Determining sustainable lignocellulosic bioenergy systems in the Cape Winelands District Municipality, South Africa December 2012 Dissertation presented for the degree of Doctor of Philosophy at the University of Stellenbosch Promoter: Prof Theo Ernst Kleynhans Faculty of AgriSciences Department of Agricultural Economics by Clemens Cornelius Christian von Doderer II DECLARATION By submitting this dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification. December 2012 Copyright ? 2012 University of Stellenbosch All rights reserved Stellenbosch University http://scholar.sun.ac.za III ABSTRACT The energy paradigm shift from fossil fuels to renewable energy sources is driven, among others, by a growing sustainability awareness, necessitating more sophisticated measurements in terms of a wider range of criteria. Technical efficiency, financial profitability, environmental friendliness and social acceptance are some of the factors determining the sustainability of renewable energy systems. The resulting complexity and conflicting decision criteria, however, constitute major barriers to processing the information and decision-making based on the information. Seeking to implement local bioenergy systems, policymakers of the Cape Winelands District Municipality (CWDM), South Africa, are confronted with such a problem. Following a case study approach, this study illustrates how life-cycle assessment (LCA), multi- period budgeting (MPB) and geographic information systems (GIS) can aid the decision-making process by providing financial-economic, socio-economic and environmental friendliness performance data in a structured and transparent manner, allowing for a comparison of the magnitude of each considered criterion along the life-cycle. However, as the environmental impacts cannot readily be expressed in monetary terms on a cardinal scale, these considerations are given less attention or are omitted completely in a market economy. By measuring the various considerations on an ordinal scale and by attaching weights to them using the multi-criteria decision analysis (MCDA) approach, this study, illustrates how to internalise externalities as typical market failures, aiding policymakers of the CWDM to choose the most sustainable bioenergy system. Following the LCA approach, 37 lignocellulosic bioenergy systems, encompassing different combinations of type of harvesting and primary transport, type of pretreatment (comminution, drying, and fast pyrolysis) and location thereof (roadside or landing of the central conversion plant), type of secondary transport from the roadside to the central conversion plant, and type of biomass upgrading and conversion into electricity, were assessed against five financial-economic viability criteria, three socio-economic potential criteria and five environmental impact criteria. The quantitative performance data were then, as part of the MCDA process, translated into a standardised ?common language? of relative performance. An expert group attached weights to the considered criteria using the analytical hierarchy process (AHP). The ?financial-economic viability? main criterion received a weight of almost 60%, ?socio-economic potential?, nearly 25% and ?lowest environmental impact?, the remainder of around 16%. Taking the prerequisite of financial- economic viability into consideration, the preferred option across all areas of the CWDM (despite various levels of productivity) comprises a feller-buncher for harvesting, a forwarder for primary Stellenbosch University http://scholar.sun.ac.za IV transportation, mobile comminution at the roadside, secondary transport in truck-container-trailer combinations and an integrated gasification system for the conversion into electricity. Stellenbosch University http://scholar.sun.ac.za V OPSOMMING Die energie paradigma verandering van fossielbrandstowwe na hernubare energiebronne word gedryf deur ?n groeiende klem op volhoubaarheid, wat ook meer gesofistikeerde meting in terme van ?n wyer verskeidenheid maatstawwe vereis. Tegniese doeltreffendheid, finansi?le winsgewendheid, omgewingsvriendelikheid en sosiale aanvaarbaarheid is sommige van die faktore wat die volhoubaarheid van hernubare energie stelsels bepaal. Die verskeidenheid oorwegings bring egter kompleksiteit en konflik mee by die verwerking van inligting en die besluitneming wat daarop berus. Beleidmakers van die Kaapse Wynland Distriksmunisipaliteit wat ten doel het om plaaslik bio-energie stelsels te implementeer, word met hierdie probleem gekonfronteer. Hierdie ondersoek illustreer aan die hand van ?n gevallestudie benadering hoe lewensiklus analise, multiperiode begroting en geografiese inligtingstelsels besluitneming kan ondersteun deur die voorsiening van finansieel-ekonomiese, sosio-ekonomiese (indiensneming) en omgewingsvriendelikheid prestasie data op ?n gestruktureerde en deursigtige wyse. Dit maak die vergelyking van die waardes van al die kriteria by elke fase van die lewensiklus moontlik. Aangesien die omgewingseffekte nie geredelik in monet?re terme op ?n kardinale skaal gemeet kan word nie, kry hulle binne die markekonomie minder aandag of word selfs buite rekening gelaat. Deur hierdie verskeidenheid kriteria op ?n ordinale skaal te meet en gewigte met behulp van multikriteria besluitneming aan hulle toe te ken, toon hierdie ondersoek hoe om eksternaliteite as tipiese markmislukkings te internaliseer om beleidmakers van die Kaapse Wynland Distriksmunisipaliteit in staat te stel om die mees volhoubare bio-energie stelsel te kies. Met behulp van lewensiklus analise is 37 lignosellulose bio-energie stelsels ge?dentifiseer as verskillende kombinasies van oes van die bome, prim?re vervoer van houtstompe, vooraf verwerking (verspaandering, droging, vinnige pirolise), die ligging van hierdie aktiwiteite (langs ?n plantasie of by ?n sentrale omsettingsaanleg), tipe sekond?re vervoer van houtspaanders vanaf die plantasie na die sentrale omsettingsaanleg en tipe biomassa opgradering en omsetting van die houtspaanders na elektrisiteit. Die verskillende stelsels is gemeet aan die hand van vyf finansieel- ekonomiese kriteria, drie indiensneming potensiaal kriteria en vyf omgewingsimpak kriteria. Die kwantitatiewe metings is deur middel van multikriteria besluitneming omgeskakel na ?n gestandaardiseerde ?gemeenskaplike taal? van relatiewe prestasie. Lede van ?n ekspertgroep het gewigte is aan die onderskeie kriteria met behulp van die analitiese hierargie proses toegeken. Aan die finansieel-ekonomiese lewensvatbaarheid hoof kriterium is ?n gewig van by die 60% toegeken, aan die indiensnemingspotensiaal bykans 25% en aan omgewingsvriendelikheid sowat 16%. Die voorkeur kombinasie vir al die areas van die Kaapse Wynland Distriksmunisipaliteit sluit in ?n Stellenbosch University http://scholar.sun.ac.za VI saag-bondelaar vir die oesproses, ?n plantasie-vragmotor vir prim?re vervoer, mobiele verspaandering langs die plantasie, ?n vragmotor-skeepshouer-treiler kombinasies vir die sekond?re vervoer van houtspaanders en ?n ge?ntegreerde vergassingstelsel vir die omsetting van houtspaanders na elektrisiteit. Stellenbosch University http://scholar.sun.ac.za VII ACKNOWLEDGEMENTS I owe my sincere gratitude to the following persons and institutions that assisted me in diverse ways to bring this study to a successful end, and without whom this task would have been impossible: ? My most heartfelt thanks go to my promoter, Professor Theo Kleynhans, for his relentless and competent guidance, his unfailing support, his sound advice and positive critique from day one of my studies at Stellenbosch University; as well as for opening up bursary opportunities. I am most grateful to have benefited from the supervision of such a distinguished academic and acknowledged expert. ? I am very grateful for the personal encouragement and academic insights of my good friends and colleagues, Dr Willem Hoffmann and Dr Shelley Johnson, whose support has been invaluable; and most importantly for offering calm port in a great number and variety of academic and other storms. ? Many thanks go to the members of the Agricultural Economics Department, whose friendly attitude and invaluable support provided the perfect foundation to the success of this study and throughout my studies at Stellenbosch University. ? I am also very grateful for the insightful comments and contributions from various members of the Forest Science Department at Stellenbosch University, including Mr Pierre Ackerman, Dr Martina Meincken, Dr Ben du Toit, Mr Cori Ham, Mr John de Wet, Prof Thomas Seifert and former member Dr Dirk L?ngin. ? I would like to pay a special tribute to Dr emeritus Kobus Theron (3 February 1943 ? 3 November 2010), former member of the Forest Science Department at Stellenbosch University, for his great support and input during my master?s studies, which provided the foundation for this dissertation. May he rest in peace. ? I also thank Dr Freddie Ellis, Dr Andrei Rozanov, Dr Ailsa Hardie from the Soil Science Department, Dr Adriaan van Niekerk from the Geography and Environmental Studies Stellenbosch University http://scholar.sun.ac.za VIII Department, as well as Mr Thomas Hugo, Mr Daniel Petrie, Ms Theari Roberts, Mr Theuns Dirkse van Schalkwyk, and Prof Johann G?rgens from the Engineering Faculty of the University of Stellenbosch for their contributions. ? Outside of Stellenbosch I am particularly grateful for the great support and encouragement of my friend Dr Johannes Gediga from PE International, whose support has been also invaluable. ? I thank Prof Harro von Blottnitz from the University of Cape Town for his great support by introducing me to the life-cycle assessment approach, and for granting me a bursary to attend the life-cycle management conference in Cape Town in 2009 which served as an ideal starting point to this study. ? Special thanks go also to Mr Greg Forsyth, Dr Willem de Lange and others from the council of scientific and industrial research (CSIR) for their contributions, particularly towards the end of the research phase of this study. ? Furthermore, I thank Prof Dr Christoph K?tsch, former member of the Department of Forest and Wood Science at Stellenbosch University, for initiating this study with the Cape Winelands District Municipality and for his support in the early stages of my postgraduate studies. ? My sincere gratitude also goes to Prof emeritus Klaus von Gadow, my former lecturer at the University of G?ttingen, Germany, for opening the door to South Africa and to Stellenbosch University for my postgraduate studies. ? Regarding funding, my thanks go to the Cape Winelands District Municipality and to South Africa?s National Energy Research Institute (SANERI) for their generous grants throughout Stellenbosch University http://scholar.sun.ac.za IX my studies at Stellenbosch University. In addition, I thank the South African National Research Foundation (NRF), as this work is based upon research supported by the NRF.1 ? I also thank most sincerely Mr Russell de la Porte and his ?WriteArt? team for working tirelessly to make this study a success. ? Finally, I thank my parents for the inspiring education they provided me. They set the foundation of who I am today, and thanks to their endless support, love and guidance I was able to go further than I would have ever imagined. Their nurture provided me, amongst many other things, with a mind-set of great curiosity and a goal-oriented approach to find sustainable solutions to the problems faced in this ever-changing world. 1 Any opinion, findings and conclusions or recommendations expressed in this dissertation are those of the author and the South African National Research Foundation does not accept any liability in regard thereto. Stellenbosch University http://scholar.sun.ac.za X After all, sustainability means running the global environment - Earth Inc. - like a corporation: with depreciation, amortization and maintenance accounts. In other words, keeping the asset whole, rather than undermining your natural capital.* *Maurice Strong, Canadian entrepreneur and former under-secretary general of the United Nations Stellenbosch University http://scholar.sun.ac.za XI TABLE OF CONTENTS DECLARATION ................................................................................................................................ II ABSTRACT ....................................................................................................................................... III OPSOMMING .................................................................................................................................... V ACKNOWLEDGEMENTS ............................................................................................................. VII TABLE OF CONTENTS ................................................................................................................... XI LIST OF ABBREVIATIONS AND ACRONYMNS ................................................................... XVII LIST OF UNITS .......................................................................................................................... XVIII LIST OF ELEMENTS AND CHEMICAL FORMULAS .............................................................. XIX LIST OF TABLES ........................................................................................................................... XX LIST OF FIGURES ...................................................................................................................... XXII LIST OF ANNEXURES ............................................................................................................... XXV 1 CHAPTER: INTRODUCTION AND ORIENTATION ............................................................... 1 1.1 Introduction and background ................................................................................................. 1 1.2 Problem statement .................................................................................................................. 3 1.3 Research goal and objectives ................................................................................................. 4 1.4 Research approach and methodologies .................................................................................. 4 1.5 Statement of hypothesis ......................................................................................................... 5 1.6 Chapter layout ........................................................................................................................ 5 2 CHAPTER: STUDY AREA AND RESOURCE BASELINE ..................................................... 7 2.1 Introduction ............................................................................................................................ 7 2.2 The Cape Winelands District Municipality ........................................................................... 7 2.3 Study area: biomass resource baseline ................................................................................... 9 2.4 Biomass definition and properties ....................................................................................... 12 2.4.1 Biomass classification .......................................................................................................... 13 2.4.2 Biomass composition ........................................................................................................... 13 2.4.3 Moisture content .................................................................................................................. 16 2.4.4 Heating value/energy content .............................................................................................. 16 Stellenbosch University http://scholar.sun.ac.za XII 2.4.5 Biomass from dedicated energy crops ................................................................................. 18 2.4.6 Short-rotation coppice systems (SRC) ................................................................................. 18 2.5 Conclusions .......................................................................................................................... 19 3 CHAPTER: LITERATURE REVIEW ....................................................................................... 21 3.1 Introduction .......................................................................................................................... 21 3.2 Life-Cycle Assessment ........................................................................................................ 21 3.2.1 Origin of LCA ...................................................................................................................... 22 3.2.2 LCA method ........................................................................................................................ 22 3.2.2.1 Goal and scope definition .................................................................................................... 23 3.2.2.2 Life-Cycle Inventory (LCI) ................................................................................................. 26 3.2.2.3 Life-Cycle Impact Assessment (LCIA) ............................................................................... 27 3.2.2.4 Interpretation ........................................................................................................................ 28 3.2.3 Types of LCA ...................................................................................................................... 29 3.2.4 LCA applied in agriculture .................................................................................................. 30 3.2.5 LCA applied in forestry ....................................................................................................... 32 3.2.6 LCA applied in biofuel and bioenergy systems ................................................................... 33 3.3 Multi-Criteria Decision-Making Analysis ........................................................................... 36 3.3.1 Basic concepts of MCDA .................................................................................................... 38 3.3.2 Phases of MCDA ................................................................................................................. 38 3.3.3 Types of MCDA .................................................................................................................. 40 3.3.4 MCDA applied in agriculture and forestry .......................................................................... 43 3.3.5 MCDA applied in biofuel and bioenergy systems ............................................................... 43 3.3.6 The combined use of LCA and MCDA ............................................................................... 45 3.4 Conclusions .......................................................................................................................... 50 4 CHAPTER: GOAL AND SCOPE DEFINITION ....................................................................... 53 4.1 Introduction .......................................................................................................................... 53 4.2 Functional unit ..................................................................................................................... 53 4.3 System boundaries ............................................................................................................... 54 Stellenbosch University http://scholar.sun.ac.za XIII 4.3.1 Technical system boundaries ............................................................................................... 54 4.3.1.1 Primary biomass production ................................................................................................ 58 4.3.1.2 Harvesting and primary transport ........................................................................................ 58 4.3.1.3 Biomass pretreatment: Comminution .................................................................................. 62 4.3.1.4 Biomass pretreatment: drying .............................................................................................. 64 4.3.1.5 Biomass upgrading: mobile fast-pyrolysis .......................................................................... 65 4.3.1.6 Secondary transport ............................................................................................................. 66 4.3.1.7 Bio-energy generation .......................................................................................................... 69 4.3.2 Natural system boundaries: biological biomass production capacity .................................. 86 4.3.3 Natural system boundaries: land-use change and ecosystem carbon storage ...................... 89 4.3.3.1 Direct land-use change ......................................................................................................... 90 4.3.3.2 Indirect land-use change ...................................................................................................... 90 4.3.3.3 Carbon stock change ............................................................................................................ 91 4.3.4 Time boundaries .................................................................................................................. 96 4.4 Conclusions .......................................................................................................................... 96 5 CHAPTER: LIFE-CYCLE INVENTORY ................................................................................. 97 5.1 Introduction .......................................................................................................................... 97 5.2 Primary production of biomass ............................................................................................ 97 5.2.1 Mechanical land preparation ................................................................................................ 98 5.2.2 Chemical land preparation and maintenance ....................................................................... 99 5.2.3 Planting of seedlings .......................................................................................................... 101 5.2.4 Fertilisation ........................................................................................................................ 102 5.2.5 Thinning of coppice shoots ................................................................................................ 105 5.3 Harvesting and forwarding ................................................................................................ 106 5.3.1 Harvesting system I ........................................................................................................... 106 5.3.2 Harvesting system II .......................................................................................................... 108 5.3.3 Harvesting system III ......................................................................................................... 110 5.3.4 Harvesting system IV ......................................................................................................... 111 Stellenbosch University http://scholar.sun.ac.za XIV 5.3.5 Harvesting system V .......................................................................................................... 113 5.4 Biomass comminution ....................................................................................................... 115 5.4.1 Mobile comminution at roadside ....................................................................................... 115 5.4.2 Stationary comminution at landing of central conversion plant ........................................ 117 5.5 Thermal pretreatment ......................................................................................................... 119 5.6 Mobile fast pyrolysis ......................................................................................................... 120 5.7 Secondary transport of bioenergy feedstock ...................................................................... 120 5.8 Bioenergy generation ......................................................................................................... 128 5.8.1 General considerations and assumptions ........................................................................... 128 5.8.2 Financial-economic considerations .................................................................................... 130 5.8.3 Emission related considerations ......................................................................................... 133 5.8.4 Bioenergy conversion system I .......................................................................................... 135 5.8.5 Bioenergy conversion system II ........................................................................................ 137 5.8.6 Bioenergy conversion system III ....................................................................................... 139 5.8.7 Bioenergy conversion system IV ....................................................................................... 143 5.8.8 Bioenergy conversion system V ........................................................................................ 146 5.9 Conclusions ........................................................................................................................ 148 6 CHAPTER: LIFE-CYCLE IMPACT ASSESSMENT ............................................................. 150 6.1 Introduction ........................................................................................................................ 150 6.2 Environmental criteria ....................................................................................................... 151 6.2.1 LCA impact categories ...................................................................................................... 151 6.2.1.1 Abiotic depletion potential ................................................................................................. 152 6.2.1.2 Acidification potential ....................................................................................................... 154 6.2.1.3 Eutrophication potential ..................................................................................................... 156 6.2.1.4 Global warming potential .................................................................................................. 158 6.2.1.5 Photochemical ozone creation potential ............................................................................ 162 6.2.1.6 Toxicity .............................................................................................................................. 163 6.2.2 Other environmental impacts ............................................................................................. 164 Stellenbosch University http://scholar.sun.ac.za XV 6.2.2.1 Impact on biodiversity ....................................................................................................... 164 6.2.2.2 Water balance .................................................................................................................... 167 6.3 Financial-economic criteria ............................................................................................... 168 6.3.1 Internal rate of return ......................................................................................................... 170 6.3.2 Cost of technology for biomass upgrading and conversion ............................................... 173 6.3.2.1 Capital expenditure ............................................................................................................ 173 6.3.2.2 Operating expenditure ........................................................................................................ 174 6.3.3 Cost other than conversion technology .............................................................................. 175 6.3.3.1 Capital expenditure ............................................................................................................ 176 6.3.3.2 Operating expenditure ........................................................................................................ 177 6.4 Socio-economic criteria ..................................................................................................... 178 6.4.1 Direct employment creation potential ................................................................................ 179 6.4.1.1 DECP I - income less than R8 000 per month ................................................................... 181 6.4.1.2 DECP II ? income from R8 000-R24 000 per month ........................................................ 183 6.4.1.3 DECP III ? income of more than R24 000 per month ....................................................... 184 6.4.2 Other socio-economic impacts: food security ................................................................... 185 6.5 Conclusions ........................................................................................................................ 185 7 CHAPTER: INTERPRETATION OF LCA RESULTS USING MCDA ................................. 189 7.1 Introduction ........................................................................................................................ 189 7.2 The analytic hierarchy process .......................................................................................... 190 7.3 Problem identification and structuring .............................................................................. 193 7.4 Model building and use ...................................................................................................... 194 7.4.1 Criteria value tree ............................................................................................................... 194 7.4.2 Normalisation of LCA results ............................................................................................ 195 7.4.3 Discussion on thresholds ................................................................................................... 201 7.4.4 Expert panel workshop ...................................................................................................... 201 7.4.5 Expert panel workshop ? outcome ..................................................................................... 202 7.4.6 Synthesising of information ? results ................................................................................ 204 Stellenbosch University http://scholar.sun.ac.za XVI 7.5 Maximisation of main criteria/sensitivity analysis ............................................................ 211 7.5.1 Maximisation of financial-economic main criterion ......................................................... 212 7.5.2 Maximisation of socio-economic main criterion ............................................................... 215 7.5.3 Maximisation of environmental impact main criterion ..................................................... 218 7.6 Conclusions ........................................................................................................................ 221 8 CHAPTER: CONCLUSIONS, SUMMARY AND RECOMMENDATIONS ......................... 226 8.1 Conclusions ........................................................................................................................ 226 8.2 Summary ............................................................................................................................ 234 8.3 Recommendations .............................................................................................................. 247 REFERENCES................................................................................................................................. 250 ANNEXURES ................................................................................................................................. 283 Stellenbosch University http://scholar.sun.ac.za XVII LIST OF ABBREVIATIONS AND ACRONYMNS AP Acidification Potential BCS Bioenergy Conversion System BII Biodiversity Intactness Index BPA Biomass procurement area CBA Cost-Benefit Analysis CLM Centrum voor Milieuwetenschappen Leiden (Institute of Environmental Sciences, University of Leiden) CS Cropping System CWDM Cape Winelands District Municipality DECP Direct Employment Creation Potential dLUC Direct Land Use Change EHV Effective heating value EP Eutrophication Potential FU Functional Unit GaBi 4 Life Cycle Assessment software (?Ganzheitliche Bilanzierung?) GHG Greenhouse Gases GWP Global Warming Potential HHV Higher heating value HV Heating value IPCC Intergovernmental Panel of Climate Change ISO International Organisation of Standardisation LBS Lignocellulosic bioenergy system LCA Life Cycle Analysis/Assessment LCI Life Cycle Inventory LCIA Life Cycle Impact Assessment LUC Land Use Change MC Moisture content MCDA Multi-Criteria Decision Making Analysis MPB Multi-Period Budgeting NREC Non-Renewable Energy Consumption NRR Non-Renewable Resources Stellenbosch University http://scholar.sun.ac.za XVIII OCE Overall Conversion Efficiency ODP Ozone Depletion Potential PM Particle Matter POCP Photochemical Ozone Creation Potential SANERI South Africa's National Energy Research Institute SRC Short Rotation Coppice system UNFCCC United Nations Framework Convention on Climate Change VOC Volatile organic compounds WF Water Footprint WSSD World Summit on Sustainable Development LIST OF UNITS ?C Temperature in Celsius kW Kilowatt MW Megawatt GW Gigawatt MJ Megajoule GJ Gigajoule Stellenbosch University http://scholar.sun.ac.za XIX LIST OF ELEMENTS AND CHEMICAL FORMULAS Carbon Methane Carbon monoxide Carbon dioxide Hydrogen Hydrogen chloride Potassium Nitrogen Di-nitrogen Monoxide (Laughing gas) Mineralised nitrogen Organic fixed nitrogen Ammonia Ammonium Non Methane Volatile Organic Compounds Nitrogen Oxide Nitrous gases Nitrate Oxygen P Phosphor Phosphate Sulphur dioxide Stellenbosch University http://scholar.sun.ac.za XX LIST OF TABLES Table 1: Unemployment in the CWDM ............................................................................................... 7 Table 2: Available biomass production areas in CWDM .................................................................... 9 Table 3: Suitable indigenous and exotic tree species for biomass production in the CWDM ........... 10 Table 4: Potential demand points for bioenergy generation in the CWDM ...................................... 12 Table 5: Chemical composition and component distribution of the bioenergy tree .......................... 15 Table 6: Density and effective heating value at different moisture content levels ............................ 17 Table 7: Examples of life-cycle approaches for different applications ............................................. 30 Table 8: Application of LCA in the agricultural context ................................................................... 31 Table 9: Application of LCA in the agricultural energy crop context ............................................... 32 Table 10: Application of LCA in the forestry context ....................................................................... 33 Table 11: Application of LCA in the biofuels and bioenergy context ............................................... 34 Table 12: Advantages and disadvantages of biomass comminution at different locations ............... 63 Table 13: Bulk densities of biomass at various levels of moisture content ....................................... 69 Table 14: Typical product weight yields obtained by different methods for pyrolysing wood ......... 79 Table 15: The range of elemental composition and properties of wood-derived pyrolysis oils ........ 81 Table 16: Pyrolysis oil yields for various feeds ................................................................................. 82 Table 17: Influence of pyrolysis temperature on bio-char properties ................................................ 84 Table 18: Silvicultural production and other indicators for selected BPAs ....................................... 88 Table 19: Proportions of changed land use by introducing SRC plantations per BPA ..................... 93 Table 20: Above- and below-ground biomass and its related carbon stock at equilibrium per land- use type and BPA ................................................................................................................. 94 Table 21: Weed control operations .................................................................................................. 100 Table 22: Planting and blanking productivity and costs (2011) ...................................................... 101 Table 23: Emission factors from synthetic nitrogen inputs (%) ...................................................... 103 Table 24: Recommended fertiliser mix per tree for different soil types .......................................... 104 Table 25: Fertiliser application over lifetime of SRC plantations ................................................... 104 Table 26: Fertiliser products, respective concentrations, and prices per ton (2011) ....................... 105 Table 27: Average fertilising cost per ha and rotation on sandy soils in the CWDM (2011).......... 105 Table 28: Harvesting system I ? productivity rate and costs per hectare for each BPA (2011) ...... 107 Table 29: Harvesting system II ? productivity rate and costs per hectare for each BPA (2011) ..... 109 Table 30: Harvesting system III ? productivity rate and costs per hectare for each BPA (2011) ... 111 Table 31: Harvesting system IV ? productivity rate and costs per hectare for each BPA (2011) ... 113 Table 32: Harvesting system IV ? productivity rate and costs per hectare for each BPA (2011) ... 115 Stellenbosch University http://scholar.sun.ac.za XXI Table 33: ?Maier drum chipper HRL 1200/ 450 x 1000 ? 8EW? drum chipper feeding line (2011) ........................................................................................................................................... 118 Table 34: Various types of HCVs for secondary transport of bioenergy feedstocks ....................... 120 Table 35: Fixed time requirements for loading, unloading, securing and weighing (in h/load) ...... 121 Table 36: Biomass and pyrolysis products mass flow ..................................................................... 124 Table 37: Number of truck configurations required for secondary transport .................................. 127 Table 38: Bioenergy conversion systems and their related efficiencies .......................................... 129 Table 39: Bioenergy conversion systems and their related capital and operational costs (2011) ... 133 Table 40: BCS I flue gas emissions per tonne biomass input .......................................................... 137 Table 41: BCS II flue gas emissions per gasifier-gas turbine system .............................................. 139 Table 42: Typical elemental distribution of bio-oil and bio-char .................................................... 142 Table 43: Elemental distribution of pyrolysis products calculated for BTG-BTL system .............. 142 Table 44: BCS III flue gas emissions of each of the pyrolysis products ......................................... 143 Table 45: Calculated elemental distribution of pyrolysis products based on Agri-Therm system .. 145 Table 46: BCS IV flue gas emissions of each of the pyrolysis products ......................................... 146 Table 47: Terrestrial ecotoxicity potential for various power-grid mixes ....................................... 164 Table 48: Benefits associated with local bioenergy production ...................................................... 178 Table 49: Bioenergy systems employment creation potential subdivided into income categories . 181 Table 50: Best- and worst-performing LBSs per selected criteria and BPA ................................... 187 Table 51: Fundamental scale for pairwise comparison in AHP....................................................... 191 Table 52: Normalised to sum one, but unweighted scores for BPA I .............................................. 198 Table 53: Ranking of LBSs based on unweighted scores ................................................................ 200 Table 54: Outcome of weighting procedure .................................................................................... 203 Table 55: Ranking of LBSs based on experts? weighted scores ...................................................... 207 Table 56: Comparison, top-ranked LBSs ? complete set of weights vs solely financial-economic criteria ................................................................................................................................ 214 Table 57: Comparison, top-ranked LBSs ? complete set of weights vs solely socio-economic criteria ........................................................................................................................................... 217 Table 58: Comparison, top-ranked LBSs ? complete set of weights vs solely ?Least environmental impact? criteria ................................................................................................................... 220 Stellenbosch University http://scholar.sun.ac.za XXII LIST OF FIGURES Figure 1: Mean annual precipitation of the CWDM ............................................................................ 8 Figure 2: Lignocellulosic biomass availability of the CWDM, including main electricity grid and electricity substations, as well as potential sites for bioenergy conversion ......................... 11 Figure 3: Phenotype and basic components of broad-leaved tree ...................................................... 14 Figure 4: Phases of a life-cycle assessment ....................................................................................... 23 Figure 5: Scheme of the main steps and flows involved in an LCA .................................................. 26 Figure 6: Relationships of the elements within the interpretation phase with the other phases of LCA ..................................................................................................................................... 28 Figure 7: Full energy chains for comparison of bioenergy and fossil energy systems producing electricity and heat ............................................................................................................... 35 Figure 8: The process of MCDA........................................................................................................ 39 Figure 9: Technical system boundaries ? schematic illustration of production phases from in-field to the roadside .......................................................................................................................... 55 Figure 10: Technical system boundaries ? schematic illustration of production phases, from roadside to central conversion facility ................................................................................. 56 Figure 11: Overview of CWDM bioenergy pathways leading to set of 37 lignocellulosic bioenergy systems (LBSs) .................................................................................................................... 57 Figure 12: Claas Jaguar 850 with SRC biomass harvesting head ...................................................... 61 Figure 13: Main conversion options for biomass to secondary energy carriers ................................ 70 Figure 14: Products from thermal biomass conversion ..................................................................... 71 Figure 15: Thermal conversion processes .......................................................................................... 72 Figure 16: Schematic description of the process of combusting a wood chip ................................... 74 Figure 17: Schematic illustration of gasification as one of the thermal conversion processes .......... 75 Figure 18: Applications for gas from biomass gasification ............................................................... 76 Figure 19: Methods of heat transfer to a pyrolysis reactor ................................................................ 77 Figure 20: Fast pyrolysis applications ............................................................................................... 82 Figure 21: Schematic representation of biomass or bio-char remaining after charring and decomposition in soil ........................................................................................................... 86 Figure 22: Biological biomass production via photosynthesis .......................................................... 87 Figure 23: The main greenhouse gas emission sources/removals and processes in managed ecosystems ........................................................................................................................... 90 Figure 24: Temporary and permanent carbon stock losses produced by increased biomass use ...... 92 Figure 25: GaBi 4.4?s LCA software interface illustrating the primary biomass production phase .. 98 Stellenbosch University http://scholar.sun.ac.za XXIII Figure 26: Proliferation pathways of nitrogen for agricultural land ................................................ 102 Figure 27: BELL Equipment?s Ultra C disc feller buncher ............................................................. 110 Figure 28: Bell Equipment?s 220A Telelogger ................................................................................ 112 Figure 29: Schematic illustration of a dedicated SRC biomass harvesting head fitted to front of a self-propelled forage harvester .......................................................................................... 114 Figure 30: Technical drawing of Lindana TP 200 PTO wood chipper ............................................ 116 Figure 31: Stationary chipping line Maier drum chipper HRL 1200/ 450 x 1000 ? 8EW .............. 117 Figure 32:Dome-Aeration Technology ............................................................................................ 119 Figure 33: Steam cycle of conventional integrated steam turbine systems ..................................... 136 Figure 34: Schematic illustration of System Johansson Gasproducer (SJG) ................................... 138 Figure 35: Schematic illustration of stationary BTG-BTL pyrolysis system .................................. 140 Figure 36: Simplified flowchart of BTG-BTL?s fast-pyrolysis system ........................................... 141 Figure 37: Agri-Therm?s MPS100 mobile fast-pyrolysis unit ......................................................... 145 Figure 38: The LBSs? abiotic depletion potential colour coded according to BPAs ....................... 153 Figure 39 Impact pathways leading to acidification ........................................................................ 154 Figure 40: The LBSs? acidification potentials colour coded according to BPAs ............................ 155 Figure 41: Impact pathways leading to eutrophication .................................................................... 156 Figure 42: The LBSs? eutrophication potentials colour coded according to BPAs ......................... 157 Figure 43: Impact pathways leading to greenhouse effect ............................................................... 159 Figure 44: The LBSs? global warming potentials colour coded according to BPAs ....................... 160 Figure 45: GWP of LBSs 2, 14, 20, 27 and 37 subdivided into production phases ........................ 161 Figure 46: Impact pathways leading to photochemical Ozone Creation ......................................... 162 Figure 47: The LBSs? photochemical ozone creation potentials colour coded according to BPAs 163 Figure 48: Influence of biodiversity on ecosystem services ............................................................ 166 Figure 49: Determining conservation importance as a function of both habitat quality and level of degradation of the natural habitat ...................................................................................... 167 Figure 50: Graphic representation of multi-period budget model components for bioenergy systems ........................................................................................................................................... 169 Figure 51: The LBSs? internal rate of return (including land value) colour coded according to BPAs ........................................................................................................................................... 171 Figure 52: The LBSs? internal rate of return (excluding land value) colour coded according to BPAs ........................................................................................................................................... 172 Figure 53: Capital expenditure of biomass upgrading and bioenergy conversion systems ............. 173 Figure 54: Operating expenditure for biomass upgrading and bioenergy conversion systems ....... 175 Stellenbosch University http://scholar.sun.ac.za XXIV Figure 55: Capital expenditure other than for conversion systems .................................................. 176 Figure 56: Operating expenditure other than for conversion systems ............................................. 177 Figure 57: Unemployment rates (1995-2007), by level of education (in years) .............................. 180 Figure 58: Mean monthly earnings (ZAR) (2003-2007), by level of education (years) .................. 181 Figure 59: DECP I ? No. of jobs with income of less than R8 000/month...................................... 182 Figure 60: DECP II ? no. of jobs with income of between R8 000 and R24 000/month ................ 184 Figure 61: DECP III ? No. of jobs with income of more than R24 000/month ............................... 185 Figure 62: Hierarchical value tree for the CWDM?s decision-making problem concerning choice of bioenergy system ............................................................................................................... 195 Figure 63: Aggregated, unweighted scores of LBSs for BPA I ....................................................... 199 Figure 64: Aggregated, weighted scores of LBSs in BPA I ............................................................ 205 Figure 65: Aggregated, weighted scores of LBSs in BPA II ........................................................... 208 Figure 66: Aggregated, weighted scores of LBSs in BPA III .......................................................... 210 Figure 67: Aggregated, weighted scores of LBSs in BPA IV ......................................................... 211 Figure 68: Aggregated weighted scores of LBSs, considering only financial-economic criteria .... 213 Figure 69: Aggregated weighted scores of LBSs, considering only socio-economic criteria ......... 216 Figure 70: Aggregated weighted scores of LBSs, considering only environmental impact criteria 219 Stellenbosch University http://scholar.sun.ac.za XXV LIST OF ANNEXURES Annexure 1: LCA results ?LBS 1 .................................................................................................... 284 Annexure 2: LCA results ?LBS 2 .................................................................................................... 285 Annexure 3: LCA results ?LBS 3 .................................................................................................... 286 Annexure 4: LCA results ?LBS 4 .................................................................................................... 287 Annexure 5: LCA results ?LBS 5 .................................................................................................... 288 Annexure 6: LCA results ?LBS 6 .................................................................................................... 289 Annexure 7: LCA results ? LBS 7 ................................................................................................... 290 Annexure 8: LCA results ? LBS 8 ................................................................................................... 291 Annexure 9: LCA results ? LBS 9 ................................................................................................... 292 Annexure 10: LCA results ? LBS 10 ............................................................................................... 293 Annexure 11: LCA results ? LBS 11 ............................................................................................... 294 Annexure 12: LCA results ? LBS 12 ............................................................................................... 295 Annexure 13: LCA results ? LBS 13 ............................................................................................... 296 Annexure 14: LCA results ? LBS 14 ............................................................................................... 297 Annexure 15: LCA results ? LBS 15 ............................................................................................... 298 Annexure 16: LCA results ? LBS 16 ............................................................................................... 299 Annexure 17: LCA results ? LBS 17 ............................................................................................... 300 Annexure 18: LCA results ? LBS 18 ............................................................................................... 301 Annexure 19: LCA results ? LBS 19 ............................................................................................... 302 Annexure 20: LCA results ? LBS 20 ............................................................................................... 303 Annexure 21: LCA results ? LBS 21 ............................................................................................... 304 Annexure 22: LCA results ? LBS 22 ............................................................................................... 305 Annexure 23: LCA results ? LBS 23 ............................................................................................... 306 Annexure 24: LCA results ? LBS 24 ............................................................................................... 307 Annexure 25: LCA results ? LBS 25 ............................................................................................... 308 Annexure 26: LCA results ? LBS 26 ............................................................................................... 309 Annexure 27: LCA results ? LBS 27 ............................................................................................... 310 Annexure 28: LCA results ? LBS 28 ............................................................................................... 311 Annexure 29: LCA results ? LBS 29 ............................................................................................... 312 Annexure 30: LCA results ? LBS 30 ............................................................................................... 313 Annexure 31: LCA results ? LBS 31 ............................................................................................... 314 Annexure 32: LCA results ? LBS 32 ............................................................................................... 315 Annexure 33: LCA results ? LBS 33 ............................................................................................... 316 Stellenbosch University http://scholar.sun.ac.za XXVI Annexure 34 LCA results ? LBS 34 ................................................................................................ 317 Annexure 35: LCA results ? LBS 35 ............................................................................................... 318 Annexure 36: LCA results ? LBS 36 ............................................................................................... 319 Annexure 37: LCA results ? LBS 37 ............................................................................................... 320 Annexure 38: LCA results ? current power grid mix of South African .......................................... 321 Annexure 39: Abiotic Depletion Potential per LBS and BPA ......................................................... 322 Annexure 40: Acidification Potential per LBS and BPA ................................................................ 323 Annexure 41: Eutrophication Potential per LBS and BPA .............................................................. 324 Annexure 42: Global Warming Potential per LBS and BPA........................................................... 325 Annexure 43: Avoided net CO2-equivalent emissions per LBS and BPA ...................................... 326 Annexure 44: Photochemical Ozone Creation Potential per LBS and BPA.................................... 327 Annexure 45: IRR per LBS and BPA ? including land value ......................................................... 328 Annexure 46: IRR per LBS and BPA ? excluding land value ......................................................... 329 Annexure 47: Net Present Value per LBS and BPA ........................................................................ 330 Annexure 48: CAPEX of bioenergy conversion systems per LBS and BPA .................................. 331 Annexure 49: OPEX of BCSs over period of 20 years per LBS and BPA ...................................... 332 Annexure 50: CAPEX other than bioenergy conversion systems per LBS and BPA ..................... 333 Annexure 51: Land value per LBS and BPA ................................................................................... 334 Annexure 52: OPEX other than BCS per LBS and BPA ................................................................. 335 Annexure 53: Employment potential subdivided in income categories per LBS and BPA ............ 336 Annexure 54: Normalised, un-weighted scores for BPA I .............................................................. 337 Annexure 55: Normalised, un-weighted scores for BPA II ............................................................. 338 Annexure 56: Normalised, un-weighted scores for BPA III ............................................................ 339 Annexure 57: Normalised, un-weighted scores for BPA IV ........................................................... 340 Annexure 58: Normalised to sum one, but un-weighted scores for BPA I ...................................... 341 Annexure 59: Normalised to sum one, but un-weighted scores for BPA II .................................... 342 Annexure 60: Normalised to sum one, but un-weighted scores for BPA III ................................... 343 Annexure 61: Normalised to sum one, but un-weighted scores for BPA IV ................................... 344 Annexure 62: Aggregated, unweighted scores of LBSs for BPA II ................................................ 345 Annexure 63: Aggregated, unweighted scores of LBSs for BPA III ............................................... 346 Annexure 64: Aggregated, unweighted scores of LBSs for BPA IV............................................... 347 Annexure 65: Participants of MCDA workshop .............................................................................. 348 Annexure 66: Normalised, weighted scores for BPA I .................................................................... 349 Annexure 67: Normalised, weighted scores for BPA II .................................................................. 350 Stellenbosch University http://scholar.sun.ac.za XXVII Annexure 68: Normalised, weighted scores for BPA III ................................................................. 351 Annexure 69: Normalised, weighted scores for BPA IV ................................................................. 352 Annexure 70: Comparison of the top-ten ranked LBSs across all four BPAs ................................. 353 Annexure 71: Maximisation of financial-economic main criterion ................................................. 354 Annexure 72: Maximisation of socio-economic main criterion ...................................................... 355 Annexure 73: Maximisation of least environmental impact main criterion .................................... 356 Annexure 74: Ranking of LBSs based on maximised financial-economic criterion ....................... 357 Annexure 75: Ranking of LBSs based on maximised socio-economic criterion ............................ 358 Annexure 76: Ranking of LBSs based on maximised environmental impact criterion ................... 359 Stellenbosch University http://scholar.sun.ac.za 1 1 CHAPTER: INTRODUCTION AND ORIENTATION 1.1 Introduction and background Worldwide about 2.6 billion people live on less than two dollars per day, while the current ecological footprint of global consumption and production patterns exceeds the earth?s capacity to regenerate (UNEP, 2010). With current energy policies and management this situation is unlikely to improve, since the world?s energy consumption is projected to more than triple between 1990 (164 exajoules, EJ) and 2035 (508 EJ) (U.S. IEA, 2011). This demand, however, cannot be satisfied by conventional energy sources such as crude oil, natural gas, coal and nuclear power combined (Lange, 2007). Finite reserves and a rapidly increasing demand for oil will inevitably force world economies to abandon oil as the primary source of energy (Laird, 2008). Another force compelling world economies to reconsider current energy policies and management is the inability of the environment to maintain its sink function, i.e. the ability to maintain its assimilating capacity without the unacceptable degradation of its future waste absorbing capacity or other important services (Goodland, 1995). There is growing scientific consensus that climate change is driven by anthropogenic emissions of greenhouse gases to the atmosphere and that the use of fossil fuels for energy is the dominant source of these emissions (IPCC, 2007). This has resulted in an entirely new energy paradigm ranging from fossil to renewable energy sources, particularly in the developed world, where the development of hydro, solar, wind and biomass-based energy systems is receiving great attention, with the aim of extending current energy mixes and replacing conventional energy systems. While significant progress can be seen in many European and North American countries, the implementation of renewable energies is still at an early stage of development on the African continent. South Africa relies on fossil fuels such as coal and oil to generate more than 90 percent of its electricity (ESKOM, 2010). While projections based on known reserves indicate sufficient coal for 114 years, pollution of the air, water and soil is causing serious environmental damage. Additionally, an outdated electricity infrastructure and low capacities of electricity generated have resulted in scheduled power cuts by the monopolistically acting national energy supplier, ESKOM, which has had a severe impact on South Africa?s economic growth. A first serious step towards introducing renewable energies was taken in 2010, when the South African government initiated a renewable energy programme aimed at procuring 3 725 Megawatt (MW) of electricity between 2014 and 2016 mainly from biomass, wind, solar energy, and small-scale hydro energy, with additional plans aimed at procuring 17 800 MW from these sources by 2030. Stellenbosch University http://scholar.sun.ac.za 2 Against this background, biomass is considered to be one of the most promising alternatives to conventional fuels and feedstocks, as it is the only renewable source of fixed carbon that can be converted to liquid, solid and gaseous fuels as well as to heat and power (Amutio et al., 2011). Moreover, biomass is considered ?carbon neutral? over its life cycle because the combustion of biomass releases the same amount of CO2 as was captured during its growth. By contrast, fossil fuels release CO2 that has been locked up for millions of years. Furthermore, biomass is considered the renewable energy source with the highest potential to contribute to the energy needs of modern society for both developed and developing economies world-wide (IEA, 2000, Bridgwater, 2002). Bioenergy has an almost closed CO2 cycle, but there are greenhouse gas emissions (GHG) in its life cycle, largely resulting from the production stages: external fossil fuel inputs are required to produce and harvest the feedstocks, in processing and handling the biomass, in operating bioenergy plants and in transporting the feedstocks and biofuels (Cherubini et al., 2009). In recent years, short- rotation woody crops such as willow, poplar and eucalyptus have turned out to be the biomass materials with the highest energy potential (Guerrero et al., 2005). The need for security and diversification of energy supplies as well as for less reliance on fossil fuels, the uncertainty surrounding oil prices, and increasing concerns over environmental degradation and climate change effects are some of the major social, political, and economic challenges that have prompted the international community to work harder at promoting renewable energy sources (Perimenis et al., 2011: 1782). However, this new energy paradigm has also demanded new ways of measuring the viability of energy sources. While in the past, the ?success? of energy carriers was mostly driven by financial considerations, leading to fossil fuels such as coal and oil being the preferred choices, the introduction of renewable energies has resulted in more of a sustainability driven approach, necessitating more sophisticated measurements of a wider range of criteria. The financial-economic competitiveness still plays an important role, but medium- and long-term aspects need to be taken into account, especially when considering the growing scarcity of fossil energy carriers. A major feature of any renewable energy product is also the degree to which it can reduce environmental impacts, e.g. carbon dioxide (CO2) emissions, associated with the use of the fossil energy that it will replace. Another important feature is the extent to which renewable energies can contribute to socio-economic potential. Bioenergy particularly is considered a local energy source, as it requires large areas to ensure a sufficient and sustainable supply, resulting not only in a change of agricultural and forestry production patterns but also in significant employment creation, particularly in rural areas. In contrast, generating fossil-fuel-driven energy is considered a large-scale, capital-intensive operation that is limited to relatively small areas, resulting not only in significant environmental impacts locally (e.g. acidification, eutrophication, Stellenbosch University http://scholar.sun.ac.za 3 human health) and globally (e.g. climate change), but also in other social challenges such as limited employment creation, migration to cities, and infrastructure and food constraints. The main goal of this study is to provide a blue print for identifying the most sustainable bioenergy system in a decision-making context, taking financial-economic viability, environmental impact and socio-economic potential criteria into consideration. 1.2 Problem statement Cost-benefit analysis (CBA) is a monetary assessment method that is traditionally used to test the financial viability of energy projects. However, the growing scarcity of fossil energy, energy security, and public and political sensitivities to environmental issues have led not only to promoting indigenous, renewable energy sources, but also to prompting the scientific community to develop assessment methods other than monetary ones, aimed at determining the environmental or socio-economic performance of alternative energy systems. A variety of studies concur that environmental, financial, and socio-economic criteria need to be considered when seeking the most sustainable alternative. However, most of them fall short in their application, as they either consider only a single dimension (finance, social or environment) or take only a very limited number of other aspects into account (e.g. only one for each dimension). This narrow measurement of ?success? may not lead to the implementation of the most sustainable alternative. The sustainability of production is, however, essential, particularly in the context of bioenergy projects, which depend on the support of many stakeholders with different perspectives; ?sustainability of production? refers to the implementation of pathways that are technically efficient, economically affordable, environmentally sound, and socially acceptable (Perimenis et al., 2011). The resulting complexity, however, constitutes a major barrier to the implementation of renewable projects, as much information of a complex and conflicting nature, often reflecting different viewpoints and often changing with time, needs to be processed. The Cape Winelands District Municipality (CWDM) in the Western Cape, South Africa, is confronted with such a decision-making problem. The insecurities in the power supply by ESKOM have prompted public decision makers of the CWDM to investigate the possibility of implementing local renewable bioenergy systems aimed at improving energy security and reducing the dependency on ESKOM, while maximising all the dimensions of sustainability. The promotion of more sustainable bioenergy systems thus called for an approach that identifies and evaluates potential bioenergy alternatives in terms of a wider variety of criteria. Stellenbosch University http://scholar.sun.ac.za 4 1.3 Research goal and objectives Aimed at supporting decision-making, this study applies life-cycle assessment (LCA) as well as complementary tools such as geographic information systems (GIS) and multi-period budgeting (MPB) to provide financial-economic, socio-economic and environmental performance data. Multi- criteria decision analysis (MCDA) is used to integrate and evaluate the provided performance data, to determine the most viable lignocellulosic bioenergy system in the CWDM. 1.4 Research approach and methodologies The life-cycle assessment (LCA) approach, originally developed as an environmental assessment tool, has gained recognition as a tool that can provide environmental performance information to support decision-making in both the private and public sectors (Basson and Petrie, 2007). There is broad agreement in the scientific community that LCA is one of the best methods for evaluating the environmental burdens associated with biofuel and bioenergy production, as it identifies energy and materials used as well as waste and emissions released to the environment; moreover, it allows the identification of opportunities for environmental improvement (Cherubini et al., 2009). Due to its structured and systematic approach, LCA appears to be well suited to being integrated with other, complementary assessment methods such as multi-period budgeting (MPB) and geographic information systems (GIS). Widely accepted and applied, these methods could assist in covering the technical, financial-economic and socio-economic aspects along a product?s life-cycle. However, while LCA and other complementary methods may be suitable methods for providing environmental, financial and socio-economic performance data, the main problem in finding the most viable/sustainable alternative in a decision environment with multiple and often conflicting objectives persists (Azapagic and Clift, 1999). To overcome this problem, an additional method is required to support decision-making that organises and synthesises the respective information, that is capable of integrating mixed sets of data (qualitative and quantitative), and that assists the decision maker to place the problem in context and to determine the preferences of the stakeholders involved. Multi-criteria decision analysis (MCDA) is an assessment tool aimed at aiding such a decision-making process. Based on a number of defined criteria, the goal of a decision maker is to identify an alternative solution that optimises all the criteria (Peremenis et al., 2011: 1784). However, in complex projects like bioenergy assessments, it is impossible to optimise all the criteria at the same time; therefore, a compromise solution needs to be actively sought by using subjective judgements of the considered criteria and by combining these as weighted scores to obtain an overall ranking of alternatives. Thus, MCDA could aid decision-making processes by integrating objective measurement with Stellenbosch University http://scholar.sun.ac.za 5 value judgement, by making subjectivity explicit, and by managing this subjectivity in a transparent and reproducible manner. 1.5 Statement of hypothesis The following hypotheses are put forward for this research: ? Hypothesis I: Life-cycle assessment (LCA) and other complementary system assessment methods including multi-period budgeting (MPB) and geographic information systems (GIS) can be used as a structured and comprehensive technique for the detailed analysis of complex lignocellulosic bioenergy systems to provide quantitative financial-economic, socio- economic and environmental performance data. ? Hypothesis II: Multi-criteria decision analysis (MCDA) can aid the decision-making process to determine the most sustainable lignocellulosic bioenergy system for the CWDM by integrating and evaluating the provided performance data. 1.6 Chapter layout This dissertation is presented in eight chapters, a list of references and 76 annexures. Chapter 1 serves as a general introduction and orientation of the research problem. Chapter 2 entails a description of the study area, and a description and definition of the bioenergy feedstock properties applicable to the study area. Chapter 3 provides the theoretical foundation of the assessment methodologies applied, as well as a summary of a variety of recent LCA and MCDA studies in the fields of agriculture, forestry and bioenergy. The combined use of both methods found in the literature is also discussed. Following the LCA approach, Chapter 4 comprises the goal and scope definition. It includes the definition of the functional unit, the technical system boundaries, geographical and time boundaries, as well as the boundaries in relation to the natural system. Chapter 5 provides the life-cycle inventory (LCI), where information is gathered on all process-related inputs and outputs in the studied system. Aimed at understanding the significance of the LCI results, Chapter 6 entails the life-cycle impact assessment (LCIA), where the environmental loads from the inventory results are translated into environmental impacts, which include, inter alia, global warming potential, acidification potential, and other categories typically not included in LCAs such as internal rate of return and direct employment creation potential. Stellenbosch University http://scholar.sun.ac.za 6 Chapter 7 presents an application of the analytical hierarchy process (AHP), one of the commonly applied multi-criteria decision analyses (MCDA). With the aim of supporting decision-making, the performance data generated in the previous chapter was translated into a common language and weighted and integrated into a single indicator, by a weighting process, resulting in a ranking of the alternatives assessed. The last chapter encompasses the conclusions, summary and recommendations for future research. Stellenbosch University http://scholar.sun.ac.za 7 2 CHAPTER: STUDY AREA AND RESOURCE BASELINE 2.1 Introduction In the following, Chapter 2 gives background information on the study area, the Cape Winelands District Municipality (CWDM), such as geographical location and extent, related unemployment figures, and climate variables (e.g. rainfall, temperature). Moreover, Chapter 2 discusses the resource baseline in terms of the availability of lignocellulosic biomass grown in short-rotation coppice (SRC) systems, based on a land and biomass availability assessment. Geographic information systems (GIS) were used to determine the extent and location of potential production sites, based on land quality considerations (e.g. soil and climate characteristics) and on avoiding biodiversity hotspots as well as urban developments, among others. This is followed by a description, definition and classification of the biomass, as well as of key feedstock parameters relevant for the generation of electrical and thermal energy. 2.2 The Cape Winelands District Municipality The Cape Winelands District Municipality (CWDM), with a total area of 22 300km2 (2.23 million ha), is one of five district municipalities in the Western Cape, South Africa. The total population of the CWDM is 679 210, with a labour force of 290 113. Of this, 230 196 people are employed (Daniels, 2011), with 202 782 workers in the formal sector and 27 414 workers in the informal sector. The official unemployment rate was estimated at 20.7% in 2010. Table 1, below, shows more detailed data for the respective municipalities within the CWDM. Table 1: Unemployment in the CWDM Municipalities in CWDM Cape Winelands Stellen- Bosch Draken- stein Br?ede Valley Witzen- berg Lange- berg Unemployment (official) 60 126 10 216 20 109 15 237 5 564 8 946 Unemployment rate (%) 20.7% 19.1% 23.2% 22.6% 13.3% 23.9% Formal employment 202 782 46 953 52 547 45 484 34 283 22 593 Informal employment 27 414 5 716 6 690 6 118 4 367 4 523 Total a 290 113 53 462 86 632 67 412 41 821 37 473 Source: Daniels (2011) Note: a Excluding district municipality area statistics. The CWDM is characterised by a Mediterranean climate and a historically strong deterministic water supply (winter rainfall) from April to August. The average mean annual precipitation (MAP) is 470mm for the CWDM, with a high geographic variation and a minimum MAP as low as 72mm Stellenbosch University http://scholar.sun.ac.za 8 for some areas (in the north-eastern parts of the CDWM); the maximum MAP reaches as high as 3 198mm in the south-western part of the CWDM. As Figure 1, below, shows, most parts of the CWDM experience even less than the South African MAP average of 450mm per year and, therefore, are prone to seasonal droughts. During the peak of summer, in February, the average maximum temperatures reach up to 45?C degrees, while in July, when winter is peaking, some areas towards the interior of the CWDM reach average minimum temperatures of minus 11?C (with an overall average of minus 2?C). The south- western part of the CWDM is mainly frost free, but some valleys experience up to 27 days of frost per year. For more details on the climate conditions of the CWDM, see Von Doderer (2009: 15-16, 70-72). Figure 1: Mean annual precipitation of the CWDM Notes: rhfa relative homogenous farming area Source: Schulze et al. (2006) Stellenbosch University http://scholar.sun.ac.za 9 2.3 Study area: biomass resource baseline The study area was assessed using geographic information systems (GIS) in order to determine the land availability and the potential productivity of available land for producing biomass in short- rotation coppice (SRC) systems (Von Doderer, 2009). Non-suitable areas, such as urban areas, areas with terrain limitations (i.e. areas that are too steep: > 35%), areas with water limitations (aridity index) and ecologically sensitive areas (e.g. protected areas, critical biodiversity areas, and water catchment areas), have been excluded, resulting in about 175 000 hectares (ha) that could be used for producing energy wood in SRC systems. Table 2 shows the available biomass production areas in the CWDM in terms of land use types and slope classes. Table 2: Available biomass production areas in CWDM Land use type Slope classes Total ? 35% ? 10% 11-20% 21-30% 31-35% ha % Intensive permanent and temporary farmland (ha) 3 028 328 34 4 3 394 2% Extensive dryland and improved grassland (ha) 53 842 5 329 631 121 59 923 34% Forest plantations (ha) 0 0 0 0 0 0% Fynbos, shrubland and bushland (ha) 51 147 31 320 21 125 8 818 112 410 64% Total (ha) 108 017 36 976 21 790 8 943 175 726 100% Total (%) 62% 21% 12% 5% 100% Source: Von Doderer (2009) Various developments in the South African forestry industry in recent years ? such as strong and continued growth of demand for wood and wood products, termination of timber production at some state plantations due to low productivity, particularly in the Southern and Western Cape (VECON-Consortium, 2006), as well as the increased use of logging residues by the existing forestry industry for generating its own energy ? has led to forest plantation residues in the CWDM not being available for generating bioenergy. The use of invasive alien plant (IAP) species, such as Black Wattle (Acacia mearnsii) and Port Jackson (Acacia saligna), can also be ruled out, as they are distributed over wide areas and, in many cases, in difficult terrain, resulting in high procurement costs. Furthermore, woody biomass sourced from invaded areas, after having been harvested, would not comprise a sustainable supply of biomass for generating electricity. IAPs pose a direct threat to South Africa?s biological diversity, to water security, the ecological functioning of natural systems, and the productive use of land. Hence, the clearance of invaded areas of IAPs without their re-establishment is desired. Stellenbosch University http://scholar.sun.ac.za 10 The biomass productivity assessment indicates that about 1.4 million tons of fresh lignocellulosic biomass could be supplied annually, assuming medium productivity (Von Doderer, 2009). Eighteen tree species were identified as being suitable for the area and climate conditions, of which four are indigenous and 14 are exotic (see Table 3, below). Indigenous species (e.g. Acacia karoo) are expected to produce higher yields in the interior, low production potential areas in the north-east of the CWDM, whereas exotic species (e.g. Eucalyptus cladocalyx) grow better in areas with higher production potential, compared with indigenous species. Table 3: Suitable indigenous and exotic tree species for biomass production in the CWDM Genera Species Common name Origi n a Re- generation E ase o f cu ltivatio n c In vas iv en ess d Ada p tab ility to sit e co n d itio n s tech n iq u e co p p ici n g Acacia karoo Sweet Thorn ind. se No - 1 5 mearnsii Black Wattle ex. se No - 5 5 saligna Port Jackson ex. se Yes 1 4 5 Casuarina cunninghamiana Beefwood ex. se - 1 3 5 glauca Swamp She-Oak ex. se - 1 3 5 Eucalyptus albens White Box ex. se Yes 1 3 3 camaldulensis Red River Gum ex. se Yes 1 3 5 cladocalyx Sugar Gum ex. se Yes 1 3 5 globulus Blue Gum ex. se Yes 1 3 5 gomphocephala Tuart ex. se Yes 1 3 3 melliodora Honey-scented Gum ex. se Yes 2 3 3 polyanthemos Red Box ex. se Yes 1 3 5 Pinus halepensis Aleppo Pine ex. se No 2 4 3 radiate Monterey Pine ex. se No 1 3 5 Rhus Lancea Karree ind. se/cu Yes 1 0 5 pendulina White Karree ind. se/cu Yes 1 0 5 Schinus Molle Pepper tree ex. se Yes 4 4 2 Ziziphus mucronata Buffalo Thorn ind. se/cu Yes 4 4 2 Source: Von Doderer (2009) Notes: a ind. = indigenous; ex. = exotic. b se = seedling; cu = cutting. c Ease of cultivation (1 ? easy, 2 ? easy-medium, 3 ? medium, 4 ? medium-difficult, 5 ? difficult). d Invasiveness (0 ? none, 1 ? low, 2 ? low-medium, 3 ? medium, 4 ? medium-high, 5 ? high). e Adaptability to site conditions (0 ? none, 1 ? low, 2 ? low-medium, 3 ? medium, 4 ? medium-high, 5 ? high). Eucalyptus cladocalyx is classified as a category two invasive species and could be commercially utilised in demarcated areas (RSA, 1983). Since it is only a potential transformer of the environment and is not quite as aggressively widespread as Acacia cyclops, it would constitute a Stellenbosch University http://scholar.sun.ac.za 11 viable wood species to be planted specifically as fuel wood (Munalula and Meincken, 2009). Current research on other fast-growing species and hybrids at the Department of Forest Science at Stellenbosch University might lead to the introduction of other species of trees for growing in SRC plantations. Figure 2, below, is showing the availability of potential sites for producing woody biomass in the CWDM. It also shows potential sites for bioenergy conversion, based on access to infrastructure such as main electricity lines, electricity substations, road networks, and potential consumers of by- products (e.g. thermal energy) from the bioenergy conversion. Figure 2: Lignocellulosic biomass availability of the CWDM, including main electricity grid and electricity substations, as well as potential sites for bioenergy conversion Source: Van Niekerk and Von Doderer (2009) Fourteen potential bioenergy conversion sites, also referred to as demand points, were identified in the CWDM (Roberts, 2009: 57) (see Table 4, below). The demand points were identified using the following determinants: proximity to substations and major grid lines, in order to minimise feed-in costs; proximity to external customers (e.g. canning industries, distilleries, cheese factories and food Stellenbosch University http://scholar.sun.ac.za 12 processing factories), to whom potential excess heat could be sold, resulting in the improved profitability of combustion and gasification plants; and electricity demand for each town within the CWDM. Furthermore, the proximity of the demand points to the road network was an important consideration, as the accessibility of the demand points affects feedstock transport efficiency and obviates the costs of additional infrastructure. Table 4: Potential demand points for bioenergy generation in the CWDM No. Potential sites Situated in industrial areas Close to electricity grid and electricity substations 1 Paarl a b 2 Franschhoek a b 3 Wolseley a b 4 Ceres a b 5 Rural Koue Bokkeveld b 6 Rural Cederberg 7 Worcester a b 8 De Doorns a b 9 Robertson a b 10 Touwsrivier a b 11 Ashton a b 12 Bonnievale a b 13 Montagu a b 14 Rural Montagu Notes: a Possibility of selling thermal energy to external customers. b Lower cost of transmitting electricity; if not close to substations, it would be necessary to build new substations or lay new transmission cables. 2.4 Biomass definition and properties Biomass refers to all organic materials that stem from green plants as a result of photosynthesis. It is a stored source of solar energy in the form of chemical energy, which can be released when the chemical bonds between adjacent oxygen, carbon, and hydrogen molecules are broken by various biological and thermo-chemical processes. Fossil fuels, including primarily coal, oil and natural gas, also originated from ?ancient? biomass that has been transformed through microbial anaerobic degradation and metamorphic geological changes over millions of years (Zhang et al., 2010; McKendry, 2002a; Kandiyoti et al., 2006). Fossil fuels are considered to be non-renewable sources of energy, considering the rate of their formation (millions of years) and consumption. In addition, burning fossil fuels releases net carbon dioxide (CO2) to the atmosphere. By contrast, biomass is a renewable resource and is considered to Stellenbosch University http://scholar.sun.ac.za 13 be CO2 neutral, as the CO2 released during combustion or other conversion processes is recaptured by the regrowth of the biomass through photosynthesis. In addition, the lower emission of environmentally detrimental gases, such as sulphur dioxide (SO2) and nitrogen oxides (NOx), during the combustion of biomass also plays a positive role in reducing global acid rain formation (Jenkins et al., 1998; Ni et al., 2006; IEA, 2007; Zhang et al., 2010). 2.4.1 Biomass classification Two classification approaches have been proposed based on the origin of the biomass and its properties (Williams, 1992; Jenkins et al., 1998). Based on origin, biomass can generally be divided into four primary classes: 1. Primary residues: by-products of food crops and forest products (for example, wood, straw, cereals, or maize); 2. Secondary residues: by-products of biomass processing for the production of food products or biomass materials (e.g. saw and paper mills, food and beverage industries, or apricot seed); 3. Tertiary residues: by-products of used biomass-derived commodities (e.g. waste, or demolition wood); 4. Energy crops. Based on properties, biomass can be classified into the following categories: 1. Wood and woody fuel (e.g. hard wood, soft wood, or demolition wood); 2. Herbaceous fuels (for example straw, grasses or stalks); 3. Waste (sewage sludge, refuse-derived fuel); 4. Derivatives (waste from paper and food industries); 5. Aquatic biomass (algae); 6. Energy crops (specifically cultivated for energy purposes). 2.4.2 Biomass composition Biomass includes a wide range of organic materials, which are generally composed of cellulose, hemicellulose, lignin, lipids, proteins, simple sugars and starches. Among those compounds, cellulose, hemicellulose, and lignin are the three main constituents (Mohan et al., 2006b, Zhang et al., 2010). Biomass also contains inorganic constituents and a fraction of water (Zhang et al., 2010, Jenkins et al., 1998). As for the elementary composition, carbon and oxygen with around 50% and 45% respectively account for more than 90% of the dry weight of a typical biomass. In addition, there are trace amounts of hydrogen (5wt.%), nitrogen (0.9wt.%), and chlorine (0.01-2wt.%). Stellenbosch University http://scholar.sun.ac.za 14 Since this study deals with bioenergy systems using woody biomass grown in a short-rotation coppice system as a feedstock (i.e. energy crop), greater attention will be given to the different components and the composition of trees suitable for this type of production system. A complete tree in its appearance can be distinguished between the part above the stump, which in forestry terms is also called whole tree, and the stump-root system. The whole tree, sometimes also called full tree, can be further divided in stem, crown branches and foliage, and in bioenergy terms can be summarised in above-ground biomass. The below-ground biomass includes the root system. Although the stump is not below-ground, it is often counted as below-ground biomass, as it is normally not used commercially and, hence, remains on site. Figure 3: Phenotype and basic components of broad-leaved tree Source: Seifert (2012) Based on the approach found in Dovey (2009), three tree components are used for bioenergy, namely, the stemwood, bark and branch biomass (sum of live and dead branch biomass, including all branches and tree tops, i.e. the portion of the stem with an over-bark diameter of less than 7cm). Dovey?s study (2009) shows that whole-tree harvesting, including the bark and branches, increases the biomass by around a half to one third, while exportation of the nutrients is increased by two to four times. Under some management practices, whole-tree harvesting may include the removal of Stellenbosch University http://scholar.sun.ac.za 15 foliage. Although foliage was not included in the study by Dovey, it accounted for 0.5-0.7% of the total above-ground biomass and 5-10% of the nutrient mass across all nutrients, for all species studied. In order to estimate the biomass availability in the CWDM, Eucalyptus cladocalyx and Acacia karroo were selected as the main species for energy wood production in an SRC system (see Von Doderer, 2009). The selection of the appropriate species depends, inter alia, on climate, site, ground and soil conditions, and cycle length, as well as on the production system. In turn, these factors influence growth rate, tree component distribution and the chemical composition of the biomass. To exacerbate matters further, the chemical composition of the different components of trees varies significantly. Vassilev et al.?s overview (2009) of the chemical composition of biomass for a variety of tree species illustrates how the bark, wood, and other biomass constituents differ. Table 5: Chemical composition and component distribution of the bioenergy tree Ultimate analysis Wt.% (dry basis) Proximate analysis Wt. % (dry basis) Carbon (C) 48.00 (45-50) Volatile matter (80-90) Hydrogen (H) 5.80 (5-7) Fixed carbon (6-18) Nitrogen (N) 0.25 (0.1-0.4) Ash (0.2-5.0) Oxygen (O)a 42.69 (40-45) Component Distributionc Wt.% Sulphur (S) 0.01 (0.0-0.2) Ash 3.25 (1-5) Bark 10.00 Branches 20.00 HHV (MJ/kg) 19.00 (18.00-20.00) Stemwood 70.00 Basic Density (kg/m3)b 720 (600-900) Sum 100.00 Notes: a Calculated by difference as assumed, inter alia, by Corujo et al. (2010) and Channiwala and Parikh (2002). b Cold-tolerant species, suitable for study area (see Von Doderer, 2009). c Biomass component distribution extrapolated from cold-tolerant species as discussed in Dovey (2009), taking the relatively young age of the stand at the time of harvesting into account (Kumar et al., 2011). However, given the vast area of study, as well as the heterogeneity thereof (including climate, ground and soil conditions), it was necessary to keep the variables for the bioenergy feedstock to a minimum, while retaining representivity to some degree for the above-mentioned species. Hence, it was decided to use a hypothetical mix of bioenergy trees comprising the same attributes, except for the growth rate, which differs for the various production areas. Table 5 shows the assumed main attributes of the ?bioenergy-tree? for the CWDM. The values in brackets are typical for other hardwood species found in the literature (Senelwa and Sims, 1999; Channiwala and Parikh, 2002; Guerrero et al., 2005; Turn et al., 2005; Garc?a-P?rez et al., 2007; Khodier et al., 2009; Munalula Stellenbosch University http://scholar.sun.ac.za 16 and Meincken, 2009; Vassilev et al., 2009; Corujo et al., 2010; Oasmaa et al., 2010; Venderbosch and Prins, 2010; Amutio et al., 2011; Bridgwater, 2011; Kumar et al., 2011; Sevilla et al., 2011). 2.4.3 Moisture content An important consideration when it comes to bioenergy is the moisture content of the feedstock. The moisture content of solid biofuels varies widely, depending on the time of harvesting; the location, type and duration of storage; and the feedstock pre-processing. The moisture is relevant not only concerning the calorific (heating) value of the biomass but also concerning its storage conditions, combustion temperature, and the amount of exhaust gas released during the energy conversion process. Two methods (dry and wet basis) are commonly used to specify the total moisture content. It is important to distinguish between them, especially when the moisture content is high. Equation 1: Moisture content of biomass (dry basis) Equation 2: Moisture content of biomass (wet basis) In the above expressions, wet weight refers to the burned condition and dry weight refers to the wood after a standardised drying process. It is important to state the basis on which total moisture content is measured. Mostly, the bioenergy feedstock moisture content is measured on a dry basis, and this method has also been used throughout this study. Moisture content is the most commonly used property of fuel wood. Its quantity is inversely proportional to the amount of heat that is recovered from conventional combustion, where the latent heat of evaporation is lost with flue gases (Nurmi, 1992: 160). 2.4.4 Heating value/energy content An important fuel property is the energy content, often expressed in megajoule per kilogram (MJ/kg). The standard measure of the energy content of a fuel is its heating value (HV), also called calorific value or heat of combustion. There are multiple measurements for the HV, depending on whether it measures the enthalpy of combustion (?H) or the internal energy of combustion (?U), and whether for a fuel containing hydrogen, water is accounted for in the vapour phase or the Stellenbosch University http://scholar.sun.ac.za 17 condensed (liquid) phase. With water in the vapour phase, the lower heating value (LHV) at constant pressure measures the enthalpy change due to combustion (Jenkins et al., 1998). The heating value is obtained by the complete combustion of a unit quantity of solid fuel in an oxygen- bomb colorimeter under carefully defined conditions. The gross heat of combustion or higher heating value (GHV or HHV respectively) is obtained using the oxygen-bomb colorimeter method, where the latent heat of the moisture in the combustion phase is recovered (Demirbas, 2009b). Hence, it is important to distinguish between the higher heating value (HHV), which refers to the energy content of absolutely dry biomass after a standardised drying process (?oven dry?), and the lower heating value (LHV) or effective heating value (EHV) of biofuels with a specific moisture content, where some energy is needed to evaporate the retained total moisture of the biomass as well as the total hydrogen of the fuel. The actual net calorific value, or EHV, of biomass containing a known percentage of water can be calculated from the HHV, which for many biofuels is available in the literature. Various formulas are available in the literature to calculate the EHV, e.g. as found in (Kaltschmitt et al., 2002) or (Nurmi, 1992). The equation from the latter was used in this study: Equation 3: Effective heating value of wet biomass Where EHV is the effective heating value of wet biomass (MJ/kg dry basis); HHV is the effective heating value of dry biomass (MJ/kg dry basis), depending on the chemical composition of the biomass; 2.45 is the energy required to vaporise water at 20?C (in MJ/kg); and MC is the moisture content of biomass (as a percentage). Based on the assumptions stated in Table 5, above, the calculated feedstock properties are provided in Table 6, below. Table 6: Density and effective heating value at different moisture content levels Moisture content (%) 0% 10% 20% 30% 40% 50% 60% 70% 80% Density (kg/m3) 720 792 864 936 1008 1080 1152 1224 1296 EHV (MJ/kg) 19.00 18.73 18.39 17.95 17.37 16.55 15.33 13.28 9.2 Stellenbosch University http://scholar.sun.ac.za 18 2.4.5 Biomass from dedicated energy crops Dedicated crops are grown first and foremost for energy, though they may also produce non-energy by-products. The ideal energy crop converts solar energy efficiently, resulting in high bioenergy yields (C4 plants are more efficient converters in high light and high temperature conditions), needing low agrochemical input, having a low water requirement, and having low moisture levels at harvest (Venturi and Venturi, 2003). When combustion is the end use of biomass, yield is probably the major decider between alternative crops, while for other end uses (e.g. ethanol production, biodiesel), quality and suitability of the crop are highly significant. The relative economic returns are likely to be the major drivers in deciding the outcome of competition for land use between bioenergy and production for food, feed and fibre. The relative returns for bioenergy compared with other land uses will be influenced by relative yields and values, which are determined by market forces and market distortions (e.g. subsidies). The yields and values of by-products (e.g. fodder) will also be significant determinants of returns (Cherubini et al., 2009). Another important aspect in determining land use is the agronomic practices, which vary with the intensity of production. In fact, increasing the intensity of cultivation (i.e. the frequency of tillage, quantity of fertiliser, use of irrigation) increases yields, but also increases GHG emissions and can challenge the goal of sustainable production. In any case, it is clear that to be acceptable, energy crops must fall within the parameters of sustainable agriculture. Dedicated energy crops can entail the added benefit of providing certain ecosystem services (e.g. C sequestration, biodiversity enhancement, salinity mitigation, and enhancement of soil and water quality). The value of these services will depend on the particular bioenergy system in question and the reference land use that it displaces. For example, these benefits would be high for a mixed species woodland planted in a cropping district suffering dry-land salinity as a result of historical land clearing, while on the other hand, if native tropical forests were displaced by bioenergy crops, the value of ecosystem services would be reduced (Cherubini et al., 2009). 2.4.6 Short-rotation coppice systems (SRC) The term SRC plantation applies to hardwood plantations (e.g. willow, poplar, gum) which are fast growing in their juvenile phase and capable of multiplication by cuttings and stump shooting. Through intensive cultivation, these properties are utilised for the production of biomass that can be used for energy production (Serup et al., 2002). Stellenbosch University http://scholar.sun.ac.za 19 In contrast to commercial forestry plantations, which are aimed at timber production and are characterised by long rotation cycles in order to produce high quality and dimension timber, SRC biomass production has far fewer requirements in terms of wood quality, and more emphasis is placed on the maximisation of volumetric production per time and area units (tonnes per hectare and year, t/ha/a). Hence, this is an agroforestry system where trees are grown for bioenergy and harvested when reaching the maximum mean annual increment (MAI). The MAI ? mean annual increment, or growth ? refers to the average annual growth a tree or a stand of trees has exhibited/experienced at a specified age (Kassier and Kotze, 2000). The MAI can be influenced, inter alia, by the number of trees planted per hectare and the applicable production method used. Generally, the more trees per ha that are planted, the sooner the MAI peak will be reached. The Institute for Commercial Forestry Research (ICFR) Bulletin (9/99) indicates, in various examples, the correlation of stems per hectare (sph) and age, when the MAI peaks (Coetzee, 1999) SRC plantations are generally characterised by conditions such as relatively flat, obstacle-free ground, small trees of uniform size growing in straight rows, uniform road spacing (in many cases), short transportation distances to the mill (in some cases), small branches, and bark characteristics differing from those of conifers (Seixas et al., 2006: 6). 2.5 Conclusions Chapter 2 elaborates on the geographical and physical boundaries of the study area, the Cape Winelands District Municipality (CWDM). These include parameters such as unemployment rate within the CWDM, and important climate data such as for rainfall and average temperatures. Based on previous work by the author, around 175 000 hectares were identified by means of a GIS as being suitable for the production of lignocellulosic biomass in short-rotation coppice (SRC) systems. With the aim of limiting the impact on the environment (e.g. biodiversity) and to avoid competition between food and biomass production, GIS was used to exclude non-suitable areas, most importantly areas with water limitations and ecologically sensitive areas. A land and biomass availability assessment for the CWDM resulted in an estimated annual supply of about 1.4 million tonnes of fresh woody biomass, assuming medium productivity of tree species such as Eucalyptus cladocalyx and/or Acacia karoo. Further, Chapter 2 describes the general characteristics of trees grown in SRC systems, as well as the chemical composition thereof, which have a great impact on the production sequences in a bioenergy system, including harvesting, processing and conversion into electrical and thermal energy. The moisture content of the bioenergy feedstock plays a particularly important role, as it affects, inter alia, handling, transport costs, and conversion efficiency. Stellenbosch University http://scholar.sun.ac.za 20 Due to differences in location, soil, climate, topography and availability of land within the CWDM, great variation in biomass productivity and resulting transport distances to potential conversion sites is expected. Further, the relevant bioenergy system components (e.g. harvesting system, transport system and conversion system) will have implications for, amongst others, financial viability, environmental impact and socio-economic considerations (e.g. employment creation potential), and as a result, for determining the most viable bioenergy system. This requires a systematic and comprehensive approach that is capable of organising and synthesising relevant information, leading to a transparent and reproducible process for determining the most viable bioenergy system. Stellenbosch University http://scholar.sun.ac.za 21 3 CHAPTER: LITERATURE REVIEW 3.1 Introduction The previous chapter gave background information on the availability of land and the resulting biomass productivity in the Cape Winelands District Municipality (CWDM), as well as on important lignocellulosic biomass characteristics relevant particularly for the bioenergy conversion processes. Variations in location, biomass availability and transport distances to potential bioenergy conversion sites ? due to differences in location, soil, climate and other factors ? result in implications for selecting the appropriate bioenergy system components and, thus, for financial viability, environmental impact and socio-economic considerations. The life-cycle analysis (LCA) method provides a sound and widely accepted approach for detailed analyses of complex systems such as is the case with bioenergy systems. Multi-criteria decision making analysis (MCDA), on the other hand, is a useful tool for integrating the generated information for further assessment and evaluation. This literature review presents the relevant literature consulted throughout the period of the research. The aim is to orientate the reader with the aid of the relevant literature. 3.2 Life-Cycle Assessment The heightened awareness of the importance of environmental protection, and the possible impacts associated with products (including product systems and service systems) manufactured and consumed has increased interest in the development of methods to better comprehend and reduce these impacts (ISO 14040, 1997: iii). Life-cycle assessment (LCA) has been postulated as an important and comprehensive technique. In an LCA study, the whole system involved in the production, use and waste management of a product or service is described (Baumann and Tillman, 2004: 19). LCA can assist in (ISO 14040, 1997: iii) ? Identifying opportunities for improving the environmental aspects of products at various points in their life-cycle; ? Decision-making in industry, governmental or non-governmental organisations (e.g. strategic planning, priority setting, product or process design or redesign); ? The selection of relevant indicators of environmental performance, including measurement techniques; and Stellenbosch University http://scholar.sun.ac.za 22 ? Marketing (e.g. an environmental claim, eco-labelling or environmental product declaration). This section gives some background information on LCA, its origin, and the general structures of the methodology. Furthermore, LCA applications in agriculture, forestry and bioenergy found in the literature are briefly discussed. 3.2.1 Origin of LCA Mainly packaging and waste management, as well as the oil crisis and the energy debate at the beginning of the 1970s have been the drivers of LCA. Pioneers are primarily from industrialised countries such as the USA, the UK, Germany and Sweden. Generally accepted as the first LCA study was a study on the consequences of packaging and manufacturing beverage containers by the Midwest Research Institute on behalf of Coca Cola (Baumann and Tillman, 2004). At the same time, other studies were initiated in Europe in both the private (Tetra Pak) and public (German Federal Ministry of Education and Science) sectors. Although public interest waned due to the ending of the energy crisis, private businesses and certain industries (e.g. product design) remained interested. With increased interest in environmental issues in the mid 1980s, the relevance of LCA rose again. The 1990s were characterised by the harmonisation and standardisation of the LCA methodology (e.g. ISO standards 14040-14044). Today, LCA represents a common environmental assessment tool and is applied in various fields, but mostly in the primary and secondary production sectors. The importance and relevance of LCA can be identified by a steadily growing LCA community. Various software suppliers, such as SimaPro, GaBi and Umberto, have developed user- friendly LCA interfaces and are specialising in data collection and the application of LCA. 3.2.2 LCA method Life-cycle assessment can be understood intuitively as a tool for analysing the potential environmental impacts and resources used throughout a product?s life cycle (from its ?cradle?, through its production and use, to its ?grave?, its disposal), i.e. from acquisition of the raw material, via the production and use phases, to waste management (Baumann and Tillman, 2004: 19). As illustrated below in Figure 4, an LCA consists of four phases, namely, goal and scope definition, inventory analysis, impact assessment, and interpretation of results. Each of the LCA phases is discussed further below. LCA is a technique for assessing the environmental aspects and potential impacts associated with a product, process or service system (ISO 14040, 1997: iii), by Stellenbosch University http://scholar.sun.ac.za 23 ? Compiling an inventory of the relevant inputs and outputs of a product, process or service system; ? Evaluating the potential environmental impacts associated with those inputs and outputs; and ? Interpreting the results of the inventory analysis and impact assessment phases in relation to the objectives of the study. Figure 4: Phases of a life-cycle assessment Source: ISO 14040 (1997: 4) 3.2.2.1 Goal and scope definition The ISO standard 14041 (ISO 14041, 1998) states that the goal definition shall ?unambiguously state the intended application, the reason for carrying out the study and the intended audience?. Further, it stresses that the goal and scope of an LCA study must be clearly defined and consistent with the intended application. The scope definition also implies important considerations, such as the functional unit to be used, the product system to be studied and the product system?s boundaries, which are discussed further below. Stellenbosch University http://scholar.sun.ac.za 24 ? Functional unit After the goal, the product(s) and the system have been decided on, the functional unit needs to be defined. The functional unit corresponds with a reference flow to which all other modelled flows of the system are related. This is why the functional unit needs to be quantitative. The functional unit provides a reference to which the input and output process data are normalised, and the basis on which the final results are presented. Generally, four types of functional units can be found in the bioenergy-LCA literature (Cherubini and Str?mman, 2011: 441): 1. Input unit related ? the functional unit is the unit of input biomass, measured in terms of either mass or energy. With this type of functional unit, results are independent of conversion processes and types of end-products. This unit can be selected by referring to studies that aim at comparing the best uses for a given biomass feedstock. 2. Output unit related ? here the functional unit is the unit of output, like units of heat or power produced, or kilometres of transportation provided. This type of functional unit is usually selected by referring to studies aimed at comparing the provision of a given service using different feedstocks. 3. Unit of agricultural land ? this functional unit refers to the hectare of agricultural land needed to produce the biomass feedstock. This unit should be the first parameter to take into account when biomass is produced from dedicated bioenergy crops. 4. Year ? results of the assessment may even be reported on a yearly basis. This type of functional unit is used in studies characterised by multiple final products, since it allows avoiding an allocation step. Typical functional units are emissions/sequestrations per unit of energy produced, emissions/sequestrations per service provided, emissions/sequestrations per unit of biomass input, and emissions/sequestrations per unit of land required. ? System boundaries The system boundaries of a product, process or service system need to be specified in terms of several dimensions (Tillman et al., 1994: 79), namely: ? Boundaries in relation to the natural system, ? Geographical boundaries, Stellenbosch University http://scholar.sun.ac.za 25 ? Time boundaries, ? Boundaries within the technical system. Boundaries in relation to natural systems. In general, activities included in the flow model of a technical system (the inventory model) are activities under human control. However, when a flow enters (or leaves) human control, it also enters (or leaves) the technical system. While it is relatively easy for non-renewable resources such as oil and minerals to be defined in the ?cradle?, i.e. during the extraction thereof, the boundaries for renewable resources, between the technical and the natural system, are less easy to draw. Renewable resources may be divided into fund resources (e.g. forests and agricultural land) and flowing resources (e.g. solar radiation and fresh water streams). In many cases, the boundary between the technical and the environmental system is obvious. However, when the life cycle includes forestry, agriculture, emissions to external wastewater systems and landfills, the system boundary needs to be explicitely defined (Finnveden et al., 2009: 5). Particularly in the context of assessing bioenergy systems, geographical boundaries are an important consideration, since certain types of biomass feedstock may be limited to certain areas, and productivities may differ from area to area, inter alia, limited by the availability of water, climate, soil or terrain conditions. Furthermore, infrastructure such as electricity production, waste management and transport systems vary from one region to another. Considering the time boundaries when defining the goal and scope of the study is an important aspect of the LCA, as it defines the type of LCA study concerned. Change-oriented LCAs are prospective. They look forward in time, since they are about alternative choices of action. Accounting LCAs ask what environmental impact a product may be made responsible for; hence, they are retrospective (Baumann and Tillman, 2004: 81). Boundaries within the technical system relate, inter alia, to production capital and personnel. Whether the environmental impact from production and maintenance of capital goods should be included in an LCA has been debated. For accounting LCAs, the guiding idea is often that the study should be as complete as possible, and the production and maintentance of capital goods should thus be included. For change-oriented LCAs, whether or not capital goods will be affected by the change has to be considered. A topic that is similar to that of capital goods is that of personnel. Processes require personnel, and personnel need food, transportation and so on. Personnel-related environmental impacts are usually not included in an LCA (Baumann and Tillman, 2004: 82). Stellenbosch University http://scholar.sun.ac.za 26 Boundaries within the technical system include those in relation to other products? life cycles. Sometimes several products (or functions) share the same process(es). If the environmental load of these processes is to be expressed in relation to one function only, then there is an allocation problem. A detailed discussion of the types of allocation problems, the principles pertaining to allocation, and specific operational allocation methods can be found in Baumann and Tillman (2004: 83-88, 110-119). 3.2.2.2 Life-Cycle Inventory (LCI) The inventory analysis involves data collection and calculation procedures to quantify the relevant inputs and outputs of a product system. These inputs and outputs may include the use of resources and releases to air, water and land associated with the system (ISO 14040, 1997). Figure 5: Scheme of the main steps and flows involved in an LCA Source: Bird et al. (2010: 57) Thus, a system model needs to be built according to the requirements of the goal and scope definition. The systems model is the flow model for a technical system with certain types of system boundaries (?cradle-to-grave?). The result is an incomplete mass and energy balance for the system. It is incomplete in the sense that only the environmentally relevant flows are considered, which more or less include the use of scarce resources and the emissions of substances considered harmful. Environmentally indifferent flows such as water vapour emissions from combustion or Stellenbosch University http://scholar.sun.ac.za 27 industrial surplus heat are disregarded. Figure 5, above, is an illustration of the main steps and flows involved in an LCA. 3.2.2.3 Life-Cycle Impact Assessment (LCIA) The impact assessment phase (LCIA) follows the LCI and involves an assessment of all relevant environmental impacts associated with the input and emissions mapped in the LCI. The LCIA thus also covers other chemically related impacts like global warming and tropospheric ozone formation, as well as the physical impacts on the land and input-related impacts or the availability of resources (Birkved and Hauschild, 2006; Wenzel et al., 1997; Hauschild and Wenzel, 1998). The level of detail, choice of impacts evaluated and methodologies used depend on the goal and the scope of the study (ISO 14040, 1997). The purpose of the LCIA is to provide additional information to help assess the results from the LCI so as to better understand their environmental significance (ISO 14040, 1997). Thus, the LCIA should translate the inventory results into their potential impacts in what are referred to as the ?areas of protection? of the LCIA (Consoli et al., 1993), i.e. the entities that are to be protected by using the LCA. Today, there is acceptance in the LCA community that the areas of protection offered by the LCA are human health, the natural environment, natural resources, and to some extent, the man- made environment (Udo de Haes et al., 2002; Finnveden et al., 2009). As mentioned above, the 1990s were characterised by the harmonisation and standardisation of the LCA methodology. Part of this process was the development of the LCIA method and consensus building, as was done in consecutive international working groups in SETAC, the Society of Environmental Toxicology and Chemistry (SETAC, 1993). Yet LCIA is a discipline still undergoing vibrant development (Finnveden et al., 2009). Today, several LCIA methods are available, and there is not always an obvious choice between them (Finnveden et al., 2009). In spite of the resemblance between some of them, there can be important differences in their results, not least for toxic impacts ? differences which can lead to conclusions that depend on the choice of LCIA method involved (Dreyer et al., 2003). An important consideration for the LCIA is the spatial differentiation concerned, as the impacts caused by an emission depend on the quantity of substance emitted, the properties of the substance, the characteristics of the emitting source, and the receiving environment (Finnveden et al., 2009). The site-generic approach (or global default) followed in current characterisation modelling includes only the first two aspects, inherently assuming a global set of average/standard conditions concerning the properties of the source and the receiving environment. For truly global impact Stellenbosch University http://scholar.sun.ac.za 28 categories like climate change and stratospheric ozone depletion, this is not a problem, since the impact is independent of where the emission occurs. For the other impacts modelled in the LCIA, however, the situation can be different. They are often regional or local in nature, and a global set of standard conditions can disregard large and unknown variations in the actual exposure to stimuli of the sensitive parts of the environment (Finnveden et al., 2009: 10). Sometimes differences in the sensitivities of the receiving environment can have a stronger influence on the resulting impact than differences in inherent properties of the substance that contribute to the impact (Potting and Hauschild, 1997; Finnveden et al., 2009). At the same time, spatial differences can be reduced in the case of sources from multiple locations, particularly when these result in uniform emission distributions. Hence, spatial differentiation can be relevant in LCIA (Udo de Haes et al., 1999), but this will increase the complexity of the LCA, requiring more information in some cases about emissions and more differentiation in the impact assessment. Whether using site-dependent factors reduces uncertainty compared with using generic defaults depends on the impact category of concern, and it may also depend on the case in question (Finnveden et al., 2009). 3.2.2.4 Interpretation Figure 6: Relationships of the elements within the interpretation phase with the other phases of LCA Source: ISO 14043 (2000: 4) The objectives of the life-cycle interpretation are to analyse results, reach conclusions, explain limitations, and provide recommendations based on the findings of the preceding phases of the LCA Stellenbosch University http://scholar.sun.ac.za 29 or LCI study, and to report the results of the life-cycle interpretation in a transparent manner. Furthermore, the interpretation phase is intended to provide a readily understandable, complete and consistent presentation of the results of an LCA or an LCI study, in accordance with the goal and scope definition of the study (ISO 14043, 2000: 3). Figure 6, above, shows the relationship of the elements within the interpretation phase with other phases of the LCA. 3.2.3 Types of LCA Finnveden et al. (2009: 3) distinguish between two types of LCAs: attributional and consequential LCAs. The attributional LCA is defined by its focus on describing the environmentally relevant physical flows to and from a life cycle and its subsystems. The consequential LCA is defined by its aim of describing how environmentally relevant flows will change in response to possible decisions (Curran et al., 2005). Similar distinctions have been made in several other publications, but often using other terms to denote the two types of LCA, and sometimes including further distinctions of subcategories within the two main types of LCA (Guin?e et al., 2002a). Baumann and Tillman (2004: 63), for instance, distinguish at least three types of LCAs: ? LCAs of the accounting type ? LCAs of the change-oriented type ? Standalone LCAs LCA studies of the accounting type are comparative and retrospective. This type of LCA is well suited to different types of eco-labelling and can be used in purchasing or procurement situations, since these applications involve a comparison of existing products. LCA studies of the change- oriented type are comparative and prospective. This makes them useful in product development, building design and process choices, since decision-making involves a comparison of options that may be implemented or produced in the future. A standalone LCA is used to describe a single product, often in an exploratory way in order to get acquainted with some important environmental characteristics of that product, identifying the ?hot spots? in the life-cycle, i.e. which activities cause the greatest environmental impact (Baumann and Tillman, 2004: 63). Examples of applications for different types of LCA methodologies are presented in Table 7, below. In general, the attributional method is the most used in LCA, but in LCA of bioenergy systems the consequential methods appears as the most broadly applied. Almost three-fourth of relevant studies reviewed by Cherubini and Str?mman (2011) compare the environmental impacts with those of a fossil reference system, as they are aimed at addressing the needs of policy makers, since consequential LCA is more relevant for decision-making. Stellenbosch University http://scholar.sun.ac.za 30 Table 7: Examples of life-cycle approaches for different applications Type of LCA Producers/ users of LCA information Life-cycle thinkinga Standalone LCAb Accounting-type LCAc Change-oriented LCAd Public policy makers/ authorities ? Development of environmental policies (e.g. producers? take- back schemes, recycling schemes and targets) ? Basis of development of producer take-back schemes ? Governmental procurement ? Development of eco-labelling criteria ? Basis for development of environmental policies (e.g. recycling schemes and targets) Industry ? Supply-chain management ? Product development ? Building design and construction ? Identification of ?hot spots? ? Environmental product declaration (based on standardised methodology) ? Purchasing ? Market communication ? Development of methodological standard for environmental product declaration ? Product development ? Building design and construction ? Process choices and optimisation ? Market communication Environ- mental NGOs ? Development of campaign ideas ? Critical evaluation of environmental strategies and measures ? Development of eco-labelling criteria ? Critical evaluation of environmental strategies and measures Consumers ? Lifestyle choices ? Eco-labelling (as users) Source: Baumann and Tillman (2004: 65) Notes: a Life-cycle thinking is less of an ?ordinary? type of quantitative LCA study, but can be described as a way of thinking that considers the cradle-to-grave implications of different activities without going into the detail of an LCA study. The principle of life-cycle thinking is often inscribed in the environmental policy of many companies. b Can be characterised as descriptive. c Can be characterised as comparative and retrospective. d Can be characterised as comparative and prospective. 3.2.4 LCA applied in agriculture An increasing number of LCA studies focus on applications in the agricultural context. A selection of LCA studies focussing on the agricultural context are listed in Table 8, below, subdivided into application area, year of publication, author(s), study area and a brief description of the respective study. The listed studies deal, inter alia, with conventional agricultural activities such as grain production, grape production/wine farming, fruit farming and related products, conventional and organic milk production or production of related products, and animal production. As mentioned above, setting geographical as well as technical system boundaries, as well as defining the functional unit have a great effect on the outcome of life-cycle assessments. Particularly LCA studies in the primary sector are affected by local differences such as land Stellenbosch University http://scholar.sun.ac.za 31 productivity, resource availability, production systems and degree of mechanisation, and variations in the energy supply mixes. This creates a significant challenge when comparing different LCA studies. Table 8: Application of LCA in the agricultural context Application Area Year Author(s) Study area Description Animal production/ products 2005 Basset-Mens and van der Werf France Current and alternative systems of pig production 2005 Van der Werf et al. France Production and on-farm delivery of concentrated feed for pigs 2011 De Boer et al. Review of studies on emissions from animal production 2011 Devers and Kleynhans Belgium, RSA Comparison of Flemish and Western Cape pork production Dairy production/ products 2000 Haas et al. Germany Grassland dairy farming differentiated in production intensities 2002 Berlin Sweden Production of semi-hard cheese 2003 De Boer Review of studies on conventional and organic milk production Fruit production/ products 2006 Mil? i Canals et al. New Zealand Apple production 2006 Mouron et al. Switzerland Life-cycle management on Swiss fruit farms: relating environmental and income indicators for apple-growing 2007 Mourad Brazil LCI of two perennial crops: green coffee and orange juice 2008 Pizzigallo et al. Italy Assessment of two wine farms (organic and semi- industrial) in the Siena territory 2009 Cholette and Venkat Italy Logistical options for delivering wine to consumers Grain production 2004 Brentrup et al. Adapted LCA for plant nutrition in arable crop production 2009 Meisterling et al. USA Conventional and organic wheat production Sugar beet production/ products 2001 Brentrup et al. Germany Sugar beet production under different fertilisation schemes 2005 Tzilivakis et al. UK Environmental impact and economic assessment of sugar beet production systems Vegetable production/ products 1998 Andersson et al. Sweden, Italy ?Hot-spots? of tomato ketchup production 2011 Cellura et al. Italy Protected crops: peppers, melons, tomatoes, cherry tomatoes, and zucchini in different types of greenhouses (tunnel and pavilion) Stellenbosch University http://scholar.sun.ac.za 32 A significant number of LCA studies deal with the production of energy crops aimed at producing biofuels such as bio-oil, bio-diesel or bio-ethanol. Among others, crops such as barley, wheat, rapeseed, sunflower and whole cropping systems, as well as sugar crops such as sugar beet or sugar cane are investigated (refer to Table 9, below). Table 9: Application of LCA in the agricultural energy crop context Application area Year Author(s) Study area Description Grain, oil seed 2005 Kim and Dale USA Corn and soybean production for biofuels, applying different cropping systems Grain 2005 Lech?n et al. Spain Agricultural production of wheat and barley grain for biofuels Oil seed 2007 Gasol et al. Spain LCA of a Brassica carinata bioenergy cropping system Grain 2008 Kim and Dale Various countries Fuel ethanol from corn grain via dry milling Various 2010 B?rjesson and Tufvesson Northern Europe Energy efficiency, greenhouse gases and eutrophication of biofuels from agricultural crops Oil seed 2010 Iriarte et al. Chile Environmental impacts, energy and water demand of rapeseed and sunflower Sugar cane 1999 Mohee and Beeharry Mauritius Energy generation from sugarcane bagasse Sugar cane 2001 Beeharry Mauritius Greenhouse gas mitigation potential of sugarcane bioenergy systems Sugar cane 2008 Macedo et al. Brazil Production and use of bioethanol from sugarcane Sugar cane 2009 Luo et al. Brazil Bioethanol from sugarcane 3.2.5 LCA applied in forestry A variety of LCA studies also deal with forestry, forestry products, or different forestry production phases, such as harvesting, forwarding or secondary transport. The increasing interest in short- rotation-coppice (SRC) systems is also reflected by the increasing number of LCA studies investigating the environmental impact of such bioenergy plantations. Table 10, below, entails a selection of LCA studies concerned with forestry operations and related products, as well as SRC plantations. Stellenbosch University http://scholar.sun.ac.za 33 Table 10: Application of LCA in the forestry context Application area Year Author(s) Country of application Nature and context of the problem Forestry operations 1997 Berg Sweden Energy use and environmental impact of forestry operations 2005 Berg and Lindholm Sweden General aspects of forestry operations 2003 Klvac et al. Energy audit of wood harvesting systems 2007 Lawes et al. Impact of colonial logging and recent subsistence harvesting in Afrotemperate forests Forestry operations/ products 2001 Karjalainen et al. Energy, carbon and other material flows of forestry and forest products Forestry products 2003 Jungmeier et al. Energy aspects in LCA of forest products 2006 Nebel et al. Germany Wood-laminated floorings Secondary transport 2009 Gonz?lez-Garc?a et al. Sweden Comparative environmental assessment of wood transport models: a case study of a Swedish pulp mill 2000 Forsberg Sweden, the Netherlands Comparison of biomass energy transport systems using LCA Short-rotation coppice (SRC) 1999 B?rjesson Sweden Maximisation of environmental benefits of energy crop cultivation (SRC forest and energy grass) 2003 Heller et al. Willow SRC plantations for bioenergy production 2007 Gruenewald et al. Germany Agroforestry systems for the production of woody biomass for energy transformation purposes 2010 Roedl, A. Germany Production and energy utilisation of wood from SRC plantations 2011 Fiala and Bacenetti Italy Economic, energetic and environmental impact in SRC harvesting operations 3.2.6 LCA applied in biofuel and bioenergy systems While only a relatively small number of LCA studies focus solely on agriculture and forestry, LCAs applied to whole biofuel/bioenergy systems receive much greater attention. This does not come as a surprise, since comparing the environmental impacts of a certain system to the environmental impacts of a reference system, both providing the same type of product or service, is one of the core aspects of an LCA. In bioenergy, this means that the selected bioenergy system is compared with a fossil reference system (Schlamadinger et al., 1997). In Figure 7, below, the full fuel chains of a bioenergy (left side) and a fossil (right side) system producing electrical and thermal energy are compared (Bird et al., 2010). Table 11, below, lists a variety of LCA studies investigating the environmental impacts of biofuel and bioenergy systems that use, among others, different types of feedstocks, conversion technologies, and whole bioenergy systems. In general, a distinction can be Stellenbosch University http://scholar.sun.ac.za 34 made between annual and perennial crops, where the former is assumed to be an intensive production system and the latter an extensive production system. The former is often converted using biochemical processes, while the latter is mostly converted using thermochemical processes (refer also to section 4.3.1.7). Table 11: Application of LCA in the biofuels and bioenergy context Application area Year Author(s) Study area Description Bioenergy 1999 Hartmann and Kaltschmitt Germany Electricity generation from solid biomass via co- combustion with coal 2004 Corti Performance analysis and LCA of biomass- integrated gasification combined cycle with reduced CO2 emissions 2005 Carpentieri et al. LCA of integrated-biomass gasification combined cycle with CO2 removal 2006 Botha and von Blottnitz South Africa Comparison of sugarcane-based production of electricity and fuel ethanol 2009 Varun et al. Review of LCAs on renewable energy for electricity generation systems 2010 Caserini et al. Italy LCA of domestic and centralised combustion Biofuels 1997 Kaltschmitt et al. LCA of biofuels under different environmental aspects 2005 Gnansounou et al. Blending of wheat-based bioethanol 2005 Larson Review of LCA studies on liquid biofuels for the transportation sector 2006 Bernesson et al. Sweden Comparing large- and small-scale production of ethanol for heavy engines 2006 Fredriksson et al. Sweden Incomplete LCAs of systems making organic farms self-sufficient in farm-produced biofuels 2006 Pehnt Dynamic LCA of renewable energy technologies 2007 Von Blottnitz and Curran Review of assessments conducted on bioethanol as a transportation fuel 2007 Zah et al. Environmental assessment of biofuels 2009 Cherubini et al. LCA of biofuel and bioenergy systems: key issues, ranges and recommendations 2009 Davis et al. Impact of biofuels 2009 Lardon et al. LCA of biodiesel production from microalgae 2010 Gonz?lez-Garc?a et al. Environmental profile of ethanol from poplar biomass as a transport fuel 2011 Cherubini and Str?mman Review of the recent bioenergy LCA literature 2011 Ren? et al. Methanol production from sugarcane bagasse 2011 Rousset et al. Brazil LCA of eucalyptus wood charcoal briquettes 2012 Nguyen and Hermansen Thailand Consequences of using molasses for ethanol production: system expansion for handling co- products in LCA of sugarcane bioenergy systems A review of the recent bioenergy LCAs in the literature has been done by Cherubini and Str?mman (2011) investigating state-of-the-art life-cycle assessments of bioenergy systems and future challenges for them. Besides discussing various parameters ? such as functional unit, allocation Stellenbosch University http://scholar.sun.ac.za 35 methods, reference systems, the use of different input data, as well as uncertainties and the use of specific local factors ? a qualitative interpretation of the LCA results is depicted, concluding that, with the exception of a few studies, most LCAs found a significant net reduction in GHG emissions and fossil fuel consumption when bioenergy replaces fossil energy. Figure 7: Full energy chains for comparison of bioenergy and fossil energy systems producing electricity and heat Source: Bird et al. (2010: 58) Stellenbosch University http://scholar.sun.ac.za 36 3.3 Multi-Criteria Decision-Making Analysis Management decisions at a corporate level in both the public and private sectors will typically involve consideration of a wide range of criteria, especially when consensus needs to be sought across widely disparate interest groups. The very nature of multiple-criteria problems is that there is much information of a complex and conflicting nature pertaining to them, often reflecting differing viewpoints and often changing with time. For instance, sustainable bioenergy systems are, by definition, embedded in social, economic, and environmental contexts and depend on support by many stakeholders with different perspectives. The resulting complexity constitutes a major barrier to the implementation of bioenergy projects (Buchholz et al., 2009: 484). In order to overcome this barrier, decision-makers are dependent on a decision support process that helps to organise and synthesise relevant information in a way that leads them to feel comfortable and confident about making a decision, minimising the potential for post-decision regret by being satisfied that all criteria or factors have properly been taken into account. Multi-criteria decision- making analysis (MCDA) is a tool aimed at aiding such a decision-making process. It can be defined as ?an umbrella term to describe a collection of formal approaches which seek to take explicit account of multiple criteria in helping individuals or groups explore decisions that matter? (Belton and Stewart, 2002: 2). In contrast with cost-benefit-analysis (CBA), which relates to the use of monetary scales when confronted with multiple dimensions of management problems, MCDA ? in its use of interval scaling and weights, and focussing on relative trade-offs within each dimension ? avoids many of the problems associated with monetary evaluation techniques, while still permitting the assessment of potential trade-offs between criteria (Stewart, 1997: 10). MCDA also has some desirable features that make it an appropriate tool for analysing complex problems such as are typically found in natural resource management (Mendoza and Martins, 2006: 1). First, it can deal with mixed sets of data, quantitative and qualitative, including expert opinion. Data pertaining to and knowledge about natural resource management systems are seldom complete, known with certainty, or fully understood. Hence, the ability to accommodate these gaps in information and knowledge through qualitative data, expert opinions, or experimental knowledge is a distinct advantage. Second, it is conveniently structured to enable a collaborative planning and decision-making environment. This participatory environment accommodates the involvement of multiple experts and stakeholders (Mendoza and Prabhu, 2003: 331). In essence, the process of MCDA involves comparing management alternatives from different viewpoints (criteria), and combining these comparisons as weighted scores to obtain an overall ranking of alternatives (De Lange, 2010). The concept of an optimum, however, does not exist in a Stellenbosch University http://scholar.sun.ac.za 37 multi-criteria framework, and thus MCDA cannot be justified with the optimisation paradigm frequently adopted in traditional operational research/management science. MCDA is an aid to decision-making, a process which seeks to (Belton and Stewart, 2002: 3): ? Integrate objective measurement with value judgement, and ? Make explicit and seek to manage subjectivity. Subjectivity is inherent in all decision-making, in particular in the choice of criteria on which to base the decision, and the relative ?weight? given to those criteria. MCDA does not dispel that subjectivity; it simply seeks to make the need for subjective judgements explicit and the process by which they are taken into account transparent (which again is of particular importance when multiple stakeholders are involved) (Belton and Stewart, 2002: 3). The purpose of decision support and decision analysis, however, is not to ?solve? a particular decision-making problem. Rather, their purpose is to facilitate learning and understanding of the problem faced; to facilitate identifying own, other parties? and organisational priorities, values and objectives; and to facilitate exploring these in the context of the problem. This process produces the insight that guides decision makers in identifying a preferred course of action, helping them make better decisions and promoting transparency (Laukkanen et al., 2002: 128). Fundamentally, MCDA has inherent properties that make it appealing and practically useful (Mendoza and Martins, 2006: 1). Belton and Stewart (2002: 5) portray some of these properties as follows: ? MCDA seeks to take explicit account of multiple, conflicting criteria in aiding decision- making; ? MCDA assists in structuring the problem concerned; ? The MCDA models used provide a focus and a common language for discussion; ? MCDA facilitates decision-making by assisting the decision maker to place the problem in context, to determine the stakeholder preferences and to present the information; ? MCDA serves to complement and to challenge intuition, acting as a sounding-board against which ideas can be tested without seeking to replace intuitive judgement or experience; ? MCDA improves the legitimacy of decisions by leading to better considered, justifiable and explainable decisions, providing and audit trail for a decision. Stellenbosch University http://scholar.sun.ac.za 38 3.3.1 Basic concepts of MCDA Before turning to the phases of MCDA, some basic concepts of multi-criteria decision-making are briefly defined (Hobbs, 1992: 1768). A criterion is a physical, biological, economic, or other characteristic of the alternatives that the decision makers consider important. ?Attribute? and ?objective? are common synonyms for criterion. Value scaling is the creation of single criterion value or utility functions that convert a criterion into a measure of worth. Let be the value of criterion for option and be the vector of values of criteria for . The creation of a single criterion value function for project costs is an example. Criterion value functions include just the decision maker?s evaluations of different levels of the criterion; utility functions, in addition, capture the decision maker?s attitudes toward risk. Amalgamation rules combine several single-criterion value functions into an overall index of worth . An example is the additive value function, in which overall worth is the weighted sum of scaled criteria: Equation 4: Additive value function ? The weights used by many rules to combine criteria are chosen by weighting methods. Some rules also ask for goals, which represent desired levels of criteria. 3.3.2 Phases of MCDA In general, MCDA can be structured in five key phases, namely, (i-ii) problem identification and structuring, (iii-iv) model building and use, and (v) development of action plans (refer also to Figure 8). In the first two phases, problem identification and structuring, the various stakeholders, including facilitators and technical analysts, need to develop a common understanding of the problem, of the decisions that have to be made, and of the criteria by which such decisions are to be judged and evaluated. Key concerns, goals, stakeholders (classified in terms of level of interest and power of influence), actions and uncertainties need to be identified. This is the most important step in the process, since a well-structured problem is halfway solved, and since a mismatch between problem and model will lead to certain failure (De Lange, 2006: 65). Phase three and four encompass problem identification and structuring, wherein a dynamic process ? after extracting the essence of the decision-making problem, the decision maker?s preferences, value trade-offs, goals, and objectives, among others ? is translated by a formal model allowing the alternative policies or Stellenbosch University http://scholar.sun.ac.za 39 courses of action under consideration to be compared in a systematic and transparent manner. The nature of the model will differ according to the nature of the problem and whether the alternatives are explicitly or implicitly defined. The fifth phase, the development of action plans, is concerned with implementing the results, by translating the analysis into specific plans of action. It should be reiterated, however, that the MCDA approach does not provide the ?right answer?, even within the context of the model used, as the concept of an optimum does not exist in a multi-criteria framework. Figure 8: The process of MCDA Adapted from Belton and Stewart (2002: 6) Iteration within and between the key phases of an MCDA can be expected, each of which is subject to a myriad of internal and external influences and pressures. This description is generic to the whole of MCDA. However, the emphasis of an MCDA is on the second phase, model building and use. This leads to a variety of MCDA approaches, which can be distinguished by the nature of the model, the information required and how the model is used (Belton and Stewart, 2002: 7). Identifi- cation of the problem/ issue Problem structuring ?Goals ?Alternatives ?Key issues ?Values ?Stakeholders ?Uncertainties ?Constraints ?External environment Model building ?Defining criteria ?Specifing alternatives ?Elicting values Using model to inform and challenge thinking ?Challenging intuition ?Creating new alternatives ?Sensitivity analysis ?Robustness analysis ?Synthesis of information Developing an action plan Stellenbosch University http://scholar.sun.ac.za 40 Although the methods of MCDA could in principle be utilised by the decision maker directly, Belton and Stewart (2002: 14) suggest that in the vast majority of non-trivial problems, the MCDA methods will be implemented by a facilitator or analyst, working with decision makers and/or interested or responsible parties. MCDA can be applied in a variety of contexts, but the output required by the user or client determines the type of MCDA used. Roy (1996) identifies four different problematiques, i.e. broad typologies or categories of problem, for which MCDA may be useful: ? The choice problematique ? where a choice from a set of alternatives must be made; ? The sorting problematique ? where management actions must be sorted or categorised (e.g. acceptable, unacceptable); ? The ranking problematique ? where management alternatives must be sorted according to a given preference ordering; ? The description problematique ? where an orderly description of actions and consequences is needed to facilitate choice. To these, Belton and Stewart (2002: 15) add the following: ? The design problematique ? where new management/decision alternatives are developed, meeting goals and aspirations revealed through the MCDA process; ? The portfolio problematique ? where a subset of alternatives from a larger set of possibilities is chosen, taking the internal and external characteristics of the individual management alternative into consideration. 3.3.3 Types of MCDA Buchholz et al. (2009: 485) distinguish between two general types of MCDA, namely, multi- objective decision-making (MODM) approaches working with an indefinite set of possible scenarios, and multi-attribute decision-making (MADM), suggesting a finite set of scenarios. For instance, linear programming follows the MODM approaches, starting with a set of principles (e.g. maximising efficiency, reducing costs) and resulting in an optimised scenario. On the other hand, MADM approaches, which are the concern in this study, start with a set of scenarios/alternatives, which are further scrutinised to determine how well they fit a set of principles. MADM approaches can be further differentiated into (Belton and Stewart, 2002: 9): ? Value measurement models, which assign a numerical score to each alternative, thus ranking scenarios depending on how they score according to a weighted list of criteria; Stellenbosch University http://scholar.sun.ac.za 41 ? Goal, aspiration and reverence level models, which are goal programming methods where ?a mathematical programming algorithm is used to approach these goals as closely as possible? (Belton and Stewart, 2002: 105); ? Outranking models, where the alternatives are compared pairwise to check which of them are preferred regarding each criterion (L?ken, 2007). After aggregation of the results for each criterion, this approach suggests to what extent the alternatives outrank one another (Buchholz et al., 2009: 485). ? Utility and value function approaches, among which multi-attribute utility theory (MAUT) and analytic hierarchy process (AHP) theory are best known in South Africa (De Lange, 2006: 66). These approaches synthesise assessments of the performance of alternatives against individual criteria (scores), together with inter-criteria information reflecting the relative importance of the different criteria (weights) to give an overall evaluation of each alternative, indicative of the decision makers? preferences (Belton and Stewart, 2002: 119). MAUT and AHP differ primarily in terms of the underlying assumptions about preference measurement, the methods used to elicit preference judgements from decision makers, and the manner of transforming these into quantitative scores (Belton and Stewart, 2002: 10). MAUT is the only technique that addresses uncertainty in its axiomatic framework by analysing the expected values. AHP assesses marginal utilities by asking for the relative strengths of preferences between each pair of possible scenarios. AHP is useful, simple and consequently, a widely used tool (De Lange, 2006: 67). In many ways, goal programming and reference point techniques represent the earliest attempts at providing formal quantitative decision aids for complex problems involving multi-criteria decisions. Goal programming (GP) was introduced by Charnes et al. (1955). This MCDM technique is regarded as the method that operationalises the Simonian ?satisficing? approach for the achievement of a DM?s objectives (Simon, 1955). A comprehensive review may be found in Lee and Olson (1999). The essential idea in goal programming is that, instead of optimising a set of objectives, the DM first sets targets for their achievement, and then an acceptable solution is found by minimising the deviations from the set of targets. The minimisation of deviations from predetermined targets can be accomplished using several alternative methods, and of these, the two most widely used ones are weighted goal programming (WGP) and lexicographic goal programming (LGP) (Rehman and Romero, 1993: 241). Reference point approaches start by having the decision maker specify Stellenbosch University http://scholar.sun.ac.za 42 achievement levels for each criterion in terms of relevant performance measures. These levels are typically of three types (De Lange, 2006: 67): ? Goal levels (performance level that will fully satisfy the goals of the decision maker); ? Exclusion levels (performance level at which, if violated, the entire scenario becomes unacceptable); ? Reference levels (expectation of the decision maker of an acceptable compromise between the conflicting demands of different criteria). The outranking approaches differ from the value function approaches in that there is no underlying aggregative value function. The output of an analysis is not a value for each alternative, but an outranking relation among the sets of alternatives (Belton and Stewart, 2002: 233). Outranking approaches represent evidence for and against the statement that one alternative is better than another. Evidence takes the form of voting between criteria. The elimination and choice translating reality (ELECTRE) family of methods and the preference ranking organisation method of enrichment evaluations (PROMETHEE) are the two most prominent outranking approaches. Outranking approaches focus pairwise on comparisons of alternatives, and are thus generally applied to discrete choice problems (Belton and Stewart, 2002: 234). Game theory approaches represent another type of multi-criteria decision-making where each criterion can be associated with a single player. Game theory synthesises the utility functions of individual players into a social utility function. It assumes that each criterion is associated with a particular ?player? and that marginal utilities can be associated with each policy scenario (Romero & Rehman, 2003: 110-113). Game theory aims at identifying solutions to the decision problem that represent the most acceptable compromise between players. Nash equilibriums ? seeking the policy scenario that maximises the product of the marginal utilities ? are the simplest forms of this type of approach (De Lange, 2006: 67). The interactive multiple-criteria decision-making approach implies the progressive evolution and definition of decision makers? preferences through interactions between them and the results generated from various runs of the model. These interactions become a dialogue in which the model responds to an initial set of the decision maker?s preferences or trade-offs, and then when this response has been examined, another set is offered, and thus the procedure progresses in an interactive and iterative way until the decision maker has found a satisfactorily outcome (Romero & Rehman, 2003: 79-102). Stellenbosch University http://scholar.sun.ac.za 43 Further details on the concepts, approaches and other related information on MCDA can be found, inter alia, in Belton and Stewart (2002), Romero and Rehman (2003), Hobbs et al. (1992), Buchholz et al. (2009) and (Mendoza and Martins, 2006). Due to its simplicity, the natural appeal of expressing relative importance by means of pairwise comparisons in ratio terms, and the resulting acceptance and common application ? the analytic hierarchy process (AHP) was selected for the multi-criteria decision-making assessment applied in Chapter 7. 3.3.4 MCDA applied in agriculture and forestry Parra-Lop?z et al. (2008) use AHP to compare the different multifunctional performances (including economic, environmental, social, cultural and technical criteria) of agricultural olive production systems, with the aim of selecting the most viable system based on the concept of sustainable agriculture. Different groups of experts were tasked to assess the hypothetically greater sustainability of organic and integrated farming over conventional farming systems in the medium/long-term under average conditions in an Andalusian/Spainish context. The multifunctional performances were determined in scoring exercises using expert choice software, followed by a weighting exercise involving the identified criteria. Despite differences in the ideological tendencies of the experts, the results indicate a superior global performance of organic and integrated agriculture. Karami (2006) also used AHP with the aim of selecting an appropriate irrigation method, determining the preference of farmers, using cluster analysis, while experts determined the priority of three irrigation methods (border, basin and sprinkler) for each group of farmers. In 74% of cases, experts confirmed the farmer?s decision in selecting the irrigation method, while questioning the appropriateness of the decisions of the remaining 26% of farmers. 3.3.5 MCDA applied in biofuel and bioenergy systems An application of MCDA in the field of renewable energy provision for the Metropolitan Borough of Kirklees in Yorkshire, UK, was done by Burton and Hubacek (2007), assessing which of small- scale or large scale-approaches could achieve energy targets in the most socially, economically and environmentally (SEE) effective way. The alternatives ? solar PV, micro-hydro, micro-wind, large- scale wind, large-scale hydro, biomass, landfill gas and energy from waste ? were assessed against a set of eight criteria (capital cost, operation and maintenance, generation capacity, lifespan, carbon emissions, noise, impact on the environment, and social score). The results indicate that small-scale schemes (micro-hydro, solar PV, micro-wind) are the most SEE effective, despite large-scale schemes (large-scale hydro, energy from waste, landfill gas, biomass) being more financially viable. Stellenbosch University http://scholar.sun.ac.za 44 Another MCDA study from the UK by Longden et al. (2007) assessed the impacts of several energy-from-waste (EfW) strategies, using geographical information systems and MCDA, in the administrative areas of Cornwall and Warwickshire for developing EfW policy options. Similarly, small-scale EfW facilities score the highest overall for the chosen criteria. The study further concludes that scale is more important than technology design in determining the overall EfW policy impact. An evaluation of the potential of MCDA to facilitate the design and implementation of sustainable bioenergy projects was performed by Buchholz et al. (2009), reviewing four MCDA tools (Super Decisions, DecideIT, Decision Lab and NAIADE), with a special focus on multi-stakeholder inclusion. Using data from a multi-stakeholder bioenergy case in Uganda, ecological (i.e. reduced completion for fertile land, reduced pollution), social (i.e. low training needs, high employment rate, diversity and certainty of ownership and business schemes, and low planning and monitoring needs), and economic criteria (i.e. increased local commerce, high cost efficiency, and high security of supply) were included. The evaluation showed that different MCDA tools may give different results, as they focus on different steps in the decision process and differ widely in their mathematical methods and structure. This highlights the importance of selecting the appropriate MCDA tool. However, in the Ugandan case study, social criteria were consistently identified by all tools as being decisive in making a bioelectricity project viable. Furthermore, the authors conclude that MCDA can assist in overcoming implementation barriers by (i) structuring the problem, (ii) assisting in identifying the least robust and/or most uncertain components in bioenergy systems and (iii) integrating stakeholders into the decision process. An approach that sets out to support the technical-scientific decision-making process for a preliminary comparative assessment of concentrated solar thermal power (CSP) technologies was presented by Cavallaro (2009). Considering the energy sector as a key role player in achieving sustainable development and in meeting environmental goals, the selection of the appropriate CSP option required the use of decision-making tools. By applying the PROMETHEE method, 12 CSP alternatives were assessed against a set of three economic (investment costs, operating and maintenance costs, levelised electricity costs) and four technical criteria (maturity of technology, environmental impacts, temperature output, solar capacity factor). Based on input-data mainly drawn from (Pitz-Paal et al., 2004), which envisaged the installation of the CSP plant in Seville, Spain, quantitative measures apply to five of the criteria, while the remaining two, being qualitative in nature, were scored by impact scales. Following the calculation procedure, the ranking obtained showed that alternatives using hybrid solar technologies with gas were favoured, while pure thermal Stellenbosch University http://scholar.sun.ac.za 45 solar power appeared not to be competitive. Cavallaro further concludes that the decision-making process for an energy project is the closing link in the process of analysing and handling different types of information, including environmental, technical, economic and social data. Thus, MCDA can help in aiding decision-making in a technical-scientific manner, with the chosen paths being clearly and consistently justifiable. A multi-criteria evaluation of cleaner development mechanism (CDM) projects, aimed at offsetting greenhouse gas emissions and at contributing to sustainable development in the host country, was presented by (Nussbaumer, 2009). The potential contribution to local sustainable development of 39 CDM projects was assessed by applying a multi-criteria method, in order to evaluate how labelled projects perform in comparison with similar non-labelled projects with respect to sustainability criteria. The goal of Nussbaumer?s analysis ? based on the so-called multi-attribute assessment of CDM (MATA-CDM), itself underpins the multi-attribute utility theory (MAUT) framework ? was purposed not at establishing a ranking among projects, but rather at comparing and discussing the contribution of initiatives aimed at promoting broad sustainable development dividends from CDM projects in relative terms. Thus, the emphasis of this study was on the scoring and on the resulting comparison of the alternatives against a set of pre-defined criteria. The weighting of the criteria and the subsequent aggregation of the weighted scores, which forms part of a conventional MCDA, were omitted, as the authors argued that the compensability of criteria would hide the potential trade-offs, which were questioned by stakeholders while the case-study was being undertaken. Furthermore, it was argued that the weighting of criteria may have been seen as arbitrary and value- driven if assigned by an individual. Each CDM project was assessed against a set of four social, environmental and economic criteria, based on the traditional ?three pillars of sustainable development? classification. Nussbaumer?s evaluation suggests that the sustainable development profile of ?premium? CDM projects tends to be comparable or slightly better than similar ordinary projects. However, the distinction between projects may very well be within the range of uncertainty intrinsic to such assessments. 3.3.6 The combined use of LCA and MCDA By definition, LCA considers only environmental issues. In reality, however, there are also other issues, such as social, economic, political and technical issues, that cannot be ignored in any decision. Therefore, LCA should be seen in a broader context, as a tool that provides information on the product?s environmental impacts for decision-making (Miettinen and H?m?l?inen, 1997: 279). Various studies have demonstrated that the structure of LCA has parallels with the decision analysis Stellenbosch University http://scholar.sun.ac.za 46 approach to decision-making (Basson and Petrie, 2007; Miettinen and M?m?l?inen, 1997, Sepp?l? and H?m?l?inen, 2001). Aiming at covering the three main aspects of sustainability, (Valente et al., 2011) used the LCA framework to examine the environmental, economic and social impacts of the potential exploitation of woody biomass resources for energy in an Alpine forest fuel supply chain, from the forest stand to the direct heating plant. The study compares only two alternatives, a traditional and an innovative logging system and assesses them against a set of three criteria, namely, global warming potential, financial costs and direct employment potential. Although discussing a multi-criteria problem, the authors do not make use of a multi-criteria decision-making aid, but rather make use of an extended discussion of the trade-offs shown in the results. Options for broadening and deepening LCA approaches are discussed by Jeswani et al. (2010). With the focus on strengthening LCA as a tool and to increase its usefulness for sustainability decision-making, the authors argue that there is a need to expand the ISO?s LCA framework by integrating and connecting it with other concepts and methods. Among others, the paper explores procedural methods/assessment methods and analytical methods. The former, which are defined as forecasting procedural methods and are used ex ante to support the decision-making process for policies and projects, include environmental impact assessment (EIA), strategic environmental assessment (SEA), sustainability assessment (SA) and multi-criteria decision analysis (MCDA). Analytical methods include those that are used to identify and analyse the environmental, as well as social and economic impacts related to policies, projects, products and substances. Most of these methods focus primarily on one particular sustainability dimension, and are often applied as part of the assessment process. Included are environmental methods such as material flow analysis (MFA), substance flow analysis (SFA), energy/exergy analysis (EA), environmental input-output analysis (EIOA), risk assessment (RA), economic methods such as life-cycle costing (LCC), cost-benefit analysis (CBA), eco-efficiency (EE), and social methods such as social life-cycle assessment (SLCA). One of the first attempts at integrating LCA into the decision-making process was made by Miettinen and M?m?l?inen (1997), showing that approaches and tools from decision analysis would be beneficial both in planning an LCA study and in interpreting and understanding the results. To illustrate their approach, the authors used an LCA study on beverage packaging systems, combining LCA with the multi-attribute value theory (MAVT). Although economy-related criteria (e.g. investments, employment, competition, logistics), consumer-related criteria (e.g. safety, price, ease of use) and environmental criteria are discussed, only the latter were applied in the decision-making Stellenbosch University http://scholar.sun.ac.za 47 analysis, with the focus on three sub-criteria, namely, resource depletion, ecological impacts and human health impacts. In 2006 an article by Soares et al. was published, proposing a new method for the identification of environmental impact category weights using a panel approach and MCDA in the weighting step of the LCIA. LCA results are assessed by means of MCDA, supporting a comparative evaluation, i.e. by aggregating them into single scores that synthesise the environmental scores of all the impact categories. In terms of a weighting method in the context of LCIA, the authors recognise that site- specificities, such as local or regional conditions and policies, need to be taken into account. Furthermore, the weighting method needed to be simple ? in order to recalculate depending on the goal and scope of the LCA study ? transparent, and simple to apply. Each life-cycle impact category (e.g. climate change, ozone depletion, abiotic resources) is scored against a set of criteria following a pair-wise comparison (distance-to-target, reversibility, duration, scale, natural resources, ecosystem health, and human health) involving weighting, employing an MCDA procedure, resulting in a single score. Again, only environmental criteria were taken into account. An approach involving considering both technical and valuation uncertainties during decision- making, supported by environmental performance information, using on an LCA was published by Basson and Petrie in 2007. Key elements included in their approach were a ?distinguish-ability analysis? to determine whether the uncertainty in the performance information was likely to make it impossible to distinguish between the alternatives, and the use of a multivariate statistical analysis approach (principal components analysis), which facilitated the rapid analysis of large numbers of parallel sets of results and enabled the identification of choices that lead to similar and/or opposite evaluations of alternatives. In demonstrating the approach in a technology selection decision for the recommissioning of a coal-based power station (involving six design scenarios/alternatives), four main evaluation criteria were taken into consideration, namely, financial, technical, environmental and social. While including 22 sub-criteria for the environmental impact, only two sub-criteria were considered for the financial and social criteria, i.e. net present value and total number of permanent jobs, respectively. The main environmental criteria were subdivided into nine inventory-level criteria, eight eco-indicator ?99 midpoint criteria (one of the LCIA impact assessment methods), three eco-indicator ?99 endpoint criteria, and two additional criteria (water use and affected land footprint). The performance of the alternatives considering all evaluation criteria was compared using value function analysis. Basson and Petrie (2007) conclude that when making decisions, it is necessary to ensure that the alternatives selected for further consideration/implementation are consistent with the value systems and preferences of the stakeholders. This requires the explicit Stellenbosch University http://scholar.sun.ac.za 48 modelling of stakeholder values and preferences rather than just ?encoding? value judgements and preferences as part of the information used to support decision-making. In 2010 El Hanandeh and El-Zein proposed ELECTRE SS, a modified version of the multi-criteria decision aid ELECTRE III, which modifies the exploitation phase through a new definition of the pre-order and through the introduction of a ranking index. The new approach accommodates cases where incomplete or uncertain preference data are present. The authors demonstrated the proposed approach by applying ELECTRE SS in the context of the municipal solid waste (MSW) of Sydney, aimed at identifying a strategic plan for the treatment of biodegradable waste. With the aim of minimising the quantity of waste sent to landfills, increasing energy production, and decreasing greenhouse gas emissions, ten alternatives, ranking from business-as-usual to various options for treating organic and paper fractions in MSW, were assessed. While acknowledging the multidimensional impact (environmental, economic, hazard to humans, as well as social, political and regulatory considerations), only seven criteria, subdivided into the environment (acidification gases, smog precursors), health hazards (heavy metals, dioxins) and regulations (GHG emission reduction, green energy recovery, landfilled waste) were taken into account. Performance values for all criteria were obtained through LCA modelling. The results indicate that anaerobic digestion is generally a better option than composting and incineration, mainly because of the higher rates of electricity generation and the associated credits gained for avoided emissions. Furthermore, the authors conclude that the results show the benefits of using an MCDA tool such as ELECTRE SS in combination with LCA modelling as a decision aid tool for MSW management planning under conditions of uncertainty, and that the tool could also be applied in other environmental management fields. Further developments in the weighting and valuation of results using environmental systems analysis tools (ESATs) such as LCAs, strategic environmental assessments, cost-benefit analyses and environmental management systems are reviewed by (Ahlroth et al., 2011). Concerning weighting and valuation, with the aim of presenting such results in a comprehensible way and making alternatives easily comparable, the authors distinguish between valuation, i.e. monetary methods (e.g. market prices, avoided costs, and revealed, stated, imputed or political willingness to pay) and weighting methods based on monetary terms or value judgements (e.g. proxy, distance-to- target, panel weighting), expressed as weights. Acknowledging that there are other rationales for classifying valuation/weighting methods, the authors further discuss strategic environmental assessment (SEA), life-cycle assessment (LCA), life-cycle cost (LCC) analysis, standardised environmental management systems (EMS), system of environmental and economic accounts Stellenbosch University http://scholar.sun.ac.za 49 (SEEA), as well as risk assessment and impact pathway analysis for decision-making with regard to the environment, in both the public and private sectors, as well as at all governance levels. Particularly in context of multi-criteria problems, LCA is referred to as a useful tool for aiding the decision-making process. An attempt at providing a basic decision support tool for the assessment of biofuels was undertaken by Perimenis et al. (2011), testing the functionality of the tool in the case of biodiesel from rapeseed in Germany. This tool integrates the most important aspects along the entire value chain (i.e. from biomass production to bio-fuel end uses), namely, the following aspects: ? Technical (energy efficiency, feedstock conversion ratio, complexity of the system, development status/current state of the technology, and implementation potential); ? Economic (capital, consumption, operational and other related costs and revenues, including from marketing the main and by-products); ? Environmental (global warming impact and primary energy demand ); and ? Social (employment creation along the entire biofuel pathway, as one of the main social drivers of the implementation of bioenergy projects (Kranjc et al., 2007)). The decision support tool Perimenis et al. used comprises a computational component that can be combined with the personal preferences of the user. The analysis provides a score for the respective pathway, which can be used to rank different options and select from them the optimal solution. The methodology involves using a constructed spread sheet-based model in order to verify the applicability of the theoretical background. The performances of the possible alternatives in terms of the selected criteria are derived from a ?screening LCA?, with the most important aspects providing the environmental input data, obtained from an economic assessment using the annuity method, as well as from technical data on the technology alternatives and the expected employment potential. The respective performances were then translated into ?grades? based on a given grade scale (?1? for a poor performance to ?5? for a good performance). For the weighting of the criteria, a simplified approach was applied using pairwise comparisons, with three types of relative importance (more important, equally important and less important). Most recently, an article by (Myllyviita et al., 2012) was accepted for the Journal of Cleaner Production, which assesses the environmental impacts of biomass production chains by means of LCA and MCDA. A panel which included experts in measuring the environmental impacts of biomass production was tasked with identifying and weighing the impact categories of two alternative raw materials (biodiesel and pulp production). New environmental impacts not included Stellenbosch University http://scholar.sun.ac.za 50 in the standard LCA were identified by panellists (e.g. impact on biodiversity), and higher weights were given to climate change, natural land-use change and biodiversity. Although Myllyviita et al. acknowledge potential financial implications when selecting the most promising alternative, the study focused solely on environmental criteria. The application of MCDA in determining the impact of the alternatives on biodiversity and land-use change, however, may not always be appropriate, especially when assessing wide areas with heterogeneous conditions. Alternatively, the application of GIS may be more suitable for strategic decision-making, since during GIS-based land availability assessments, ?hot-spots? such as nature reserves and other ecologically sensitive areas can be screened out, minimising the effect on biodiversity or land-use change. Besides the newly identified environmental impacts, a set of 14 environmental impact categories grounded on the ReCiPe life- cycle impact assessment method (Goedkoop et al., 2009) were used in the first phase of the MCDA, for which the simple multi-attribute rating technique (SMART), an application of an MCDA method, was applied. As for the AHP method, SMART is one of the most easily applicable MCDA methods, as it is simple and easy to modify in its application. While Myllyviita et al. concluded that it would be beneficial to include MCDA in LCA, since it results in new perspectives on traditional LCA and indicates that standard LCA may not be comprehensive. Furthermore, the authors note that utilising default weights, such as in the ReCiPe method does, and focussing only on environmental impacts can potentially lead to inaccurate results. 3.4 Conclusions Often only monetary assessment methods such as cost-benefit analysis are used when seeking to implement alternative ways of generating energy. However, this narrow measurement of ?success? may not lead to implementation of the most sustainable alternative, as most decision-making problems are embedded not only in financial contexts but also in social, environmental and technical contexts, and depend on the support of many stakeholders with different perspectives. This is no different when seeking to implement bioenergy projects, where much information of a complex and conflicting nature, often reflecting different viewpoints and often changing with time, need to be processed. Thus, the promotion of a more sustainable pathway calls for an approach that evaluates potential alternatives in terms of a wider variety of criteria, e.g. by incorporating environmental, financial- and socio-economic factors in the decision-making process. As shown in this literature review, the life-cycle assessment (LCA) method could aid such a decision-making process, as it provides a structured and comprehensive technique for detailed analyses of complex systems. Originally developed as an environmental assessment tool, it is aimed at capturing the environmental impacts along the entire life-cycle of a product or a service (from its Stellenbosch University http://scholar.sun.ac.za 51 cradle to its grave). The LCA method is structured in four phases, namely (1) goal and scope definition, (2) life-cycle inventory (LCI) analysis, (3) life-cycle impact assessment (LCIA), and (4) interpretation of the results. Along similar lines, in this study the goal and scope, including the various system boundary dimensions are defined in Chapter 4, and in Chapter 5, all relevant inputs and outputs of the considered systems are brought together in the life-cycle inventory (LCI). In the third phase, all potential environmental impacts associated with these inputs and outputs are evaluated, by translating the environmental loads into impacts, which makes the results more environmentally relevant, comprehensible and easier to communicate. Several LCIA methods exist, and there is not always an obvious choice between them. Common areas of protection covered by LCAs are human health, natural environment, natural resources, and to some extent, the man-made environment. However, other environmental concerns, such as impact on biodiversity and water balance, which are more difficult to specify, are not included in the LCA method. Other assessment methods dealing with these impacts, which could be potentially be integrated with LCA are described in Chapter 6. However, as discussed in section 2.3, instead of quantifying the potential impacts on biodiversity and water balance, this study deploys a geographic information system to minimise these impacts, by a priori excluding particularly sensitive areas. Due to its systematic and transparent approach, LCA is well suited to being extended to measure a product?s financial and social aspects along with its life-cycle. Some LCA software packages, such as GaBi 4.4, integrate additional features such as life-cycle costing (LCC) and life-cycle working environment (LCWE), allowing a quantitative assessment of the financial aspects of a product or service. This is done by tracking the various cost factors and social aspects on the basis of working time per value added relating to a process or flow throughout the product or service?s life-cycle. While the conventional LCA method has reached a great level of maturity, proven by ISO standards 14040-14044 and a variety of studies assessing a multitude of products and systems across all sectors, complementary assessment methods covering financial and social aspects are still relatively immature, requiring additional research into and development of the methodological approach, particularly in terms of the definition of new indicators/assessment criteria. As is shown in this study, multi-period budgeting, a sound and proven method, particularly in the agricultural economics context, could serve as a complementary tool to LCA to assess financial viability. Instead of using LCC, which provides only limited financial-economic information in terms of cost factors, MPB models can aid the decision-making process by also providing profitability indicators such as internal rate of return (IRR) and net present value (NPV). In addition, the productivity and performance assumptions made in the LCA and the MPB can be used as socio-economic indicators to derive the employment potential for various income categories. Stellenbosch University http://scholar.sun.ac.za 52 However, while LCA and MPB are suitable methods for providing environmental, social and financial performance data in a structured and comprehensive way, an additional method that organises and synthesises the respective information, integrating mixed sets of data and assisting decision makers to place the problem in context and to determine the preferences of the stakeholders involved is required to support decision-making. Multi-criteria decision analysis (MCDA), which is defined as umbrella term to describe a collection of formal approaches, fulfils these requirements. It does this by improving the legitimacy of decisions, leading to better- considered, justifiable and explainable decisions, while providing an audit trail for a decision. Various MCDA methods exist, generally classified as value measurement models; goal, aspiration and reverence level models; and outranking models. Studies using a similar approach, i.e. combining LCA and MCDA to support decision-making, were discussed in the last section of this chapter. A variety of studies concur that environmental, financial- and socio-economic criteria need to be considered when seeking the most sustainable alternative. most of them fall short in their application, as they consider either only the environmental aspects (in most instances only LCA-based criteria) or they take a very limited number of financial or social aspects into account (e.g. only one for each aspect). The immaturity of complementary assessment methods, the data intensity and the lengthy process of generating the respective information are given as explanations for omitting other sustainability indicators. By using performance data that is generated during an LCA, and complementary financial-and socio-economic assessment methods, the first part of this study (Chapters 4-6) is aimed at supporting decision-making and at minimising subjectivity and single-dimensionality by providing performance data in an objective, transparent and reproducible manner. The analytical hierarchy process (AHP) ? one of the commonly applied and accepted MCDA approaches, characterised by its simplicity and possessing the natural appeal of expressing relative importance by means of pairwise comparisons in ratio terms ? is applied in Chapter 7 to integrate and evaluate the performance data provided, in order to determine the most viable lignocellulosic bioenergy system for the Cape Winelands District Municipality. Stellenbosch University http://scholar.sun.ac.za 53 4 CHAPTER: GOAL AND SCOPE DEFINITION 4.1 Introduction The introduction of lignocellulosic biomass based bioenergy systems in the Cape Winelands District Municipality may, inter alia, enhance the energy supply, create employment and additional income opportunities, but may also affect the environment. Sustainability of production is key, hereby meaning the implementation of pathways that are technically efficient, financial- economically affordable, environmentally friendly and socially acceptable. Therefore, when decision makers, such as local governments, seek to determine the optimal bioenergy system, a decision-aiding approach is required, that supplies information on possible pathways over the whole life-cycle from the production and supply of biomass to the conversion into energy, in a systematic, coherent, transparent and reproducible manner. The life-cycle assessment (LCA) framework is an approach fulfilling these requirements. Thus, the intention of this life-cycle assessment is to provide information on the performance of lignocellulosic biomass based bioenergy systems for the CWDM in terms of environmental criteria, as well as in terms of financial-economic and socio-economic criteria. The previous chapter entails, inter alia, a description of the life-cycle assessment framework (refer to section 3.2), subdivided in four phases, namely goal and scope definition, life-cycle inventory, life-cycle impact assessment and the interpretation of the results. Chapter 4 encompasses the first phase, the goal and scope definition, which shall ?unambiguously state the intended application, the reasons for carrying out the study and the intended audience? (ISO 14041, 1998). This chapter sets the foundation of this study by defining goal and scope and by specifying functional unit and the different dimensions of system boundaries. The latter includes also the technical system boundaries, i.e. the alternative lignocellulosic biomass based bioenergy system pathways are defined, providing also background information on state-of-the-art technologies and systems for each phase of the life-cycle. 4.2 Functional unit The functional unit provides a reference to which the input and output process data are normalised and the basis on which the final results are presented. Similar to that in a study by Petrie et al. (2004: 381), the functional unit used in this study is a combination of functional unit types two and four (see section 3.2.2.1), i.e. a dual time and product basis is used, in which the burdens calculated for an average year?s operation are normalised to the total electrical power produced per year: an annual electricity generation of 39 600 megawatt hours (MWh). This is based on a 5-MW system Stellenbosch University http://scholar.sun.ac.za 54 generating electricity for 7 920 hours per year (330 days of full production). The reasoning was to allow a direct comparison of the different alternatives, and to compare lignocellulosic-biomass bioenergy systems with the current South African energy mix. 4.3 System boundaries As stated in section 3.2.2.1, the system boundaries in a life-cycle assessment (LCA) need to be defined in terms of several dimensions, namely, geographical boundaries, boundaries in relation to the natural system, technical system boundaries, as well as time boundaries. The geographical boundaries are set by the political boundaries of the Cape Winelands District Municipality (CWDM), as described in section 2.2. The boundaries in relation to the natural system are specified in two ways, by describing the woody biomass feedstock and by defining the properties thereof (see section 2.3-2.4), and the biomass production capacity (per section 4.3.2). A general description of the technical system boundaries and a discussion of relevant parameters for each aspect of the life-cycle are given in the subsequent section. 4.3.1 Technical system boundaries This section includes a general description of the technical systems of the life cycle of lignocellulosic-biomass bioenergy systems in the CWDM, which can be structured in five production phases: (i) Primary production of biomass in short-rotation-coppice (SRC) plantations, (ii) Harvesting and primary transportation of the biomass from in-field to the roadside, (iii) Pretreatment of the biomass, (iv) Secondary transport of the bioenergy feedstock from the roadside to a central conversion plant, and (v) Further processing of the feedstock and its conversion into electricity. Important aspects of each production phase are also discussed. Figure 9 and 10, below, are schematic illustrations of the technical system boundaries for the bioenergy systems compared. For all activities/processes throughout the life cycle, best operating practices (BOP) are assumed. Stellenbosch University http://scholar.sun.ac.za 55 Figure 9: Technical system boundaries ? schematic illustration of production phases from in-field to the roadside Stellenbosch University http://scholar.sun.ac.za 56 Figure 10: Technical system boundaries ? schematic illustration of production phases, from roadside to central conversion facility Figure 11, below, translates the schematic illustrations into a set of 37 separate lignocellulosic bioenergy systems (LBSs), which are characterised by different combinations: type of harvesting and forwarding in the SRC plantation, type of pretreatment (comminution, fast pyrolysis) and location thereof (roadside or landing of the central conversion plant), type of secondary transport from the roadside to the central conversion site, and type of bio-electricity generation system. Stellenbosch University http://scholar.sun.ac.za 57 LBS Plantation Roadside Road Central Conversion Site LBS 01 Primary Production of Biomass Motor-Manual Harvesting (whole tree) Forwarding with Forestry Machinery (whole tree) Mobile Chipping of Biomass Transport of Comminuted Biomass Direct Combustion of Biomass 01 02 Direct Gasification of Biomass 02 03 Central Pyrolysis + Conversion 03 04 Mobile Pyrolysis Transport of Mobile Pyrolysis Products Combustion of Pyrolysis Products 04 05 Bio-Oil in Gas Turbine/Bio-Char sold to Industry 05 06 Transport of Uncomminuted Biomass Stationary Chipping at Landing Direct Combustion of Biomass 06 07 Direct Gasification of Biomass 07 08 Central Pyrolysis + Conversion 08 09 Motor-Manual Harvesting (log) Forwarding with Agricultural Machinery (manual loading of logs) Mobile Chipping of Biomass Transport of Comminuted Biomass Direct Combustion of Biomass 09 10 Direct Gasification of Biomass 10 11 Central Pyrolysis + Conversion 11 12 Mobile Pyrolysis Transport of Mobile Pyrolysis Products Combustion of Pyrolysis Products 12 13 Bio-Oil in Gas Turbine/Bio-Char sold to Industry 13 14 Transport of Uncomminuted Biomass Stationary Chipping at Landing Direct Combustion of Biomass 14 15 Direct Gasification of Biomass 15 16 Central Pyrolysis + Conversion 16 17 Harvesting with Forestry Machinery (whole tree) Forwarding with Forestry Machinery (whole tree) Mobile Chipping of Biomass Transport of Comminuted Biomass Direct Combustion of Biomass 17 18 Direct Gasification of Biomass 18 19 Central Pyrolysis + Conversion 19 20 Mobile Pyrolysis Transport of Mobile Pyrolysis Products Combustion of Pyrolysis Products 20 21 Bio-Oil in Gas Turbine/Bio-Char Sold to Industry 21 22 Transport of Uncomminuted Biomass Stationary Chipping at Landing Direct Combustion of Biomass 22 23 Direct Gasification of Biomass 23 24 Central Pyrolysis + Conversion 24 25 Forwarding with Agricultural Machinery (loading of whole trees with three-wheeler) Mobile Chipping of Biomass Transport of Comminuted Biomass Direct Combustion of Biomass 25 26 Direct Gasification of Biomass 26 27 Central Pyrolysis + Conversion 27 28 Mobile Pyrolysis Transport of Mobile Pyrolysis Products Combustion of Pyrolysis Products 28 29 Bio-Oil in Gas Turbine/Bio-Char sold to Industry 29 30 Transport of Uncomminuted Biomass Stationary Chipping at Landing Direct Combustion of Biomass 30 31 Direct Gasification of Biomass 31 32 Central Pyrolysis + Conversion 32 33 Harvesting with Agricultural Machinery (whole tree) Forwarding with Agricultural Machinery (whole tree) Transport of Comminuted Biomass Direct Combustion of Biomass 33 34 Direct Gasification of Biomass 34 35 Central Pyrolysis + Conversion 35 36 Mobile Pyrolysis Transport of Mobile Pyrolysis Products Combustion of Pyrolysis Products 36 37 Bio-Oil in Gas Turbine/Bio-Char sold to Industry 37 Figure 11: Overview of CWDM bioenergy pathways leading to set of 37 lignocellulosic bioenergy systems (LBSs) Stellenbosch University http://scholar.sun.ac.za 58 4.3.1.1 Primary biomass production The initial phase of the bioenergy life cycle is characterised by the primary production of the biomass, comprising mechanical and chemical land preparation of the SRC plantation, planting of fast-growing trees, which can be aggregated to the establishment of the site. This is followed by the maintenance of the plantation (in forestry terms also called tending). The maintenance of SRC plantations consists of weed control operations prior to and after planting, and fertilising operations in order to enhance the growth rate of the trees, particularly during the first years after planting, until canopy closure is reached, after which competition from weeds is eliminated (Little et al., 1997). A post-harvesting activity, thinning, also forms part of the primary biomass production phase. After clear-felling a plantation, coppice shoots are allowed to re-sprout in order to regenerate the section. Once coppice shoots reach a height of between 1.5-2.0m, they should be reduced to between one or two shoots per stump (Little and Du Toit, 2003). The aim is to achieve the same tree density that was obtained in the first planting operation, relative to the stand target concerned. More than one shoot per stump can be left to make up for the mortality of neighbouring stumps (Von Doderer, 2009: 42). As can be seen in Figure 9 and 11, above, all activities/processes prior to harvesting are assumed to be the same for all 37 bioenergy alternatives. With the exception of the mechanical land preparation, all activities/processes prior to harvesting deploy manual labour in order to minimise intermediate capital investments and the potential emissions thereof, as well as to create employment opportunities, particularly in the low-skills category. 4.3.1.2 Harvesting and primary transport Once the maximum annual increment is reached (refer to section 4.3.2), the trees grown in SRC plantations are harvested. The harvesting and primary transportation of biomass are often aggregated in the term harvesting system. Harvesting refers to the felling of trees and, in some cases, to the in-field pretreatment thereof (e.g. de-branching, topping and cross-cutting). Primary transport (also referred to as forwarding or extraction) denotes the in-field movement of woody biomass (e.g. felled trees) from the stump to the forest road, roadside, or landing (collectively referred to as the primary landing) (Morkel, 2000). A variety of biomass harvesting machines and systems exists (Stokes et al., 1986; Seixas et al., 2006; Spinelli et al., 2007; Spinelli and Hartsough, 2006; Ashton et al., 2007; R?ser et al., 2011; Abbas et al., 2011). In general, large and highly mechanised harvesting and forwarding machinery Stellenbosch University http://scholar.sun.ac.za 59 is more cost effective than small-scale systems, due to its greater productivity, notwithstanding the high capital investment costs (Ashton et al., 2007). However, given that in most cases within the context of the CWDM biomass production will be an additional farming enterprise in an existing farming business, existing agricultural machinery and equipment may be available, and therefore preferred, since little or no additional investments will be required and greater levels of utilisation of the existing intermediate capital could be reached. Conventional forestry equipment and systems are commonly used across the northern hemisphere to harvest SRC plantations. However, one problem of using conventional machines is tree size, since the harvesting costs are generally lower for larger trees (Seixas et al., 2006). According to Hartsough and Yomogida (1996), the best machines, mainly from Scandinavia, are based on advanced harvesters for traditional crops such as corn and sugar cane, involving relatively minor adaptions, such as headers specifically designed for harvesting small-diameter hardwoods. Tricycle or articulated rubber-tyred drive-to-tree feller-bunchers are by far the cheapest commercially available machines for felling and bunching trees in the 10-25cm DBH range. Felling and bunching is followed by skidding, whole-tree forwarding or wood-mobile chipping. Tricycle or articulated rubber-tyred drive-to-tree feller-bunchers cause more soil disturbance than other felling methods, and cannot load onto forwarders or trailers. Rubber-tired or tracked limited-area (excavator-style) feller-bunchers are more expensive than drive-to-tree machines, but travel on a single track, causing little surface disturbance (Seixas et al., 2006). Another recently adopted harvesting system option is the ?harwarder? (harvester-forwarder), a machine combining a harvester with a forwarder (e.g. Valmet 801 Combi/330 Duo, Ponsse Harwarder). The harwarder is able to compete with the harvester and forwarder system over short forwarding distances, with lower traffic movements (Seixas et al., 2006: 8). A study by Sir?n (2003) compared a harwarder model with three other small harvesters especially developed for thinning. The results showed that costs were higher for harwarders than for other systems using harvesters and forwarders. With the increasing interest in agroforestry in the late 1990s to early 2000s, particularly in Europe, initiated by the search for renewable energy supplies, alternative harvesting technologies to conventional forestry machinery were investigated. Generally, conventional forestry harvesting systems are well adapted to large trees, but not to small trees grown in an SRC plantation system, resulting in high production costs and low efficiencies. Hence, ways of harvesting were assessed to improve productivity as well as cost-efficiency. Various ?cut and chip? harvesters have been Stellenbosch University http://scholar.sun.ac.za 60 developed to meet these requirements. Combine harvesters have been modified and fitted with a header for harvesting short-rotation biomass. Examples of these are the Claas HS1-HS2, Kemper, and Krone headers (e.g. HTM WoodCut 1500), as well as the Italian-based GBE company with its GBE1 and GBE2 headers. Given the local conditions and the technological suitability, five different harvesting systems (HS) combining three modes of harvesting with three types of forwarding are modelled in this study (refer also to Figure 11): (i) Harvesting system I entails a motor-manual operation, where the trees are felled and left in- field for air-drying until a moisture content level of around 40% (dry basis) has been reached, with the whole trees being extracted using dedicated forestry machinery, i.e. a ?forwarder? fitted with a crane for mechanical loading and unloading. (ii) Harvesting system II represents a manual labour-intensive option: The trees are felled, de- branched, topped and cross-cut using chainsaws, and then left for air-drying in-field. Only the logs are used for bioenergy feedstock, leaving around 30% of the biomass (branches and tops) behind (refer to section 2.4.2). Agricultural machinery, i.e. tractors coupled with modified pole-trailers are used to extract the logs. To load and unload the logs, manual labour is assumed, creating considerable employment opportunities, particularly in the low- skills segment. (iii) A greater degree of mechanisation is assumed for harvesting system III, as dedicated forestry equipment is used for both harvesting and forwarding. A feller-buncher is used to fell the trees and bunch them in rows, butt ends facing in the same direction, allowing the forwarder to easily load by crane, and to forward the whole-trees to the roadside. (iv) Harvesting system IV also involves using a feller-buncher for the harvesting operation, but a tractor coupled with a pole-trailer is used for forwarding. For whole-tree loading and unloading such as this, additional machinery such as three-wheeler loaders are required. (v) Whole-tree utilisation is also assumed for the harvesting system V. Modified agricultural machinery, i.e. self-propelled forage harvesters equipped with dedicated SRC biomass harvesting heads are used to fell and comminute the trees in a single operation (see Figure 29, below). A tractor coupled with a container-trailer drives alongside the harvester, which simultaneously blows the comminuted fresh biomass into the container (see Figure 12, below). Harvesting systems I-IV allow a time delay between felling and forwarding until the biomass has dried from 80% at the time of harvesting to around 40% moisture content (dry basis), the commonly Stellenbosch University http://scholar.sun.ac.za 61 accepted level for secondary transport. This offers several advantages, such as reduced biomass weight, and therefore, reduced costs and emissions from transporting and drying the biomass to the required moisture content levels for the respective bioenergy conversion processes. Furthermore, by storing the biomass in-field for several weeks, the felled trees defoliate to some extent. Thus, fewer nutrients are removed from the plantation. On the other hand, additional handling is required further downstream, resulting in increased resource input, costs and related emissions elsewhere in the bioenergy value chain. Drier biomass also has an adverse effect on the quality of the comminuted biomass, as the risk increases of producing under-sized particles not suitable for the bioenergy conversion process. Figure 12: Claas Jaguar 850 with SRC biomass harvesting head Source: Regione-Lombardia (2008) In harvesting system V, trees are cut and comminuted simultaneously, obviating additional handling as with comminution at the roadside or the landing. Hence, both the field and the roadside are left clean after harvesting operations. Since with this system fresh biomass is comminuted, fewer under- sized particles are expected to be produced. However, since the whole tree including foliage is processed and removed from the plantation, the result is a greater loss of nutrients. Hence, additional fertilisation may be required. Another negative aspect is the increased transport costs and related emissions due to the relatively greater mass of the biomass as a result of the higher moisture content. A potential threat comes from the self-ignition of fresh chipped biomass when stored on a pile or in a container. Natural decomposing processes caused by fungi and bacteria lead to a rise in Stellenbosch University http://scholar.sun.ac.za 62 temperature, resulting not only in a loss of biomass but also potentially causing self-ignition of the biomass (Serup et al., 2002: 19). Generally, the larger the wood chips are, the slower the decomposition process is. Three types of activities/processes are aggregated in the term biomass pretreatment, namely, (i) Mechanical pretreatment, i.e. biomass comminution into wood chips or chunks meeting the conversion technology?s feedstock particle size requirements, (ii) Thermal pretreatment, i.e. drying of the comminuted biomass to meet the respective conversion technology?s moisture content-level requirements, and (iii) Thermo-chemical pre-processing, i.e. fast pyrolysis, where the comminuted biomass is converted into pyrolysis oil and char (also called bio-oil and bio-char). As illustrated in Figure 9, 10 and 11, depending on the bioenergy system?s configuration, all three pretreatment activities take place at the roadside or at the landing of the central conversion plant. 4.3.1.3 Biomass pretreatment: Comminution Biomass-feedstock-based conversion technologies stipulate various requirements in terms of the physical properties of the bioenergy feedstock. These include feedstock dimensions, size distribution, moisture content, ash content, and pollutants (stones, soil, sand, etc.). In order to meet size requirements, the biomass is comminuted into a smaller, more homogeneous bioenergy feedstock (sometimes also called fuelwood) such as wood chips or chunks. Various types of biomass comminuters exist, such as hammer mills, shredders and chippers, with the latter being the most common for biomass comminution. Chippers can be classified into three different categories: disc chippers, drum chippers, and screw chippers, differing only in their way of chipping (Serup et al., 2002: 18). In commercial forestry systems, the comminution of biomass is considered at four locations, namely, ? At the source of the biomass, i.e. in-field, ? At the roadside, close to the source, but accessible for secondary transport, ? At a terminal in remote areas, where the biomass is concentrated for further transport to the final destination, or ? At the landing of the conversion site. Stellenbosch University http://scholar.sun.ac.za 63 Each comminution location requires a specific machinery setup. General advantages and disadvantages are discussed in Table 12, below. Table 12: Advantages and disadvantages of biomass comminution at different locations Location In-field Roadside Terminal Landing Adva n tag es ? Chipping and forwarding with same unit ? Small roadside storage facility required ? Clean areas after harvesting operations ? Chipping and secondary transportation with same unit ? No hot chain ? Chips can be supplied to several small plants ? Small harvesting sites ? Short transport distances for uncomminuted biomass ? Effective chipping operation (all season) ? No hot chain ? Good quality management of chips ? Continuous supply ensured ? No hot chain ? Large-scale production ? Powerful comminution ? Most cost-efficient supply chain with relatively short transport distances for chips Disa d va n tag es ? Ineffective chipping ? Size limitation of chipper?s container due to terrain ? Long forwarding distances ? Prone to breakdowns ? Long transportation distances for chips; thus high costs and emissions ? Size and weight limitation of container ? Long transportation distances for chips; thus high costs and emissions ? Large storage space at roadside required ? Untidy roadside storage areas after harvesting operations ? Establishment expenses of new terminal ? Identifying appropriate terminal areas ? Extra handling times ? Relatively high total supply chain costs ? Long transportation distances for uncomminuted biomass; thus high costs and emissions ? Large storage facility at roadside and landing required ? Untidy roadside storage areas after harvesting operations In Figure 9 and 10, two locations for the comminution of the biomass are presented: at the roadside and at the landing of the central conversion plant. Roadside comminution encompasses several small-scale mobile chipping systems, whereas for the latter, a single large-scale stationary chipping system is assumed. The mobile roadside chipping system is characterised, inter alia, by its mobility, its user- friendliness (even small tractors can be used to power the chipper when PTO-driven, and can be operated by semi-skilled operators), and its relatively low capital investment costs. Another advantage may be that by running several mobile chippers at the same time, even if one system is not running due to maintenance, a continuous supply of wood chips can be ensured. On the other hand, mobile chippers are relatively small in size and, therefore, are less productive. Furthermore, mobile chippers are more prone to maintenance downtime as they are less robust than stationary systems (e.g. requiring sharpening or replacing of chipping blades and other spares). Stellenbosch University http://scholar.sun.ac.za 64 Stationary chipping systems are characterised by considerably higher capital investment costs, greater complexity of the system, requiring more qualified operators, and if the system is down for a considerable period, the conversion system might be affected due to a lack of feedstock. However, due to their greater capacity, they reach higher productivity levels and are less prone to maintenance downtime, since these systems are more robust and allow for quicker maintenance and repairs. Generally, it could be said that chipping at a central point is more cost effective than chipping at the plantation. However, the increased handling and transportation associated with centralised comminution will negatively affect the transportation cost, especially over a short distance (Johansson et al., 2006). Wood chips are typically 5-50mm long in the direction of the fibre, and can be classified as coarse (stay in 8mm), accepts (stay in 3-7mm) and fines (pass 3mm) (Serup et al., 2002: 14). However, as mentioned above, feedstock particle size requirements depend on the conversion technology. For instance, the Carbo-Consult CCE-SJG Gasifier (Eckermann, 2009) discussed in Von Doderer (2009) has a throat diameter of approximately 22cm, allowing chunks of up to 22cm to be fed into the system. The particle size requirements for conversion also play an important role in productivity, cost-effectiveness and emissions from the comminution process (see also Van Belle, 2006). The quality of fuelwood affects costs and productivity in several ways. Contaminants, such as stones, metal and sand, can damage chipper blades, which then require more frequent replacement or sharpening. Dull blades impair chipper productivity and result in an undesirable chip size. Dry residues increase costs by 10-20%, and the presence of contaminants raises the costs even more (Richardson et al., 2002: 137). 4.3.1.4 Biomass pretreatment: drying Thermal pretreatment, i.e. the drying of biomass, represents an essential step toward turning green biomass into electricity, and is linked to the storage conditions thereof. As discussed in section 2.4.4, the moisture stored in biomass has a direct effect on the calorific value of the fuelwood, i.e. the drier the biomass, the higher the calorific value. However, drying does not seriously affect the volume of woody biomass (Hamelinck et al., 2005: 122). Fuelwood is stored either uncomminuted or in comminuted form. Biomass produced in an SRC production system can be stored in the field, at the roadside, at a terminal, and/or at the landing of the central conversion plant. The rate of transpiration or ?air- drying? depends on many factors, including ambient temperature, relative humidity, wind speed, Stellenbosch University http://scholar.sun.ac.za 65 season, rainfall pattern, tree species, and tree size. The best season for drying is when the vapour pressure deficit of the ambient air is low (Richardson et al., 2002: 108). Additionally, the in-field storage of biomass in small heaps allows the foliage to remain in the plantation, resulting in reduced nutrient loss and better fuel quality. Usually, transpiration, or air-drying, is more efficient in small heaps than in roadside windrows. The weight losses associated with fungal decay are smaller when stored in windrows than when stored as chipped material. Heat development, a problem with chip storage, is also eliminated (Richardson et al., 2002: 110). 4.3.1.5 Biomass upgrading: mobile fast-pyrolysis A significant portion of biomass feedstock costs can be attributed to the ?handling? associated with its movement from the point of production to the point of conversion and end use (Sokhansanj, 2002). Traditionally, handling includes harvesting, chipping, and loading of the biomass onto trucks, and transporting it to end-use points. Additionally, handling includes the operations at the end-use point, including weighing, dumping, screening, grinding, storage, various conveying operations, and metering into the end-use system (Badger and Fransham, 2006: 321). Handling solid forms of biomass is expensive for a number of reasons, including the number of operations required and the low bulk density of the feedstocks (Badger, 2002). If raw biomass could be densified into liquid (e.g. bio-oil) and/or into solids (e.g. bio-char) it would simplify the handling, transportation, storage, and usage of the feedstock. Additionally, bio-oil and bio-char have a much greater energy density than raw biomass. The process capable of achieving this is fast pyrolysis. Pyrolysis is one of the important thermo-chemical conversion processes. It is carried out either in the complete absence of oxygen or with a limited supply of it. When significantly less oxygen is present than required for complete combustion, pyrolysis can convert the biomass into usable high energy content products such as pyrolysis oil (also referred to as bio-oil), bio-char, and bio-gas. The relative proportions of oil, char, and gas depend significantly on the pyrolysis method employed and the characteristics of the biomass, as well as on reaction time and temperature (Kumar et al., 2010). For a more detailed discussion of pyrolysis principles, types and product properties, refer to section 4.3.1.7, 5.3.4 and 5.3.5. The mobile fast pyrolysis process that is proposed for a number of bioenergy alternatives encompasses the installation of a modular and transportable conversion plant, allowing it to be located close to the source of the biomass (e.g. at the roadside), and the subsequent transportation of high-energy and dense bio-oil and bio-char to a central conversion plant. This is particularly appealing for remote areas characterised by a lack of infrastructure and long transportation distances. Stellenbosch University http://scholar.sun.ac.za 66 4.3.1.6 Secondary transport For environmental and economic reasons, biomass for energy conversion is usually considered to be a local resource (Forsberg, 2000: 18). This is due to the large areas of land required to produce and source biomass, resulting in the development of harvesting systems and transportation networks, leading to the increase in costs and greenhouse gas emissions associated with producing biomass on large areas of land. Evidently, an optimum must be reached between economy of scale of the conversion plant and the variable associated transportation costs (Schlamadinger et al., 2006: 2). The term ?secondary transport? refers to the movement of timber, woody biomass or other related products, from the primary landing to the processing plant. This may be accomplished by a single mode of transport using a vehicle, or it may encompass a number of modes of transport (road, rail, and water) and many vehicles (Morkel, 2000: 382). Given the local circumstances in the CWDM, road transport appears to be the most viable option (Roberts, 2009: 28). Research by Van Dam et al. (2009) has also shown that transporting biomass over a short distance (taken to be less than 100km) using trucks is the preferred option, especially where multiple sites have to be visited, and also where rail and waterway infrastructures do not exist. Wood transportation from a forest landing to forest-based industries uses large amounts of energy. In the case of Sweden, where forest operations are highly and efficiently mechanised, this stage consumes more fossil fuels than other elements of the wood supply chain (such as silviculture and logging operations) (Gonz?lez-Garc?a et al., 2009: 3530). Important guiding principles and decision-making guidelines on appropriate vehicles for forestry and bioenergy applications exist, and these can be summarised for the relevant application (an appropriate vehicle is one that is specifically matched to a defined application), legislation of the specific country, and cost efficiency (Krieg, 2000: 388). Additionally, when deciding on the appropriate truck configuration, global technological developments need to be considered (Webster, 2000). This is a function of several factors, namely, the type and volume of commodity to be transported; the terrain conditions, and their influence on road width and curvature; the gravel-to- paved surface ratio; the back-haul requirements; as well as the number of demand points, their locations, and product requirements (Brink and Krieg, 2000: 378). Four main features are typical for commercial road haulage (Rogers and Brammer, 2009: 1368), namely, the high annual distance covered by each truck; the low proportion of time spent for loading and unloading; the loads achieved between different destinations, in order to reduce unloaded travel time; and the large proportion of journeys on motorways and major trunk roads. In Stellenbosch University http://scholar.sun.ac.za 67 contrast, given the nature of bioenergy systems, fleets of trucks shuttling between biomass plantations and conversion plants are required. A much greater proportion of time is spent on loading and unloading, as well as on travelling on slower, rural roads. Consequently, their cost structure will vary from those of commercial freight haulers (Rogers and Brammer, 2009). Especially in forestry or bioenergy applications, truck power requirements are regarded as a crucial factor, since long periods of time are spent transporting heavy loads across gravel and dirt roads in mountainous terrain, as is commonly found in the CWDM. Hence, the truck used in this environment requires greater power and torque than, for instance, that used for long-distance transportation on good roads (Roberts, 2009: 30). Besides fulfilling power and torque requirements, when selecting the appropriate truck, great attention should be given to the feedstock itself. Woody biomass is characterised, inter alia, by its low bulk density, often limiting the load by volume rather than by mass capacity. The economy of scale in transporting low bulk material invariably dictates that the load must either be compacted or the load space extended. In practice, the load space must be built according to the maximum allowable dimensions. Nevertheless, in many cases, the maximum allowable weight will not be achieved. The load extension may also have to be adjusted for some locations where access to the raw material is restricted by low-quality roads (Ranta and Rinne, 2006: 231). The transporter capacity is expressed in terms of the mass to be transported: Equation 5: Transport capacity Where is the wet mass of biomass (t), the wet bulk density of biomass (t/m 3), the volume capacity of the pole-trailer/container-trailer (m3), and the coefficient represents less than maximum payload scenarios. Furthermore, in order to determine the effective transport rate, the total transport time per load needs to be established. For the total transport time per load, three aspects are of importance, namely, loading time, travelling time, and unloading time (Roberts, 2009: 28). Equation 6: Total transportation time per load Stellenbosch University http://scholar.sun.ac.za 68 Where represents the total transportation time per load (h), the forwarding time per load, the return time per load, the loading time per load, the unloading time per load, and the efficiency factor for the transport equipment due to obstacles that may increase transportation time (<1). The effective transport rate ( ) is a function of the actual mass of the commodity to be transported ( ) and the total transportation time per load ( ), and can be calculated as follows: Equation 7: Effective transport rate stands for the rate of mass transport (wet t/h), for the wet mass of biomass (t/m3), and for the total transport time per load (h). has a maximum value based on the weight limit of the road. In other words, if exceeds the legal limits, then or has to be reduced (Sokhansanj et al., 2006: 843). Following the production steps prior to secondary transport, six types of bioenergy feedstocks are to be transported to a central conversion facility. They can be categorised as uncomminuted or comminuted biomass, and as mobile fast-pyrolysis products: ? Uncomminuted biomass: o Whole tree including branches and top o Logs/stemwood (motor-manual harvesting with manual loading and unloading) ? Comminuted biomass o Whole tree including branches and top o Logs/stemwood ? Mobile fast pyrolysis o Bio-oil o Bio-char Table 13, below, shows the bulk densities for various types of woody biomass feedstock in relation to different moisture content levels, as determined by De Wet (2010). As is commonly accepted in industry, an average moisture content of 40% (dry basis) for the biomass is assumed for secondary transport (De Wet, 2010; Ranta and Rinne, 2006). The exception to this is the one-pass harvesting Stellenbosch University http://scholar.sun.ac.za 69 method, where the trees are felled and comminuted in a single operation. In this case, a moisture content of 80% is assumed. Table 13: Bulk densities of biomass at various levels of moisture content Moisture content (%, dry basis) 0% 20% 40% 60% 80% Bulk density (t/m3) Whole tree/utilisable biomass (incl. bark, branches, etc.) 0.14 0.17 0.2 0.23 0.31 Log/stemwood 0.48 0.57 0.67 0.77 1.03 Comminuted biomass from whole tree 0.24 0.28 0.33 0.37 0.51 Comminuted biomass from log/stemwood 0.29 0.34 0.40 0.46 0.62 Note: Transport bulk density is generally assumed to be 40% moisture content for dry matter, except for the one-pass harvesting method, where a bulk density of 0.51t/m3 (80% MC) is assumed. Other bulk density levels are extrapolated from data provided by (De Wet, 2010). It should be noted, however, that further research is required to validate the above-mentioned bulk densities for the different woody biomass types. In Finland, for instance, Ranta and Rinne (2006) assume a bulk density for chipped biomass from logging residues of 0.36-0.46t/m3, but 0.15- 0.20t/m3 for uncomminuted logging residues. 4.3.1.7 Bio-energy generation The exploration of alternative energy sources, particularly renewable energy sources, is driven, in part, by the negative environmental impact and limited supply of conventional, non-renewable fossil fuels (DeSisto et al., 2010: 2642). Bioenergy is a renewable and clean energy source that is derived from biomass. The conversion routes for producing energy carriers from biomass are plentiful (Faaij, 2006: 345). Figure 13 illustrates the main conversion routes that are used or are under development for producing heat, power, and transport fuels. While biological processing using biological catalysts is usually very selective and produces a small number of discrete products with high yields, thermal conversion with inorganic catalysts often gives multiple and complex products over very short reaction times, and is often used to improve the product quality or spectrum (Bridgwater, 2011: 1; Czernik and Bridgewater, 2004). Stellenbosch University http://scholar.sun.ac.za 70 Figure 13: Main conversion options for biomass to secondary energy carriers Source: Turkenburg et al. (2000: 223) Among the options for renewable energy production, the thermal conversion of biomass is receiving considerable attention (Bridgwater et al., 1999; Oasmaa et al., 2010; DeSisto et al., 2010). This study focuses solely on the thermo-chemical conversion processes available for converting biomass to a more useful energy form, namely, combustion, gasification and pyrolysis, since electrical energy is the main product desired when using lignocellulosic biomass as a feedstock. Figure 14, below, summarises the markets for the products of these processes. Their relationships to one another and their oxygen requirements are summarised in Figure 15, below. Combustion is a well-established commercial technology with applications in most industrialised and developing countries, and associated development is concentrated on resolving environmental problems (Koppejan and van Loo, 2009). Gasification has been practiced for many years, and while there are many examples of demonstration and pre-commercial activities, there are still surprisingly few successful commercially operational units (Bridgwater, 2011: 1). Fast pyrolysis is an advanced emerging technology representing either an integrated process for producing a liquid fuel that can be used directly, or an intermediate pretreatment step for converting solid biomass into a form of higher-energy-content transportable liquid for subsequent processing to be converted to heat, power, biofuels, and chemicals (Bridgwater, 2011: 1). Stellenbosch University http://scholar.sun.ac.za 71 Figure 14: Products from thermal biomass conversion Source: Bridgwater (2011: 2) When analysing bioenergy systems, it should be borne in mind that the different conversion technologies in the process of generating energy, the different fuels, and the different forms of fuel processing result in varying environmental effects. The chief characteristics of conversion technologies are the following (Jungmeier et al., 2003: 102): ? Conversion efficiency from fuel to electricity and/or heat (?el, ?th), ? Ratio of electricity to heat (? = ?el/?th), ? Emissions to air (flue gas cleaning system), and ? Ash treatment. Generally, great emphasis is given to the first characteristic. However, not only the final energy- carrier-to-electricity-efficiency ratio is important, but also the conversion efficiency of the pretreatment steps prior to the final conversion, e.g. when converting biomass into pyrolysis products, etc. All conversion and pretreatment steps influence the overall biomass-to-energy efficiency ratio, and therefore, the mass balance of each particular system, resulting in the amount of land required to supply sufficient biomass to ensure the continuous generation of five megawatts of electricity in 7 920 hours per year (refer to section 4.2). Stellenbosch University http://scholar.sun.ac.za 72 Figure 15: Thermal conversion processes Source: Bridgwater (2002: 2) ? Combustion Combustion is the most widely used process for biomass conversion. It accounts for over 97% of bioenergy production in the world. In some less-developed countries, combustion of traditional biomass plays an important role in people?s lives, as it is the main source of energy available for cooking and heating. Regarded as a proven low-cost, but highly reliable technology, combustion is relatively well understood and commercially available (Demirbas and Demirbas, 2007; IEA, 2002). The product is heat, which must be used immediately to heat and/or generate power, as storage is not a viable option. Production of heat (domestic and industrial) and electricity, or combined heat and power (CHP), is possible through a portfolio of options. The larger-scale combustion of biomass for the production of electricity (as well as heat and process steam) is undertaken commercially worldwide. Many plant configurations have been developed and deployed over time. Basic combustion concepts include pile burning, various types of grate firing (stationary, moving, vibrating), suspension firing, and fluidised bed concepts (Faaij, 2006). As mentioned in section 2.4, in comparison with fossil fuels, biomass fuels have relatively low heating values. This can be explained by two of their distinct characteristics: high moisture and high Stellenbosch University http://scholar.sun.ac.za 73 oxygen content. The high moisture content is one of the most significantly disadvantageous features of using biomass as a fuel. Although the combustion reactions are exothermic, the evaporation of water is endothermic. To maintain a self-supporting combustion process, the moisture content (on a wet basis) of biomass fuels cannot be higher than 65% (Jenkins et al., 1998: 37). In addition, the heating value of the fuel is negatively correlated with the relative moisture content, even when this is within the maximum acceptable limit (Zhang et al., 2010: 971; Demirbas, 2004). Biomass combustion is a complex process consisting of consecutive homogeneous and heterogeneous reactions, which can be subdivided in three main stages: drying; pyrolysis and reduction; and combustion of volatile gases and solid char (IEA, 2002). Figure 16 gives a schematic description of these processes for wood. The composition and physico-chemical properties of the fuel are the determining factors for the duration and rate of each step of the process (Khan et al., 2009). The combustion of volatile gases contributes more than 70% of overall heat generation. This takes place above the fuel bed and is generally evident from the presence of yellow flames. Char is combusted in the fuel bed and is evident from the presence of small blue flames (Zhang et al., 2010; Quaak et al., 1999; IEA, 2002). The combustion of biomass on a large scale is still considered to be a complex process with technical challenges associated with the biomass fuel characteristics, types of combustors, and the co-firing processes. The overall efficiencies of converting biomass to power tend to be rather low due to the process? inherent natures (i.e. the fundamental thermodynamic limitations associated with their operating pressures and temperatures (Envergent Technologies, 2010), which are typically 15% for very small plants and up to 35% for larger and newer plants. However, existing technologies are widely available commercially, and there are many successful working examples throughout North America and Europe that frequently utilise forestry, agricultural, and industrial wastes. Emissions and ash handling remain technical problems though (Bridgwater, 2002). The co-combustion of biomass, particularly in coal-fired power plants, offers several advantages: the overall efficiency is high (usually around 40%) due to the economies of scale of the existing plant, and investment costs are negligible when high-quality fuels such as pellets are used. Also, directly avoided emissions are high due to the direct replacement of coal. Co-firing combined with the fact that many coal-fired power plants in operation are fully depreciated usually makes it a very attractive GHG mitigation option. In addition, biomass firing leads to the lowering of sulphur and other emissions (Faaij, 2006). The most utilised combustors for biomass applications are stoker- fired and fluidised bed designs, with the latter rapidly becoming the preferred technology because of the low amount of NOx emissions (Caputo et al., 2005). Stellenbosch University http://scholar.sun.ac.za 74 Figure 16: Schematic description of the process of combusting a wood chip Source: Khan et al. (2009: 29) ? Gasification Gasification converts biomass through partial oxidation into a gaseous mixture of syngas, consisting of hydrogen (H2), carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2) (Knoef, 2005; Higman and van der Burgt, 2003). The oxidant or gasifying agent can be air, pure O2, steam, CO2, or a combination thereof (Wang et al., 2008: 574). The gas can be burned directly for cooking or heating purposes, or can be used in internal combustion engines or gas turbines for producing electricity or shaft power (Abdul Salam and Bhattacharya, 2006: 228). Figure 17, below, shows a schematic illustration of the processes involved in biomass gasification. Stellenbosch University http://scholar.sun.ac.za 75 Figure 17: Schematic illustration of gasification as one of the thermal conversion processes Source: Swaaij et al. (2002: 1) There are three main types of gasifiers: fixed-bed, moving-bed and fluidised-bed (Knoef, 2005; Higman and van der Burgt, 2003; Basu, 2006). Both fixed-bed and moving-bed gasifiers produce syngas, with large quantities of either tar and/or char, due to the low and non-uniform heat and mass transfer between the solid biomass and the gasifying agent (Wang et al., 2008: 574). Both are simple and reliable designs and can be used to economically gasify very wet biomass on a small scale (Basu, 2006). Fluidised-bed gasifiers, which consist of a large percentage of hot inert bed materials such as sand and 1-3% biomass have been used widely in biomass gasification (Basu, 2006). Fluidised bed gasification can achieve a high heating rate, uniform heating and high productivity (Van der Drift et al., 2001). Although not common, entrained bed reactors, e.g. cyclone reactors, have also been used for biomass gasification. The performance of a gasifier depends on the design of the gasifier, the type of fuel used and the airflow rate, among others (Abdul Salam and Bhattacharya, 2006). As illustrated in Figure 17, when aiming at generating thermal and/or electrical energy, the syngas can be used either in a combustor or directly fed to gas engines. Compared with direct biomass combustion, the syngas from biomass gasification can increase the bio-based fuel percentage in existing pulverised coal combustors, without posing any concern about the plugging of the coal- feeding system during the co-firing of biomass coal (Wang et al., 2008). Biomass gasification can reduce the potential of ash slugging or other ash-related problems, because the gasification Stellenbosch University http://scholar.sun.ac.za 76 temperature is lower than combustion and clean syngas is supplied to the combustor. Furthermore, a gasification process can use a variety of biomasses with large variations in their properties, such as moisture content and particle size (Raskin et al., 2001). However, if syngas is combusted directly to generate steam for generating power via a steam turbine, the electricity efficiency is limited by the theoretical limit of a steam turbine. High-quality syngas with almost no tar and dust, and a high heating value can be fed to gas engines directly (Wander et al., 2004; Sridhar et al., 2001) or to gas turbines (Gabra et al., 2001a; Rodrigues et al., 2003; Gabra et al., 2001b; Miccio, 1999) to generate power. Gas turbines can transform hot syngas into mechanical energy and thus increase the energy efficiency of conversion. A typical ?biomass-integrated gasification combined-cycle? (BIGCC) involves combustion of the hot syngas from a gasifier in a gas turbine to generate electricity in a topping cycle (Wang et al., 2008). The hot exhaust gas from the turbine is used to generate steam through a heat-recovery steam generator (Rodrigues et al., 2003; Carpentieri et al., 2005). The steam is used in a steam turbine to generate additional electricity in a bottom cycle, or is used as processing heat. Figure 18: Applications for gas from biomass gasification Source: Bridgwater (2002: 10) Further information and literature on the thermal gasification of biomass is abundant ? inter alia, refer to Abdul Salam and Bhattacharya, 2006; Basu, 2006; Bridgwater et al., 2002; Caputo et al., 2005; Klass, 1998; Clini et al., 2008; Cummer and Brown, 2002; Demirbas, 2009a; Di Blasi, 2009; Stellenbosch University http://scholar.sun.ac.za 77 Faaij et al., 1997; Francisco V. Tinaut, 2008; Gabra et al., 2001a; Gabra et al., 2001b; Gil et al., 1999; Higman and van der Burgt, 2003; Mamphweli and Meyer, 2009; McKendry, 2002b; Melchior et al., 2009; Miccio, 1999; Persson, 2009; Quaak et al., 1999; Querleu, 2009; Rezaiyan and Cheremisinoff, 2005; Sandvig et al., 2003; Turn et al., 2005; Wander et al., 2004; and Wang et al., 2008. ? Pyrolysis Pyrolysis has been applied for thousands of years to produce charcoal. In this conventional, or slow- wood, pyrolysis process, biomass is heated slowly (at a heating rate of 5-7?C/min) up to 500?C, where the vapour residence time varies from 5 to 30 minutes (Bridgwater et al., 2001). Vapours do not escape as rapidly as they do during fast pyrolysis. Thus, components in the vapour phase continue to react with each other, resulting in more char formation. It is only during the last 30 years that fast pyrolysis at moderate temperatures of around 500?C and very short reaction times of up to two seconds has gained in appeal and application. This is because the process gives direct high yields of liquids of up to 75 weight percent (wt.%), which can be used directly in a variety of applications (Czernik and Bridgewater, 2004), or as an efficient energy carrier (Bridgwater, 2011). Hence, it has advantages for energy efficiency, because mainly liquid fuels and solid char are formed, both of which are easy to store and transport (Van de Velden et al., 2008). Figure 19: Methods of heat transfer to a pyrolysis reactor Source: Bridgwater (2002: 11) The production of the liquid fraction of bio-oil is receiving growing interest. This is because its use is especially advantageous when biomass resources are remote from where the energy is required or where the specific technology is located, since the liquid can be stored or transported more easily Stellenbosch University http://scholar.sun.ac.za 78 than solid heterogeneous feedstock. In this way, it is possible to decouple liquid fuel production from its utilisation, separate the minerals inherently present from the minerals at the site where the liquid fuel is produced, transport the bio-oil to bio-refineries for its upgrading to obtain transportation fuels and valuable chemicals, or to transport it to large power plants to convert it into heat and power (Cherubini, 2010). Pyrolysis is thermal decomposition in the complete absence of an oxidising agent (air or oxygen), or with such a limited supply of the oxidising agent that combustion or gasification does not occur to an appreciable extent. Pyrolytic cracking of biomass yields mainly liquids, together with a solid residue (char) and gas. In comparison to gasification, pyrolysis occurs at relatively low temperatures (300-600?C) (Van de Velden et al., 2008). Lower process temperatures and longer vapour residence times favour the production of charcoal. High temperatures and longer residence times increase biomass conversion to gas, while moderate temperatures and a short vapour residence time are optimum for producing liquids. Three products are always produced, but the proportions can be varied over a wide range by adjusting the parameters of the process (Bridgwater, 2011). These essential parameters include (Van de Velden et al., 2008; Bridgwater, 2011): ? Very high heating rates and high heat transfer at the biomass particle reaction surface usually require a finely ground biomass feed of typically less than 3mm, as biomass generally has low thermal conductivity; ? Carefully controlled pyrolysis reaction temperatures around 500?C to maximise the liquid yield for most biomass, and temperatures for the vapour phase of 400-500?C; ? Short, hot vapour residence times of typically less than two seconds to minimise secondary reactions; ? Fast char separation to avoid secondary cracking, and ? Rapid cooling of the pyrolysis vapours. In addition to the above-mentioned essential parameters, feedstock properties ? such as moisture content or particle size ? as well as its feed rate, and the process used for fast pyrolysis of the biomass influence pyrolysis product yields and the properties thereof. In general, higher temperatures and longer residence times maximise gas formation, and minimise char formation (Bridgwater et al., 1999; DeSisto et al., 2010). Because of the complexity of the process, reactor types and feedstock variation, the effect of operating conditions on the bio-oil properties is very process specific (DeSisto et al., 2010). The relative proportions depend on the pyrolysis method, the Stellenbosch University http://scholar.sun.ac.za 79 characteristics of the biomass, and the reaction temperature (Amutio et al., 2011; Kumar et al., 2010; Demirbas, 2009a). Table 14 indicates the product distribution obtained from different modes of pyrolysis, showing the considerable flexibility achievable by changing process conditions. Table 14: Typical product weight yields obtained by different methods for pyrolysing wood Mode Conditions Product yield (wt.%, dry basis) Liquid Solid Gas Fast ~500?C, short hot vapour residence time: ~1s 75% 12% char 13% Intermediate ~500?C, hot vapour residence time: ~10-30s 50% in two phases 25% char 25% Carbonisation (slow) ~400?C, long vapour residence time: hours?days 30% 35% char 35% Gasification ~750-900?C 5% 10% char 85% Torrefaction ~290?C,solids residence time ~10-60min 0%, unless condensed, then up to 5 % 80% solid 20% Source: Bridgwater (2011: 2) The main product of fast pyrolysis, bio-oil, is obtained in yields of up to 75wt.% on a dry-feed basis, together with the by-products char and gas, which can be used within the process to provide the process? heat requirements, so there are no waste streams other than flue gas and ash. The liquid yield depends on biomass type, temperature, hot vapour residence time, char separation, and biomass ash content, with the last two having a catalytic effect on vapour cracking (Bridgwater, 2011: 3). The pyrolysis process itself requires about 15% of the energy in the feed. The gas has a medium heating value and can be used internally to provide process heat, recirculated as an inert carrier gas, or exported, for example, for feed drying (Cottam, 1995). A fast pyrolysis process includes drying the feed to typically less than 10% water in order to minimise the water in the product liquid oil, grinding the feed to give sufficiently small particles to ensure rapid reaction, fast pyrolysis, rapidly and efficiently separating solids (bio-char), and rapidly quenching and collecting the liquid product (bio-oil) (Bridgwater, 2011: 3). Virtually any form of biomass can be considered for fast pyrolysis. Nonetheless, most work has been carried out on wood because of its consistency and comparability for testing ? over 100 different biomass types have been tested by many laboratories, ranging from agricultural wastes such as straw, olive pits and nut shells to energy crops such as miscanthus and sorghum, forestry Stellenbosch University http://scholar.sun.ac.za 80 residues such as bark, and solid wastes such as sewage sludge and leather wastes (Bridgwater, 2011: 3). At the heart of a fast pyrolysis process is the reactor. Although it probably represents only 10-15% of the total capital costs of an integrated system, most research and development has focused on developing and testing different reactor configurations on a variety of feedstocks. However, increasing attention is now being paid to controlling and improving liquid quality, and improving liquid collection systems (Bridgwater, 2011: 3). The rest of the fast pyrolysis process comprises biomass reception, storage, handling, drying and grinding; and product collection, storage, and when relevant, upgrading. Different fast pyrolysis types are available, such as bubbling fluid bed reactor systems, circulating fluid bed and transported bed reactor systems, rotating cone reactor systems, and ablative pyrolysis systems. Several comprehensive reviews of fast pyrolysis processes for liquid production are available, such as Mohan et al. (2006a), Kersten et al. (2005), and Bridgwater ( 2002, 2003, 2011). As illustrated in Figure 10, above, where bioenergy alternatives encompass mobile or centralised stationary pyrolysis, bio-oil and bio-char are the utilisable products for further conversion into electricity. Hence, a more detailed description of these pyrolysis products follows. Pyrolysis liquid is referred to by many names, including pyrolysis oil, bio-oil, bio-crude-oil, bio- fuel-oil, wood liquids, wood oil, liquid smoke, wood distillates, pyroligneous tar, pyroligneous acid, and liquid wood. The crude pyrolysis liquid is typically almost black through to dark red-brown, and approximates to the original feedstock in its elemental composition (refer also to Table 15, below). It is composed of a very complex mixture of oxygenated hydrocarbons and water (Bridgwater, 2002). Water is obtained from the original moisture in the feedstock (typically about 12wt.%, dry basis) and from the dehydration reactions occurring during pyrolysis, ranging from 15wt.% to an upper limit of about 30-50 wt.%, depending on the feed material and how it was produced and subsequently collected. The water yield increases slightly when the temperature is increased, as a result of the increase in the secondary cracking-dehydration reactions (Amutio et al., 2011), resulting in a total of around 25 wt.% moisture content. Solid char and dissolved alkali metals from ash may also be present (Huffman et al., 1993). The liquid is formed by rapidly quenching and thus ?freezing? the intermediate products of the flash degradation of hemicellulose, cellulose, and lignin. The liquid thus contains many reactive species, which contribute to its unusual attributes (Bridgwater, 2011: 10). Bio-oil can be considered a micro- emulsion in which the continuous phase of an aqueous solution of holocellulose decomposition Stellenbosch University http://scholar.sun.ac.za 81 products stabilises the discontinuous phase of pyrolytic lignin macromolecules through mechanisms such as hydrogen bonding. Aging or instability is believed to result from a breakdown in this emulsion. In some ways bio-oil could be considered analogous to the asphaltenes found in petroleum (Bridgwater, 2002: 16). Table 15: The range of elemental composition and properties of wood-derived pyrolysis oils Physical properties Pyrolysis conditions Water content (wt%) 15-30 Temperature (K) 750-825 pH 2.8-3.8 Gas residence time (s) 0.5-2 Density (kg/m3) 1500-1250 Particle size (?m) 200-2000 Elemental analysis (wt%, moisture free) C 55-65 Moisture content (wt%) 2-12 H 5-7 Cellulose (wt%) 45-55 N 0.1-0.4 Ash (wt %) 0.5-3 S 0.00-0.05 Yields (wt.%, dry basis) O Balance Organic liquid 60-75 Ash 0.01-0.30 Water 10-15 HHV (MJ/kg) 16-19 Char 10-15 Viscosity (315K, cP) 25-1000 Gas 10-20 Solubility (wt%) ASTM vacuum distillation (wt%) 430 K ~10 Hexane ~1 466 K ~20 Toluene 15-20 492 K ~40 Acetone >95 Distillate ~50 Acetic acid >95 Source: Venderbosch and Prins (2010: 182) Assuming typical feed material, with a specification of a maximum of 10% moisture in the dried feed material, fast pyrolysis oil has a HHV of about 17MJ/kg (compared with 43-46MJ/kg for conventional fuel oils), and is produced with about 25wt.% water, which cannot readily be separated. While the liquid is widely referred to as ?bio-oil?, it will not mix with any hydrocarbon liquids due to its high oxygen content of 35-40wt.%, which is similar to that of biomass. It is composed of a complex mixture of oxygenated compounds comprising more than 230 organic chemicals (Kumar et al., 2010: 163), which provide both the potential and the challenge for utilisation (Bridgwater, 2011). The density of the liquid is about 1 200kg/m3, which is higher than that of light fuel oil (around 850kg/m3) and significantly higher than that of the original biomass (Demirbas, 2009b). This means that bio-oil has about 42% of the energy content of fuel oil on a weight basis, but 61% on a volumetric basis. Stellenbosch University http://scholar.sun.ac.za 82 Table 16: Pyrolysis oil yields for various feeds Biomass feedstock type Typical pyrolysis oil yield (wt.%, dry basis) Hardwood 70-75 Hardwood bark 60-65 Softwood 70-80 Softwood bark 55-65 Corn fibre 65-75 Bagasse 70-75 Waste paper 60-80 Source: Streff (2010) Table 16 presents some typical bio-oil yields for a variety of biomass feedstock types. Below, Figure 20 shows some possible applications of pyrolysis oil. However, while pyrolysis oil can be upgraded to transport fuels or other usable chemicals, this study focuses solely on its application for the generation of electricity, with thermal energy as a by-product. Figure 20: Fast pyrolysis applications Source: Bridgwater et al. (2002: 187) Findings by Venderbosch et al. (2010) indicate that bio-oil could, for the time being, be used to substitute fossil fuels in heat and power production, through combustion in conventional boilers or co-combustion in power stations. Over the next few years, the focus will be on producing oil and on applying simple and cheap applications. As the technology advances, larger amounts of oil will become available for the development and commercial-scale demonstration of other bio-oil Stellenbosch University http://scholar.sun.ac.za 83 applications, such as turbines or diesel engines. Chiaramonti et al. (2007) investigated the use of pyrolysis oil gas turbine combustors, showing that the fuel needs to be preheated to reduce its viscosity, as well as filtered to reduce its ash and solid contents. In general, experience with bio-oil combustion in gas turbines is still fairly limited (refer also to Lupandin et al., 2005), and further research is required until its application on a commercial scale is achieved. All pyrolysis systems produce some char as a by-product (Gaunt and Lehmann, 2008), which is often referred to as bio-char or sometimes as ?agri-char? when used as a soil amendment. Biochar is very stable compared with uncharred biomass (Baldock and Smernik, 2002) and has an inherent energy value that can be utilised to maximise the energy efficiency of the pyrolysis facility. Various parameters determine the proportions of the three pyrolysis products (see above). Fixed carbon and the carbon content in the char are enhanced by increasing the temperature, and the latter, for instance, can constitute 80wt.% at 500?C and 92wt.% at 600?C, as shown in Table 17, below (Amutio et al., 2011). The relatively low ash content in lignocellulosic biomass is reflected in the low ash yields from bio-char (DeSisto et al., 2010; Kumar et al., 2010; Oasmaa et al., 2010; Venderbosch and Prins, 2010; and Cu?a Su?rez et al., 2010). The study by Amutio et al. (2011) also shows that temperature affects the calorific value of the initial biomass by 50% at 600?C. The heating values of the chars obtained at 500?C and 600?C are much higher (30.4 and 39.9MJ/kg respectively) than for other solid fuels such as soft coal (29MJ/kg) and lignite (20MJ/kg). Biochar has been described as a possible means to improve soil fertility as well as other ecosystem services, and to sequester carbon to mitigate climate change (Laird, 2008; Lehmann et al., 2009; Lehmann et al., 2011). The observed effects on soil fertility have been explained mainly in terms of a pH increase in acid soils (Van Zwieten et al., 2010), or improved nutrient retention through cation adsorption (Liang et al., 2008). However, it has been established, both through field research (Lehmann et al., 2003a; Rondon et al., 2007) and observation of situations where historically bio- char has been applied to soil (Lehmann et al., 2003b), that applying biochar to soil enhances plant growth. When applied to soil, biochar improves the supply of nutrients to crops as well as the soil?s physical and biological properties (Glaser et al., 2002). This results in increased crop yields in low- input agriculture, and an increased crop yield per unit of fertiliser applied (fertiliser efficiency) in high-input agriculture, as well as in reductions in off-site effects such as run-off, erosion, and gaseous losses (Gaunt and Lehmann, 2008). Stellenbosch University http://scholar.sun.ac.za 84 Table 17: Influence of pyrolysis temperature on bio-char properties 400?C 450?C 500?C 600?C Ultimate analysis C (wt.%) 73.3 75.2 82.7 89.4 H (wt.%) 3.7 3.6 2.9 1.4 N (wt.%) 0.2 0.1 0.1 0.1 O (wt.%) 20.6 18.7 11.4 5.7 Proximate analysis Volatile matter (wt.%) 37.6 33.3 23.5 14.1 Fixed carbon (wt.%) 60.2 64.3 73.6 82.5 Ash (wt.%) 2.2 2.4 2.9 3.4 LHV (MJ/kg) 21.6 27.4 30.4 39.9 Surface characteristics BET surface (m2/g) 1.9 2.2 16.2 73.2 Average pore diameter (?) 472.1 443.5 389.2 64.6 Source: Amutio et al. (2011: 7) Charcoal, also referred to as black carbon (BC), is hypothesised to have several positive impacts on soils (Glaser et al., 2002). ? Charcoal is an adsorbent, and when present in soils, it increases the soil?s capacity to adsorb plant nutrients and agricultural chemicals, thereby reducing leaching of these chemicals to surface and ground water. ? Charcoal contains most of the plant nutrients that were removed when the biomass was harvested and has the capacity to slowly release these nutrients to growing plants. ? Charcoal is a relatively low-density material that helps to lower the bulk density of high-clay soils, increasing drainage, aeration, and root penetration, and it also increases the ability of sandy soils to retain water and nutrients. ? Charcoal is a liming agent that will help offset the acidifying effects of N fertilisers, thereby reducing the need for liming. Some of the unintended consequences from applying biochar to soil are discussed by Kokaana et al. (2011), highlighting the physical and chemical characteristics of biochar, which can impact on the sorption, hence efficacy and biodegradation, of pesticides. As a consequence, weed control in Stellenbosch University http://scholar.sun.ac.za 85 biochar-amended soils may prove more difficult as pre-emergent herbicides may be less effective. Since biochars are often prepared from a variety of feedstocks (including waste materials), the potential introduction of contaminants needs to be considered before land application. Plant growth, as well as soil microbial and faunal communities may be affected particularly by metal contaminants. Furthermore, biochar may also influence a range of soil chemical properties, and rapid changes to nutrient availability, pH, and electrical conductivity need to be considered to avoid unintended consequences for productivity. However, most negative effects of biochar are due to the way in which biochar was manufactured and what feedstock was used for the production of biochar. Particularly when using lignocellulosic biomass from SRC plantations as a biochar feedstock, the level of toxic compounds are not considered a serious problem for applying biochar to soil. The half-life of C in soil charcoal is in excess of 1000 years (Glaser et al., 2002). This means that charcoal applied to the soil will make a lasting contribution to soil quality, and the C in the charcoal will be removed from the atmosphere and sequestered in the soil for millennia (Laird, 2008). Preliminary research by Rondon et al. (2005) suggests that nitrous oxide (N2O) and methane (CH4) emissions from the soil may be significantly reduced by applying biochar. It was found that CH4 emissions were completely suppressed and N2O emissions were reduced by 50% when biochar was applied to soil. Yanai et al. (2007) also found a suppression of N2O when biochar was added to soil. The mechanisms by which N2O and CH4 emissions are reduced are not clear. However, the reduction in N2O emissions observed by these authors is consistent with the more widespread observation that fertiliser is used more efficiently by crops in situations where biochar has been applied to the soil (Gaunt and Lehmann, 2008). It should be stressed, however, that the effectiveness of using bio-char to mitigate climate change rests on its relative recalcitrance against microbial decay, and thus on its slower return of terrestrial organic C as carbon dioxide to the atmosphere (Lehmann, 2007). Both the composition of the decomposer community as well as the metabolic processes of a variety of soil organismal groups may be important in determining to what extent bio-char is stable in soils, as these have been established for wood decay (Fukami et al., 2010). The majority of the C in the biochar is in a highly stable state and has a mean residence time of 1000 years or longer at a 10?C mean annual temperature (Roberts et al., 2010; Cheng et al., 2008; Lehmann et al., 2009) (refer also to Figure 21, below). Stellenbosch University http://scholar.sun.ac.za 86 Figure 21: Schematic representation of biomass or bio-char remaining after charring and decomposition in soil (A) C remaining from biomass decomposition after 100 years; C remaining after charring or pyrolysis. (B) range of biomass C remaining after decomposition of crop residues. Source: Lehmann et al. (2006: 406) 4.3.2 Natural system boundaries: biological biomass production capacity Biological biomass production is a central element of a bioenergy-related LCA, where all biological biomass production-related inputs and outputs are accounted for. The CO2 uptake (and/or carbon uptake) of biomass via photosynthesis must be included. In most cases, a dynamic uptake model is not adequate or applicable in LCA studies; therefore, a simplified static approach to calculating the CO2 uptake should be used (Jungmeier et al., 2003). The three main elements in biomass are Stellenbosch University http://scholar.sun.ac.za 87 carbon, oxygen and hydrogen, accounting for more than 96% of its composition, with the remainder being trace elements such as nitrogen, sulphur, or potassium. Figure 22 gives a schematic representation the biological biomass production process via photosynthesis (PE International, 2011), illustrating that CO2, radiation from the sun, and water are used to produce glucose, which is then converted in the metabolic cycle into lignin, cellulose and hemicellulose. Additionally, oxygen is generated; water is used as a reducing agent and is partially emitted; and the sun?s radiation is converted into chemical energy. Figure 22: Biological biomass production via photosynthesis Note: For the production of one tonne of fresh biomass, 80wt.% MC, dry basis Quantification of the amount of sequestered and stored CO2 is based on the method proposed by Zimmer and Wegener (1996), who used a modified photosynthesis equation (R?dl, 2008). The general photosynthesis equation (refer to Equation 8, below) describes the structure of a hexose with a carbon content of 40%. In order to accommodate the difference in mass ratio, Zimmer and Wegener (1996) developed a molecule which is shown in equation (refer to Equation 9, below). Based on this equation, the inputs required to produce one tonne of lignocellulosic biomass can be calculated (refer to Equation 10, below) assuming that the energy required is equal to the energy contained in the biomass, i.e. 9.2MJ/kg (80% moisture content). However, it should be noted that this represents a simplified approach, since it is unknown how much actual energy is required to generate one tonne of wood. Equation 8: General photosynthesis equation Equation 9: Adapted photosynthesis equation Stellenbosch University http://scholar.sun.ac.za 88 Equation 10: Inputs and outputs for producing one tonne of lignocellulosic biomass Table 18: Silvicultural production and other indicators for selected BPAs Biomass demand area (BPA)a BPA I BPA II BPA III BPA IV Demand point Paarl Worcester Ashton Rural Cederberge Relatively Homogeneous Farming Area Groupa 1-2 3-4 5-6 7-8 Potential tree speciesb Eucalyptus cladocalyx (Sugar Gum) Acacia karoo (Sweet Thorn) Drought resistanceb High High Frost resistanceb Medium Medium Ease of cultivationb Easy - Invasivenessb Medium None Adaptability to site conditionsb High High Rotation length (years) 5 7 10 15 No. of rotations (coppices) 4 (3) 4 (3) 4 (3) 4 (3) Stems per hectare (sph) 2000 2200 1800 1250 Usual height attained (m) 15 15 15 6 DBH attained (cm)c 10-14 10-14 10-14 10-14 MAId, whole tree (stems only)e, t/ha/a 0% MC 15.0 (10.5) 10.0 (7.0) 5.0 (3.5) 2.8 (1.9) 20% MC 18.0 (12.6) 12.0 (8.4) 6.0 (4.2) 3.3 (2.4) 40% MC 21.0 (14.7) 14.0 (9.8) 7.0 (4.9) 3.9 (2.7) 80% MC 27.0 (18.9) 18.0 (12.6) 9.0 (6.3) 5.0 (3.5) Potential yield per rotation (stems only)e, (t/ha) 0% MC 75 (53) 70 (49) 50 (35) 42 (29) 20% MC 90 (63) 84 (59) 60 (42) 50 (35) 40% MC 105 (74) 98 (69) 70 (49) 59 (41) 80% MC 135 (95) 126 (88) 90 (63) 75 (53) Notes: a Four biomass procurement areas (BPAs) were selected based on their respective biomass productivity rates, which were estimated by an expert group (Theron et al., 2008) applying climate data for the so-called Relatively Homogeneous Farming Areas (RHFAs) (Von Doderer, 2009). b Suitable tree species were identified (Theron et al., 2008) based on their expected productivity, drought and frost resistance, ease of cultivation, invasiveness, and adaptability to site conditions (Poynton, 1984). c Diameter at Breast Height (DBH). d Mean Annual Increment (MAI). e The values in brackets indicate the biomass available when leaving branches, tops, etc. behind and only using the stemwood as bioenergy feedstock ? see also Dovey (2009) and Kumar et al. (2011). In this study, natural system boundaries refer, inter alia, to the biological biomass production capacity. The biological biomass production capacity is a function of a variety of variables such as Stellenbosch University http://scholar.sun.ac.za 89 the soil, ground and climate conditions of the location of the SRC plantations. As mentioned in section 2.3, 14 potential bioenergy conversion sites (demand points) were identified in the CWDM. Four demand points/biomass procurement areas (BPAs) were selected (namely, Paarl, Worcester, Ashton and Rural Cederberge), based on their different site conditions, with estimated productivity rates for woody biomass grown in an SRC system (relatively high, medium, low, very low). The productivity rates for each of the sites were estimated by an expert group (Theron et al., 2008) based on the climate data for each of the so-called Relatively Homogeneous Farming Areas (RHFAs), spatial units with relative homogeneity in terms of climate, terrain, soils, and resulting farming pattern (Elsenburg Landbou-Ontwikkelingsinstituut, 1990a, 1990b, 1991), which were grouped according to their respective climate data and proximity (Von Doderer, 2009). Table 18, below, shows the biomass productivity and other relevant silvicultural data for the biomass procurement areas (BPA) of the four selected demand points. As is also mentioned in section 2.3, although actual drought and frost resistant species such as E. cladocalyx and A. karoo were identified for producing woody biomass in the CWDM, in order to keep the number of variables for the bioenergy feedstock to a minimum, but somewhat representative sample, a hypothetical ?bioenergy tree? with average values for its chemical composition (refer to Table 5) was used in the LCA model. 4.3.3 Natural system boundaries: land-use change and ecosystem carbon storage Land use and management influence a variety of ecosystemic processes that affect greenhouse gas fluxes (see Figure 23, below), such as photosynthesis, respiration, decomposition, nitrification/denitrification, enteric fermentation, and combustion. These processes involve transformations of carbon and nitrogen that are driven by biological (activity of microorganisms, plants, and animals) and physical processes (combustion, leaching, and run-off) (Paustian et al., 2006). Land use is recognised as the main driver of soil degradation, although impacts on soil quality can also be beneficial, depending on land management practices. In fact, in contrast to annual crops, perennial cropping systems, such as SRC willow systems, tend to accumulate soil organic carbon (SOC) and can serve to remediate contaminated soil (Brand?o et al., 2010). Land-use change due to bioenergy production can occur in two ways: (i) directly, when uncultivated land, pasture, etc. is converted to produce energy crops (e.g. grassland is used to cultivate cereals for bioethanol), or (ii) indirectly, through displacing food and feed crop production to new land areas previously not used for cultivation. From an LCA perspective, direct land-use Stellenbosch University http://scholar.sun.ac.za 90 change is often straightforward and easy to include in the assessment ? see, e.g. Reijnders and Huijbregts (2008) ? although there are often uncertainties in the levels of carbon stock changes due to variations in local conditions and a lack of reliable field trial data. Figure 23: The main greenhouse gas emission sources/removals and processes in managed ecosystems Source: Paustian et al. (2006: 1.6) 4.3.3.1 Direct land-use change Direct land-use change (dLUC) occurs when new agricultural land is taken into production for feedstock for biofuel purposes, displacing a prior land use (e.g. conversion of forest land to sugarcane plantations), thereby generating possible changes to the carbon stock of that land. Depending on the previous use of the land and the crop to be established, there could be a benefit or a disadvantage: when a forest is converted to agricultural land for biofuel production, there will be a loss of carbon stocks; on the other hand, when set-aside land is taken into production, the carbon stock may increase (Cherubini et al., 2009: 437). 4.3.3.2 Indirect land-use change Indirect land-use change (iLUC) (or leakage) occurs when land currently used for feed or food crops is switched to bioenergy feedstock production, and the demand for the previous land use (i.e. feed, food) remains, because the displaced agricultural production will move to other land where Stellenbosch University http://scholar.sun.ac.za 91 unfavourable land-use change could occur. GHG emissions from indirect land-use change are claimed to be even more important than emissions from direct land-use change (Cherubini et al., 2009: 438). Indirect land-use change occurs outside the system boundary because of the displacement of services (usually food production) that were previously provided by the land now used for bioenergy. Emissions from iLUC are not as easy to calculate as dLUC because there are many drivers of land-use change. Therefore, it is difficult to ascertain precisely which land-use change is a result of the bioenergy system (Bird et al., 2010). Hence, indirect land-use change is not included in this study. 4.3.3.3 Carbon stock change Changes in land use can potentially alter carbon stocks by releasing or sequestering soil and vegetation carbon (Mills et al., 2011, in preparation). Carbon is stored within the woody biomass of vegetation, the root biomass, the soil surface biomass, and in soil organic matter (microbial biomass) to varying degrees. Similarly, carbon stocks between areas may differ and change spatially and temporally as a result of natural variations in temperature and rainfall, successional dynamics, and disturbances (such as fire, diseases and pests). Six factors affecting the accumulation of C within an ecosystem are identified by Mills et al. (2005: 183): ? C storage is a function of mean annual precipitation (MAP) and temperature. Soil C tends to increase with an increase in mean annual precipitation (Dalal and Mayer, 1987; Hontoria et al., 1999), probably because primary productivity tends to be a function of rainfall (Knapp and Smith, 2001), and organic matter inputs into the soil tend to be greater in mesic than in arid regions. ? C storage will increase with an increase in woody biomass. ? Frequent fires will lead to a decrease in C storage in both biomass (Tilman et al., 2000) and soils (Bird et al., 2000). ? Tillage will reduce C storage in biomass and soils (Tiessen et al., 1992; Gregorich et al., 1994; Aslam et al., 2000; Francis et al., 2001). ? The establishment or maintenance of a permanent cover of vegetation (e.g. pasture, thicket) will maintain or increase soil C (Dalal and Chan, 2001; Dominy and Haynes, 2002). The effect of pasture establishment on biomass C depends on the structure of the natural Stellenbosch University http://scholar.sun.ac.za 92 vegetation. Pastures may accumulate more biomass C than natural grassland if a dense grass sward is established, but will have less biomass C than woody systems. ? Any of the above effects will be dependent on changes to, and the inherent chemical and physical properties of the soil (Oades, 1993; Zech et al., 1997; Percifal et al., 2000). The establishment of plantations on former grassland, for example, may be expected to reduce soil water content, improve soil aeration, and therefore reduce soil C storage (Birch, 1958). The possible change of carbon storage pools in the forest (i.e. trees, soil and litter) brought about by removing wood from forests should be considered, at least as a qualitative description (Schlamadinger et al., 1997). The most important carbon compartments in forest ecosystems are living vegetation (trees and ground vegetation), dead organic matter and the forest soil (Jungmeier et al., 2003). Figure 24: Temporary and permanent carbon stock losses produced by increased biomass use Source: Bird et al. (2010: 62) In interpreting the carbon cycle, it is important to consider the following aspects: assumed rotation period of the forest ecosystem, changes to carbon storage pools, landfill by wood-based waste, and recycling. Figure 24 illustrates the potential temporary and permanent losses of carbon over time due to land-use change or increased biomass use. Conversely, in some cases of land-use change, the carbon stock may increase; for instance, when changing from intensive agriculture to extensive forestry. In order to account for the carbon stock change when introducing lignocellulosic biomass production in each of the biomass procurement areas, the current land-use types, their proportions Stellenbosch University http://scholar.sun.ac.za 93 and their respective carbon stocks had to be determined and compared with the expected carbon stocks of SRC plantations. The respective proportion for each land-use type that would be replaced by an SRC plantation for each biomass procurement area ? presented in Table 19, below ? has been derived from the data used in the land availability assessment (Von Doderer, 2009), which in turn is based on the original land cover categories developed by CSIR-ARC-Consortium (2000). Table 19: Proportions of changed land use by introducing SRC plantations per BPA No. Identified land for biomass productiona Land coverb Biomass procurement area I II III IV 1 Extensive dryland and improved grassland CTCDc - 60.6% 51.4% 44.9% IGd - 0.4% 1.6% 0.1% 2 Fynbos, shrubland, and bushland SLFe 50.0% 31.5% 33.2% 51.9% TBCFf 50.0% 7.5% 2.8% 3.1% 3 Intensive, permanent and temporary farmland CPCIg - - 10.4% - CTCIh - - 0.6% - Notes: a Land cover categories as applied in Von Doderer (2009) b Original land cover categories in CSIR-ARC-Consortium (2000) c Cultivated, temporary, commercial dryland (CTCD) d Improved grassland (IG) e Shrubland and Low Fynbos (SLF) f Thicket, Bushland, Bush Clumps, High Fynbos (TBCF) g Cultivated, Permanent, Commercial, Irrigated (CPCI) h Cultivated, Temporary, Commercial, Irrigated (CTCI) Based on the land availability assessment, six of the land cover types present in the CWDM would be affected if SRC plantations were introduced. Each of the BPAs has an unique distribution of land cover types affected. However, due to the large extent of the area and the heterogeneity thereof, assessing the soil and production types of each potential site would go beyond the scope of this study. Thus, some generalisations were necessary. Only organic carbon (i.e. above- and below- ground carbon) is taken into consideration, since some factors contributing to the total carbon stock ? such as the inherent chemical and physical properties of the soil ? are unknown. Furthermore, each of the land cover types themselves already represent a variety of production types, e.g. cultivated, permanent, commercial, irrigated, which include any type of intensive agricultural production where permanent irrigation systems are installed, such as vineyards, deciduous fruit orchards, etc. Hence, production systems had to be selected that were somewhat representative for each land-use category, in order to give some carbon stock change estimates (see Table 20, below). Stellenbosch University http://scholar.sun.ac.za 94 Table 20: Above- and below-ground biomass and its related carbon stock at equilibrium per land-use type and BPA Land-use type: Fynbos, Shrubsa Extensive (dryland) farmingb Intensive farmingc Biomass productiond Production system: - Wheat Lemon orchard SRC plantation Biomass procurement area: I II III IV I II III IV I-IV I II III IV Biomass Above-ground biomass (kg/ha) 11351 10568 7492 6187 8571 2857 1429 2571 23 755 37500 35000 25000 20 830 Below-ground biomass (kg/ha) 13258 12343 8750 7226 4615 1538 769 1385 10 933 31 500 29 400 21 000 17 500 Average above- and below-ground biomass (kg/ha) 24609 22911 16242 13412 13187 4396 2198 3956 34 688 69 000 64 400 46 000 38 330 Carbon Above-ground organic carbon (kg/ha) 5 676 5 284 3 746 3 093 3 429 1 143 571 1 029 19 954 18 000 16 800 12 000 10 000 Below-ground organic carbon (kg/ha) 6 629 6 172 4 375 3 613 1 846 615 308 554 9 183 15 120 14 110 10 080 8 400 Average below- and above-ground carbon (kg/ha) 12 305 11 456 8 121 6 706 5 275 1 758 879 1 582 29 138 33 120 30 910 22 080 18 400 Carbon content in biomass (wt%) 50 40 50 48 Root-shoot ratio (%) 117 54 46 48 Ecosystem lifespan (a) 10 1 25 20 28 40 60 Notes: a Source: Cockx (2002) Fynbos biomass quantity and carbon content depend on age of Fynbos vegetation; assuming an age of 10 years, values are derived from above-ground formula ) and below-ground formula ). b Source: Agenbag (2011) The expected grain yields are 3000, 1000, 500 and 900kg/ha for BPAs 1-4 respectively, assuming a yield-biomass ratio of 0.35, a root-shoot ratio of 0.65, and a carbon content of 40%. c Assuming a lemon orchid as a reference land-use type for intensive farming ? all irrigated, average spacing 500 trees/ha, reaching full production after 8 years, with a plantation life span of 25 years, dry mass 144kg/tree (Goldschmidt and Golomb, 1982: 207), leaves renewed every two years, average carbon content assumed, 50%. d Dry matter, based on productivity rates provided in Table 5, above, assuming a root-shoot ratio of 0.48 and a carbon content of 48%. Stellenbosch University http://scholar.sun.ac.za 95 To determine an average organic carbon stock for intensive farming activities, the perennial production of lemons under irrigation was assumed. As mentioned above, C storage is a function of MAP and temperature. Since the lemon orchards are assumed to be irrigated, no distinctions are made for the different BPAs in producing biomass, neglecting the possible impact of temperature. Each ?off-tree? has a dry mass of 144kg with a carbon content of 50% and reaches full production after eight years. Assuming a spacing of 500 trees per hectare and a plantation lifespan of 25 years, as well as leaf renewal every two years, an average above- and below-ground carbon stock of around 29 tonnes per hectare was assumed. Considerably less organic carbon is expected for annual farming activities, such as wheat production in a rain-fed dryland farming scenario. Average grain yields of 3, 1, 0.5 and 0.9t/ha (dry basis) are expected for BPAs I-IV respectively (Agenbag, 2011), and combined with a yield- aboveground biomass ratio of 0.35, a shoot-root-ratio of 0.65, as well as a carbon content of 40%, average above- and below-ground carbon contents of 5.2, 1.7, 0.8 and 1.6 t/ha for BPAs I-IV respectively have been taken into account. For areas that are not actively used commercially, such as Fynbos areas, shrubland, and bushland, a Fynbos ecosystem was assumed. Based on the above- and below-ground biomass equations in Cockx (2002), an average age of ten years until fires destroy current vegetation ? allowing the Fynbos vegetation to reproduce ? and a carbon content of 50%, Fynbos average above- and below- ground carbon stocks of 12.3, 11.5, 8.1 and 6.7 t/ha for BPAs I-IV respectively have been assumed. These values for the different land-use ecosystems assume an average biomass/carbon stock, incorporating initial growth, consumption, and decomposition processes. In SRC plantations, both above- and below-ground biomass is developed during the first rotation. Following the planting of the trees, the above-ground biomass is removed after each rotation (harvesting). Once the below- ground biomass is fully established during the first cycle, the biomass/carbon level is not expected to change until the stumps have been exterminated with spray, resulting in decomposition of the below-ground biomass, followed by re-establishment with improved genetic material. With 48% carbon content in the biomass (refer to section 2.4.2), BCA 1 accumulates an above- and below- ground living organic content of 33 tonnes per hectare, with organic content levels of 30.9, 22.1, and 18.4 for the remaining BPAs respectively. Thus, when substituting current land-use types with a biomass SRC production system, the carbon stock will in most cases increase to varying degrees, except when substituting intensive farming production (i.e. lemon orchards) in BPAs III and IV (see Table 20, above). Stellenbosch University http://scholar.sun.ac.za 96 4.3.4 Time boundaries Since this study is aimed at comparing bioenergy systems and identifying the most sustainable one, this LCA can be defined as change-oriented and thus prospective. The functional unit, the annual production of electricity, also specifies the time boundary for the LCA, i.e. one year. However, for the financial assessment, a longer time boundary is required in order to determine the profitability of the different bioenergy alternatives in terms of net present value (NPV), and internal rate of return (IRR). As is commonly accepted for the financial-economic analysis, an economic business cycle of 20 years for the conversion plant has been assumed. Depending on the BPA/demand point, this time boundary has been extended by the respective rotation lengths for the SRC plantations, since a sustainable supply of biomass needs to be ensured. Hence, the time boundary is 25 years for BPA I, 27 for BPA II, 30 for BPA III and 35 for BPA IV. 4.4 Conclusions Chapter four encompasses the goal and scope definitions, the first phase of the life-cycle assessment of lignocellulosic-biomass bioenergy systems in the Cape Winelands District Municipality. To provide a reference, the functional unit was defined, for which the input and output process data of the following chapter are normalised, providing the basis on which the final results are presented. Furthermore, the system boundaries were discussed, which were specified in terms of several dimensions, such as boundaries in relation to the natural system (i.e. biological biomass production capacity and land-use change-related carbon stock changes); time boundaries, which are strongly linked to the functional unit; as well as the technical system boundaries. At the beginning of section 4.3.1, schematic illustrations (Figure 9 and Figure 10) show the various bioenergy system production phases or pathways leading to a set of 37 bioenergy system alternatives (refer to Figure 11). Each production phase, subdivided into primary biomass production; harvesting and primary transport; biomass pretreatment, including comminution, drying and mobile fast pyrolysis; as well as secondary transport; biomass upgrading and final conversion into electricity were also discussed. This gives some general background information on available technologies and applications. The second phase of a life-cycle assessment (LCA), the life-cycle inventory (LCI), is presented in the following chapter. Information is gathered about the inputs and outputs for all processes and activities of the 37 lignocellulosic bioenergy systems (LBSs), which not only includes the specification of each unit process on productivity and environmentally relevant flow data, which is commonly required for an LCI, but also financial-economic data, in terms of costs, as well as socio- economic data, in terms of direct employment creation potential. Stellenbosch University http://scholar.sun.ac.za 97 5 CHAPTER: LIFE-CYCLE INVENTORY 5.1 Introduction The previous chapter describes the first phase of a life-cycle assessment, defining the goal and scope for assessing a set of 37 alternative lignocellulosic bioenergy systems (LBSs). The functional unit is set as 39.6GWhel, the annual output of electrical energy of a 5-megawatt conversion system on 330 days per year. The geographical boundaries for the LCA are set by the extent of the Cape Winelands District Municipality, describing also the natural system boundaries in terms of biomass productivity and land use, and related carbon stock changes for four so-called biomass procurement areas (Paarl, Worcester, Ashton and the Rural Cederberge). Furthermore, the technical system boundaries are defined, subdivided into various production phases such as primary biomass production, harvesting and primary transport; biomass pretreatment, including comminution, drying and fast-pyrolysis; as well as secondary transport, biomass upgrading and final conversion into electricity. Each production phase is discussed and general background information on available technologies and applications is given. The life-cycle inventory (LCI) for each of the 37 lignocellulosic bioenergy systems is discussed in Chapter 5, representing the second phase of an LCA. In the life-cycle inventory analysis, information is gathered about all process-related inputs and outputs in the studied system. For each process, qualitative and quantitative data, i.e. relating to machinery and equipment, are assumed, and related productivity is specified, not only in terms of environmental input and output flows, which are typical for an LCI, but also by considering related financial-economic (capital and operational expenditures, income from selling electricity and related by-products such as thermal energy or bio-char), as well as socio-economic (direct employment creation potential) data. 5.2 Primary production of biomass This represents the initial phase in the life cycle, which is the same for all 37 lignocellulosic bioenergy systems. Besides taking all the activities and processes in the establishment and maintenance of the SRC plantation into account, this phase also includes the carbon stock change per hectare and the land-use type of the respective biomass procurement areas (BPAs). Figure 25, below, shows the GaBi 4.4 LCA software interface for the primary production of biomass (PE International, 2011). The establishment of an SRC plantation requires sufficient site preparation. Site preparation entails the clearing and cultivation of plantation areas. The clearing can be divided into two steps, mechanical and chemical land preparation. Mechanical land preparation consists of the removal of Stellenbosch University http://scholar.sun.ac.za 98 shrubs and trees in order to improve access for establishment operations, to allow effective cultivation, and to remove cover to deter browsing animals. Chemical land preparation is aimed at removing the remaining competing vegetation. Figure 25: GaBi 4.4?s LCA software interface illustrating the primary biomass production phase Source: PE International (2011) 5.2.1 Mechanical land preparation In the case of a heavy infestation of shrubs and trees, as well as when dealing with poor site conditions (i.e. instability), very rough ground conditions and steep slopes, ripping should be Stellenbosch University http://scholar.sun.ac.za 99 undertaken with heavy machinery such as bulldozers. However, since more than 80% of the identified SRC?s biomass production areas are less steep than 20% (see Table 2) and sandy soils in the CWDM are predominant, it was assumed for all alternatives that a medium to light infestation of competing vegetation was a given, and that, therefore, using agricultural equipment (e.g. a tractor-plough combination) would be sufficient. Research on commercial timber and pulp wood production in South Africa shows that complete strip ploughing is the best method for establishing eucalypts (Viero, 2004). Based on data from the Guide to Machinery Costs (Lubbe et al., 2011), it takes about 2.47h/ha for mechanical land preparation using a four-wheel-drive, high-power-demand tractor (67kW) combined with a five-shank, spring-tine chisel plough. The fuel consumption was assumed to be 12.06 litres per hour, resulting in fuel costs of R297.88 per hectare (ha) (a conservative fuel price for both diesel and unleaded petrol of R10/l was used throughout the multi-period budgeting models). The costs for the tractor operator were assumed to be R26.45/h (R65.33/ha). The capital investment costs for the tractor and for the plough were estimated at R435 to R450 and R32 to R100 respectively. Taking depreciation; licencing and insurance; repairs and maintenance costs, etc. into account, but excluding interest, the hourly cost for the tractor amounted to R201.59 and R15.41 for the plough. A figure for interest per annum of 10% for the machinery was included, and thus the total costs for mechanical land preparation were assumed to be R677.92/ha. Resource consumption and emissions for the mechanical land preparation are captured in the LCA using the LCI process for soil cultivation (heavy ploughing), as provided in the software database (PE International, 2006). 5.2.2 Chemical land preparation and maintenance Chemical land preparation and maintenance are required not only to remove the remaining competing vegetation, but also to allow purposeful and effective fertilisation, i.e. for their secondary effects, and therefore to enhance the growth rate of the trees, particularly during the first years after planting, until canopy closure is reached. Table 21, below, indicates the assumed time of herbicide applications, the number of applications and the amount of herbicide per hectare applied throughout the lifetime of the SRC plantation. It must be noted that weed control operations might differ, depending on factors such as site conditions, the magnitude of infestation of competing vegetation and the growth rate/competitiveness of the grown crop. Additional information on weed control in plantation forests/woodlots can be found in Little et al. (1997). For both weed control prior to and post Stellenbosch University http://scholar.sun.ac.za 100 harvesting, the same herbicide can be applied, but at different levels of concentration Du Toit (2009). Two to three harvests after coppicing can be done following the first harvest after the planting of the trees. If the SRC plantation is to be continued after 3-4 rotations, it might be beneficial to plant improved genetic material. To do this the existing stumps must be killed, and the new trees must be planted between the original lines, allowing the old stumps to decompose. Table 21: Weed control operations Ro ta tio n Y ea r Broadcast spray Cone spray Total (l/ha) Comment l/ha l/ha 1 0 6 4 10 One broadcast spray prior to planting; one cone spray operation post planting 1 8 8 Two (cone spray) weed control operations per year at 4l/application 2 8 8 ... 4 4 To be continued until canopy closure is reached 2 0 8 8 Two (cone spray) weed control operations per year at 4l/application to support coppice shoots 3 0 8 8 4 0 8 8 X X 6 6 Elimination of competing vegetation and killing of old stumps as preparation for new crop Total: 12 48 60 Source: Du Toit (2009) In order to account for the impact of weed control in the LCA, an average amount of herbicides per hectare was allocated per rotation over the lifetime of the SRC plantation. In total, 60 litres of herbicide are applied per hectare over the lifetime of the plantation, i.e. 15l/ha are sprayed per rotation. The cost for herbicides per hectare and rotation were estimated at R421. Additional material costs of R73/ha/rotation were added for the pressure sprayers for the manual operation (the purchase price is R389 per unit). Weed control operations post planting are more time-consuming, so only a manual cone spray operation is done to avoid damage to the crop. Following the silvicultural guidelines of best operating practices (BOP) (Forestry Solutions, 2007j), the total treatment of an area prior to planting takes about 1.4 to 1.6 workdays per hectare (assuming a vegetation ground cover of 40- 59% and a slope of up to 25%), whereas the coneing of the same area, i.e. weed control post Stellenbosch University http://scholar.sun.ac.za 101 planting, will require 3.1 to 3.3 workdays. Over the lifetime of the plantation, a total of 41.4 workdays are required, assuming an average value of 1.5 and 3.2 workdays respectively. Based on 9 hours per workday, farm workers will spend a total of 373 hours per hectare for weed controlling operations over the lifetime of an SRC plantation. With an agricultural minimum wage (AMW) of R6.74 per hour (RSA, 2011), the labour costs amount to R628.50 per hectare and rotation. Furthermore, additional costs of R189 were allocated per hectare and rotation for transportation, i.e. the supply of the chemicals to the farm, as well for as the transportation of farm workers, chemicals and other materials to and from the SRC plantation were specified as R2.82 per kilometre using a light duty vehicle (LDV) (two-wheel drive, diesel), commonly used on South African farms (Lubbe et al., 2011). In total, the cost for weed control amount to R1 312/ha/rotation. 5.2.3 Planting of seedlings A central operation for the establishment of an SRC plantation is the unit process ?planting of seedlings?, which includes preparation of planting pits at the required spacing (depending on the recommended stems per hectare, planting of the seedlings, and blanking). The term blanking refers to the replacement of dead seedlings shortly after planting; replanting describes, in forestry terms, the total replanting after clear-felling. The production costs of seedlings in nurseries, as well as the transport costs from the nurseries to the biomass production sites were not included. Table 22: Planting and blanking productivity and costs (2011) Planting Unit Biomass procurement area (BPA) I II III IV No. of trees/ha sph 2 000 2 200 1 800 1 250 Work hours per ha a h/ha 45 49.5 40.5 28 Labour costs per ha b R/ha 303 334 273 190 Planting material c R/ha 2 000 2 200 1 800 1 250 Total planting costs R/ha 2 303 2 534 2 073 1 440 Blanking Unit I II III IV Mortality rate % 5 5 5 5 Trees to be replaced No. of trees/ha 100 110 90 63 Work hours per had h/ha 0.43 0.47 0.39 0.33 Labour costs per ha b R/ha 2.90 3.17 2.63 2.22 Planting material c R/ha 100 110 90 63 Total blanking costs R/ha 103 113 93 65 Notes: a Source: Forestry Solutions (2007i). b Based on an agricultural minimum wage of R6.74 per hour. c Assuming a seedling costs of R1.00/plant for Eucalyptus spp. and Acacia karoo. d Source: Forestry Solutions (2007g). Stellenbosch University http://scholar.sun.ac.za 102 Based on best operating practices, a worker is assumed to plant around 44 trees per hour, which includes both preparing the planting pits and planting the seedlings (Forestry Solutions, 2007i). Furthermore, a seedling mortality of 5% was assumed, resulting in a blanking productivity of 0.43, 0.47, 0.39 and 0.33 for biomass procurement areas I-IV respectively. Costs for labour of R6.74 per hour (RSA, 2011) and for seedlings of R1.00 per plant (Du Toit, 2009) were assumed. 5.2.4 Fertilisation An important variable in LCA studies is the contribution to net greenhouse gases (GHG) emissions of N2O, which results from the application of nitrogen fertiliser and the decomposition of organic matter in the soil (Stehfest and Bouwman, 2006). Applying fertiliser to agricultural land has an effect on the nutrient balance of the soil. Emissions from fields vary, depending on soil type, climate, crop, tillage method, and fertiliser and manure levels (Larson, 2005). The uncertainties concerning actual emissions are magnified by the high global warming potential of N2O, which is 298 times greater than that of CO2 (Forster et al., 2007). The impacts of N2O emissions are especially significant for annual biofuel crops, since fertilisation levels are greater for these than for perennial crops. Crops grown in high rainfall environments or under flood irrigation have the highest N2O emissions, as denitrification, the major process leading to the production of N2O, is favoured under moist soil conditions where oxygen availability is low (Wrage et al., 2005). Nitrogen fertilisers contribute to the environmental impact of bioenergy systems because (i) their production is energy intensive, (ii) their production releases significant quantities of nitrous oxide, and (iii) a proportion of the nitrogen added to agricultural soils, in the form of fertiliser, is converted to N2O, a potent greenhouse gas, and released to the atmosphere. Figure 26: Proliferation pathways of nitrogen for agricultural land Source: PE International (2007) Stellenbosch University http://scholar.sun.ac.za 103 Many LCA studies neglect N2O emissions; those that include N2O often utilise default emission factors published by the Intergovernmental Panel on Climate Change (IPCC), which estimates emissions from several sources (IPCC, 2006) as follows (refer also to Figure 26, above): ? Volatilisation of N as NH3 at a rate of 10% of total N in the case of synthetic N application, or 20% of total N in the case of manure application. Another study estimates these percentages to be much lower, around 2% (Van den Broek et al., 2000). One percent of the N in the NH3 is then converted to N2O. ? Direct soil emissions of N2O at 1% in the case of synthetic N, and 2% in the case of manure (mean values). With respect to runoff and leaching to groundwater as nitrate (30% of total N applied), 0.75% thereof is converted to N20. The resulting effect is that 1.325% of synthetic N fertiliser is emitted as N in N2O. However, for dry climates, as found in the CWDM, where leaching is unlikely to occur, the IPCC guidelines suggest a lower value of 1.1wt.% for the conversion factor of synthetic N inputs to N2O. The nitrous oxide release (kg/ha/a) can therefore be calculated as outlined in Table 23, below: Table 23: Emission factors from synthetic nitrogen inputs (%) Emission factors a Source Conversion factor b (CF) Formula N2 D?mmgen (2006) 10.0 N2O-N IPPC (de Klein et al., 2006: 2492), Stephenson et al. (2010) 1.1 NH3-N Doehler et al. (2002), Van den Broek et al. (2000) 2.0 NO D?mmgen (2006) 0.7 Stephenson et al. (2010) 0.0 Notes: a from synthetic nitrogen inputs b CF, % from input Similar for weed control, the assumed fertilising operations are based on general recommendations for sandy and clayey soils typically found in the CWDM (Du Toit, 2009). For individual sites, however, it is advisable to employ a soil chemist to undertake a soil chemical analysis in order to identify the appropriate fertiliser mix for the maximised growth of the SRC plantation. A Nitrogen Stellenbosch University http://scholar.sun.ac.za 104 (N), Phosphate (P), and Potassium (K) mix for both a sandy as well as for a clayey soil type was recommended (see Table 24, below). For the LCA, in all primary production scenarios, a sandy soil type is assumed with a fertiliser mix (N-P-K) per tree of 30, 20 and 15 grams respectively. Table 24: Recommended fertiliser mix per tree for different soil types Active ingredient On sandy soils (g/tree) On clayey soils (g/tree) Nitrogen (N) 30 15 Phosphate (P) 20 15 Potassium (K) 15 10 Source: Du Toit (2009) In total, 12 fertiliser applications are recommended over the lifetime of the SRC plantation (see Table 25, below). Therefore, accounting for the impact of fertilisation in the LCA, an average of three fertiliser applications per rotation were assumed. Table 25: Fertiliser application over lifetime of SRC plantations Rotation Year Number of applications Comment 1 0 3 One application at time of planting; two more applications during course of the first year 1 3 Three applications each in year one, and two after planting to enhance growth rate/competitiveness of crop 2 3 ... 0 Fertilisation to be continued until canopy closure has been reached 2 0 1 To support above-ground biomass growth, one fertiliser application post harvesting 3 0 1 4 0 1 X X - If SRC plantation is to be continued, it is recommended to plant new, improved genetic material; hence, fertilisation cycle starts anew. Total 12 Source: Du Toit (2009) The costs of the respective fertilisers were obtained from Yara (2009) and are listed together with the required concentrations and amounts in Table 26, below. For sandy soils, the cost per Stellenbosch University http://scholar.sun.ac.za 105 application and tree are R0.92, and for clayey, soils R0.59 respectively. The cost per hectare depends on the amount of assumed stems per hectare (sph) ? see Table 18, above. Table 26: Fertiliser products, respective concentrations, and prices per ton (2011) Active ingredient Fertiliser Product Concentration of active ingredient Price/ ton (R/t) Amount (g/tree) of fertiliser required Sandy soils Clayey soils Nitrogen (N) KAN28 28% R3 350 107 54 Phosphate (P) Maxifos 20P 20% R3 780 100 75 Potassium (K) KCL 50% R5 980 30 20 Similar to the weed control operation, the manual labour costs are based on the agricultural minimum wage for 2010, i.e. R6.74 per hour, and ? extrapolated from the best operating practices (BOP) ? work efficiency for fertilising circles (fertiliser buried) of 0.93 minutes per tree (Forestry Solutions, 2007h). Additional costs were included for the transportation of fertilisers to the farm, and the transportation of farm workers, fertilisers and other material to the SRC plantation, as stated in Table 27, below. In total, the cost of fertilisation amounts to R2 654/ha/rotation for BPA I; and R2 919, R2 389, and R1 659 for BPAs II, III, and IV respectively. Table 27: Average fertilising cost per ha and rotation on sandy soils in the CWDM (2011) Cost item BPA I BPA II BPA III BPA IV Fertiliser (R/ha) R1 833 R2 016 R1 649 R1 145 Transport (R/ha) R194 R214 R175 R121 Labour (R/ha) R627 R690 R564 R392 Total (R/ha) R2 654 R2 919 R2 389 R1 659 5.2.5 Thinning of coppice shoots After clear-felling an SRC plantation, coppice shoots are allowed to resprout in order to regenerate the section. Once coppice shoots reach a height of between 1.5m and 2m, they should be reduced to between one or two shoots per stump (Little and Du Toit, 2003). The aim is to achieve the same tree density that was used in the first planting operation, relative to the stand target concerned. More than one shoot per stump can be left to make up for the mortality of neighbouring stumps. As mentioned above, this procedure can be repeated at least two to three times after the initial planting. Thereafter, it is recommended that new, genetically improved tree material be planted Stellenbosch University http://scholar.sun.ac.za 106 between the original planting lines, after killing the original stumps with contact herbicides and allowing them to decompose (Du Toit, 2008). Thinning, as the reduction of coppice shoots is also called, takes about 16 hours per hectare, resulting in manual labour costs of R108/ha, based on an AMW of R6.74 per hour. Furthermore, costs of R28/ha (R2.82/km for LDV, two-wheel drive, diesel) for transporting farm workers and materials to and from the site were also included. For the LCA, an average of 7.5km per rotation was assumed. 5.3 Harvesting and forwarding As mentioned in section 4.3.1.2, five harvesting systems (HS) are modelled in this study, entailing three different harvesting technologies and three types of primary transportation (also referred to as forwarding or extraction). The harvesting technologies modelled are motor-manual machinery, mechanised forestry machinery, and modified agricultural machinery. A forwarder fitted with a crane; a tractor pole-trailer combination loaded and unloaded, either manually or with a three- wheeler loader; or a tractor-container trailer combination were assumed for primary transport. 5.3.1 Harvesting system I In the case of harvesting system I (LBSs 1-8, see Figure 11), whole trees are felled using chain- saws, left in-field for several weeks for air-drying, and then transported to the roadside with a forwarder. The loading and unloading are executed by the forwarder operator, using a crane fitted to the forwarder. As suggested by Schif (2010), the STIHL MS 361 chainsaw (3.4kW) is well suited to small-tree harvesting, consuming an assumed 1.7 litres (two-stroke blend) per productive machine hours (PMH) and 1kg/PMH of lubricants (e.g. chain oil). The fixed cost per PMH was assumed to be R9.52; the variable costs (e.g. repairs and maintenance, fuel and lubricants) amount to R29.72/PMH, resulting in total machinery costs of R39.24/PMH (R21.80/h). The chainsaw operator costs are R3 000/month (R16/h, R28.8/PMH), assuming a shift length (also referred to as a workday) of nine hours, of which five are counted as PMH, the remainder being allocated to non- productive work such as setting up, servicing and walking in the compartment. Hence, the total costs per PMH are R68.04 or R37.80/h. A productivity rate of 54 trees per hour (felling only) was extrapolated from data provided by Forestry Solutions (2007a), based on industrial daily production targets which, inter alia, depend on variables such as tree species, tree height, tree branching, competing vegetation and ground conditions. Stellenbosch University http://scholar.sun.ac.za 107 For the LCA, the Stihl MS 441 chainsaw process dataset provided in the GaBi database (PE International, 2006) was modified according to the above-stated input specifications and emission data provided by Schif (2010). Table 28, below, sets out the productivity rates and costs for harvesting system one for each of the activities per biomass procurement area based on the corresponding number of trees per hectare (stems per hectare or sph) and the biomass yield per hectare and rotation. Table 28: Harvesting system I ? productivity rate and costs per hectare for each BPA (2011) Biomass procurement area (BPA) BPA I BPA II BPA III BPA IV Stems per hectare (sph) 2 000 2 200 1 800 1 250 Yield/rotation t/ha (80% MC, whole tree) 135 126 90 75 t/ha (40% MC, whole tree) a 105 98 70 59 Motor- manual harvesting Productivity (trees/hour) b 54 Time (h/ha) 37 41 33 23 Productivity (t/h) 3.65 3.09 2.70 3.24 Cost (R/ha) R2 520 R2 772 R2 268 R1 575 Extraction with forwarder c Max. payload capacity 14t/load (22.40m3/load) Bulk density (logs) 0.375 Cycle length (min) d 42 Productivity (t/h) 12 Time (PMH/ha) 8.75 8.17 5.83 4.92 Cost (R/ha) R5 122 R4 781 R3 415 R2 878 Cost (R/t) Harvesting R19 R22 R25 R21 Forwarding R49 R49 R49 R49 Total R67 R71 R74 R70 Notes: a Motor-manual harvesting entails felling only. b Productivity for chainsaw application derived from industrial production standards (Forestry Solutions, 2007a). c Forwarder John Deere/Timberjack 1410D. d Forwarding productivity derived from industrial production standards (Forestry Solutions, 2007f). Following the air-drying of the biomass to moisture content levels of around 40% (dry basis), the biomass is extracted with dedicated forestry machinery. For this study, the John Deere/Timberjack 1410D (129kW) with a payload capacity of 14t/load (22.40m3/load) and a fuel consumption of 10.28l/PMH was assumed. As practical experience indicates that the volumetric capacity limitations for primary transport are often exceeded to increase the productivity rate per load up to a level of maximum mass payload capacity, a bulk density of 0.375t/m3 is suggested for primary transport (see Table 28, above). The forwarding productivity or cycle length of the forwarder was also derived from Industrial Production Standards (Forestry Solutions, 2007f) taking the average lead distance (501-600m), Stellenbosch University http://scholar.sun.ac.za 108 slope conditions (7-20%), ground conditions (moderate) and ground roughness (uneven) into account. This results in a cycle length of 42 minutes or 1.43 cycles per hour. Hence, around 12t/h of the whole-tree biomass is forwarded to the roadside when using a forwarder. A purchase price of R2.9 million for the forwarder, plus R145 000 for spares, an economic lifetime expectancy of 15 000PMH, a salvage value of 10% of the purchase price, fixed costs (licence and insurance), and variable costs (fuel consumption of 23l/PMH, and repairs and maintenance costs of R232/PMH, etc.) result in a machinery cost of R529/PMH. Adding operator costs of R56.36/h gives a total of R585.40 per hour, translating into forwarding costs of R49 per tonne for harvesting system I. Since no complete forwarder dataset for the LCA was available, an agricultural combine harvester dataset was modified according to the forwarder specifications. 5.3.2 Harvesting system II LBSs 9-16 envisage motor-manual harvesting, i.e. felling, de-branching, topping and cross-cutting into two logs per stem, leaving the branches and tops behind, followed by biomass extraction with a tractor coupled to a modified trailer, with a delay of several weeks for air-drying. With logs, the ease of handling improves significantly, and this harvesting system accommodates manual loading and unloading of the feedstock, resulting in increased job opportunities, particularly in the low- income sector. Similar to the ?felling-only? operation above, the productivity rates for chainsaw operation were derived, assuming best operating practices, from industrial production standards (Forestry Solutions, 2007a), resulting in a productivity rate of 26 trees per hour. The average cost per PMH is the same as for the ?felling-only? application (R37.80/h, or R68.04/PMH). This motor-manual harvesting application is considerably more expensive due to its lower efficiency, as shown per biomass procurement area in Table 29, below. Forwarding using agricultural machinery entails using a 67kW, four-wheel drive, high-power-demand tractor coupled with a modified pole-trailer. Based on a cycle length of 50 minutes per load (Forestry Solutions, 2007b), assuming the same forwarding conditions as for the forwarder mentioned above, and a maximum load capacity of 10 tonnes (30m3), around 12t/h are extracted with the tractor-trailer combination. Purchase prices of R411 369 for the tractor and of R270 000 for the trailer, both of which have an economic lifetime expectancy of 12 000PMH, were derived from the Guide to Machinery Costs (Lubbe et al., 2011). Based on the assumptions of fixed and variable costs made in the guide, the Stellenbosch University http://scholar.sun.ac.za 109 total tractor-trailer combination cost per PMH was calculated at R269.80, translating into a per ton cost of R22.48 (logs, 40% MC). The above-stated tractor specifications and the production-related emissions were taken into account in the LCA by modifying the ?universal tractor? dataset provided in the Gabi database (PE International, 2006). Table 29: Harvesting system II ? productivity rate and costs per hectare for each BPA (2011) Biomass procurement area (BPA) BPA I BPA II BPA III BPA IV Stems per hectare (SPH) 2 000 2 200 1 800 1 250 Logs per hectare 4 000 4 400 3 600 2 500 Yield/rotation t/ha (80% MC, whole tree) 135 126 90 75 t/ha (40% MC, logs) a 74 69 49 41 Motor-manual harvesting Productivity (trees/hour) b 26 Time (h/ha) 77 85 69 48 Productivity (t/h) 0.96 0.82 0.71 0.85 Cost (R/ha) R2 908 R3 198 R2 617 R1 817 Manual loading and unloading Loading (trees/worker/hour) c 38 Unloading (trees/worker/hour) d 76 Loading time/area unit (h/ha) 106 116 95 66 Unloading time/area unit (h/ha) 53 58 47 33 Total time (h/ha) 159 174 142 99 Cost (R/ha) f R1 072 R1 173 R957 R667 Forwarding with tractor- trailer combination Max. payload capacity 10t/load (30m3/load) Bulk density (logs) 0.67 Cycle length (min) g 50 Load capacity (t/h) 12 Time (PMH/ha) 6.2 5.8 4.1 3.5 Cost (R/ha) R1 653 R1 542 R1 102 R925 Cost (R/t) Harvesting R71 R83 R96 R80 (Un)loading R14 R17 R20 R16 Forwarding R22 R22 R22 R22 Total R108 R122 R138 R119 Notes: a 30% of biomass (branches, tops, etc.) remains in-field unutilised. b Motor-manual harvesting (felling, debranching, topping and cross-cutting) derived from Industrial Production Standards (Forestry Solutions, 2007a). c Manual loading derived from Industrial Production Standards (Forestry Solutions, 2007d), assuming a team of six workers (2 040 trees/shift). d For unloading, half of the loading time was assumed. e Assuming the agricultural minimum wage of R6.74/hour. f Forwarding cycle length derived from Industrial Production Standards (Forestry Solutions, 2007b). Stellenbosch University http://scholar.sun.ac.za 110 Based on Forestry Solution?s Industrial Production Standards for the manual loading of logs, a team of six workers, with four loading and two positioning on the transportation unit, was assumed (Forestry Solutions, 2007d). Taking log volume, time after felling, ground conditions, and carrying distance into account, a team is expected to load 2 040 logs per workday, resulting in 38 logs/worker/hour. For unloading, a productivity rate of double that for loading was assumed. 5.3.3 Harvesting system III Harvesting system III (LBSs 17-24) represents a fully mechanised application, using modified forestry machinery for both harvesting and forwarding. A variety of companies specialising in dedicated forestry machinery (such as John Deere/Timberjack, Valmet, Ponsse, Tigercat, Caterpillar, Logset, Konrad, Bell Equipment, to name a few) have developed harvester configurations well suited for small-tree harvesting. For this study, machinery from a local manufacturer was selected, namely, the Bell Equipment Ultra C disc feller-buncher (see Figure 27, below), with a net engine power of 82.5kW, an operating weight of 7 950kg, and a maximum cutting diameter of 46cm. While other harvesting systems allow felling, de-branching, topping, cross-cutting, and in some cases, also de-barking, and are more commonly used for timber or pulp wood harvesting, tricycle or articulated rubber-tyred drive-to- tree feller-bunchers only fell trees and bunch them next to the skidding track. This represents by far the cheapest commercially available machine for this type of operation (Seixas et al., 2006). They are also advantageous as the felled trees can be left in-field for air-drying and to leave foliage behind, resulting in reduced nutrient loss. Figure 27: BELL Equipment?s Ultra C disc feller buncher The productivity rate extrapolated from the guidelines for best operating practices for harvesting with a feller-buncher is assumed to be 240 trees per hour, resulting in an average across all four Stellenbosch University http://scholar.sun.ac.za 111 biomass procurement areas of 14.1 tonnes per hour (Forestry Solutions, 2007c). Table 30, below, shows for each BPA the respective productivity rates (in h/ha and t/h). Table 30: Harvesting system III ? productivity rate and costs per hectare for each BPA (2011) Biomass procurement area (BPA) BPA I BPA II BPA III BPA IV Stems per hectare 2 000 2 200 1 800 1 250 Yield/rotation (t/ha, 80% MC) 135 126 90 75 Yield/rotation (t/ha, 40% MC) 105 98 70 59 Harvesting: feller-buncher a Productivity (trees/hour) b 240 240 240 240 Time (PMH/ha) 8.3 9.2 7.5 5.2 Productivity (t/h) 16.2 13.8 12.0 14.4 R2 644 R2 931 R2 389 R1 657 Extraction with forwarder c Max. payload capacity 14t/load (22.40m3/load) Bulk density (logs) 0.375 Cycle length (min) d 42 Productivity (t/h) 12 Time (PMH/ha) 8.75 8.17 5.83 4.92 Cost (R/ha) R5 122 R4 781 R3 415 R2 878 Cost (R/t) Feller-buncher R20 R23 R27 R22 Forwarder R49 R49 R49 R49 Biomass at roadside R68 R72 R75 R71 Notes: a Bell Equipment Ultra C disc feller-buncher. b Harvesting productivity extrapolated from Industrial Production Standards (Forestry Solutions, 2007c). c Forwarder John Deere/Timberjack 1410D. d Forwarding productivity derived from Industrial Production Standards (Forestry Solutions, 2007f). The capital investment cost quoted for this machine is R1.7 million, and it has an economic lifetime expectancy of 20 000PMH. A fixed cost of R83/PMH, taking inter alia licence, insurance and depreciation into account, plus variable costs of R180/PMH (including repairs and maintenance, as well as fuel costs of R120/PMH), less a salvage value, results in a total machinery cost of R262 per productive machine hour. With harvester operator costs of R56/PMH, the total costs are set at R319/PMH. The same financial, productivity and LCA-relevant data for the John Deere/Timberjack 1410D forwarder specified for HS I are proposed. 5.3.4 Harvesting system IV LBSs 25-32 comprise harvesting with a feller-buncher and forwarding with a tractor-pole trailer combination. Additional machinery is required for the loading and unloading of the whole tree biomass, since harvesting with a feller-buncher accommodates only the felling operations, and the Stellenbosch University http://scholar.sun.ac.za 112 agricultural machinery is not fitted with a crane for loading/unloading. Commonly used for loading in South African forestry is the three-wheeler loader (e.g. Bell Equipment 220A telelogger) as shown in Figure 28, below. The so-called ?three-wheeler? is a South African invention characterised by its high mobility and manoeuvrability, but it is limited by its carry load capacity. Other disadvantages of the machine are that it cannot build high stacks or handle long lengths (Langenhoven, 2000). The feller-buncher dataset deployed for HS III, as well as the tractor-pole trailer dataset from HS I also apply to this harvesting system, though the forwarding differs concerning the assumed bulk density. As for HS I, the whole-tree bulk density for primary transport is assumed to be 0.375t/m3. However, the mass capacity is limited to 10t/load so that the volumetric capacity limitation of 30m3 will not be reached. The Bell Equipment 220A Telelogger is powered with a 49kW engine, has an operating weight of 5 100kg and can grab up to 0.35m3. The average fuel consumption according to the Guide to Machinery Costs (Lubbe et al., 2011) is estimated at 7 litres per hour. The intermediate capital investment cost of R495 000, together with fixed and variable costs, result in a total machinery cost of R162.43 per hour. Adding R25.62/h for operator costs gives a total of R188.05 per hour. Figure 28: Bell Equipment?s 220A Telelogger Derived from the industrial standards for loading with a three-wheeler loader (Forestry Solutions, 2007e), the three-wheeler loads 65 trees per hour, taking variables such as time after felling (six Stellenbosch University http://scholar.sun.ac.za 113 weeks), average lead distance (11-20m), moderate landing conditions, and uneven ground roughness into consideration. 200% efficiency gain is assumed for unloading. Table 31: Harvesting system IV ? productivity rate and costs per hectare for each BPA (2011) Biomass procurement area (BPA) BPA I BPA II BPA III BPA IV Stems per hectare 2 000 2 200 1 800 1 250 Yield/rotation (t/ha, 80% MC) 135 126 90 75 Yield/rotation (t/ha, 40% MC) 105 98 70 59 Harvesting: feller buncher a Productivity (trees/hour) b 240 240 240 240 Time (PMH/ha) 8.3 9.2 7.5 5.2 Productivity (t/h) 16.2 13.8 12.0 14.4 Cost (R/ha) R2 644 R2 931 R2 389 R1 657 Three-wheeler loader c Loading (trees/hour) d 98 98 98 98 Unloading (trees/hour) e 196 196 196 196 Time (h/ha) 31 34 28 19 t/h 3.43 2.91 2.54 3.08 Forwarding with tractor- trailer combination Max. payload capacity 10t/load (30m3/load) Bulk density (whole tree) 0.375 Cycle length (min) f 50 Load capacity (t/h) 12 Time (PMH/ha) 6.2 5.8 4.1 3.5 Cost (R/ha) R1 653 R1 542 R1 102 R925 Cost (R/t) Feller-buncher R16 R19 R22 R18 Tractor-trailer R21 R21 R21 R21 Loading/Unloading with three-wheeler R55 R65 R74 R 61 Biomass at roadside R92 R105 R117 R100 Notes: a Bell Equipment Ultra C disc feller-buncher. b Harvesting productivity extrapolated from the Industrial Production Standards for feller-buncher harvesting (Forestry Solutions, 2007c). c Bell Equipment 220A Telelogger. d Three-wheeler loading productivity derived from Industrial Production Standards (Forestry Solutions, 2007e). e Unloading assumed to be twice as efficient as loading. f Forwarding cycle length derived from Industrial Production Standards (Forestry Solutions, 2007b). 5.3.5 Harvesting system V Unlike the aforementioned harvesting systems, which have their origin in conventional forestry applications, harvesting system V has its roots in agriculture. A modified self-propelled forage harvester fitted with a dedicated biomass harvesting head cuts and chips the SRC crop in a single operation, simultaneously blowing the comminuted biomass into a container-trailer coupled to a tractor. Figure 29, below, is a schematic illustration of such a biomass harvesting head (Fiala and Bacenetti, 2011: 2). Stellenbosch University http://scholar.sun.ac.za 114 Figure 29: Schematic illustration of a dedicated SRC biomass harvesting head fitted to front of a self-propelled forage harvester Source: Fiala and Bacenetti ( 2011: 2) For this study, the GBE2 header coupled with the Claas Jaguar 880 forage harvester was assumed. This system can work with trees with a basal diameter of about 12-14cm, more than that managed by other headers on the market. After being cut, shoots are sent to the forage chipper. The header ? 2.5m wide, 2.7m long and 1.4m high, with a mass of 2 050kg ? receives its power from the self- propelled harvester (engine power = 343kW) via cardanic joint (Fiala and Bacenetti, 2011). The system can harvest up to 50 tonnes per hour consuming 40-50 litres per hour. A productivity of 35/h with a fuel consumption of 40l/h was assumed. With a combined purchase price for the harvester and biomass harvesting heads of R3.11 million, less a salvage value of 10%, plus licence and insurance costs, as well as variable costs, such as repairs and maintenance and fuel consumption, result in a production cost of R2 167/PMH. Based on a salary of R10 568 per month, the operator cost is R56.36/PMH. Hence, the total cut-and-chip harvesting costs add up to R2 224/PMH. Primary transport costs of R197/PMH for a tractor (67kW, four-wheel drive, high-power demand) and a container-trailer with a capacity of 30m3, derived from the Guide to Machinery database (Lubbe et al., 2011) were also taken into account. Added to this are R26/h for tractor operator costs, resulting in an operating cost of R267/PMH for the primary transport operation. At least two forwarding tractor-trailor combinations are required to ensure a continuous harvest with the combine harvester transporting the comminuted biomass from in-field to roadside. Stellenbosch University http://scholar.sun.ac.za 115 Table 32: Harvesting system IV ? productivity rate and costs per hectare for each BPA (2011) Biomass procurement area (BPA) BPA I BPA II BPA III BPA IV Stems per hectare 2 000 2 200 1 800 1 250 Yield/rotation (t/ha, 80% MC) 135 126 90 75 Harvesting: feller buncher a Productivity (t/h)1 35 35 35 35 Time (PMH/ha) 3.9 3.6 2.6 2.2 Cost (R/ha) R8 577 R8 006 R5 718 R4 765 Forwarding with tractor- container trailer combination Max. payload capacity 30m3/load Bulk density b 0.51 Time (PMH/ha) 3.9 3.6 2.6 2.2 Cost (R/ha) R2 062 R1 924 R1 374 R1 145 Cost (R/t) Cut-and chip harvester R64 R64 R64 R64 Tractor-container trailer R15 R15 R15 R15 Biomass at roadside R79 R79 R79 R79 Notes: a Productivity varies between 35-50t/h, depending on wood density (Nardin, 2009). b For fresh, comminuted whole-tree biomass (80% MC dry basis). For the LCA, the ?universal tractor? provided in the GaBi database was modified according to the above-stated specifications (PE International, 2006). Similarly, relevant data was changed for the corn harvesting process to meet the specifications of the modified combine harvester. 5.4 Biomass comminution As briefly discussed in section 4.3.1.3, two locations for biomass comminution are proposed, i.e. mobile comminution at the roadside and stationary comminution at the landing of the central conversion plant. 5.4.1 Mobile comminution at roadside In the case of mobile comminution at the roadside, the biomass is fed manually into the chipping system, and the chipped biomass is simultaneously blown into a container. This system is characterised by its flexibility and mobility, and offers great employment potential, particularly in the low-skills segment. However, low productivity results in low cost efficiency. For mobile roadside comminution, the Danish-made Lindana TP 200 PTO wood chipper was selected. This all-round wood chipper, characterised by its functionality and flexibility, is mounted onto and driven by tractor (e.g. the above-mentioned 67kW, four-wheel drive, high-power demand tractor). The disc chipper, equipped with a hydraulic feed, three chipping knives and three anvils, can be fed with trees of up to 200mm in diameter, resulting in a hardwood chipping capacity of 6- 10m3/hour (12-18m3/h for softwood). For this study, a chipping capacity of 5.0t/PMH for whole Stellenbosch University http://scholar.sun.ac.za 116 trees and 6.7t/PMH for logs was proposed, assuming a biomass moisture content of 40% (dry basis) and a wood density of 720kg/m3. During the chipping operation a fuel consumption of 6-8l/h is expected, using a conservative consumption of conservative 8l/PMH for the financial and LCA calculations. Figure 30: Technical drawing of Lindana TP 200 PTO wood chipper The cost price for the 700kg heavy equipment is R155 000, with an economic lifetime expectancy of 20 000PMH. An annual cost of R24 492 for spares such as chipping knives, square wipers and hydraulic filters needs to be added to the financial assessment, assuming an annual usage of 2 000PMH. The costs for the tractor driving the chipper are R411 369 based on the Guide to Machinery Costs (Lubbe et al., 2011). Taking fixed and variable costs for the whole chipping system into account, the machinery cost adds up to R264/PMH. To ensure a continuous biomass feed into the chipper, one tractor operator, as well as five workers for manual feeding are taken into account, resulting in labour costs of R59/PMH. A total production cost of R323/PMH translates into R65/t for chipping whole trees and R49/t for logs. Similar to the processes in the life cycle prior to comminution, the ?universal tractor? from the GaBi database (PE International, 2006) has been modified to meet the specifications stated above. Based on Jungmeier et al. (2003), a 5% material loss of biomass during mobile comminution and transport was stipulated for feedstock and energy loss, assuming natural decomposition of these losses over time. Stellenbosch University http://scholar.sun.ac.za 117 5.4.2 Stationary comminution at landing of central conversion plant Generally, compared with a mobile system, a stationary chipping line is characterised by significantly higher capital investment costs but also by its greater chipping capacity, resulting in lower unit production costs (R/t). Assuming the same biomass properties for chipping as stated above, the Maier drum chipper HRL 1200/ 450 x 1000 ? 8EW stationary chipping line caters for biomass of up to 160mm, producing chips of 30-35mm (Rahlmeyer, 2011). Incorporating potential downtimes for maintenance and repairs, an average chipping capacity for logs of around 16.8t/h and 12.6 for whole trees (40% MC, dry basis) was assumed, although potentially up to 28t can be processed hourly. The capital investment costs, fixed costs, and variable costs of the stationary chipping line are listed in Table 33, below. Assuming a capacity of 17t/h for logs and 13t/h for chipping whole tree biomass, the total comminution costs per tonne add up to R36 and R42 respectively. The electrical energy consumption of the whole chipping line is 20-25kW per tonne of produced chips. The required electricity is assumed to be provided by the national electricity supplier ESKOM at a tariff of R0.75 per kWh. Maintaining the computer-controlled system requires one qualified engineer for supervision, and a technician. Concerning material losses during transport and comminution, a similar approach to that for mobile comminution was used, i.e. 2% of the biomass was specified for feedstock and energy loss. Figure 31: Stationary chipping line Maier drum chipper HRL 1200/ 450 x 1000 ? 8EW as proposed by German-based Maier company (Rahlmeyer, 2011) Stellenbosch University http://scholar.sun.ac.za 118 Table 33: ?Maier drum chipper HRL 1200/ 450 x 1000 ? 8EW? drum chipper feeding line (2011) Designation Unit price (?) Unit price (ZAR) a E L E b (yea rs ) Q ty. c Annualised costs (ZAR/a) d Cost per hour (ZAR/h) Fixed costs: Chipper feeding line 110 000 1 227 600 20 1 61 380 15.35 Storage cross chain conveyor 180 000 2 008 800 20 1 100 440 25.11 MAIER drum chipper HRL 1200/450x1000 ? 8EW1 250 000 2 790 000 20 1 139 500 34.88 Discharge screw 35 000 390 600 20 1 19 530 4.88 L-shaped trough chain conveyor 50 000 558 000 20 1 27 900 6.98 Installation cost 50 000 558 000 20 1 27 900 6.98 Variable costs: Spare and wear parts for chipping unit 30 000 334 800 1 1 334 800 83.70 Spare and wear parts for conveyor units 5 000 55 800 2 2 55 800 6.98 Maintenance (3% of capital investment) 187 500 2 092 500 20 1 104 625 26.16 Labour (1x engineer/1x qualified technician) 450 000 1 1 450 000 112.50 Sum: 1 293 975 323.49 Energy (22kW/t produced chips) e Feedstock type Chipping capacity (t/h) Energy required (kWh) Cost per hour (R) f Logs 16.8 369.6 277.20 Whole trees 12.6 277.2 207.90 Unit cost Feedstock type Total cost per hour (R/h) Unit cost (R/t) g Logs 600.69 35.76 Whole trees 531.39 42.17 Source: Rahlmeyer (2011) Notes: a Assuming an exchange rate of R11.16 to the Euro. b Economic Lifetime Expectancy (ELE). c Quantity (Qty). d Based on 250 working days with two shifts of 8 hours each. e Energy consumption of whole chipping line is 20-25kW/t produced chips. f Assuming an electricity tariff of R0.75/kWh and an energy supply from the national grid. g Based on 40% moisture content (dry basis). Stellenbosch University http://scholar.sun.ac.za 119 5.5 Thermal pretreatment Both the location of the stored biomass and the shape of the biomass (comminuted or uncomminuted) depend on the harvesting system applied. In the case of harvesting systems I-IV, uncomminuted biomass is stored in-field to air dry for several weeks until the biomass has reached moisture content levels of around 40% (dry basis). Once this level has been reached, the biomass is forwarded to the roadside for further processing. In the case of harvesting system V, the trees are felled and comminuted in a single process, resulting in wood chips with moisture content levels of around 80% (dry basis). Irrespective of whether the biomass has been air-dried in-field prior to comminution or not, additional drying is required to meet the moisture content requirements of the respective conversion technologies, i.e. the MC levels need to be further reduced to at minimum less than 20% (dry basis). This can be achieved by additional air-drying in a storage system such as the relatively inexpensive so-called dome-aeration-technology (Grosse, 2008) and/or by using the exhaust heat from the conversion process in a conveyor belt drier, a drum drier, chamber, or container drier. However, for all 37 LBSs, the assumption was made that by using the exhaust heat of the respective conversion system, no additional energy would be required to reach the stipulated moisture content levels of the bioenergy feedstock. Hence, no additional costs and emissions arise from the active drying processes. Figure 32:Dome-Aeration Technology Source: Brummack and Bartha (2005); Trois and Polster (2007) Stellenbosch University http://scholar.sun.ac.za 120 5.6 Mobile fast pyrolysis Pyrolysis entails the thermal degradation of biomass in the absence of an oxidising agent, whereby the volatile components of a solid carbonaceous feedstock are vaporised in primary reactions by heating, leaving a residue consisting of char and ash. Pyrolysis always produces a gas vapour that can be collected as a liquid and a solid char. Fast-pyrolysis processes are designed and operated to maximise the liquid fraction by up to 75%wt on a dry-biomass feed basis (Bridgwater et al., 2001). Although fast-pyrolysis can be understood as some form of pretreatment of the biomass, it also represents one of the possible pathways for upgrading low-bulk-density biomass into densified, more homogeneous energy carriers. Hence, detailed information on the application of this technology can be found in section 5.8, which deals with generating bioenergy. Section 5.8.6 discusses the application of a centralised, stationary fast-pyrolysis system, whereas sections 5.8.7-8 are concerned with a mobile/portable configuration for fast-pyrolysis. 5.7 Secondary transport of bioenergy feedstock Table 34: Various types of HCVs for secondary transport of bioenergy feedstocks Commodity to be transported (h/load) Uncomminuted biomass Comminuted biomass Mobile fast pyrolysis Whole tree logs/ stemwood Whole tree logs/ stemwood bio-oil bio- char Bulk density (t/m3) 0.20 0.67 0.33 (0.51) b 0.40 1.20 0.5 Truck configuration a c c d d e d Trailer type Pole Pole Container Container tanker Container Max. permissible combination mass (t) 49.5 49.5 56.0 56.0 49.5 56.0 Payload capacity (t) 31.98 31.98 38.48 38.48 31.98 38.48 Payload capacity (m3) 160 160 114 114 20 114 Limitation due to Mass Mass Volume Mass Volume Mass Effective payload capacity (t) 31.98 31.98 37.68 38.48 24 38.48 Notes: a Based on assumptions made by Roberts (2009: 77) using the Road Freight Association?s Vehicle Cost Schedule (RFA, 2009). b In the case of harvesting system V fresh biomass (80% moisture content) is to be transported. c Six-axle articulated vehicle. d Seven-axle articulated vehicle. e Five-axle articulated vehicle. Three truck configurations (or heavy commercial vehicles, HCV) were selected for transporting the bioenergy feedstocks. When transporting uncomminuted biomass, a six-axle articulated vehicle fitted with a pole-trailer (payload capacity of 31.98t or 160m3), and a seven-axle articulated vehicle capable of transporting three containers, with a total payload capacity of 114m3 (54t), were assumed Stellenbosch University http://scholar.sun.ac.za 121 for transporting comminuted biomass or bio-char. Bio-oil from a mobile fast-pyrolysis system needs to be transported in a different truck configuration, i.e. in a dedicated truck-tanker configuration characterised by a volume capacity of 20 000 litres. Based on a bulk density of 1.2t/m3 for bio-oil, up to 24 tonnes can be transported per load. Determining the mass transport rate, the following additional assumption has been made: in all cases, the trucks must complete a whole number of round trips from the central conversion plant to the roadside where the commodity is to be procured and back in a nine-hour day. Table 35, below, shows the assumed fixed time requirements, i.e. the time per load required, irrespective of the time needed for travelling. This includes the loading and securing of the load prior to travelling, and once the destination has been reached, the clearing of the load, i.e. unloading and weighing prior to and after unloading. Since the fixed time requirements for transporting uncomminuted whole trees and log/stemwood for the selected tree species were not available, the values used were extrapolated from data pertaining to the transportation of uncomminuted whole trees provided by Ranta and Rinne (2006), and for logs/stemwood by Forestry Solutions (2007e). Further research in this matter is recommended. The total fixed time requirements for transporting whole trees is estimated at 3.15 hours, and for logs/stemwood, 2.82 hours. In the case of transporting comminuted biomass, a total fixed time of 1.25h was assumed. Similarly, 1.25h are required for bio-char and 1.17h for bio-oil. The fixed time assumed for the latter was derived from Rogers and Brammer (2009: 1370). Table 35: Fixed time requirements for loading, unloading, securing and weighing (in h/load) Commodity to be transported Uncomminuted biomass Comminuted biomass d Mobile fast pyrolysis whole tree b logs/ stemwood c whole tree logs/ stemwood c bio-oil e bio-char f Loading 1.52 1.36 0.75 0.75 0.50 0.75 Securing of load 0.22 0.20 0.17 0.17 0.07 0.17 Weighing a and unloading 1.41 1.26 0.33 0.33 0.60 0.33 Total fixed time requirements 3.15 2.82 1.25 1.25 1.17 1.25 Notes: a Weighing of truck prior to and after unloading. b Extrapolated from Ranta and Rinne (2006: 233). c Extrapolated from Forestry Solutions (2007e). d Fixed time requirements for comminuted biomass derived from Rogers and Brammer (2009: 1368) and Badger (2002). e The required fixed time for bio-oil was also derived from Rogers and Brammer (2009: 1370). f The same fixed time requirements as for comminuted biomass were assumed. Stellenbosch University http://scholar.sun.ac.za 122 Bio-oil typically has a density of 1 200kg/m3, so the truck?s load will be limited by its weight. Much research has been done on the nature of bio-oil, possible commercial standards for bio-oil, its storage, and handling characteristics (Bridgwater, 2011; Oasmaa et al., 2010; Venderbosch and Prins, 2010; Zhang et al., 2010; Czernik and Bridgewater, 2004; Huffman et al., 1993). The pH, viscosity, high density, poor miscibility and aging characteristics mean that dedicated tankers are needed for bio-oil (Rogers and Brammer, 2009: 1370). It has been proposed that bio-oil be moved by gravity from delivery tankers into a reception well (Badger and Fransham, 2006). In this situation the unloading time will be governed by the viscosity of the bio-oil and the dimensions of the unloading pipe. The Hagen Poiseuille law governs the flow of Newtonian liquids through pipes: Equation 11: The Hagen-Poiseuille law Where is the pressure, r is the radius of the pipe, is the viscosity and is the length of the pipe. If typical figures are taken from the Dynamotive bio-oil data sheet (Dynamotive Energy Systems, 2011), with a temperature of 40?C, a loading head of 5m, a pipe length of 2m and six parallel 200mm diameter loading pipes, a 28t payload tanker could be loaded in 15 minutes. If allowance is made for connecting and disconnecting hoses, this will lead to a loading time of the order of 30 minutes (Rogers and Brammer, 2009). If it is assumed that the unloading time is the same as the truck loading time and a 10 minute allowance is made for weighbridge operations, the non-driving time per round trip will be 70 minutes. This figure has been used to calculate the travelling time for bio-oil. As mentioned in Section 2.3, above, 14 demand points/biomass procurement areas were identified based on various considerations, such as proximity of electricity substations and major grid lines; proximity of industrial consumers in order to sell excess heat/thermal energy produced in the conversion process as a by-product; and proximity to the road network, to ease feedstock transport and to avoid the additional costs of infrastructure; as well as the projected electricity demand in the CWDM (Roberts, 2009: 50-58). Four demand points, Paarl, Worcester, Ashton and the Rural Cederberge, were selected for further assessment, inter alia, based on the respective biomass productivity levels in each biomass procurement area (relatively high, medium, medium-low, and low ? see section 4.3.2) and spatial distribution within the CWDM. The first three demand points are characterised by their proximity to infrastructure in terms of road network, electricity lines and electricity substations, as well as to industrial consumers interested in using thermal energy for Stellenbosch University http://scholar.sun.ac.za 123 production. The Rural Cederberge demand point, on the other hand, is characterised by its remoteness and its lack of scope for additional income from selling excess heat/thermal energy, since no industrial consumers are located in the vicinity. Also, additional investments in electricity infrastructure are required to supply electricity to the grid. The average transport distance for each demand point is a function of the supply and demand of bioenergy feedstock. The supply depends, inter alia, on the availability of land for biomass production, the willingness of landowners to participate by offering their land for lignocellulosic- biomass production and the productivity rate of the respective areas. The demand component is mainly driven by the conversion efficiency of the respective bioenergy system, but also by feedstock losses during the procurement and pretreatment of the feedstock. Biomass productivity and land availability in the CWDM were determined by Von Doderer (2009) using geographic information systems (GIS) and were further assessed by Roberts (2009) in a transport optimisation model using GIS and LINGO, where various landowner participation levels were assessed. For this study, a participation level of 50% was assumed for landowners. The feedstock demand, i.e. the biomass required to ensure a supply for continuously generating 5MW electricity, is mainly driven by the conversion efficiency of the respective bioenergy system. However, due to losses of biomass in the value chain prior to the conversion, more biomass needs to be produced than is used in the conversion process. Biomass losses, and therefore energy feedstock losses, occur for instance in-field, where only logs are used further in the process (less 30% of the fresh biomass), as is the case for LBSs 9-16, or in a one-pass harvesting system (as is the case for LBSs 33-37), where biomass is harvested and comminuted in a single operation, resulting in a loss of 5% of fresh biomass. The same quantity of loss of biomass (5% at 40% moisture content) is assumed when comminuting biomass at the roadside, as is assumed in a generic biomass comminution dataset provided in the GaBi database (PE International, 2006). Since greater capacity and efficiency are assumed for stationary comminution at the landing of the central conversion plant, a biomass feedstock loss of 2% (at 40% moisture content) was adopted for the LBSs using stationary comminution. Table 36, below, illustrates the mass flow of biomass and pyrolysis products over the life-cycle of each respective lignocellulosic bioenergy system (LBS). Stellenbosch University http://scholar.sun.ac.za 124 Table 36: Biomass and pyrolysis products mass flow Location Plantation/in-field Roadside Road Central conversion plant No. Biomass (BM) prior to harvesting (t/a) Harvesting Biomass at forwarding (t/a) For- war- ding Pretreatment Secondary transport Bioenergy Feedstock for secondary transport (t/a) Pretreatment Electricity generation BM required for conversion (t/a) BM required for conversion at 80% MC 1 64 729 HMMwh. tree (-40% MC) 50 345a FFM COMR (-5% BM) TCB40 47 828 CCB 40 995c 61 493 2 55 722 43 339 a 41 172 CGB 35 291 c 52 937 3 91 865 71 451a 67 878 CPC CCP 53 333d 87 272 4 78 754 61 253 a CPM TPP 22 860/ 12469 CCP 45 721 d 74 816 5 103 966 80 863 a 30 179/ 16 461 CGP 60 358 d 98 768 6 62 748 48 804 a TUB40 48 804 COML (-2% BM) CCB 40 995 c 61493 7 54 016 42 013 a 42 013 CGB 35 291 c 52 937 8 89 053 69 264a 69 264 CPC CCP 53 333 d 87 272 9 92 471 HMMlog (-40% MC, - 30% BM remaining in- field) 50 345 a FAMlog COMR (-5% BM) TCB40 47 828 CCB 40 995 c 61 493 10 79 603 43 339 a 41 172 CGB 35 291 c 52 937 11 131 236 71 451 a 67 878 CPC CCP 53 333 d 87 272 12 112 506 61 253 a CPM TPP 22 860/ 12469 CCP 45 721 d 74 816 13 148 523 80 863 a 30 179/ 16 461 CGP 60 358 d 98 768 14 89 640 48 804 a TUB40 48 804 COML (-2% BM) CCB 40 995 c 61 493 15 77 166 42 013 a 42 013 CGB 35 291 c 52 937 16 127 219 69 264 a 69 264 CPC CCP 53 333 d 87 272 17 64 729 HFM (-40% MC) 50 345 a FFM COMR (-5% BM) TCB40 47 828 CCB 40 995 c 61 493 18 55 722 43 339 a 41 172 CGB 35 291 c 52 937 19 91 865 71 451 a 67 878 CPC CCP 53 333 d 87 272 20 78 754 61 253 a CPM TPP 22 860/ 12469 CCP 45 721 d 74 816 21 103 966 80 863 a 30 179/ 16 461 CGP 60 358 d 98 768 22 62 748 48 804 a TUB40 48 804 COML (-2% BM) CCB 40 995 c 61 493 23 54 016 42 013 a 42 013 CGB 35 291 c 52 937 24 89 053 69 264 a 69 264 CPC CCP 53 333 d 87 272 25 64 729 50 345 a FAMwh. tree COMR (-5% BM) TCB40 47 828 CCB 40 995 c 61 493 26 55 722 43 339 a 41 172 CGB 35 291 c 52 937 27 91 865 71 451 a 67 878 CPC CCP 53 333 d 87 272 28 78 754 61 253 a CPM TPP 22 860/ 12469 CCP 45 721 d 74 816 29 103 966 80 863 a 30 179/ 16 461 CGP 60 358 d 98 768 30 62 748 48 804 a TUB40 48 804 COML (-2% BM) CCB 40 995 c 61 493 31 54 016 42 013 a 42 013 CGB 35 291 c 52 937 32 89 053 69 264 a 69 264 CPC CCP 53 333 d 87 272 33 64 729 HAM (-5% BM) 61 493 b FAMCB TCB80 61 493 CCB 40 995 c 61 493 34 55 722 52 936b 52 936 CGB 35 291 c 52 937 35 91 865 87 272 b 87272 CPC CCP 53 333 d 87 272 36 78 754 74 816 b CPM TPP 22 860/ 12469 CCP 45 721 d 74 816 37 103 966 98 768 b 30 179/ 16 461 CGP 60 358 d 98 768 For notes and explanations of acronyms, please refer to next page. Stellenbosch University http://scholar.sun.ac.za 125 Notes: a 40% moisture content (dry basis). b 80% moisture content (dry basis). c 20% moisture content (dry basis). d 10% moisture content (dry basis). BM Biomass. MC Moisture content. HMMwh. tree Motor-manual harvesting: felling only. HMMlog Motor-manual harvesting: felling, de-branching and cross-cutting. HFM Harvesting with forestry machinery: whole tree. HAM Harvesting with agricultural machinery: whole tree (80% MC; loss of 5% of biomass due to chipping). FFM Forwarding of whole trees (40% MC) with forestry machinery: loading/unloading with fitted crane. FAMlog Forwarding of logs (40% MC) with agricultural machinery: manual loading/unloading. FAMwh. tree Forwarding of whole trees (40% MC) with agricultural machinery: loading/unloading with three-wheeler. FAMCB Forwarding of comminuted biomass (80% MC) with agricultural machinery: COMR Mobile comminution at roadside: including loss of 5% BM (40% MC). COML Stationary comminution at landing of conversion plant: loss of 2% BM assumed (40% MC). TUB40 Transport of uncomminuted biomass at 40% MC. TCB80 Transport of comminuted biomass at 80% MC. TCB40 Transport of comminuted biomass at 40% MC. TPP Transport of pyrolysis products: Bio-oil/bio-char. CPM Mobile fast pyrolysis at roadside (10% MC required). CPC Stationary fast pyrolysis at central conversion plant (10% MC required). CCB Combustion of biomass in integrated boiler-steam turbine system (20% MC required). CGB Gasification of biomass in integrated gasifier-gas turbine system (20% MC required) CCP Combustion of bio-oil and bio-char in integrated boiler-steam turbine system CGP Bio-oil in direct-injection gas turbine/bio-char sold to industrial consumer. Based on the primary biomass production requirements, which are listed in column 2 of Table 36, above, the weighted average transport distance (WATD) for each demand point and the respective bioenergy conversion system were determined by Van Niekerk (2011) using GIS and FlowMap. Since off-road and on-road transport times and costs differ considerably, road-type factors of 1 for major roads, 1.2 for minor roads, and 1.5 for gravel roads were included to determine average transport distances. Hence, the WATD should be considered to be a relative index of transport and not a true distance. Table 37, below, comprises the WATD for each LBS in relation to the demand points/biomass procurement areas. The number of truck configurations required to ensure a continuous supply of bioenergy feedstock for each LBS is a function of the number of required shuttle trips from the conversion plant to the roadside and back, the total transport time per load, and the time availability of the truck configuration, as shown in Table 37, below. The number of shuttle trips depends on the type and Stellenbosch University http://scholar.sun.ac.za 126 bulk density of bioenergy feedstock to be transported (t/a) and the effective payload capacity (t/load) of the respective truck configuration. The time availability is limited by the working days per year and the shift length per day. As mentioned above, trucks must complete a whole number of shuttle/round trips in a nine-hour working day on 250 working days a year. The total transport time per load encompasses a fixed time component (see Table 36, above) and a variable time component, the travelling time per round trip. The travelling time depends on the WATD, the types of roads used, and the average travelling speed per road type. For this study, 10% of the WATD was allocated for travelling on major roads at an average speed of 82km/h, 40% for minor roads at 70km/h, and for the remainder, an average travelling speed of 27km/h (gravel road and off-road) was assumed. The average speed for the respective road types is based on default values for a EURO 3 Norm, diesel-driven truck from the GaBi database (PE International, 2006). This dataset was also used throughout the LCA model for the related secondary transport inputs and emissions, applying the respective transport distances and masses per load to be transported. As proposed in Roberts (2009: 59), Concept 11 of the Vehicle Cost Schedule of the Road Freight Association (RFA, 2009) was used to determine the secondary transport costs. The capital investment costs per truck were assumed at R938 875 with a salvage value of 25% and an economic lifetime expectancy (ELE) of 550 000km. Annual insurance and licence costs for the truck are 7.5% of the purchase price and R8 235 respectively. The purchase price for the trailer is R273 719 with a 0% salvage value and an ELE also of 550 000km. The insurance and licence costs for the trailer are 5% of the purchase price and R5 304 respectively. Furthermore, overhead costs (administration and operation) of R135 738 were proposed for Concept 11. The employment costs per truck-trailer combination are R17 680/month for the truck and R6 445/month for an assistant. The total annual fixed costs depend on the annual kilometres driven, which also determine the economic lifetime expectancy in years. The variable costs comprise fuel, lubricants, maintenance and tyres. The fuel consumption was proposed as 55 litres per 100 kilometres. With a diesel price of R10/l, the fuel cost is R5.5/km. Lubricants were specified as 2.5% of the fuel cost. Furthermore, maintenance costs of R1.52/km and R1.05/km for tyre usage were proposed. Stellenbosch University http://scholar.sun.ac.za 127 Table 37: Number of truck configurations required for secondary transport No. Biomass prior to harvesting (t/a at 80% MC) WATD per BPA1 Bioenergy feedstock for secondary transport (t/a) Type of commodity to be transported Effective payload capacity (t/load) No. of shuttle trips required2 Total transport time per load (h/load) Truck configurations required per BPA i ii iii iv i ii iii iv i ii iii iv 1 64 729 41 22 15 18 47 828 Comminuted biomass (whole tree40) 37.683 1 270 3.3 2.4 2.0 2.2 3 2 2 2 2 55 722 38 21 15 17 41 172 37.683 1 093 3.2 2.3 2.0 2.1 3 2 2 2 3 91 865 49 26 18 23 67 878 37.683 1 802 3.7 2.4 2.2 2.4 4 3 2 3 4 78 754 45 24 17 21 22 860/ 12 469 Bio-oil/ bio-char 24.000/ 38.480 953/ 325 3.5/ 3.5 2.4/ 2.5 2.0/ 2.1 2.2/ 2.3 2/ 1 2/ 1 1/ 1 1/ 1 5 103 966 51 27 19 25 30 179/ 16 461 24.000/ 38.480 1 258/ 428 3.8/ 3.8 2.5/ 2.6 2.1/ 2.2 2.4/ 2.5 3/ 1 2/ 1 2/ 1 2/ 1 6 62 748 41 22 15 18 48 804 Uncomminuted biomass (whole tree40) 31.980 1 527 5.2 4.3 3.9 4.1 7 4 4 4 7 54 016 38 21 15 17 42 013 31.980 1 314 5.1 4.2 3.9 4.0 6 3 3 3 8 89 053 49 26 18 23 69 264 31.980 2 166 5.6 4.5 4.1 4.3 9 5 5 5 9 92 471 49 26 18 23 47 828 Comminuted biomass (logs40) 38.480 1 269 3.7 2.6 2.2 2.4 3 2 2 2 10 79 603 45 24 17 21 41 172 38.480 1 096 3.5 2.5 2.1 2.3 3 2 2 2 11 131 236 58 29 20 29 67 878 38.480 1 790 4.2 2.7 2.3 2.7 4 3 3 3 12 112 506 53 27 19 27 22 860/ 12 469 Bio-oil/ bio-char 24.000/ 38.480 953/ 325 3.9/ 3.9 2.5/ 2.6 2.1/ 2.2 2.5/ 2.6 2/ 1 2/ 1 1/ 1 2/ 1 13 148 523 60 31 21 33 30 179/ 16 461 24.000/ 38.480 1 258/ 428 4.2/ 4.3 2.7/ 2.7 2.2/ 2.3 2.8/ 2.9 3/ 1 2/ 1 2/ 1 2/ 1 14 89 640 49 26 18 23 48 804 Uncomminuted biomass (logs40) 31.980 1 558 5.3 4.1 3.7 4.0 7 4 4 4 15 77 166 45 24 17 21 42 013 31.980 1 314 5.1 4.0 3.7 3.9 6 3 3 3 16 127 219 58 29 20 29 69 264 31.980 2 198 5.8 4.3 3.8 4.3 9 5 5 5 17 64 729 41 22 15 18 47 828 Comminuted biomass (whole tree40) 37.683 1 270 3.3 2.4 2.0 2.2 3 2 2 2 18 55 722 38 21 15 17 41 172 37.683 1 093 3.2 2.3 2.0 2.1 3 2 2 2 19 91 865 49 26 18 23 67 878 37.683 1 802 3.7 2.4 2.2 2.4 4 3 2 3 20 78 754 45 24 17 21 22 860/ 12 469 Bio-oil/ bio-char 24.000/ 38.480 953/ 325 3.5/ 3.5 2.4/ 2.5 2.0/ 2.1 2.2/ 2.3 2/ 1 2/ 1 1/ 1 1/ 1 21 103 966 51 27 19 25 30 179/ 16 461 24.000/ 38.480 1 258/ 428 3.8/ 3.8 2.5/ 2.6 2.1/ 2.2 2.4/ 2.5 3/ 1 2/ 1 2/ 1 2/ 1 22 62 748 41 22 15 18 48 804 Uncomminuted biomass (whole tree40) 31.980 1 527 5.2 4.3 3.9 4.1 7 4 4 4 23 54 016 38 21 15 17 42 013 31.980 1 314 5.1 4.2 3.9 4.0 6 3 3 3 24 89 053 49 26 18 23 69 264 31.980 2 166 5.6 4.5 4.1 4.3 9 5 5 5 25 64 729 41 22 15 18 47 828 Comminuted biomass (whole tree40) 37.683 1 270 3.3 2.4 2.0 2.2 3 2 2 2 26 55 722 38 21 15 17 41 172 37.683 1 093 3.2 2.3 2.0 2.1 3 2 2 2 27 91 865 49 26 18 23 67 878 37.683 1 802 3.7 2.4 2.2 2.4 4 3 2 3 28 78 754 45 24 17 21 22 860/ 12 469 Bio-oil/ bio-char 24.000/ 38.480 953/ 325 3.5/ 3.5 2.4/ 2.5 2.0/ 2.1 2.2/ 2.3 2/ 1 2/ 1 1/ 1 1/ 1 29 103 966 51 27 19 25 30 179/ 16 461 24.000/ 38.480 1 258/ 428 3.8/ 3.8 2.5/ 2.6 2.1/ 2.2 2.4/ 2.5 3/ 1 2/ 1 2/ 1 2/ 1 30 62 748 41 22 15 18 48 804 Uncomminuted biomass (whole tree40) 31.980 1 527 5.2 4.3 3.9 4.1 7 4 4 4 31 54 016 38 21 15 17 42 013 31.980 1 314 5.1 4.2 3.9 4.0 6 3 3 3 32 89 053 49 26 18 23 69 264 31.980 2 166 5.6 4.5 4.1 4.3 9 5 5 5 33 64 729 41 22 15 18 61 493 Comminuted biomass (whole tree80) 38.480 1 599 3.3 2.4 2.0 2.2 4 3 2 2 34 55 722 38 21 15 17 52 936 38.480 1 376 3.2 2.3 2.0 2.1 3 2 2 2 35 91 865 49 26 18 23 87272 38.480 2 268 3.7 2.6 2.2 2.4 5 4 3 4 36 78 754 45 24 17 21 22 860/ 12 469 Bio-oil/ bio-char 24.000/ 38.480 953/ 325 3.5/ 3.5 2.4/ 2.5 2.0/ 2.1 2.2/ 2.3 2/ 1 2/ 1 1/ 1 1/ 1 37 103 966 51 27 19 25 30 179/ 16 461 24.000/ 38.480 1 258/ 428 3.8/ 3.8 2.5/ 2.6 2.1/ 2.2 2.4/ 2.5 3/ 1 2/ 1 2/ 1 2/ 1 Notes: 1 Weighted Average Transport Distances (WATD) for the respective demand points/biomass procurement areas : Paarl (i), Worcester (ii), Ashton (iii), Rural Cederberge (iv); Source: Van Niekerk (2011) . Stellenbosch University http://scholar.sun.ac.za 128 5.8 Bioenergy generation This section deals with the conversion of lignocellulosic biomass into electrical energy. As shown in Table 38, below, five different bioenergy conversion system (BCS) configurations are modelled in this study. The first bioenergy conversion system (BCS I) entails an integrated steam-turbine system, where the biomass at max. 20% MC (dry basis) is combusted to generate steam, which is then used in a steam turbine to generate electricity. The same MC is required for BCS II, an integrated gasifier-gas turbine system, where the biomass is upgraded to bio-gas, which in turn, is fed into a gas turbine. BCS III consists of a stationary fast-pyrolysis plant converting biomass (10% MC) into bio-oil and bio-char. The upgraded products are then fed into an integrated boiler-steam turbine system to generate electricity. An integrated steam-turbine system is also assumed for BCS IV, also using bio-oil and bio-char that is produced in a mobile fast-pyrolysis system at the roadside, close to the primary biomass production sites. The last bioenergy conversion system (BCS V) also encompasses mobile fast-pyrolysis systems, but differs in the final conversion step, where only bio-oil is used to generate electricity by directly injecting the liquid into a gas turbine. Also transported to a central facility, the bio-char by-product is assumed to be sold to the fertilising industry, which uses it as an additive for soils. 5.8.1 General considerations and assumptions For all bioenergy conversion systems (BCSs), an electrical output of 5MWel was assumed, based on findings by Roberts (2009), indicating that at an output of 5MWel, the farm gate price for biomass, is the least sensitive to a biomass producer participation factor. Hence, compared to larger conversion plants, this option shows the lowest risk. The potential gain of larger plants (conversion plants of up to 15MWel were considered) could be offset against the higher risk involved, should a lower percentage of farmers choose to participate. Furthermore, electricity is generated on 330 days per year over three shifts of eight hours each, resulting in 7 920 hours of production or 39.6 GWhel per year (refer also to 3.2, the functional unit). As in a study by Petrie et al. (2004), transmission, distribution, and use of the generated power are not covered. The different LBSs thus provide inventories of undelivered electricity. In addition, only process-related emissions are assessed. The environmental burdens associated with the running and maintenance of offices, workshops, etc. at the respective conversion stations are not incorporated in the assessment. Furthermore, the building and commissioning of conversion plants are not included; neither are the environmental burdens associated with the materials used in construction, and maintenance materials, as was also the case in the study done by Berglund and B?rjesson (2006). Stellenbosch University http://scholar.sun.ac.za 129 Table 38: Bioenergy conversion systems and their related efficiencies Loca- tion Bioenergy Conversion System (BCS) I II III IV V R o ad si d e Upgrading system n/a a n/a a n/a a Mobile fast pyrolysis Mobile fast pyrolysis Upgraded product(s) Bio-char, bio-gasb and bio-oil Bio-char, bio-gasb and bio-oil Road Commodity to be transported Biomass Biomass Biomass Bio-oil and bio-char Bio-oil and bio-char C en tral c o n v ersi o n sit e Upgrading system n/a a n/a a Stationary fast pyrolysis n/a a n/a a Upgraded product(s) Bio-char, bio-gasb and bio-oil Feedstock at storage facility Comminuted biomass Comminuted biomass Bio-oil and bio-char Bio-oil and bio-char Bio-oil (and bio-char) Upgrading unit Boiler Gasifier Boiler Boiler - Upgraded product Steam Fuel gas Steam Steam Bio-oil Final conversion Steam turbine Gas turbine Steam turbine Steam turbine Gas turbine System description Integrated boiler-steam turbine system Integrated gasifier-gas turbine system Integrated boiler-steam turbine system Integrated boiler-steam turbine system Direct injection gas turbine Conversion system I II III IV V A n n u al e le ctr ic al a n d t h er m al e n er g y g en er at io n Net electrical energy output 39.6 GWhel./a Energy carrier to electricity efficiency (%) 24.1% 22.2% 24.1% 24.1% 26.0% Required energy input (%)c 11.1 1.0 21.1 14.4 3.3 Feedstock upgrading efficiency (%)d Biomass to steam: 87.2 Biomass to bio-gas: Bio-oil to steam: 89.0% Bio-char to stem: 80.0% Bio-oil to gas: Thermal excess energy ratioc 3.6 0.66 3.9 3.7 2.5 Thermal energy GWhth./a 142.5 GWhth./a 26.2 GWhth./a 155.4 GWhth./a 146.8 GWhth./a 102.3 GWhth./a Bio-char (t/a) n/a a n/a a n/a a n/a a 16 461 Bi o m as s in p u t fe ed r eq u ire m en ts Moisture contente 20 wt.% 20 wt.% 10 wt.% 10 wt.% 10 wt.% Biomass at HHVf 34 163 t/a 29 409 t/a 48 485 t/a 41 565 t/a 54 871 t/a Biomass at required MC (t/a)f 40 995 35 291 53 333 45 721 60 358 Energy content at required MC 209.38 GWh/a 180.25 GWh/a 277.45 GWh/a 237.85 GWh/a 314.00 GWh/a g Pyrolysis products Bio-char (t/a) n/a1 n/a1 5 613 12 469 16 461 Bio-oil (t/a) 34 944 22 860 30 179 Pyrolysis con- version h Bio-char (wt.%) 12 30 30 Bio-gas (wt.%)i 16 15 15 Bio-oil (wt.%) 72 55 55 Notes: a Not applicable (n/a). b Bio-gas produced in fast pyrolysis process is used to fuel fast pyrolysis system. c Based on net electrical energy output. d Preserved energy in energy carrier from inherent energy stored in biomass. e Dry basis; biomass pre-dried to required moisture content using exhaust gases from conversion process. f Losses due to pretreatment are not included; values refer only to net biomass requirements. g In case of bioenergy system V, bio-char is sold directly for industrial consumption (e.g. fertilising company); hence, 135.22 GWh stored energy in the bio-char is not utilised. h Biomass-to-pyrolysis-products conversion rate based on dry matter of biomass. i Bio-gas assumed to fuel the pyrolysis systems. Stellenbosch University http://scholar.sun.ac.za 130 Table 38, above, gives an overview of the five BCS configurations, briefly describing each system in terms of location and type of upgrading and conversion unit, its related intermediate product, as well as its respective upgrading and conversion efficiencies. Furthermore, the net biomass feed requirements, based on the respective efficiencies, and the chemical energy input from the raw material are discussed. 5.8.2 Financial-economic considerations Various issues and assumptions concerning the financial-economic assessment of the bioenergy systems are discussed below: ? In search of appropriate bioenergy conversion systems, in some cases it was necessary to rely on information from the literature or technology providers based overseas (e.g. USA, Canada, Netherlands or Germany), for which an exchange rate of R11.00 to the Euro and R7.90 to the US dollar was assumed. ? Some of the data used is based on conversion systems working at different production capacities than the above-stipulated 5MWel. Hence, it was necessary to estimate the cost of the conversion plant where cost data were not available for the particular size or capacity involved. Predictions can be made by using the power relationship known as the six-tenths factor rule, if the proposed BCS is similar to one of another capacity for which cost data are available. According to this rule, if the cost of a given unit at one capacity is known, the cost of a similar unit with times the capacity of the first is times the cost of the initial unit. The application of the 0.6 rule of thumb for most equipment purchased was, however, an oversimplification, since the actual values of the largest capacity exponent vary from less than 0.3 to greater than 1.0. Because of this, the 0.6 power should be used only in the absence of other information (Peters et al., 2003): Equation 12: Six-tenth factor rule Source: Peters et al. (2003: 242) ? The economic lifetime expectancy (ELE) for each of the BCSs is assumed to be 20 years (refer also to 3.3.4). ? The renewable energy feed-in tariff (REFIT): In June 2007, the national energy regulator of South Africa (NERSA) commissioned a study of the renewable energy feed-in tariff to support renewable energy, which culminated in the approval of the REFIT guidelines in March 2009. Stellenbosch University http://scholar.sun.ac.za 131 Based on the levelised cost of electricity, a feed-in tariff of R1.18 per kWhel for bioenergy from solid biomass was announced (NERSA, 2009: 17), with the term of the REFIT power purchase agreement being 20 years, and potential income from the clean development mechanism (CDM) scheme, i.e. so-called carbon credits, being excluded from the REFIT. This means that bioenergy plant operators could separately apply to qualify for the CDM, resulting in additional income. In March 2011, however, NERSA sought to consult with stakeholders on a review of the tariff levels set in 2009 for renewable energy technologies under the REFIT programme (NERSA, 2011). Until then no REFIT project had commenced due to issues relating to institutional arrangements, although it was assumed that the REFIT programme would commence immediately after the tariff approvals. In approving the 2009 REFIT tariffs, NERSA also undertook that the REFIT tariffs would be reviewed on an annual basis for the first five years of the REFIT programme and every three years thereafter. Due to changes in financial and economic parameters used in the tariff determination of 2009, NERSA revised, inter alia, the REFIT tariff for bioenergy from solid biomass, proposing R1.060/kWhel for 2011, R1.084/kWhel for 2012, and R1.108/kWhel for 2013. The REFIT tariff used in this study is based on the 2011 level, i.e. R1.06/kWhel. However, recent developments have seen another reconsideration of the REFIT tariffs. In August 2011, the South African Department of Energy invited prospective bidders to submit a proposal for the financing, construction, operation and maintenance of renewable energy generation facilities as part of the so-called Renewable Energy Independent Power Producer Procurement Programme (refer to http://www.ipp-renewables.co.za). ? Excess heat or thermal energy can be sold to industrial consumers for heating or, as is more likely in South African conditions, for cooling purposes. Potential buyers of thermal energy are canning factories, fruit wholesalers, etc. In three of the four selected biomass procurement areas, namely, Paarl, Worcester and Ashton, which are located near industrial areas, income is generated not only from selling electricity, but also from selling thermal energy to industrial consumers. Assuming that a potential thermal energy consumer would otherwise have to buy coal (HHV: 30MJ/kg) in order to run a steam boiler to generate thermal energy (80% coal-to- steam conversion efficiency), which is traded at around R1 000 per tonne in the Cape region, a thermal energy tariff of R0.15/kWhth is taken into account. Since thermal energy is a by- product, capital or operational costs are not included. Stellenbosch University http://scholar.sun.ac.za 132 ? In order to promote the participation of the developing world in efforts to reduce greenhouse gasses (GHG), the so-called clean development mechanism (CDM) was developed. After the Kyoto protocol (UN, 1998) came into effect in 2005, it became possible for companies in the developing world (including South Africa) to initiate projects with the aims of reducing GHG emissions, obtaining certified emission reduction (CER) certificates for these projects, and then selling these CERs (also called carbon credits) into the carbon market (Promethium, 2011). Using the South African Power-Grid Mix (SAPGM) provided in the GaBi database (PE International, 2006) as a baseline, the avoided net CO2-equivalent emissions were calculated (in tonnes) to determine the amount of carbon credits per LBS and biomass procurement area. With the functional unit (39.6 GWhel.) as a reference, the SAPGM causes fossil fuel-based CO2- equivalent emissions of 44 951 tonnes per year (refer to Annexure 38). The CO2-equivalent (fossil) emissions-avoided are specified as the CO2-equiv. emissions of the reference system less the CO2-equiv. emissions caused during the life-cycle of the LBSs (refer also to UNCFCCC (2010a, 2010c, 2010b). The latter is a function of the biotic CO2-equiv. sequestered less emissions from the biotic and fossil CO2-equiv. Equation 13: Calculation of CER certificates . Equation 14: Net CO2 emissions Besides selling electrical and thermal energy, carbon credits, assuming a tariff of R100/t of CO2 emissions avoided, are included in this study and therefore contribute to the income of the LBSs. However, the application of bio-char to soils as a form of carbon capture and storage is currently not included in the CDM methodologies and, thus, is not incorporated in the determination of carbon credits. The financial-economic information for each bioenergy conversion system is presented in Table 41, below. It is subdivided into capital expenditure (CAPEX) and operating expenditure (OPEX). Furthermore, distinctions are made for CAPEX between the biomass upgrading unit (e.g. into bio- oil or bio-char), the conversion unit and the final production step. Similarly, OPEX are subdivided into operation and maintenance (O&M) costs, as well as employment costs (including cost-to- company) for both the upgrading and conversion units. Stellenbosch University http://scholar.sun.ac.za 133 Table 39: Bioenergy conversion systems and their related capital and operational costs (2011) Bioenergy Conversion System (BCS) I II III g IV g V C A P E X a U p g ra d in g u n it Base module cost - R1 600 000 R82 520 874 R11 848 344 R11 848 344 No. of units - 22 1 21 27 Installation cost - -d R66 016 700 - - Total - R28 160 000c R148 537 574 R248 815 216 R319 905 278 Biomass drying system - R8 802 227 - - - C o n v ers io n u n it Base module cost R107 387 165 R1 100 000 R107 387 165 R107 387 165 R43 376 002i No. of units 1 22 1 1 1 Installation cost R30 367 682 R300 000d R 30 367 682 R30 367 682 R34 700 802h Total R137 754 846 R25 960 000c R137 754 846 R137 754 846 R78 076 804 CAPEX ? total R137 754 846 R62 922 2273 R286 292 420 R386 570 062 R397 982 082 O P E X O&M ? upgrading unit - k - k R4 126 044e R4 976 305f R6 398 106f Employment ? upgrading unit - k - k R2 040 000 R4 140 000 R4 980 000 O&M ? conversion unit R4 951 584 R1 293 203 R4 951 584 R4 951 584 R867 520j Employment ? conversion unit R1 430 000 R2 030 000 R1 430 000 R1 430 000 R1 810 000 OPEX ? total R6 381 584 R3 323 203 R12 547 628 R15 497 889 14 055 626 Notes: a Capital investment costs (CAPEX) in South African rands (ZAR). b Annual operating costs (OPEX) in South African rands (ZAR). c Due to the scale of economics, a rebate of 20% on the gasifier and genset units is assumed. d Combined installation cost for both the upgrading and conversion units. e OPEX of upgrading unit assumed as 5% of CAPEX of upgrading unit. f OPEX of upgrading unit assumed as 2% of CAPEX of upgrading unit. g Conversion unit the same as for BCS I. h 80% of conversion unit cost. i Six-tenth rule applied. j OPEX of conversion unit assumed as 2% of CAPEX of conversion unit. k OPEX of upgrading unit included in OPEX of conversion unit. 5.8.3 Emission related considerations Efficient and complete combustion is a prerequisite of utilising wood as an environmentally desirable fuel. In addition to a high rate of energy utilisation, the combustion process should therefore ensure the complete destruction of the biomass and avoid the formation of environmentally undesirable compounds. The fuel has an influence on the combustion efficiency. At complete combustion, carbon dioxide and water (H2O) are formed. An incorrect mixture of fuel, type of heating system, and introduction of air may result in an unsatisfactory utilisation of the fuel and a resultant undesirable environmental effect. Efficient combustion is a function of variables such as high temperature, excess oxygen, combustion time and mixture of fuel. Stellenbosch University http://scholar.sun.ac.za 134 Besides factors relating to the conversion technology (such as reactor type or filter technology) or to the conversion system (such as reactor temperature or residence time), biomass feedstock properties such as its elemental composition have a great influence on the properties of intermediate products, as well as after its final combustion, on the related exhaust gas emissions. The determination of exhaust gas emissions for the BCSs proved to be challenging, since no actual emission data for each of the BCSs using biomass with the specifications stated in 1.6 is available. Some studies concerned with bioenergy conversion are based on hardwoods such as eucalyptus (Cu?a Su?rez et al., 2010; Oasmaa et al., 2010; Corujo et al., 2010), but in most cases where conversion emission data is available, biomass such as softwoods (e.g. pine) was used, differing considerably in elemental composition, calorific value and biomass density. In order to overcome this problem, a simplified approach was used to determine the emissions generated in the process of converting woody biomass-based fuel into energy, applying the so- called thermo-chemical equilibrium and theoretical or stoichiometric oxygen or air requirement (Perry, 1997: 25). Assuming complete combustion of the feedstock, emissions are based on the amount of oxidant (oxygen or air) that is just sufficient to burn the carbon, hydrogen, and sulphur in a fuel to carbon dioxide, carbon monoxide, water vapour, and sulphur dioxide. Stoichiometry includes the basic laws of chemistry, such as the law of conservation of mass and the law of definite proportions. In general, chemical reactions combine in definite ratios of chemicals. Since chemical reactions can neither create nor destroy matter, nor transmute one element into another, the amount of each element must be the same throughout the overall reaction. For example, the amount of C on the reactant side must be equal to the amount of C on the product side. In other words, only that much carbon can be emitted, as carbon contained in the feedstock is combusted during the conversion process. Gas stoichiometry is the quantitative relationship (ratio) between reactants and products in a chemical reaction with reactions that produce gases. It applies when the gases produced are assumed to be ideal, and the temperature, pressure, and volume of the gasses are all known. Hence, the gas ratios applied in this study represent a lower limit of emissions, but give some indication of the emissions to expect. Software packages, such as the NASA chemical equilibrium programme or ASPEN could have been used for more accurate emission estimates, requiring additional information on enthalpy and combustion conditions. However, this would have entailed a study in itself, therefore, going beyond the scope of this study. Stellenbosch University http://scholar.sun.ac.za 135 Assuming complete combustion of the bioenergy feedstock (biomass, bio-oil, bio-char or bio-gas) and based on various considerations, the following assumptions have been made. Of the carbon in the feedstock, 99.9% is assumed to be emitted as CO2, with the remainder (0.1%) emitted as carbon monoxide. A greater proportion of CO emissions would represent a significant loss of energy. All hydrogen contained in the feedstock is assumed to be emitted as H2O. Depending on the combustion conditions, nitrogen forms mostly NO which oxidises into NO2 upon contact with the air. Hence, it is assumed that all N is released as NO2. Due to the low content of S in the biomass, technology providers often present the sulphur (S) value as zero. However, over a long period, a significant release of S gasses can be expected. S emissions are not dependent on the combustion conditions and, therefore, do not differ for the various conversion processes. It is assumed that of the S contained in the bioenergy feedstock, 25% (Meincken, 2010) is released as a gas (assuming 100% SO2). Generally, more than 85% SO2 is formed, with the difference mostly being SO3. If SO2 gas emissions appear to be too high, the gas can be cleaned using scrubbers. The remaining constituents of the bioenergy feedstock are assumed to be completely released to the air in the form of dust/particle matter (PM). Cyclones or flue/exhaust gas filter systems could be fitted for the removal of PM in the exhaust gas. Some of the bioenergy conversion systems require a considerable amount of water during the electricity generation process. However, it is assumed that in each case a closed water cycle is implemented. Thus, potential water consumption during the conversion process has not been included in the LCA. Extracting biomass from plantations entails removing important nutrients, which in some cases can increase acidification and decrease productivity, if not compensated for. Recirculating ash, the only ?waste? in lignocellulosic bioenergy systems, could be an option for reducing fertiliser inputs. However, so far, large-scale ash recirculation systems are not commercially available and are therefore not demonstrated (Forsberg, 2000: 23). Thus, if not released to the air as particle matter, woody biomass ash is considered to be used as landfill or as construction material. 5.8.4 Bioenergy conversion system I The first bioenergy conversion system consists of a biomass-fired integrated steam turbine system, representing the most established commercial option for the production of electricity from wood. The biomass is burned in a boiler to produce hot gasses, producing steam from the hot gasses via a heat exchanger, and then generating power from the steam using a steam turbine. Stellenbosch University http://scholar.sun.ac.za 136 Figure 33: Steam cycle of conventional integrated steam turbine systems Source: Envergent Technologies (2010) The assumptions made for BCS I are based on data from previous projects in South Africa (Nukor, 2010), namely, a 5.25 MWel CHP plant combining a John Thompson boiler with a MAN steam- turbine. The turnkey cost, which includes all system components as well as installation costs, amount to R137.8 million, with annual operating costs of almost R6.4 million. The latter encompasses various cost components such as employment costs (cost-to-company) for one plant manager and one engineer, each earning R400 000/year, and R70 000 per year for nine plant operators (three per shift), costs for water conditioning, maintenance costs (1.5% of capital investment costs), and others. Around 10% of the energy produced is required to maintain the energy generated, resulting in an electrical energy production of 5.5MWel. The boiler efficiency (biomass-to-steam efficiency) is 87.2%, i.e. the remainder of the energy inherent in the biomass is lost with the exhaust gases. The exhaust gases, however, are used to dry the biomass to the required moisture content level of 20% (dry basis). The steam-turbine is expected to reach a conversion efficiency of 24.1%. Thus, the net biomass input requirements to ensure continuous energy generation are estimated at 40 995 tonnes per year (20% MC). However, more biomass needs to be produced in order to compensate for potential losses such as occur during harvesting, pretreatment and transport. As mentioned above, the exhaust gas emissions from the conversion process that are taken into account in the LCA are based on mass balance calculations and the thermo-chemical equilibrium. The approximated emission values used for BCS I are presented in Table 40, below, using the combustion of one tonne of biomass (assuming 20% MC, dry basis) as a reference unit: Stellenbosch University http://scholar.sun.ac.za 137 Table 40: BCS I flue gas emissions per tonne biomass input Biomass elemental composition Wt.% Dry matter content a Exhaust gas emissions b Element Gasses formed % Stochiometric ratio c Emissions (kg/t) d C 48.00 400.0 C CO2 99.9 44/12 1 466.52 H 5.80 48.3 CO 0.1 28/12 0.09 N 0.25 2.1 H H2O 100.0 18/1 870.00 S 0.01 0.1 N NO2 100.0 46/14 6.85 O e 42.69 27.1 S SO2 25.0 64/32 0.04 Ash 3.25 355.8 Ash PM f 100.0 - 27.1 Total: 100.00 833.3 Total: 2 370.58 Notes: a Elemental composition of one tonne of biomass less 20% moisture content (dry basis): 1 000kg/1.2=833.33kg b Assuming complete combustion; exhaust gas emission conversion ratios based on Perry (1997) c Mass of gas product (kmol)/ relative atomic mass (kmol), also called relative atomic mass ratio d Exhaust gas emissions in kg per tonne biomass input (20% MC dry basis) e Calculated by difference f Particle matter 5.8.5 Bioenergy conversion system II For BCS II, the South African-designed and -made System Johansson Gas producer (SJG) by Carbo Consult and Engineering (PTY) LTD was adopted (CCE, 2010). This modular system is based on parallel series of integrated 450Nm3/h gasifier-gas-turbine systems, each generating an electrical energy output of 255kWel. Since the thermal energy is fed back into the system, the thermal excess energy ratio is relatively low at 0.66 of the electrical energy output. Figure 34, below, is a schematic illustration of the downdraft wood gasification SJG system. Up to 225kg of biomass (max. 20% MC) are fed into the gasifier, where under vacuum conditions, the biomass is converted into a raw gas, which is passed through a cyclone to remove coarse particles. In a gas scrubber, fine particles are removed and the gas is cooled down to an ambient temperature. Of the energy in the gas, 22% is lost when cooling to an ambient temperature. However, this loss is partially recovered by a heat exchanger. After passing through two more filters, the gas is fed into a generator powering an internal combustion engine. Due to the design of the reactor, together with a high gasification temperature and a long gas residence time, the gas is tar free. Electrical conversion efficiencies of around 22.2% are reached. The energy input is relatively low at around 1% compared with the electrical energy output, due to the relatively low degree of automation of the system. The net biomass requirements at 20% moisture content are 35 291 tonnes per year, resulting in a net biomass energy input of 18.25GWh. Stellenbosch University http://scholar.sun.ac.za 138 Figure 34: Schematic illustration of System Johansson Gasproducer (SJG) Source: Eckermann (2009) Although only 20 units would suffice to ensure the continuous generation of 5MW of electricity, a total of 22 units were assumed in order to accommodate downtime due to maintenance or repairs. The capital investment costs for the gasifier units were quoted in total at R35.2 million (R1.6 million per unit), and for the generating unit and alternator (also called genset), a total of R24.2 million (R1.1 million per unit) was assumed. Including a discount of 20% and installation costs of R300 000 per unit set, as well as the purchase and installation of a drying unit (R8.8 million), the total capital investment costs were estimated at R62.9 million. The main portion of the operating costs of, in total, R3.32 million per year, arise from employment: seven operators per shift (i.e. 21 operators each at R70 000/a), a plant manager, as well as one engineer (each at R280 000/a) are accounted for. Other operating costs comprise maintenance and repairs of the SJG systems of R853 092, as well as R440 111 for biomass handling, including drying. In the LCA, the flue gas emissions taken into account for BCS II, similar to BCS I, are based on the thermo-chemical equilibrium. Again, this represents only an approximation. Since the feed-in Stellenbosch University http://scholar.sun.ac.za 139 capacity of each gasifier is limited to 225kg/h (20% MC, dry basis), the emission values presented in Table 41, below, are based on hourly feedstock input for each gasifier. Table 41: BCS II flue gas emissions per gasifier-gas turbine system Biomass elemental composition Wt.% Dry matter content a Exhaust gas emissions b Element Gasses formed % Stochiometric ratio c Emissions (kg/t) d C 48.00 90.00 C CO2 99.9 44/12 329.98 H 5.80 10.86 CO 0.1 28/12 0.02 N 0.25 0.47 H H2O 100.0 18/1 195.75 S 0.01 0.02 N NO2 100.0 46/14 1.54 O e 42.69 80.04 S SO2 25.0 64/32 0.01 Ash 3.25 6.09 Ash PM f 100.0 - 6.09 Total 100.00 187.50 Total 533.27 Notes: a Elemental composition of dry matter of one tonne of biomass less 20% moisture content (dry basis): 225kg/1.2=187.50kg b Assuming complete combustion c Mass of gas product (kmol)/ relative atomic mass (kmol), also called relative atomic mass ratio d Exhaust gas emissions in kg per 225kg biomass input (20% MC dry basis) e Calculated by difference f Particle matter 5.8.6 Bioenergy conversion system III The third biomass conversion system consists of a stationary fast-pyrolysis system for upgrading the biomass feedstock into the intermediates bio-oil and bio-char, which are then used to fuel an integrated steam-turbine system to generate electricity. As for all BCSs, a total electrical energy output of 39.6GWhel is set as a production target. The steam turbine efficiency is assumed to be the same as for BCS I, namely, 24.1%. The upgrading efficiency of the pyrolysis products into steam is 89% for bio-oil and 80% for bio-char. Together, the pyrolysis system, as well as the integrated steam-turbine system reach a thermal excess energy ratio of 3.9 based on the electrical energy output, resulting in 155.4GWhth. The energy input to sustain both electricity generation, as well as for the fast-pyrolysis process is relatively high at 21.1% of the electrical energy output. In order to maintain the continuous generation of electricity, a net biomass supply of 53 333 tonnes, at a 10% moisture content per year is required, resulting in a net input of biomass energy input of 277.45 GWh. The data used for the stationary fast-pyrolysis system is based on a rotating cone reactor (RCR) from the Dutch-based Biomass Technology Group (BTG) BioLiquids B.V. (BTG-BTL, 2010). Stellenbosch University http://scholar.sun.ac.za 140 Biomass particles at an ambient temperature and hot sand particles are introduced near the bottom of the cone, where the solids are mixed and transported upwards by the rotating action of the cone (see Figure 35, above). The organic material is rapidly heated to 450-600?C in the absence of air. Under these conditions, organic vapours, permanent gases and bio-char are produced. The vapours are condensed into bio-oil, resulting in a bio-oil output of 72% (dry basis). Almost 12% of the biomass is converted into bio-char, and the remainder is converted into bio-gas, which is used in the pyrolysis process (Venderbosch and Prins, 2010: 191). The fast-pyrolysis system annually yields 34 944 tonnes of bio-oil and 5 613 tonnes of bio-char. Figure 35: Schematic illustration of stationary BTG-BTL pyrolysis system Source: Venderbosch and Prins (2010: 191) For the integrated steam-turbine system, the same assumptions are made as for BCS I, the difference being only in terms of the feedstock-to-steam efficiency, i.e. the boiler efficiency based on the inherent energy content of the feedstock is assumed to be 89% and 80% for bio-oil and bio- char respectively. The turnkey cost for the biomass upgrading unit, i.e. the pyrolysis system, is quoted at R148.5 million, which includes a capital cost of R82.5 million and R66.0 million for its installation. The operating costs of the pyrolysis system total R6.17 million per year, including costs for operation and maintenance (O&M) of R4.13 million (assumed to be 5% of CAPEX, excluding the installation Stellenbosch University http://scholar.sun.ac.za 141 cost) and a labour cost (cost-to-company) of R2.04 million. The latter can be subdivided into operator costs of R840 000 (4 operators per shift, 3 shifts per day) and R 1.2 million for management and engineering (one manager and two engineers, each earning R400 000 per year). For the conversion unit, the same assumptions as for BCS I were made, i.e. a capital cost including installation of R137.8 million and an operating cost of R6.38 million per year (including O&M and employment). In total, the capital costs amount to R286.3 million, with operating costs of R12.55 million per year. Figure 36: Simplified flowchart of BTG-BTL?s fast-pyrolysis system For the LCA and the related emissions, a number of assumptions had to be made. As mentioned above, the feedstock properties and the conversion system have a great influence on the proportions of the upgraded products (i.e. bio-char, bio-gas and bio-oil), their respective chemical compositions and their respective exhaust gas distributions on combustion. After reviewing the related literature ? refer, inter alia, to Sevilla et al. (2011); Kumar et al. (2010); Kumar and Gupta, (1992); Kumar et al. (2011); Bridgwater (2011); Amutio et al. (2011); Venderbosch and Prins (2010); Cu?a Su?rez et al. (2010); Oasmaa et al. (2010); Corujo et al. (2010); Vassilev et al. (2009); Khodier et al. (2009); NREL (2006); and Turn et al. (2005) ? the typical elemental composition of the wood-derived pyrolysis products bio-oil and bio-char is Stellenbosch University http://scholar.sun.ac.za 142 summarised in Table 42, below. In general, the elemental composition of bio-oil is similar to the original feedstock. Around 75wt.% of the bio-char consists of carbon, with a much lower oxygen proportion of around 10wt.%. The mass balance for ash can differ considerably, depending on various parameters of the pyrolysis process. However, for this study, it was assumed that all of the ash (mineral elements, except N and S) contained in the feedstock remains in the bio-char, and that the mass of the bio-char product includes the mass of the stable carbon, ash and volatile matter (refer also to Roberts et al., 2010: 829). Table 42: Typical elemental distribution of bio-oil and bio-char Elemental components Distribution of wood derived pyrolysis products (wt.%) Bio-oil Bio-char Carbon (C) 45.00-60.00 60.00-90.00 Hydrogen (H) 5.20-7.20 0.50-3.00 Nitrogen (N) 0.07-0.79 0.50-1.00 Oxygena (O) 30.00-45.00 8.00-12.00 Sulphur (S) 0.00-0.10 0.00-0.10 Ashb 0.00-0.10 0.00-30.00 Notes: a Calculated by difference b Most of the ash is contained in the bio-char, with negligible amounts in the bio-oil and bio-gas Table 43: Elemental distribution of pyrolysis products calculated for BTG-BTL system Bio-oil Bio-char Bio-gasa Biomassb HHV 18.77MJ/kg 29.57MJ/kg 6.08MJ/Nm3 c 19.00MJ/kg Mass balance (dry matter) 72.07 wt.% 11.58 wt.% 16.35 wt.% - Elemental component distribution (wt.%) Carbon (C) 49.80 63.33 29.21 48.00 Hydrogen (H) 6.00 1.76 7.78 5.80 Nitrogen (N) 0.10 0.64 0.64 0.25 Oxygen (O) d 44.10 6.11 62.37 42.69 Sulphur (S) 0.00 0.09 0.00 0.01 Ash2 0.00 28.08 0.00 3.25 Notes: a Calculated as difference assuming law of conservation of mass b Elemental composition of biomass feedstock as reference c 1kg biomass feed results in around 2Nm3 gas containing approximately 5.9-6.25MJ/kg chemical energy d Calculated by difference As described above, the mass balance (wt.%, dry basis) for the BTG-BTL system results in 72wt.% of the biomass feedstock being converted into bio-oil and nearly 12wt.% into bio-char. Based on the Stellenbosch University http://scholar.sun.ac.za 143 law of the conservation of mass, the bio-gas produced in the pyrolysis process was specified as the the original feedstock less the bio-oil and the bio-char. The elemental compositions listed in Table 43, above, were selected arbitrarily, since no data for the BTG-BTL system using hardwood feedstock with the properties listed in the last column of Table 43 was available. However, the values selected are somewhat representative of each pyrolysis product, while maintaining a constant mass balance. Table 44: BCS III flue gas emissions of each of the pyrolysis products Biomass elemental composition Wt.% Dry matter content a Exhaust gas emissions b Ultimate analysis Gasses formed % St. ratio c Bio- char Bio- gas Bio- oil kg/t biomass input (10% MC) C 48.00 436.36 C CO2 99.9 44/12 244.15 159.06 1 195.20 H 5.80 52.73 CO 0.1 28/12 0.16 0.10 0.76 N 0.25 2.27 H H2O 100.0 18/2 16.67 104.07 353.80 S 0.01 0.09 N NO2 100.0 46/14 2.21 3.10 2.15 O e 42.69 388.09 S SO2 25.0 64/32 0.05 0.00 0.00 Ash 3.25 29.55 Ash PM f 100.0 - 29.55 0.00 0.00 Total: 100.00 909.09 Total: 292.78 266.33 1 551.92 Notes: a Elemental composition of one tonne of dry biomass less 10% moisture content (dry basis): 1000kg/1.1=909.09kg b Assuming complete combustion c Stoichiometric (St.) ratio: Mass of gas product (kmol)/ relative atomic mass (kmol); also called relative atomic mass ratio d Exhaust gas emissions in terms of kg per tonne biomass input (10% MC dry basis) e Calculated by difference f Particle matter Similar to the combustion of biomass, the exhaust gas emissions from combusting each of the pyrolysis products are based on the emission assumptions made above. The last three columns of Table 44 show the calculated emissions for combusting each of the pyrolysis products based on inputting one tonne of biomass at 10% moisture content. 5.8.7 Bioenergy conversion system IV Mobile/portable fast-pyrolysis at the roadside is proposed for BCS IV. After having been transported to and stored at the central conversion site, the pyrolysis products bio-oil and bio-char are further used in an integrated steam turbine system to generate electricity. The assumptions made for the mobile fast-pyrolysis system are based on the MPS100 system from the Canadian based Agri-Therm Inc. company. The collapsible pyrolysis unit is fitted to a heavy- Stellenbosch University http://scholar.sun.ac.za 144 duty, standard-sized towing tractor for easy transportation and setting up. The fluidised bed reactor is fitted with a patented heat recovery system, allowing the pyrolysis process to operate at higher temperatures and lower input energy requirements. The MPS100 can support variable feedstock sizes ? up to 2.5cm in diameter and 10cm in length. The feeding capacity per unit is up to 416kg per hour at a 10% moisture content (dry basis) with an output of 208kg of bio-oil (55wt.% based on biomass dry matter input) and 113kg bio-char (30wt.%). The bio-gas produced in the pyrolysis process is used to fuel itself. Similar to those for BCS I, the assumptions made for the conversion unit are based on a system combining a John Thompson boiler and a MAN steam turbine. The same boiler efficiencies for the combustion of bio-oil and bio-char as for BCS III are assumed, i.e. 89% and 80% respectively. The steam turbine reaches conversion efficiencies of up to 24.1%. Besides a net annual electrical output of 39.6 GWhel, excess thermal energy of 146.8GWhth is also produced, assuming a thermal excess energy ratio of 3.7. The energy input for the conversion unit based on the electrical energy output is 14.4%. To maintain the continuous generation of electricity, at least 45 721 tonnes (10% MC) of biomass are required annually, which translates into 237.85 GWh of chemical energy inherent in the biomass. Unlike the stationary upgrading units, which are assumed to have an uptime of 24 hours on 330 days per year, the mobile fast-pyrolysis units only produce for 16 hours or two shifts a day, which results in a total of 21 upgrading units being required in order to ensure continuous electricity generation (14 units would be required if 24 hourly production was assumed). The upgrading unit?s capital costs, including the transportation unit are R11.9 million ($1.5million), or a total of R248.8 million for all 21 upgrading systems. Any discount due to the scale of economics was not suggested by the provider. As for BCS I and III, the basic module costs for the conversion unit are estimated at R107.4 million, to which R30.4 million for installation needs to be added. The total capital investment costs are therefore R386.6 million. One skilled operator is required to run a mobile fast-pyrolysis unit, resulting in a total of 42 upgrading unit operators. The conversion unit requires three operators per shift, one engineer for technical support, as well as one general manager supervising the whole operation. Thus, total employment costs including cost-to-company costs add up to R5.57 million per year. Operation and maintenance costs of the upgrading unit are assumed to be 2% of the unit?s capital cost, i.e. R236 967/unit (R4.98 million in total). Including the O&M costs for the conversion unit of almost R5.0 million, the total operating costs for BCS IV are R15.5 million per year. Stellenbosch University http://scholar.sun.ac.za 145 Figure 37: Agri-Therm?s MPS100 mobile fast-pyrolysis unit Following the same approach as for BCS III, the flue/exhaust emissions of the respective pyrolysis products are specified by assuming complete combustion and are based on the law of the conservation of mass. The elemental composition of bio-oil and bio-char, which are presented in Table 45, below, were chosen arbitrarily, but are within the boundaries of typical product properties and maintain mass balance. The elemental composition of biogas is calculated as the difference between the elemental composition of the biomass feedstock less the bio-oil and the bio-char. Table 45: Calculated elemental distribution of pyrolysis products based on Agri-Therm system Bio-oil Bio-char Bio-gas a Biomass b HHV 18.77MJ/kg 29.57MJ/kg 6.08MJ/Nm3 c 19.00MJ/kg Mass balance (dry matter) 55.00 wt.% 30.00 wt.% 15.00 wt.% - Elemental component distribution (wt.%) Carbon (C) 43.21 73.97 13.62 48.00 Hydrogen (H) 7.73 3.22 3.88 5.80 Nitrogen (N) 0.14 0.46 0.23 0.25 Oxygen (O) d 48.92 11.51 82.24 42.69 Sulphur (S) 0.01 0.11 0.03 0.01 Ash2 0.00 10.84 0.00 3.25 Notes: a Calculated as the difference assuming the law of conservation of mass b Elemental composition of biomass feedstock as reference c 1Kg biomass feed results in around 2Nm3 gas containing around 5.9-6.25MJ/kg chemical energy d Calculated by difference Stellenbosch University http://scholar.sun.ac.za 146 Flue gas emissions from bio-gas arise during the pyrolysis process, where they are used to fuel the upgrading unit. The bio-oil and bio-char are combusted in the respective boiler systems at the conversion unit. The emissions to air for each pyrolysis product taken into account in the LCA are presented in Table 46, below. Table 46: BCS IV flue gas emissions of each of the pyrolysis products Biomass elemental composition Wt.% Dry matter content a Exhaust gas emissions b Ultimate analysis Gasses formed % St. ratio c Bio- char Bio- gas Bio- oil kg/t biomass input (10% MC) C 48.00 436.36 C CO2 99.9 44/12 738.96 68.05 791.39 H 5.80 52.73 CO 0.1 28/12 0.47 0.04 0.50 N 0.25 2.27 H H2O 100.0 18/2 79.04 47.66 347.85 S 0.01 0.09 N NO2 100.0 46/14 4.12 1.05 2.30 O e 42.69 388.09 S SO2 25.0 64/32 0.01 0.2 0.01 Ash 3.25 29.55 Ash PM f 100.0 - 29.55 0.00 0.00 Total 100.00 909.09 Total 852.15 116.82 1 142.06 Notes: a Elemental composition of dry matter of one tonne of biomass less 10% moisture content (dry basis): 1000kg/1.1=909.09kg b Assuming complete combustion c Stoichiometric (St.) ratio: Mass of gas product (kmol)/relative atomic mass (kmol), also called relative atomic mass ratio d Exhaust gas emissions in kg per tonne biomass input (20% MC dry basis) e Calculated by difference f Particle matter (PM) 5.8.8 Bioenergy conversion system V Like BCS IV, the fifth biomass conversion system encompasses a mobile pyrolysis system as an upgrading unit. For the conversion, however, a different technology is assumed. After transportation of both pyrolysis products bio-oil and bio-char to a central conversion site, only the bio-oil is used to generate electricity, i.e. in a direct-injection gas turbine at a central conversion site. The bio-char is not used to generate electricity. Instead, it is assumed to be sold to agro-chemical companies as a fertiliser additive. For the upgrading unit, the same assumptions as for BCS IV are made, i.e. up to 416kg of biomass at a moisture content of 10% can be fed into the system, yielding 208kg of bio-oil and 113kg of bio- char (55wt.% and 30wt.% respectively based on dry biomass, i.e. dry basis). The conversion unit is based on a Tarsus 60 X1 combined-cycle gas turbine plant ? refer to the Gas Turbine World Handbook (2010) and Farmer and De Biasi (2010). Due to its high oxygen content and the presence Stellenbosch University http://scholar.sun.ac.za 147 of a significant proportion of water, the heating value of bio-oil is much lower than for fossil fuel (Venderbosch and Prins, 2010: 197). These and other differences in fuel properties result in relatively low conversion efficiencies for bio-oil in gas turbines. For this study, a conservative 26.0% was assumed, similar to values found in Lupandin et al. (2005), resulting in a net biomass requirement of 60 358 tonnes per year at 10% moisture content. Around 3.3% energy input on an electrical energy output basis is required for the system. Besides the electrical energy output of 5MWel, a thermal excess energy of 12.5MWth is generated (thermal excess energy ratio of 2.5). Another by-product is the marketable bio-char, which amounts to 16 461 tonnes per year. The conversion unit generates electricity for 24 hours on 330 days per year, whereas the upgrading units produce bio-oil and bio-char only during two shifts, each of eight hours a day. Only bio-oil is used for generating electricity. Both, the time and product constraints, result in 27 mobile fast- pyrolysis units being required, ensuring continuous production of the conversion unit. Therefore, the total capital costs for the upgrading units are calculated at R319.91 million. In order to conform to the proposed 5MW electrical energy output requirements, the six-tenth factor rule had to be applied for the conversion unit. Since the Tarsus 60 X1 has a proposed electrical energy output of 7.3 MW and a capital cost of R54.44 million ($6.89 million), the base module costs for the gas turbine are calculated as R43.38 million, and together with installation costs of R34.70 million (80% of the base module cost), the total capital expenditure for the conversion unit is R78.08 million. Similar to BCS IV, one skilled operator is required to run a mobile fast-pyrolysis unit, resulting in 54 skilled operators being required for the upgrading units. They are supervised by two upgrading unit managers, as well as three engineers. One plant manager, one engineer and three operators are assumed to be required for the conversion unit. The total annual employment costs are calculated as R6.79 million. Expenditure for O&M for both the upgrading units as well as for the conversion units is assumed to be 2% of the base unit costs, i.e. R6.40 million for the mobile fast-pyrolysis systems and R0.87 million for the gas turbine, resulting in a total operating expenditure of R14.06 million per year. For the LCA, the flue gas emissions of the mobile fast-pyrolysis units are as assumed for BCS IV (refer to column 9 in Table 45, above). Similarly, the emissions to air from the compressed combustion of bio-oil in a gas turbine are also per the combustion of bio-oil for BCS IV (refer to column ten in Table 45, above). However, the flue gas emissions per produced energy unit are less for BCS V than for BCS IV, due to the greater conversion efficiency. Stellenbosch University http://scholar.sun.ac.za 148 As mentioned above, the transmission, distribution, and use of the generated power are not covered. Similarly, the transport to and usage of bio-char by industrial consumers is not included in the assessment. Nevertheless, the bio-char is assumed to be sold to fertiliser companies for addition to soils. The stability of bio-char does vary with feedstock, processing, and environmental conditions. For this assessment, high yields of stable carbon are assumed. With this in mind, a conservative estimate of 80% of the C in the char as being stable is assumed (Lehmann et al., 2009; Baldock and Smernik, 2002). The remaining 20% of the C is labile and is released into the atmosphere as biogenic CO2 within the first few years of applying it to the soil (Roberts et al., 2010). 5.9 Conclusions In the goal and scope definition (the first phase of a life-cycle assessment), as described in Chapter 4, a set of 37 lignocellulosic bioenergy systems using lignocellulosic biomass grown in short- rotation coppice systems as a feedstock was defined. This included a definition of the functional unit and system boundaries. Within the Cape Winelands District Municipality, which forms the geographical boundaries, four biomass procurement areas, differing in biomass productivity and availability of biomass production sites, were selected. The second phase of a life-cycle assessment is defined as a life-cycle inventory analysis (LCI), as described in Chapter 5 involves data collection and calculation procedures to quantify the relevant inputs and outputs of a product system (ISO 14040, 1997). Thus, based on the LCA framework, each process/activity illustrated in Figure 11 leading to the set of 37 lignocellulosic bioenergy systems for the Cape Winelands District Municipality was specified, not only in terms of environmental input and output flows, as defined in the ISO standards 14040-14044, but also in terms of financial-economic and socio-economic performance. The financial-economic data comprises capital and operating expenditure for each unit-process, as well as expected revenues from selling electricity, the main product, and from selling by-products such as thermal energy for cooling or heating, or bio-char to the fertilising industry. Furthermore, for each system, the amount and type of the direct employment creation potential, a socio-economic indicator, were determined. Since each LBS consists of at least five production phases, namely, primary biomass production; harvesting and forwarding; biomass pretreatment including comminution, drying and fast- pyrolysis; secondary transport; and biomass upgrading and electricity generation, a myriad of information and data across the four biomass procurement areas has been collected and processed. The following chapter encompasses the life-cycle impact assessment (LCIA), the third phase of an LCA, which is ? from conventional LCA perspective? aimed at assessing the results of the life- cycle inventory to better understand their environmental significance by translating the Stellenbosch University http://scholar.sun.ac.za 149 environmental loads of each LBS into environmental impacts, such as global warming potential or eutrophication potential. Furthermore, the relevant data for the financial-economic assessment is translated by means of multi-period budgeting into key parameters, such as internal rate of return or risk of investment in terms of cost, describing the financial performance of each LBS per biomass procurement area. Similarly, the socio-economically relevant data describing the potential of creating direct employment is translated into three income categories, also to allow a comparison of the LBSs. Stellenbosch University http://scholar.sun.ac.za 150 6 CHAPTER: LIFE-CYCLE IMPACT ASSESSMENT 6.1 Introduction The previous chapter covers the life-cycle inventory, based on the LCA framework. This involves data collection and calculation procedures to quantify the relevant inputs and outputs occurring during the production phases for each of the lignocellulosic bioenergy systems (LBSs) considered. This chapter deals with the life-cycle impact assessment (LCIA), which is the third phase of the life-cycle assessment as described in the international standard (ISO 14040, 1997). The purpose of the LCIA is to assess a product system?s life-cycle inventory results, to better understand their environmental significance (ISO 14042, 2000). The impact assessment is achieved by translating the environmental loads from the inventory results into environmental impacts, such as acidification, ozone depletion, and global warming potential (Baumann and Tillman, 2004: 129). There are several reasons for translating environmental loads into impacts, such as to make the results more environmentally relevant, comprehensible and easier to communicate, as well as to improve the readability of the LCI results. The number of result parameters for the latter can range from 50 to 200 or even more (Baumann and Tillman, 2004: 129). Through the LCIA, the number of parameters can be reduced by grouping the environmental loads of the inventory results into environmental impact categories. The LCIA is also useful for making results more comparable, which is particularly relevant when comparing a set of alternatives, as is the case in this study. Other important considerations in terms of environmental impacts are, for instance, the effects of introducing bioenergy systems on biodiversity, as well as on water balance. The biodiversity intactness index or the water footprint are assessment methods which have the potential to determine such environmental impacts, but since they are not included in the commonly accepted LCIA methods, only a general discussion is given below. In addition, both environmental impacts have been dealt with a priori in a land suitability assessment by means of geographic information systems (GIS). Furthermore, using the LCA framework as a guideline, a set of financial-economic and socio- economic criteria are defined, against which the LBSs are assessed. By means of multi-period budgeting (MPB), financial-economic data is translated into key parameters describing the performance of each LBS, making them more comparable. The financial-economic criteria are used to describe the LBSs? profitability and cost structures, the former being an indicator of overall performance, and the latter being an important consideration in terms of risk of investment. This Stellenbosch University http://scholar.sun.ac.za 151 allows for a comparison of LBSs, as well as for a comparison of each LBS in terms of biomass procurement area. The socio-economic impact of the LBSs in terms of employment creation potential are subdivided into three income categories, based on the productivity data of each production phase used in the MPB models. Similar to the environmental impacts biodiversity and water balance, food security, another socio-economic impact, is briefly discussed. 6.2 Environmental criteria This section deals with not only the impact assessment categories commonly found in life-cycle impact assessments such as abiotic depletion potential and global warming potential, among others, but it also presents a brief discussion on biodiversity and water balance. 6.2.1 LCA impact categories The LCA software package GaBi 4.4 offers a variety of life-cycle impact assessment (LCIA) methods, such as the so-called CML 2001, Eco-Indicator 95, EDIP 2003, Impact 2002+ and TRACI. Commonly used is the frequently updated CML 2001 method, which is also applied in this study. CML 2001 is a collection of impact assessment methods that restricts quantitative modelling to the relatively early stages in the cause-effect chain, to limit uncertainties and group LCI results in mid- point categories, according to themes. These themes are common effects (e.g. climate change) or commonly accepted groupings of these (e.g. ecotoxicity) (PE International, 2010). The version of the CML 2001 normalisation factors used for this study is from November 2009. The data for the impact categories CML 2001 are from the Centre of Environmental Science (?Centrum Milieukunde Leiden? or CML) at the University of Leiden, The Netherlands, published in the ?Handbook on Life Cycle Assessment? (Guin?e et al., 2002b). Furthermore, a spreadsheet presents characterisation factors for more than 1 700 different flows (PE International, 2011). The CML 2001 normalisation data is based mainly on conditions for the Netherlands and Western Europe according to the information of the CML. Data for other countries and geographical units are from computations and information supplied by PE International GmbH (PE International, 2006). The normalisation data mostly being based on European conditions may be reason for criticism, but due to the lack of localised data, it was the only available source. A summary of the results for each impact category discussed in this section can be found in Annexures 38-44, subdivided into LBSs and biomass procurement areas. The complete and detailed Stellenbosch University http://scholar.sun.ac.za 152 results for each LBS and biomass procurement area, including all CML 2001-Nov. 2009 impact categories are also detailed in the Annexures (see Annexures 1-37). 6.2.1.1 Abiotic depletion potential How impact assessments of resource depletion should be done, is one of the most debated topics (Baumann and Tillman, 2004: 145). In general, resources can be divided into renewable and non- renewable resources or into abiotic and biotic resources. Abiotic resources are natural resources (including energy resources) such as iron ore, crude oil and wind energy, which are regarded as non-living; biotic resources are ?living?, i.e. those with a biological character. Three types of abiotic resources can be distinguished: deposits, funds and flows. Deposits, which are sometimes also called non-renewable resources, are resources that are not regenerated within human lifetimes, e.g. fossil fuels, minerals and clays. Funds are resources that can be regenerated within human lifetimes, e.g. ground water and top soil. Flows are resources that are constantly being regenerated, e.g. rivers, wind and solar energy. Another term for flows is renewable resources (Baumann and Tillman, 2004: 146). The CML 2001 impact assessment method collection encompasses two types of abiotic depletion potentials (ADPs), namely, ADP elements (measured in kg stibnite or Sb-equivalent) and ADP fossil (measured in MJ), where the stock of the resource itself is considered a key problem. The characterisation model is a function of the natural reserves of the resources, combined with their rates of extraction. The method is made operational for many elements and fossil fuels (more specifically, the energy content of fossil fuels). The natural reserves of these resources are based on ?ultimate reserves?, that is on concentrations of the elements and fossil carbon in the earth?s crust (Van Oers et al., 2002: 12). The characterisation factor is the abiotic depletion potential (ADP). This factor is specified for the extraction of elements (ADPelements), a relative measure, with the depletion of the element ?antimony? as a reference, and the consumption of fossil fuels (ADPfossil) measured in energy units (e.g. Gigajoule) respectively (Van Oers et al., 2002: 12). Only ADPfossil (GJ) is taken into consideration in this study, since it deals with the generation of energy and not with the extraction and use of elements. The results generated for the ADPelements of the LBSs support this, with the abiotic resource consumption of the elements by the LBSs being one to a maximum of four kg per year (refer to Annexures 1-37). Figure 38, below, illustrates the performance of the LBSs regarding their respective ADPfossil (GJ) and the respective biomass procurement area, i.e. Paarl (blue bars), Worcester (orange bars), Ashton Stellenbosch University http://scholar.sun.ac.za 153 (green bars) and the Rural Cederberge (red bars). The worst performing alternative across all locations, having the greatest abiotic depletion potential, is LBS 16, which can be explained by the relatively low overall conversion efficiency of BCS 3 (centralised fast-pyrolysis combined with a steam-turbine conversion system), resulting in greater biomass demand and, thus, requiring more up-stream activities. This is intensified by the low biomass output efficiency during motor-manual harvesting, since only logs are used further on in this process, necessitated by manual loading and unloading. LBS 34, which falls within BCS II, a gasification bioenergy system, is characterised by a relatively higher overall conversion efficiency, resulting in fewer up-stream activities being required. In addition, harvesting with a combine harvester, where the trees are felled and comminuted in a single operation, results in a lower combined fuel consumption and thus in a lower ADPfossil compared with harvesting systems where harvesting and comminution occur in separate phases. This greater efficiency even compensates for the greater emissions during the secondary transportation of the comminuted biomass, which contains 80% moisture content (dry basis), instead of 40% in the case of the other harvesting systems. Figure 38: The LBSs? abiotic depletion potential colour coded according to BPAs For the LBS key, refer to Figure 11 0 10 000 20 000 30 000 40 000 50 000 60 000 70 000 80 000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Abi o tic deple ti o n p o ten ti a l, f o ss i (G J ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 154 Using the same assumptions made for the functional unit, i.e. an electrical energy output of 39.6 GWh per year, the South African power-grid mix (SAPGM) (PE International, 2006) causes an ADPfossil of 429 458GJ (refer to Annexure 38), compared with an average for the LBSs across all biomass procurement areas of 32 310GJ, which is less than 8% of the SAPG mix. Worcester?s LBS 34 accounts for the smallest ADP with 14 311GJ (3.3% of the SAPGM), while Ashton?s LBS 16 accounts for the greatest ADP with 74 736GJ (17.4% of the SAPGM). 6.2.1.2 Acidification potential The major acidifying pollutants are SO2, NOx, HCl and NH3. Acid rain is only one form in which acid deposition occurs. Fog, snow, and dew also trap and deposit atmospheric pollutants. Furthermore, dry acidic particles and aerosols are converted into acids when they dissolve in surface water or contact moist tissues (e.g. in the lungs) (Baumann and Tillman, 2004: 155). Figure 39 Impact pathways leading to acidification Source: Heijungs et al. (1992) The acidification of soil and water occurs predominantly through the transformation of air pollutants into acids. This leads to a decrease in the pH levels of rain water and fog from 5.6 to levels lower than 4. This damages ecosystems, of which forest dieback is the most well-known impact. Acidification has direct and indirect damaging effects (such as nutrients being washed out of soils or an increased solubility of metals in soils). But even buildings and building materials may be damaged. Examples include metals and natural stones, which are corroded or disintegrated at an increased rate (PE International, 2010). However, actual acidification varies depending on where the acidifying pollutants are deposited. The actual impact is governed by, for example, the buffering capacity of soils and waters, climatic conditions, (amount of light and temperature) and the rate of harvesting (Baumann and Tillman, 2004: 155). Thus, although considered a global problem, the regional effects can vary. Various approaches to accounting for local differences have been SO2 NOX H2SO44 HNO3 Stellenbosch University http://scholar.sun.ac.za 155 suggested, but to date, there are few easily applicable methods. Figure 39 displays the primary impact pathways of acidification. What acidifying pollutants have in common is that they form acidifying H+ ions. A pollutant?s potential for acidification can thus be measured by its capacity to form H+ ions. This fact has been used in characterisation modelling in LCAs. The acidification potential (AP) is defined as the number of H+ ions produced per kg of substance relative to SO2 ? (Heijungs et al., 1992). The acidification potential thus reflects the maximum acidification a substance can cause. When analysing the AP results, graphically illustrated in Figure 40, it becomes quite apparent that the main drivers for AP are the respective BCSs with their overall conversion efficiencies. At least 85% of the AP originates from the BCSs (refer to Annexures 1-37). Figure 40: The LBSs? acidification potentials colour coded according to BPAs For LBS key, refer to Figure 11 Based on the same functional unit, the South African power grid mix (SAPGM) shows an acidification potential of 531 tonnes per year (refer to Annexure 38), at least 2.2 times that of the 0 50 100 150 200 250 300 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Acidi fic a ti o n P o ten ti a l (t S O 2 -E q u iv a len t) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 156 proposed LBSs. The best case-scenario shows (BPA II? and BPA IV?s LBS 34) a comparatively impressive ratio of 6.5. All LBSs using BCS II, the gasifier-gas-turbine system, show the lowest AP, with 83 to 96 tonnes per year (tpa). The LBSs using BCS V have the second-lowest acidification potential, varying from 114 to 123tpa. The LBSs using BCS I cause an AP of 124 to 143tpa, followed by those using BCS IV, with 181 to 190tpa. The highest acidification potential is expected for those LBSs using BCS III, i.e. centralised fast-pyrolysis combined with a boiler-steam-turbine, causing an AP of between 214 and 241tpa. A small variation of around +/- 1 tonnes per year is estimated among the different biomass procurement areas, additionally indicating the great effect of the BCS in causing AP. 6.2.1.3 Eutrophication potential Eutrophication is generally associated with environmental impacts involving excessively high levels of nutrients that lead to shifts in species composition and increased biological activity, for example, as algal blooms. In the LCA, the eutrophication category, sometimes also called nutrification, covers not only the impact of nutrients, but also those of degradable organic pollution and sometimes also waste heat, since they all affect biological productivity in some way (CML, 2002). These pollutants have one aspect in common, which is useful for characterisation modelling, i.e. they all lead to oxygen consumption. Discharges of degradable organic matter into water are broken down by micro-organisms, which consume oxygen, resulting in lower oxygen levels in the water and detrimental effects on aquatic ecosystems. Flows of nutrients as well as waste heat into the water lead to increased biological productivity and biomass formation, which in turn also lead to increased oxygen consumption when the biomass is being decomposed (Baumann and Tillman, 2004: 156). The causes of eutrophication are displayed in Figure 41, below: Figure 41: Impact pathways leading to eutrophication Source: Heijungs et al. (1992) Eutrophication is a phenomenon that can influence terrestrial as well as aquatic ecosystems. Nitrogen (N) and phosphorus (P) are the two nutrients most implicated in eutrophication. Other Waste water Air pollution Fertilisation PO4-3 NO3- NH4+ NOX N2O NH3 Stellenbosch University http://scholar.sun.ac.za 157 substances are rarely constraints. In most terrestrial ecosystems, the amount of nitrogen is the limiting nutrient and an increase of nitrogen will stimulate plant growth. In eutrophicated soils, an increased susceptibility of plants to diseases and pests is often observed, as is a degradation of plant stability. If the nitrification level exceeds the amount of nitrogen necessary for a maximum harvest, it can lead to an enrichment of nitrate. This can cause, by means of leaching, an increased nitrate content in groundwater (PE International, 2010). In aquatic ecosystems, phosphorus is normally the limiting factor for growth in fresh water, while nitrogen is the limiting factor in marine ecosystems. Nitrogen ending up in aquatic ecosystems comes from a number of different sources. Agricultural fertilisers and effluents from sewage works are major sources of nitrogen, but also parts of the atmospheric emissions of NOx eventually end up in aquatic ecosystems (Baumann and Tillman, 2004: 156). Figure 42: The LBSs? eutrophication potentials colour coded according to BPAs For LBS key, refer to Figure 11 Since different ecosystems are limited by different nutrients, actual eutrophication varies geographically. As with acidification potential, this complicates characterisation, and the simplest solution is to disregard the geographical variation. Hence, eutrophication potentials reflect the maximum eutrophying effect of a substance. Maximum eutrophication assumes that all airborne 0 10 20 30 40 50 60 70 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 E ut ro p h ic a ti o n P o ten ti a l (t P h o sp h a te -E q u iv a len t) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 158 nutrients eventually end up in aquatic systems, and includes all emissions of N and P substances to both air and water in the category together with emissions of organic matter (Baumann and Tillman, 2004: 156). Eutrophication potential (EP) is expressed as a phosphate-equivalent ( , but given the molar ratios of the chemical formulae, phosphate-equivalents can easily be converted into or equivalents. The EP of the different LBSs subdivided into biomass procurement areas is presented in Figure 42, above. Similar to acidification potential, the greatest influence on this impact comes from the conversion systems, which account for more than 90% of eutrophication potential. This also explains the low variation of EP of around one tonne per year per LBS and biomass procurement area. Those LBSs using bioenergy conversion systems with a relatively high overall conversion efficiency show the lowest eutrophication potential, and vice versa. Hence, with higher overall conversion efficiency, less biomass and, therefore, less input in the value chain prior to the conversion is required, resulting in a relatively lower eutrophication potential. 6.2.1.4 Global warming potential Climate change may lead to a broad range of impacts on ecosystems and our societies, but greenhouse gases (GHG) have one property in common, which is useful for characterisation in an LCA. Characterisation of GHGs is based on the extent to which they enhance the radiative forcing in the atmosphere, i.e. their capacity to absorb infrared radiation and thereby heat in the atmosphere (Baumann and Tillman, 2004: 149). The mechanism of the greenhouse effect can be observed on a small scale, as the name suggests, in a greenhouse. These effects also occur on a global scale. Short-wave radiation from the sun reaches the earth?s surface and is partially absorbed and partially reflected as infrared radiation. The reflected fraction is absorbed by greenhouse gasses (GHGs) in the troposphere and is re-radiated in all directions, including back to earth. This results in a warming effect on the earth?s surface (PE International, 2010). This effect is amplified by human activities, in addition to the natural mechanism. Carbon dioxide is not the only gas that causes climate change. Methane, chlorofluorocarbons (CFCs), nitrous oxide and other trace gases also absorb infrared radiation. Compared with CO2, they absorb much more effectively. The potential contribution of a substance to climate change is expressed as its global warming potential (GWP) (Baumann and Tillman, 2004: 149). Stellenbosch University http://scholar.sun.ac.za 159 Figure 43: Impact pathways leading to greenhouse effect Source: Heijungs et al. (1992) Figure 43, above, shows the main processes of the anthropogenic greenhouse effect. GHGs are calculated in carbon dioxide equivalents (CO2-equivalent), i.e. the greenhouse potential of an emission is given in relation to CO2. Since the residence time of the gases in the atmosphere is incorporated into the calculation, a time range for the assessment must be specified, with a period of 100 years commonly being applied. A distinction needs to be made between GHG emissions from fossil fuels and fuels from biotic sources. The former emit additional greenhouse gasses GHGs into the air, as the carbon has been stored over millions of years, whereas the latter take up carbon during its growth, via photosynthesis, resulting in a sequestration of carbon. Hence, when using biomass as a feedstock to generate energy, the CO2-balance of the feedstock itself is zero. However, procuring biomass as well as, in some cases, negative carbon stock changes in soils cause additional GHG emissions. As long as bioenergy systems show lower GHG emissions than fossil-fuel energy systems, a substitution in terms of GHG emissions can be justified. The LBSs? overall performance in terms of global warming potential is presented in Figure 44, below (for detailed results refer to Annexure 42). Comparatively, Ashton?s LBS 27 has the greatest global warming potential (GWP100years), with a CO2-equivalent of 3 690t per year. The best- performing LBS in terms of GWP100years is no. 13 for the Paarl-BPA, which shows a net GWP100years balance of minus 36 448 tonnes per year. To put this in perspective, the South African Power-Grid Mix (SAPGM) has a GWP100years of 44 951 t CO2-equivalent assuming the same functional unit (PE International, 2006 ? refer to Annexure 38), i.e. even the LBS with the greatest GWP100years reaches only around eight percent of the SAPGM. Significantly, different results can be seen for LBSs 5, 13, 21, 29 and 37. These alternatives have bioenergy system V in common, where only bio-oil produced in mobile fast-pyrolysis units is used CO2 CH4 CFCs UV - radiation Absorption Reflection Infrared radiation Trace gases in the atmosphere Stellenbosch University http://scholar.sun.ac.za 160 for electricity generation. The other product from the fast-pyrolysis process, bio-char, is assumed to be sold to the fertilising industry for application to soil. Eighty percent of the bio-char is assumed to be stable in the soil, resulting in negative GWP levels of more than 32 000 t CO2-equivalent across all biomass procurement areas for LBSs 5, 13, 21, 29 and 37. Figure 44: The LBSs? global warming potentials colour coded according to BPAs For LBS key, refer to Figure 11 For the other LBSs, a similar observation can be made as for the acidification and eutrophication potential impact categories: the greater the overall-conversion efficiency of the bioenergy conversion system applied, the fewer up-stream activities are required and the lower the GWP. However, when comparing the GWP of an LBS for each biomass procurement area, significant variation can be found. This can be explained by the positive effects of carbon stock changes when introducing SRC plantations. As mentioned in section 3.3.3.3, primary biomass productivity is, inter alia, a function of rainfall. BPA I (Paarl) is characterised by a higher level of mean annual precipitation, resulting in a greater biomass productivity and carbon stock storage capacity compared with the other biomass procurement areas. Thus, some LBSs also show for BPA I (and II) a slightly negative net GWP, depending on whether the increase in carbon stock due to land-use -40 000 -35 000 -30 000 -25 000 -20 000 -15 000 -10 000 -5 000 0 5 000 10 000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 G lo b a l W a rm in g P o ten ti a l , 1 0 0 y ea rs (t C O 2 -E q u iv a len t] ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 161 change compensates for the GHG emissions caused during harvesting, forwarding, pre-processing and secondary transportation. Figure 45: GWP of LBSs 2, 14, 20, 27 and 37 subdivided into production phases For LBS key, refer to Figure 11 Figure 45, above, shows the performance of five selected LBSs (2, 14, 20, 27 and 37) per BPA in terms of GWP, subdivided into production phases. The first group (the first four bars) represents LBS 14, which uses bioenergy conversion system I; the second group, LBS 2, which uses BCS II. BCS III is the assumed conversion technology for LBS 27, represented by the third group of bars, followed by LBS 20, which uses BCS IV. The last group of bars in Figure 45 illustrates the GWP100years of LBS 37, which uses BCS V. For detailed results, refer to Annexure 2, Annexure 14, Annexure 20, Annexure 27, and Annexure 37. The relatively large fraction of GWP for the harvesting phase for LBS 14 can be explained by the 30 percent of unutilised biomass remaining infield. Although there is no direct relation between the harvesting and decomposition of the unutilised biomass, it is during the harvesting phase that the trees are felled, de-branched and cross-cut, leaving the tops and branches behind. LBS 37 entails an unstable carbon fraction. When using biochar as additive to soil around twenty percent are assumed to be unstable, resulting in the decomposition thereof. -100000 -80000 -60000 -40000 -20000 0 20000 40000 60000 80000 100000 LBS 2, BDA I LBS 2, BDA II LBS 2, BDA III LBS 2, BDA IV LBS 14, BDA I LBS 14, BDA II LBS 14, BDA III LBS 14, BDA IV LBS 20, BDA I LBS 20, BDA II LBS 20, BDA III LBS 20, BDA IV LBS 27, BDA I LBS 27, BDA II LBS 27, BDA III LBS 27, BDA IV LBS 37, BDA I LBS 37, BDA II LBS 37, BDA III LBS 37, BDA IV G lo b a l W a rm in g P o ten ti a l, 1 0 0 y ea rs (t C O 2 -E q u iv a len t) Lignocellulosic bioenergy systems 2, 14, 20, 27 and 37 Primary production of biomass Harvesting Forwarding Mobile comminution Mobile fast-pyrolysis Secondary transport Centralised comminution Upgrading and conversion Unstable bio-char in soil Stellenbosch University http://scholar.sun.ac.za 162 6.2.1.5 Photochemical ozone creation potential Photo-oxidants are secondary pollutants found in the lower atmosphere, derived from NOx (generic term for the mono-nitrogen oxides NO and NO2) and hydrocarbons in the presence of sunlight. These substances are characteristic of photochemical smog, also known as summer smog, a known cause of health problems such as the irritation of respiratory systems and damage to vegetation (Baumann and Tillman, 2004: 153). Ozone is one of the most important photo-oxidants; others are peroxyacetyl nitrate (PAN), hydrogen peroxide, and various aldehydes. The smog phenomenon is crucially dependent on meteorological conditions and the background concentrations of pollutants. It can extend from being a local problem to one on a regional or even continental scale when emissions of NOx and hydrocarbons are widespread and ozone is transported by wind (Harrison, 1990). Figure 46: Impact pathways leading to photochemical Ozone Creation Source: Heijungs et al. (1992) Ozone is formed when and sunlight are present (refer also to Figure 46, above). Ozone production is increased when the air also contains organic substances. Different hydrocarbons react at different rates and efficiencies. In LCAs, photochemical ozone creation potential (POCP) is referred to in ethylene-equivalents ( -equiv.). Noteworthily, when interpreting the POCP results, characteristics of local conditions and weather patterns should be considered. Similar to the outcome of the impact categories acidification and eutrophication potential, Figure 47, below, shows that there is a strong relationship between the overall conversion efficiency of the LBSs and their respective photochemical ozone creation potentials. Those LBSs having relatively high overall conversion efficiencies show the least POC potential (between 5 and 11 t ethylene- equivalent per year), whereas those LBSs comprising BCS III, which are characterised by a relatively low overall conversion efficiency, show a POCP of 14 to 24 t ethylene-equivalent per year. Hydroca bons Nitrogen oxides Dry and warm climate Hydrocarbons Nitrogen oxides Ozone Stellenbosch University http://scholar.sun.ac.za 163 Figure 47: The LBSs? photochemical ozone creation potentials colour coded according to BPAs For LBS key, refer to Figure 11 6.2.1.6 Toxicity Toxicity is another complicated impact category, with a variety of characterisation methods. As yet, there is no coherent framework for characterising the toxicological impact pollutants, but research and methodology development in this area is in progress internationally (Baumann and Tillman, 2004: 151). They are considered troublesome impact categories for several political as well as scientific reasons. One has been the lack of inventory data for emissions, creating data gaps; others are linked to the models used and related data (Finnveden et al., 2009: 12). A reason why the toxicity category is complicated is that it includes many types of impacts and substances. For example, organic solvents, heavy metals and pesticides all cause different types of toxic impacts. Some substances cause neurological damage; others are carcinogenic or mutagenic, among others. Toxic substances tend to spread: pesticides used for agriculture can end up in waterways, causing harm to aquatic organisms as well as making drinking water inconsumable. The toxicity category is therefore often divided into human toxicity and eco-toxicity (CML, 2002). Eco- toxicity, in turn, can be divided into aquatic toxicity and terrestrial toxicity. Furthermore, aquatic toxicity can be divided into freshwater and marine toxicity (Baumann and Tillman, 2004: 151). 0 5 10 15 20 25 30 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 P h o to che m ic a l O zo ne Cr ea ti o n P o ten ti a l ( t E then e- E q u iv a len ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 164 The CML 2001 (Nov 09) impact assessment collection comprises four toxicity impact categories: ? Human toxicity potential (HTP) [kg Dichlorobenzene (DCB)-equivalent] ? Terrestrial ecotoxicity potential (TETP) [kg DCB-equivalent] ? Freshwater aquatic ecotoxicity potential (FAETP) [kg DCB-equivalent] ? Marine aquatic ecotoxicity potential (MAETP) [kg DCB-equivalent] However, toxicity impact categories are not included in this study due to a lack of consistency in the field for hazardous substances and heavy metals (Gediga, 2011), potentially resulting in incorrect conclusions stemming from inconsistent data. In addition, and partially as a result of this lack of consistency, the characterisation factors for toxicity impacts will change due to further development and refinement of the methodology. To highlight the inconsistency, Table 47, below, shows the terrestrial ecotoxicity potential for two power-grid mixes for LBS 1 (BPA I), based on the functional unit, i.e. 39.6 GWh/year. Table 47: Terrestrial ecotoxicity potential for various power-grid mixes Terrestrial ecotoxicity potential (TETP) in t DCB-equivalent/functional unit (39.6 GWh/year) South Africa Power-Grid Mix Great Britain Power-Grid Mix Bioenergy alternative 1, BPA I CML 96 75 928 25 984 1 549 CML 2001 (Nov. 2009) 146 19 1 Source: PE International (2006) 6.2.2 Other environmental impacts A general discussion on the impact of lignocellulosic bioenergy systems on biodiversity and water balance is given below. 6.2.2.1 Impact on biodiversity Biological diversity, normally referred to as biodiversity, is defined by the United Nations Convention Biological Diversity (UNCBD, 1973) and the Millennium Ecosystem Assessment Board (MEA, 2005) as: ?the variability among living organisms from all sources including, inter alia, terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems?. The term is used to cover all forms of life, but for practical purposes, it is often used in reference to specific taxa, e.g. biodiversity of plants, biodiversity of mammals, biodiversity of insects, and others. In its Stellenbosch University http://scholar.sun.ac.za 165 most common usage it refers to the disappearance or decrease in abundance of naturally occurring (endemic or indigenous) species that are implied when ?loss of biodiversity? is being discussed (Von Maltitz et al., 2010). When considering biodiversity, it is often convenient to subdivide the landscape into units of similar biodiversity, such as habitat types or ecosystems. The habitat type is typically defined by eco-regions, biomes or broad vegetation type such as lowland forest, dry deciduous forest, grassland, wetlands when working at a global or national level, but it could be a more detailed local classification when working at a plantation level (Von Maltitz et al., 2010). Biodiversity is important in all ecosystem services, directly or indirectly, although the relationship is often quite complex and subtle. There is firm evidence that diverse ecosystems, in general, are both more productive and more resilient to stress than less diverse ecosystems (MEA, 2005). Figure 48, below, shows the pathways and processes by which biodiversity influences ecosystem services, and ecosystem services influence human wellbeing. The value of supporting services, most of the value of regulating services, and most of the aspects of biodiversity are contained within the value of the directly used provisioning and cultural services. These underlying elements can influence the direct services through altering the mean magnitude of the service (?) or its variability in time (?) or its variability in space (?) (Amezaga et al., 2010: 85). The deliberate simplification of ecosystems, for instance, through mechanised monocultural cropping using high inputs of nutrients, water and pesticides has been the key mechanism for increased provisioning services such as food and fuel over the past century. This has generally been at the cost of other services ? even of other provisioning services ? such as water and biodiversity (MEA, 2005). Biofuel expansion, if not carefully regulated, has the potential to have very high impacts on biodiversity, especially as a consequence of habitat loss. It is counter-productive to fight one global environmental problem ? climate change ? and simultaneously exacerbate a second global environmental problem by increasing biodiversity loss. This is, however, a complex trade- off, since climate change is also predicted to have profound impacts on biodiversity (Thomas et al., 2004). Changes in temperature and rainfall regimes will displace habitats. The predicted rise in temperatures will displace the zone of climate preference for most species polewards or to higher altitudes. It is likely that a significant fraction of species will lose their current habitats completely, and will thus ultimately become extinct unless steps are taken to intervene (Hannah et al., 2002; Thomas et al., 2004). Though biofuels can mitigate climate change impacts in part, this positive impact is likely to be very small compared with the high negative land transformation costs. The Stellenbosch University http://scholar.sun.ac.za 166 synergistic impact of both land transformation and climate change will be a double blow to biodiversity, with transformed habitats making it much harder for species to adapt to climate change (Von Maltitz et al., 2010). Figure 48: Influence of biodiversity on ecosystem services Source: Von Maltitz et al., 2010: 85) Two aspects underpin the severity of biodiversity impacts. One is the importance of the habitat for biodiversity protection, and the other is the degree to which the proposed land is degraded or already transformed. A simple matrix (Von Maltitz et al., 2010) illustrates that it is untransformed areas of high biodiversity importance that are likely to have the greatest biodiversity conservation value (see Figure 49, below). A potential approach in determining the impact on biodiversity due to land-use change is the biodiversity intactness index (BII). The BII is a measure of the change in abundance across a wide range of well-known elements of biodiversity, relative to their levels in a chosen reference case. It is an indicator of the average abundance of a specified set of organisms (or functional groups of organisms) in a given geographical area. The BII is intended to provide a single, integrated measure of biodiversity, and the principles underlying the BII are discussed in Scholes and Biggs (2005) and ? ? ? insurance value mostly negative feedbacks Provisioning: food, fibre, water, wood medicines Cultural : aesthetics tourism spiritual Society Individual human wellbeing Freedom and choice Security Material needs Health Social relations market & nonmarket values Regulating: Climate, floods pests & disease Supporting: Ecosystem processes, Habitat provision Response Functional Landscape Diversity Types Diversity Biodiversity Non nature-based sources of goods and services actions to protect species and ecosystems ecosystem use decisions Stellenbosch University http://scholar.sun.ac.za 167 Biggs (2005). The application of the BII, however, would have gone beyond the scope of this study, and therefore, it has not been included. Figure 49: Determining conservation importance as a function of both habitat quality and level of degradation of the natural habitat Source: Von Maltitz et al. (2010: 92) For this study, another approach aimed at minimising the impact on biodiversity has been applied. In a previous study (refer to Von Doderer, 2009: 9-28), suitable land for biomass production in the CWDM was identified by means GIS. Ecologically sensitive areas, such as protected areas (e.g. nature reserves, national parks), critical biodiversity areas, water catchment areas, waterbodies and wetlands and other sensitive areas from ecological and aesthetical points of view, as identified by an expert group, were amongst other unsuitable land use types excluded. 6.2.2.2 Water balance Natural capital ? air, land, habitats and water ? is essential for the natural environment, which performs functions essential for human existence and life on earth (Costanza and Daly, 1992), such as providing biomass. The availability of fresh water is a prerequisite for the growth of biomass. Solar radiation is the principal driving force behind the evaporation of water (Gerbens-Leenes et al., 2009: 1055). Q ua lity o f l an d i.t .o . it n ot be en de gr ad ed o r tr an sf or m ed G oo d Ba d Degraded or transformed land in high conservation value habitat Conservation value dependent on degree of degradation and possibilities of reclamation Biodiversity importance Low High Good condition natural habitat of high conservation importance Very high biodiversity conservation value Totally transformed or badly degraded land of an original habitat type of low conservation importance Very low biodiversity conservation value Good condition natural habitat of low conservation importance Low overall conservation value ? but large scale conversion could alter conservation state Stellenbosch University http://scholar.sun.ac.za 168 Various concepts and tools have been developed to determine the water requirements of crops, for instance, CROPWAT, a FAO-developed computer programme for farmers, for irrigation planning and management (FAO, 2011, Allen et al., 1998); or the water footprint (WF) concept introduced by Hoekstra and Hung (2002), who define the WF as the total volume of fresh water used to produce the goods and services related to certain consumption patterns. The WF of a product (commodity, good or service) is defined as the volume of fresh water used for the production of that product at the place where it was actually produced (Hoekstra and Chapagain, 2008). Most of the water used is not contained in the product itself. In general, the actual water content of products is negligible compared with their WF (Gerbens-Leenes et al., 2009). An assessment has been done by Gerbens-Leenes et al. (2009) of the WF of energy from biomass, and the related consequences of an increasing share of bioenergy in the supply of energy. Various primary energy carriers derived from biomass are expressed as the amount of water consumed to produce a unit of energy (m3/GJ), showing considerable differences among the WFs for specific types of primary bioenergy carriers. The WF depends on the crop type, agricultural production system, and climate. The WF of biomass is 70 to 400 times larger than the WF of other primary energy carriers (excluding hydropower). Water balance is a location-specific issue, but is likely to be a constraining factor, particularly in the future, when climate change will have a severe impact on agricultural and other activities. However, although likely to be a constraining factor, the WFs of the bioenergy systems in this study have not been included, since WF is a location-specific issue. Areas not meeting the minimum water requirements were excluded a priori in the land availability assessment by applying the so- called aridity index (Von Doderer, 2009). In some cases, the introduction of SRC plantations may have a positive effect on the water balance, e.g. when replacing intensive agriculture under irrigation or when establishing SRC plantations on land that is infested with so-called undesired alien invader plants (AIPs). 6.3 Financial-economic criteria Budgeting is perhaps the most widely used method of financial planning. Budgeting, as a non- optimising method, evaluates plans in physical and financial terms (Hoffmann, 2010). The popularity of budgets stems from their simplicity of use and the fact that they aid in the heuristic approach to decision-making, rather than imposing an analytic framework on the decision maker (Rehman and Dorward, 1984: 181). Budgets are often used as comparable quantitative techniques and play an important role in benchmarking. Stellenbosch University http://scholar.sun.ac.za 169 Budgeting methods have been employed since the inception of agricultural economics and extension. During this time, standard accounting methods have been employed to generate comparable information for analyses and to serve as benchmarking information. Since budgeting is considered straightforward and practical, not much attention is given to it in the academic literature (Malcolm, 1990: 35). Figure 50: Graphic representation of multi-period budget model components for bioenergy systems Budget models are, in essence, simulation models, normally developed using spreadsheet programs, where complex and sophisticated calculations and relationships can be expressed in a relatively simple way. The sophistication of budget models lies in their ability to allow for detail, adaptability and user-friendliness (Keating and McCown, 2001). Incorporating physical as well as financial parameters, budgets usually generate information on profitability such as net income or cash flow. With some adaptation, system budget models may also be extended over time to calculate returns Input component ?Physical bioenergy system description ?Biomass procurement area and related biomass productivity (MAI, rotation length, etc.) ?Electricial conversion efficiency ?Thermal excess energy ratio ?Upgrading efficiency ?Weighted average transport distances ?Operational assumptions ?Bioenergy system configuration (alternatives) ?Inflow variables ?Biomass yields ?Electricity tariff ?By-products' prices ?Carbon credits ?Outflow variables ?Variable costs ?Overhead costs ?Intermediate capital costs ?Expenditure for land ?Fixed improvements Calculation component ?Rotation and mass calculations ?Overhead and fixed costs calculations ?Asset replacement ?Entire bioenergy system profitability and cash flow Output component ?Captial expenditure of conversion system ?Operational expenditure of conversion system ?Capital expenditure other than conversion system ?Operational expenditure other than conversion system ?Net annual flow ? Internal rate of return on capital investment (IRR) Stellenbosch University http://scholar.sun.ac.za 170 on capital invested and to calculate profitability indicators such as the internal rate of return on capital investments (IRR) or net present value (NPV). The components of the calculation model are shown below in Figure 50. This figure illustrates the input component (refer also to Chapters 2, 4 and 5), calculation component and output component of the multi-period budgeting (MPB) model. Each component consists of various parts. More information on the concept of MPB or farm modelling can be found in Hoffmann (2010) or Strauss (2005). 6.3.1 Internal rate of return A common measure of profitability or project worth in investment analysis is the internal rate of return (IRR), expressed as a percentage, where the incremental net benefit stream or incremental cash flow is used to measure the worth of a project. This is done by finding the discount rate that makes the net present value of the incremental net benefit stream or incremental cash flow equal to zero. It is the maximum interest that a project could pay for the resources used if the project were to recover its investment and operating costs and still break even (Gittinger, 1982: 329; Reilly and Brown, 1997: 529, 1058). The performance of each LBS per biomass procurement area in terms of IRR is illustrated in Figure 51 (refer also to Annexure 45), below, showing significantly different results for each. LBS 34 shows the best IRR across all biomass procurement areas, with 11.18%, 15.26%, 10.13% and 8.25% for BPAs I, II, III and IV respectively. This can be explained by the relatively high overall conversion efficiencies of BCS II, as well as by the high harvesting system efficiencies of the modified combine harvester. The LBSs employing BCS II show on average ? with one exception (No. 15) ? IRRs of around ten percent across all BPAs. LBS 11 shows the least favourable results in terms of IRR: For BPA I, the IRR was so negative that no result was given in the spreadsheet-based MPB model. BPAs II-IV also give a negative outcome, with -2.00%, -4.32% and 2.83% respectively. This can be explained by the relatively low overall conversion efficiencies, together with the low harvesting efficiencies, due to the motor-manual harvesting and manual loading and unloading of logs onto the tractor-pole-trailer combinations, leaving 30% of the biomass in form of branches and tops behind. In general, LBSs employing BCS III exhibit negative to marginally positive IRRs. Similar to most environmental impact category criteria, when using the same harvesting system, LBSs employing BCS II show the best comparative results, followed by BCS I, BCS V, BCS IV and BCS III. LBSs 33-37, which are characterised by their high degree of mechanisation in terms of Stellenbosch University http://scholar.sun.ac.za 171 harvesting system, show more favourable results than, for instance, LBSs 9-16, which have the lowest degree of mechanisation. Figure 51: The LBSs? internal rate of return (including land value) colour coded according to BPAs For LBS key, refer to Figure 11 When comparing the LBSs, significant differences between the biomass procurement areas can be seen. The Worcester biomass procurement area (BPA II) yields generally better results than the other biomass procurement areas, followed by BPAs I, III and IV. This can be explained by the relationship between land value, land productivity and rotation length of the SRC plantations per biomass procurement area. Typical land values for each of the BPAs were obtained from Adval Valuation Centre (2011) ? refer also to Von Doderer and Kleynhans (2010). Data on biomass productivity per hectare and rotation length for each BPA can be found in section 3.3.2. BPA I (Paarl) has the highest productivity (MAI 27 t/ha/a, fresh biomass) and the shortest rotation length (five years), but is also characterised by the highest land value of R50 000 per hectare, due to the great demand for it and its competition with other land use activities. BPA II?s land value, at R8 -5% 0% 5% 10% 15% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Int ern a l ra te o f re turn (% ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 172 000/ha, is considerably lower than BPA I?s land value, but it still has a reasonably good annual productivity of 18 t/ha/a and a seven-year rotation length. A land value of R5 000/ha, a MAI of 9 t/ha/a, and a rotation length of ten years is suggested for BPA III (Ashton). Due to BPA IV?s remote location (Rural Cederberge), as well as the generally low agricultural productivity of the land, its value is relatively low ? R1 000/ha ? but so is its biomass productivity, at 5 t/ha/a, resulting in a relatively long waiting period of 15 years until harvesting. The effect of the land value becomes apparent when comparing Figure 51, above, with Figure 52, below. The latter shows the IRR of the LBSs excluding the land value (refer also to Annexure 46). LBS 34 still proves to be the best-performing alternative in terms of IRR, resulting in yields of 26.57%, 20.00%, 13.16% and 9.03% for BPAs I-IV respectively. LBS 11, proving to be the least favourable in terms of IRR, shows a positive 0.69% for BPA I, but remains negative for the other BPAs, with -0.87%, -3.52% and -2.61% respectively. Figure 52: The LBSs? internal rate of return (excluding land value) colour coded according to BPAs For LBS key, refer to Figure 11 -5% 0% 5% 10% 15% 20% 25% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Int ern a l ra te o f re turn (% ) ex clud in g l a n d v a lu e Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 173 6.3.2 Cost of technology for biomass upgrading and conversion Since both the capital expenditure and operating expenditure of the bioenergy conversion systems contribute largely to the overall performance in terms of profitability of each LBS, they are discussed separately. The investment in biomass upgrading and bioenergy conversion technologies is a venture that requires an assessment of possible technology types and providers, inter alia, in terms of product to be produced, conversion efficiency, how well the technology is established, the degree of mechanisation and integration, and the related capital and operational expenditures. Different upgrading and conversion technology options and their properties are discussed in section 4.3.1.7. The selected five bioenergy conversion systems (BCS) are described in detail in section 5.8, taking general characteristics, financial-economic and environmental criteria into consideration. 6.3.2.1 Capital expenditure Figure 53: Capital expenditure of biomass upgrading and bioenergy conversion systems For LBS key, refer to Figure 11 The establishment of a bioenergy conversion system represents a capital intensive venture, carrying a significant risk. The BCS factors contributing to this risk are, inter alia, a sufficient supply of feedstock, continuity of production, reliability of the conversion technology and all ancillary systems, and a guaranteed market for the products produced. In the case of BCSs, the risk is 0 50 100 150 200 250 300 350 400 450 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 C a pit a l ex pen d it ure f o r bi o ene rg y u p g ra di n g a n d co n v er si o n (Z AR m ill io n ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 174 normally carried by either a single or a few private investors, a public investor, or a joint venture between the public and private sectors. Investors from the private sector, particularly, are expected to have a great short-term interest not only in the maximisation of their return on investment (ROI) but also in the sustainable development of their investment. Public investors, on the other hand, may not have the maximisation of ROI as their priority, but rather an interest in the sustainable creation of employment opportunities, as well as the sustainable supply of energy. Hence, public investors may not seek the most profitable alternative, but may take the opportunity costs into account, in order to create jobs, infrastructure and to ensure a reliable energy supply. Figure 53 shows the CAPEX required for the biomass upgrading and bioenergy conversion systems (CAPEXconversion) for each LBS (refer also to section 4.8.2 and to Annexure CAPEX-C). As mentioned above, five types of biomass upgrading and conversion combinations were considered, with LBSs employing BCS II being the least CAPEXconversion intensive (R62.9 million). BCS I comes second (R137.8 million), followed by BCSs III, IV and V (R286.3 million, R386.6 million and R398.0 million respectively). The considerably higher CAPEXconversion for LBSs employing BCS IV and V can be explained by the great numbers of mobile fast-pyrolysis units required, which are relatively costly, as the technology is still relatively immature at this stage. Also, unlike the stationary upgrading and conversion systems, which can be in production for 24 hours a day, the mobile fast-pyrolysis units are assumed to convert biomass into bio-oil and bio-char during two shifts per day (16 hours), resulting in a greater number of units required. The costs of property and ancillary infrastructure have not been taken into account, resulting in an equal CAPEXconversion of the bioenergy conversion systems for each LBS across all biomass procurement areas. 6.3.2.2 Operating expenditure The operating expenditures of the bioenergy conversion systems (OPEXconversion) for each LBS, over an economic lifetime of 20 years, are shown in Figure 54, below. As for the CAPEXconversion, no distinction was made in terms of costs for the different biomass procurement areas. Although the degree of automation of BCS II is relatively low, which may result in increased operating cost, LBSs employing BCS II still show the lowest operating cost. Again, the second best BCS is no. I, followed by BCS III. BCS V employs a direct-injection gas-turbine only, using bio-oil as a feedstock, which requires a greater degree of automation, resulting in lower operating costs compared with BCS IV. Detailed results for annual operating costs can be found in Table 39, above, or in Annexure 49. Stellenbosch University http://scholar.sun.ac.za 175 Figure 54: Operating expenditure for biomass upgrading and bioenergy conversion systems For LBS key, refer to Figure 11 6.3.3 Cost other than conversion technology Costs other than those for the conversion technology include all expenses along the value chain prior to biomass upgrading (i.e. fast pyrolysis) and bioenergy conversion, i.e. from land value, primary production of biomass, harvesting, forwarding, comminution, secondary transport, amongst others. In contrast with the costs of the conversion systems, which are expected to be carried by a single investor or a limited number of investors, the costs occurring during the other production phases are carried by a variety of investors, such as land owners and entrepreneurs. All operations prior to upgrading and conversion, for instance, could be taken care of by contractors specialising in the harvesting of SRC plantations, forwarding, comminution, or secondary transport. As for the IRR, the time period taken into consideration differs from BPA to BPA, taking the economic lifetime expectancies of the conversion units plus one rotation length into account to ensure a continuous supply of biomass. Thus, 25, 27, 30 and 35 years are assumed for BPAs I, II, III and IV respectively. 0 50 100 150 200 250 300 350 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 T o ta l o per a ti o n a l ex pen d it ure o f bi o ene rg y u p g ra di n g a n d c o n v er si o n (in Z AR m ill io n ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 176 6.3.3.1 Capital expenditure Capital expenditures other than those of the bioenergy conversion systems (CAPEXother) for each LBS and biomass procurement area are illustrated in Figure 55, below (for detailed results refer to Annexure 50). Significantly higher CAPEXother can be seen for the biomass procurement area Paarl, which can be explained by the 50 times greater land value than for BPA IV (Rural Cederberge). Furthermore, the weighted average transport distances for BPA I are more or less double those of the other biomass procurement areas, resulting in significantly higher secondary transport costs. In general, as discussed in section 5.31, the total land costs for producing biomass in an SRC system are a function of the land value per hectare, the biomass productivity, and the rotation length. The effect of these costs is considerable, therefore, also affecting profitability. Figure 55: Capital expenditure other than for conversion systems For LBS key, refer to Figure 11 When comparing the LBSs with one another, a similar picture as for the IRR can be seen. LBS 34 shows the lowest requirement CAPEXother across all biomass procurement areas, which can be explained by the relatively good overall conversion efficiency, resulting in less biomass being required, as well as the relatively low unit costs of the harvesting system. A more complex result is obtained for the highest CAPEXother: in BPA I, LBS 13 has the highest CAPEXother, since it requires 0 50 100 150 200 250 300 350 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 C a pit a l ex pen d it ure o ther t h a n bi o ene rg y u p g ra di n g a n d c o n v er si o n (in Z AR m ill io n ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 177 the most biomass prior to harvesting, due to losses during harvesting (30% of the biomass remains in-field), losses during comminution (less 5%), and due to the fact that only bio-oil is used in generating electricity. Hence, for the primary biomass production, more than 5 000 hectares are required to ensure a continuous supply of energy feedstock, resulting in a total land value of more than R250 million, and causing a total CAPEXother of almost R290 million. In BPA I, a difference of about R3 million exists between LBSs 13 and 21. Although the latter initially requires less biomass, and therefore less land, the MPB model shows that for LBS 21 more investment in intermediate capital equipment, such as three-wheelers, forwarders, and mobile-chipping units is necessary. This is also why LBS 21 comes first in terms of CAPEXother, of R155m, R165m and R128m respectively, for the other biomass procurement areas. 6.3.3.2 Operating expenditure Operating expenditure other than for conversion systems (OPEXother) includes all operating costs prior to biomass upgrading and bioenergy conversion, such as primary biomass production, harvesting, forwarding, comminution and secondary transport. The results for OPEXother are illustrated in Figure 56. Figure 56: Operating expenditure other than for conversion systems For LBS key, refer to Figure 11 0 100 200 300 400 500 600 700 800 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 T o ta l o per a ti o n a l ex pen d it ure o ther t h a n bi o ene rg y u p g ra di n g a n d c o n v er si o n ( in Z AR m ill io n ) Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 178 As for CAPEXother, the LBS with the lowest OPEXother is LBS 34, which can be explained by the same reasons as those in section 5.3.1. Again, LBS 13 exhibits the least favourable results. However, when comparing each LBS, a different picture emerges. Whereas for CAPEXother BPA I shows comparatively higher costs, mainly due to the cost of land, for OPEXother BPA I has the lowest value. This can be explained by the relatively higher biomass productivity per hectare, resulting in the lowest land requirements. Vice versa, BPA IV, has the lowest biomass productivity and the longest rotation length, requiring considerably more land to ensure a continuous supply of biomass. This means, for instance, that more land needs to be prepared and more SRC plantations need to be established, as well as maintained. 6.4 Socio-economic criteria Biomass utilisation, bioenergy technologies, their market share, and research interests in these issues vary considerably among countries. Nevertheless, in most of the countries, the socio- economic benefits of using bioenergy can clearly be identified as a significant driving force in increasing the share of bioenergy in the total energy supply. In most countries, regional employment created and economic gains are probably the two most important issues regarding using biomass to produce energy (Domac et al., 2005: 98). Table 48: Benefits associated with local bioenergy production Dimension Benefit Social aspects ? Increased standard of living o Environment o Health o Education ? Social cohesion and stability o Migration effects (mitigating rural depopulation) o Regional development o Rural diversification Macro economic level ? Security of supply/risk diversification ? Regional growth ? Reduced regional trade balance ? Export potential Supply side ? Increased productivity ? Enhanced competitiveness ? Labour and population mobility (induced effects) ? Improved infrastructure Demand side ? Employment ? Income and wealth creation ? Induced investment ? Support of related industries Source: Madlener and Myles (2000) Stellenbosch University http://scholar.sun.ac.za 179 The essence of the sustainability of bioenergy projects from a social perspective is how they are perceived by society, and how different societies benefit from these activities (refer also to Table 48, below). Avoiding carbon emissions, environmental protection, security of energy supply on a national level, or other ?big issues? are added bonuses for local communities in pursuing these projects, but the primary forces are much more likely employment or job creation, contribution to the regional economy, and income improvement. Consequently, benefits such as these that stem from the introduction of an employment and income generating source will result in increased social cohesion and stability (Domac et al., 2005: 98). The introduction of bioenergy systems could also help to mitigate adverse social and social cohesion trends (e.g. high levels of unemployment, rural depopulation, among others.), having positive effects on rural labour markets by, firstly, introducing direct employment and, secondly, supporting related industries and employment in these. Demand side effects constitute the focal point of the majority of socio-economic impact studies, and are concentrated on for several reasons. Most notably, they are relatively easy to define and the scale of the investment?s impact can be quantified with reasonable accuracy. Moreover, it is the economic impact that is most important to regional developers and decision makers (Domac et al., 2005). Thus, since an entire social impact assessment (SIA) would have gone beyond the scope of this study, a simplified approach was used to determine the socio-economic impact, i.e. by focussing on the direct employment opportunities offered by each LBS. Indirect employment opportunities, the mitigating effects of rural depopulation, and other socio-economic criteria were not captured. However, more information on the assessment of social impacts of bioenergy projects can be found, inter alia, in Tiwari et al. (2010) and Ngepah (2010). 6.4.1 Direct employment creation potential Direct employment results from operation, construction and production. In the case of bioenergy systems, this refers to the total labour necessary for the crop production, harvesting and pre- processing, and transportation of the biomass, as well as the construction, operation, and maintenance of the conversion plant (Domac et al., 2005: 102). As for the LCA (refer to section 4.8.1), the building and commissioning of conversion plants is not included in determining potential. Current levels of unemployment for the CWDM are given in Table 1, indicating a current unemployment rate of around 20 percent. Figure 57, below, shows the unemployment rate between 1995 and 2007 in terms of level of education (in years) for South Africa (SA), as well as for the Stellenbosch University http://scholar.sun.ac.za 180 Western Cape province (WC) (Burger, 2011). Although significantly lower compared with the national average, the WC?s unemployment rate for unskilled to semi-skilled labourers is still remarkably high, with more than one out of five being unemployed. With an increase in level of education, particularly of tertiary education, the risk of unemployment reduces significantly. This also results in South Africa having one of the most unequal income distributions in the world. Much of this inequality derives from the large wage disparities between workers of different skill groups (Burger, 2010). The latter is well illustrated in Figure 58, below, which shows the mean monthly earnings in terms of level of education for the South African average, and in comparison with the agricultural sector in the Western Cape. Figure 57: Unemployment rates (1995-2007), by level of education (in years) Source: Burger (2011) In a study on the wage dispersion in South Africa, Burger (2010) uses four skills groups, i.e. unskilled, semi-skilled, skilled and highly skilled. Bioenergy systems, however, require some understanding of operations by farm and forest workers, resulting in the combination of unskilled and semi-skilled groups. Based on this, full-time jobs created for each LBS have been subdivided into three income categories (refer also to Table 49, below), namely, number of jobs with an income of less than R8 000/month (direct employment creation potential, DECP I), number of jobs with an income of between R8 000 and R24 000/month (DECP II), and number of jobs with an income greater than R24 000 per month (DECP III). The productivity data applied for each production 0% 10% 20% 30% 40% 50% 60% 0 2 4 6 8 10 12 14 16 U n em p lo y m ent r a te ( % ) Years of formal education WC National Stellenbosch University http://scholar.sun.ac.za 181 phase in the multi-period budget models was used to quantify the direct employment potential for each of the categories. Figure 58: Mean monthly earnings (ZAR) (2003-2007), by level of education (years) Source: Burger (2011) Table 49: Bioenergy systems employment creation potential subdivided into income categories Direct employment creation potential Description Income (R/month) DECP I (< R8 000/month) Farm and forest worker R1 264 Chainsaw operator R3 000 Tractor operator Three-wheeler loader operator R5 000 Conversion plant operator R5 833 Secondary transport assistant R6 445 DECP II (R8 000-R24 000/ month) Combine harvester operator Feller-buncher operator Forwarder-operator R10 568 Stationary comminution unit technician R12 000 Secondary transport driver R17 680 DECP III (> R24 000/month) Stationary comminution engineer R25 500 SRC plantation supply chain manager R29 167 Conversion plant manager Conversion plant engineer R33 333 6.4.1.1 DECP I - income less than R8 000 per month This category comprises the number of jobs created for unskilled to semi-skilled labourers by each LBS, including farm and forest workers, chainsaw, tractor, three-wheeler loader, and conversion - 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 45 000 0 2 4 6 8 10 12 14 16 M ea n m o nthl y e a rnin g s (Z AR ) Years of formal education WC - agriculture National Stellenbosch University http://scholar.sun.ac.za 182 plant operators, as well as assistants to truck drivers during secondary transport. The performance in terms of DECP I of each LBS and for each biomass procurement area is illustrated in Figure 59, below. Similar to the LCA impact categories discussed above, the number of jobs created, particularly for this income category, is a function of the overall conversion efficiency of the bioenergy conversion system, the productivity rate of the harvesting system, as well as of the potential biomass losses occurring during harvesting and comminution. Figure 59: DECP I ? No. of jobs with income of less than R8 000/month For LBS key, refer to Figure 11 Besides employment created directly for the bioenergy conversion systems, all upstream employment creation potential depends on the overall conversion efficiency ? the less efficient, the more jobs are created. Hence, LBS 13, with 333, 379, 429 and 403 jobs for BPAs I-IV respectively, is the least favourable in terms of environmental and financial-economic criteria, but is the most favourable in terms of employment potential pertaining to jobs with an income of less than R8 000/month. However, LBS 13 is characterised not only by a relatively low overall conversion efficiency, resulting in greater land requirements and therefore in relatively greater employment force requirements for all upstream activities, but it also uses a relatively inefficient harvesting system, which, together with its labour intensive activities such as the manual loading and 0 50 100 150 200 250 300 350 400 450 500 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Dir ec t em pl o y m en t cr ea ti o n p o ten ti a l - N o . o f jo b s w it h inc o m e les s th a n R 8 0 0 0 / m o nt h Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 183 unloading of the logs, as well as the manual feeding of the mobile comminution units, contributes to the high number of jobs created in terms of DECP I. Another contributing factor is the high number of mobile fast-pyrolysis units (27), which require at least one skilled operator each per shift. The least employment creation potential offered in terms of income category I is by LBS 33, with 61, 66, 78 and 78 for BPAs I-IV respectively. This can be explained by the relatively high conversion efficiency of BCS I, resulting not only in relatively less employment created upstream, but also in only three conversion plant operators being required per shift. The LBSs employing BCS II, which is assumed to have the best overall conversion efficiency, require at least seven operators per shift, due to the conversion technology?s low degree of automation. 6.4.1.2 DECP II ? income from R8 000-R24 000 per month Direct employment creation potential category II includes all skilled labourers such as operators of combine harvesters, feller-bunchers, forwarders, or service technicians for the stationary comminution units, and truck drivers. Figure 60, below, shows the performance in terms of DECP II for each LBS and biomass procurement area. For DECP II, the overall conversion efficiency of the bioenergy conversion systems does not play a more significant role than it does for DECP I. The number of truck drivers transporting the bioenergy feedstock from the roadside to a central conversion plant represents the greatest proportion of DECP II. As discussed in section 4.7, the number of trucks, and therefore truck operators, depends, inter alia, on the commodity to be transported and the amount thereof, the payload capacity of the truck, the transportation distance, the total transportation time per load and the number of trips per working day completed. LBSs 9 and 10 yield the lowest employment creation potential for this category, with 3, 2, 2 and 2 for BPAs I-IV respectively, which can be explained by the low biomass demand, due to the relatively high overall conversion efficiencies, but more importantly, by the low level of mechanisation and automation used during the harvesting and forwarding of the biomass feedstock. Only truck operators are required in this category. The greatest employment creation potential is exhibited by LBS 24 (15, 11, 11, 11 for BPAs I-IV respectively), which is a result of more biomass being required by BCS III, resulting in more biomass having to be transported to the central conversion site, and the use of feller-bunchers and forwarders during harvesting. Stellenbosch University http://scholar.sun.ac.za 184 Figure 60: DECP II ? no. of jobs with income of between R8 000 and R24 000/month For LBS key, refer to Figure 11 6.4.1.3 DECP III ? income of more than R24 000 per month Highly skilled labourers having a monthly income of more than R24 000, such as engineers and managers for the conversion plant as well as for the supply chain, are aggregated in the category DECP III. For each LBS, one SRC plantation supply chain manager is included. The LBSs encompassing centralised, stationary comminution require one qualified engineer to supervise the system; the LBSs using BCSs I and II require both one operations manager, as well as one engineer; the LBSs using BCSs III and IV require one manager and two engineers for the upgrading unit and one each for the conversion units. Due to the large number of upgrading units required for the LBSs using BCS V, two unit managers and three engineers are required, with one engineer and one manager being in charge of the conversion unit. Thus, the LBSs using BCSs I and II, together with mobile comminution, create the least employment in DECP category III. On the other hand, those LBSs that use BCS V as an upgrading and conversion system, create the most employment. 0 2 4 6 8 10 12 14 16 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Dir ec t em pl o y m en t cr ea ti o n p o ten ti a l - N o . o f jo b s w it h inc o m e fr o m R 8 0 0 0 t o R 2 4 0 0 0 / m o nt h Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 185 Figure 61: DECP III ? No. of jobs with income of more than R24 000/month For LBS key, refer to Figure 11 6.4.2 Other socio-economic impacts: food security The competition for land between food, feed and energy production can in some instances lead to a shortage in food supply on a local, regional and national level, affecting, inter alia, social cohesion and stability; particularly in the African context, food security plays an important role. However, concerns regarding food security are dealt with in the land availability assessment (Von Doderer, 2009), and by selecting trees as a bioenergy feedstock. Assuming that land will always be used to its highest potential, based on sustainability, and given natural conditions (climate, soil, terrain, among others), trees will only be grown in SRC plantations on marginal and idle land, unlike canola or other bioenergy crops, which compete directly with high-potential food crops. A potential measure for land use at its highest potential is the land type suitability index value (Naud? et al., 2012), which indicates the soil suitability or potential for annual or perennial crops. However, this index does not reflect the demand side. 6.5 Conclusions Covered in the LCA framework, this chapter assesses the life-cycle impact of the 37 lignocellulosic bioenergy systems, not only in terms of the LCIA categories, but also in terms of financial- 0 1 2 3 4 5 6 7 8 9 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 E m pl o y m ent c re a ti o n p o ten ti a l - N o . o f jo b s w it h inc o m e g re a te r th a n R 2 4 0 0 0 / m o nt h Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 186 economic viability and socio-economic potential. The results of the life-cycle inventory discussed in Chapter 5 are translated using the ?CML 2001 impact assessment method collection? into environmental impact categories, such as abiotic depletion potential, acidification potential, eutrophication potential, global warming potential, and photochemical ozone creation potential, allowing for a comparison of the LBSs with one another and for a comparison of each biomass procurement area. Furthermore, a general discussion on the environmental impact of the LBSs in terms of biodiversity and water balance is given. The performances of the LBSs in their respective biomass procurement areas are assessed against a set of profitability and cost criteria by means of multi-period budgeting. As a socio-economic indicator, the direct employment creation potential, subdivided into three income categories, is determined by using productivity data for each production phase used in the MPB modelling. In general, it can be concluded that the main driver for each criterion, whether it be of an environmental, financial-economic or socio-economic nature, is the overall conversion efficiency (OCE) of the biomass upgrading and bioenergy conversion system. The greater the OCE, the less biomass is required, resulting in fewer upstream activities and less land required for biomass production. In terms of the environmental impact of the LBSs, a greater OCE is desired, resulting in lower total emissions and, therefore, in lower impacts for each life-cycle impact category. Similarly, for the financial-economic viability of the LBSs, a greater OCE results in lower costs, both in terms of capital and operating expenditure, as well as in higher internal rates of return on the capital invested. Particularly for biomass procurement area I (Paarl), a lower OCE results in higher biomass and, thus, higher land requirements. This has a negative impact particularly on the profitability of the LBSs, due to the very high land value of BPA I, which is only partially compensated for by its higher biomass productivity. The opposite picture emerges for the socio-economic criteria, expressed in employment creation potential. The lower the OCE, the greater the employment creation potential, particularly for the unskilled and semi-skilled income categories. Another important driver is the efficiency of the harvesting system, which has an effect similar to the OCE. The greater the degree of mechanisation and automation, the lower the environmental impact and the higher the cost-effectiveness and profitability. LBSs 9 to 16, for instance, comprise motor-manual harvesting with chainsaws, where trees are felled, de-branched and cross-cut to allow for the manual loading of logs, leaving a considerable amount of biomass in the form of branches and tops behind. As only logs are used further on in the process, easing the handling of the biomass, 30 percent of the biomass is left unutilised and, thus, more land needs to be cultivated for SRC plantations, in compensation. The greatest harvesting system efficiency is reached by the modified Stellenbosch University http://scholar.sun.ac.za 187 combine harvester system (LBS 33-37), where felling and comminution occur in a single operation, resulting in a relatively low environmental impact and low production cost, in turn, resulting in a positive impact on overall profitability. However, this harvesting system is also characterised by a relatively low direct employment creation potential. Table 50, below, shows the best- and worst-performing alternative(s) for each of the environmental, financial-economic and socio-economic criteria discussed in Chapter 6. LBS 34 performs best in terms of most of the environmental and financial-economic criteria, which can be explained by the relatively good OCE and the relatively good harvesting system efficiency. Only in terms of GWP and POCP are other LBSs favoured: LBS 37 for the former criterion, which can be explained by the great carbon storage capacity of biochar, resulting in a negative GWP; and LBS 18 for the latter criterion, which, however, shows only marginally better results than LBS 34. Table 50: Best- and worst-performing LBSs per selected criteria and BPA Best performing LBS a Worst performing LBS a BPA I II III IV I II III IV Environmental criteria Abiotic depletion potential 34 34 34 34 16 16 16 16 Acidification potential 34 34 34 34 16 16 16 16 Eutrophication potential 34 34 34 34 16 16 16 16 Global warming potential 37 37 37 37 27 27 27 27 Photochem. ozone creation potential 18 18 18 18 16 16 16 16 Financial-economic criteria Internal rate of return 34 34 34 34 11 11 11 11 Capital expenditure ? BCS b b b b c c c c Operational expenditure ? BCS b b b b d d d d Capital expenditure ? other 34 34 34 34 13 21 21 21 Operational expenditure ? other 34 34 34 34 13 13 13 13 Socio-economic criteria (i.e. direct employment creation potential) DECP I (< R8 000/month) 13 13 13 13 33 33 33 33 DECP II (R8 000-R24 000/month) 24 24 24 24 e e e e DECP III (> R24 000/month) f f f f g g g g Notes: a For LBS key, refer to Figure 11. b LBSs 2, 7, 10, 15, 18, 23, 26, 31 and 34 show the same results. c LBSs 5, 13, 21, 29 and 37 show the same results. d LBSs 4, 12, 20, 28 and 36 show the same results. e LBSs 9 and 10 show the same results. f LBSs 5, 8, 13, 16, 21, 24, 29, 32 and 37 show the same results. g LBSs 1, 2, 9, 10, 17, 18, 25, 26, 33, 34 show the same results. Stellenbosch University http://scholar.sun.ac.za 188 A different picture is presented for the socio-economic criteria. Here, LBS 13 shows the greatest potential for DECP I and, amongst others, for DECP III. DECP II is led by LBS 24. LBS 13 creates between 333 and 429 jobs with an income of less than R8 000/month for the different biomass procurement areas. In comparison, LBS 34 creates 65 to 81 jobs, only marginally more for this income category than the worst performing LBS. On the other hand, when using IRR as a point of reference, LBS 34 yields between 8.25 and 15.26%. LBS 13, however, gives an IRR of between 1.42 and 3.54%. Thus, only in purely monetary terms would LBS 34 be the obvious choice, resulting in a trade-off in socio-economic terms, as it only reaches a DEPC I of around 20% compared with LBS 13. When comparing LBS 24 and LBS 34 in terms of DEPC II and GWP, another trade-off becomes apparent. While LBS 24 has a DEPC II of 11-15, LBS 34 reaches only between 3 and 4. In contrast, LBS 24 has a GWP of between 864 and 2 768 t CO2-equivalent, whereas LBS 34 varies between 0 and 958, also showing a trade-off of at least 864 or up to 1 472 t CO2-equivalent. The results presented in this chapter set a good example of the complexity of bioenergy systems, constituting a major barrier to the implementation of bioenergy projects, as they are, by definition, embedded in social, economic and environmental contexts and depend on the support of many stakeholders with different points of view. In order to overcome these trade-offs, a tool is required that integrates the various points of view, by helping decision makers to organise and synthesise such information, so that they feel comfortable and confident about making a decision, minimising the potential for post-decision regret by being satisfied that all criteria or factors have properly been taken into account. Multi-criteria decision-making analysis (MCDA) is a tool aimed at aiding such a decision-making process. In the following chapter, the results provided are translated into a common language (scores). During a workshop, experts attached weights to the selected criteria using the commonly accepted and applied analytic hierarchy process (AHP). The combination of the weighted criteria with the scores results in a ranking of the LBSs, with the aim of providing decision support for the CWDM in deciding on what bioenergy system may be the preferred option for implementation. Stellenbosch University http://scholar.sun.ac.za 189 7 CHAPTER: INTERPRETATION OF LCA RESULTS USING MCDA 7.1 Introduction The complexity of bioenergy systems constitutes a major barrier to the implementation of bioenergy projects, as they are embedded in economic, social and environmental contexts. Such complexity and the resulting decision-making problem is illustrated by the trade-offs between the defined alternatives presented in the previous chapter, where the performances of 37 lignocellulosic bioenergy systems (LBSs) are compared against a set of 13 key criteria. Overall conversion and harvesting system efficiency are dominating factors contributing to the positive performances of the LBSs in terms of most of the selected economic and environmental criteria. However, besides being concerned about financial-economic profitability and low environmental impact, the public decision makers and other stakeholders may be also interested in improving the socio-economic situation, for instance, by supporting the creation of direct employment. The resultant trade-offs cause a decision- making problem, as those LBSs which do well in terms of the financial-economic and least environmental impact criteria show less favourable performances in terms of the socio-economic criteria. In this chapter, multi-criteria decision-making analysis (MCDA) is applied with the aim of supporting the public decision makers of the CWDM in their decision-making process. MCDA, which can be defined as a ?formal approach which seeks to take explicit account of multiple criteria in helping individuals and groups explore decisions that matter?, stands in contrast to single goal optimisation and approaches using ?unifying units? to offset poor performance in terms of one criterion by good performance in terms of another criterion. This latter approach is adopted in cost- benefit analyses, which use monetary values assigned to parameters, allowing for substitution and comparability between criteria. MCDA with its use of interval scaling and weights and with its focus on relative trade-offs within each dimension avoids many of the problems associated with monetary evaluation techniques, while still permitting the assessment of potential trade-offs between criteria. Based on the analytic hierarchy process (AHP), one of the commonly applied MCDA approaches, the performances of the LBSs in terms of the selected criteria were translated into a common language (scores). The aggregation of the (unweighted) scores resulted in a ranking of the LBSs, but without taking the conflicting natures of some of the criteria as well as the different viewpoints of potential stakeholders into consideration. With the support of experts, reflecting the various stakeholder perspectives, the relative importance of the selected criteria was determined by attaching weights using the AHP-based Expert-Choice software. In a next step, the weighted criteria Stellenbosch University http://scholar.sun.ac.za 190 were multiplied by the scores of the LBSs and aggregated into a single indicator, resulting in a ranking of the LBSs. This made subjective judgements explicit and transparent, and served as a basis for further discussion, but did not solve the actual decision-making problem for the final decision maker, as trade-offs between the various alternatives could not be resolved. 7.2 The analytic hierarchy process The MCDA method employed in this study is the analytic hierarchy process (AHP), developed by Saaty (1980). In its execution, it has many similarities with the multi-attribute value theory (MAVT) approach (Belton and Stewart, 2002; Hobbs et al., 1992), both being based on evaluating plausible alternatives in terms of an additive preference function. Although based on different assumptions about value measurement, AHP can be viewed as an alternative means of eliciting a value function. However, it was developed independently of decision theory, and some AHP proponents insist that it is not a value function method at all (Saaty, 1980). Nevertheless, the evidence for the similarity of the AHP and MAVT approaches is the convergence of supporting software, with a number of available packages supporting both eliciting approaches (De Lange, 2010). As with MAVT, the initial step in AHP is to develop a hierarchy of criteria (criteria value tree) and to identify or develop possible alternatives to be used as inputs. The major factors that differentiate AHP from MAVT are the use of pairwise comparisons of (a) alternatives with respect to criteria and (b) criteria within families, and the use of ratio scales for all judgements. In the standard procedure, alternatives are not differentiated from criteria, but are treated as the bottom level of the hierarchy, and all comparisons follow the same procedure. Rather than constructing a value function or an explicit qualitative scale against which the performances of alternatives are assessed, the decision maker is required to respond to a series of pairwise comparisons, usually using a nine-point scale, which leads to an implied numerical evaluation of the alternatives according to each criterion (Belton and Stewart, 2002: 152). Table 51 shows the fundamental scale for the pairwise comparisons. Generally, a weighting procedure for the criteria with respect to a goal as well as a scoring procedure of the alternatives with respect to each of the criteria are included in the AHP, which can be made in any order. The scoring procedure is undertaken by means of pairwise comparisons of the alternatives and aims to establish the relative preference order of such alternatives against the goal/objective (Saaty, 2004: 5). Pairwise comparisons of alternatives simply present two alternatives against each other and record the relative preference for one above another in terms of a given criterion on a numerical or semantic scale. In contrast with direct comparison, which Stellenbosch University http://scholar.sun.ac.za 191 compares various alternatives simultaneously (the more alternatives, the more difficult it becomes), pairwise comparison is significantly more simple (Belton and Stewart, 2002: 153). In comparing alternatives with respect to one particular criterion (e.g. implications in terms of financial profitability), participants are requested to express their preferences across alternatives only with respect to that particular criterion (e.g. financial profitability). Table 51: Fundamental scale for pairwise comparison in AHP Intensity of importance Definition Explanation 1 Equal importance Two criteria contribute equally to the objective 3 Moderate importance of one over another Experience and judgement slightly favour one criterion over another 5 Essential or strong importance Experience and judgement strongly favour one criterion over another 7 Very strong importance or demonstrated importance A criterion is favoured very strongly over another, its dominance demonstrated in practice 9 Extreme importance The evidence favouring one criterion over another is of the highest possible order of affirmation 2, 4, 6, 8 Intermediate values between the two adjacent judgements When compromise is needed Source: Saaty (1990: 15) Once all pairs of alternatives have been compared, the numeric values corresponding to the judgements made are entered into a pairwise comparison matrix, where all diagonal entries are by definition equal to one. The method interprets the strength of preference in terms of a ratio. Thus, if for example, alternative ? ? is preferred to alternative ? ?, with a strength of preference given by (where is the entry in the row and the column of the comparison matrix), then the comparison of with is the reciprocal of that value, i.e. (Belton and Stewart, 2002: 154). The first step in synthesising the comparison matrix is to reduce it to a comparison vector (a set of scores representing the relative performance of each criterion). The values in the pairwise comparison matrix are interpreted as ratios of underlying scores. Within this context, the aim of the AHP is to find the set of values (weights) that approximates the set of ratios. The standard method for doing this is an eigenvalue analysis of matrices, which is aimed at extracting the eigenvector corresponding to the maximum eigenvalue of the pairwise matrix. This procedure is iterative and not easily performed by hand, but software programs such as Expert Choice extract the relevant values quite easily. The elements of the vector of scores are normalised to sum one (unity), which implies that for each criterion the scores are standardised, such that when a new criterion is added or Stellenbosch University http://scholar.sun.ac.za 192 a current one is deleted from the comparison, the whole structure is changed consistently (Saaty, 2004: 13; De Lange, 2010: 21). The weighting procedure consists of pairwise comparisons of criteria to elicit the relative preferences of decision makers. This step compares all criteria within each criteria group, using the same pairwise comparison procedure described above to derive the vector indicating the contribution of the criteria relative to the group of criteria. Respondents are asked questions similar to the procedure described above, but now compare criteria against each other within the same group. The weighting is done on the same numerical or semantic scale as for the scoring procedure. The preferences are again aggregated by working upwards from the bottom of the hierarchy (bottom-up approach). These weights are then multiplied by the relative performance (score) of each alternative against the said criteria, to present a weighted score for the particular criteria (Saaty, 2004: 13). The aggregated final score of all criteria for each alternative facilitates a ranking of alternatives, which feeds directly into the decision-making process. Worth noting is that the preference ordering should be done with a set goals/objectives in mind. The strength of the approach depends on the way it is used to facilitate understanding, learning and discussion, which in turn depends on the interaction of the participants and the effectiveness of displaying the information to participants (De Lange, 2010: 22). Sensitivity analysis may then be used to investigate the significance of missing information, to explore the effect of decision makers? uncertainty about their values and priorities, or to offer a different perspective on the problem. However, there may be no practical or psychological motivation for changing values; the exploration may be driven simply by a wish to test the robustness of the results. Nonetheless, Saaty (1996) developed a consistency index that compares the scores/weights with a value derived by generating random reciprocal matrices of the same size, to give a consistency ratio which is meant to have the same interpretation no matter what the size of the matrix. A consistency ratio of 0.1 or less is generally seen as acceptable (Saaty, 2004: 9). It is clear from the algebraic structure of the additive model used both in MAVT and AHP that the weight parameters define the desirable levels of trade-offs between performances in terms of the different criteria when measures of performances are given in scores. Because of the scaling of the partial scores to sum unity, the implied meaning of ?weight? in the standard AHP procedure is the relative value of the total (or average) score for different criteria ? thus it can only be defined by all the alternatives under consideration, which is much more complex to conceptualise. It is not at all Stellenbosch University http://scholar.sun.ac.za 193 evident that decision makers have this interpretation in mind when they express relative weight ratios. In addition, AHP assumes that all comparisons can be made on a ratio scale, which implies the existence of a natural reference point. This makes sense for comparisons of distance or area or monetary units, but it does not make sense for qualitative comparisons such as for comfort, image or quality of life, for which no clear reference level exists. Kahneman and Tversky (1979, 1981) have illustrated that reference points are strongly influenced by the framing of problems, while the framing will almost inevitably change from one pairwise comparison to another, so that in general, stable reference points cannot be expected to occur (Kahneman and Tversky, 1979; Tversky and Kahneman, 1981; Kahneman, 2003). A number of reasons have led to the popularity of AHP, despite the criticisms mentioned above: psychological research shows that the human brain can consider only a limited amount of information, so all factors cannot be resolved in one?s head (Belton and Stewart, 2002: 2). Experience and other anecdotal evidence suggests (Belton and Stewart, 2002: 114) that decision makers are quite happy to express their opinions regarding relative importance in ratio terms, e.g. the one criterion being more important than the other. AHP makes direct use of such intuitive statements, by allowing decision makers to give verbal descriptions of relative importance in terms such as ?moderately?, ?strongly? or ?absolutely? more important, which are converted into assumed ratios. The natural appeal of such semantic scales for expressing relative importance explains, inter alia, the popularity of AHP. However, this study employed only parts of the MCDA procedure, requiring judgements only for the weighing of the defined set of criteria in order to overcome the trade-offs between the LBSs indicated in section 6.5. The performances of the LBSs were determined in terms of quantitative measures using standardised, quantitative assessment methods, where natural reference points existed. Thus, the performances were based on sound, objective data, rather than on subjective judgements by decision makers or ?objective? experts, who often rely on their ?gut-feel?. 7.3 Problem identification and structuring The first MCDA phase, problem identification and structuring, is covered in the LCA part of this study, where the goal is defined as ?supporting the public decision maker in determining the best- suited or ?optimal? bioenergy system for the CWDM?. The alternatives are defined in Chapter 4, ?Goal and scope definition? (refer also to Figure 11); their features and key issues are discussed in Chapter 5, ?Life-cycle inventory?, while Chapter 6 presents the life-cycle impact assessment, where the LBSs are assessed against a set of predefined criteria. Stellenbosch University http://scholar.sun.ac.za 194 7.4 Model building and use As mentioned in section 3.3.2, during the second phase (model building and use) of a conventional MCDA, after extracting the essence of the decision problem, the decision maker?s preferences, value trade-offs, goals and objectives, and other requirements are translated by developing a formal model so that the alternatives under consideration can be compared relative to one another in a systematic and transparent manner. 7.4.1 Criteria value tree The initial step during the second phase of an MCDA is to develop a hierarchy of criteria (criteria value tree), which consists of a goal, at which the decision making process is aimed; main criteria, which describe relatively broad general interests (e.g. social, economic and environmental concerns); and various levels of sub-criteria, which are more specific (e.g. IRR, GWP). Figure 62 shows the proposed hierarchical value tree for the CWDM?s decision-making problem concerning the choice of bioenergy system, based on the criteria applied in Chapter 6. The goal was defined, as ?Identifying the most viable/sustainable for the CWDM?. The main criteria were based on the ?three-legged stool? of sustainability (Brady, 2005: 33), i.e. (i) financial- economic viability, (ii) socio-economic potential (i.e. direct employment creation potential), and (iii) least environmental impact. The sub-criteria, which are based on the criteria defined in Chapter 6, were grouped accordingly. The financial-economic viability criterion was divided into three sub-criteria, namely internal rate of return, cost of conversion system, and cost other than conversion system. The latter two were further subdivided into capital expenditure and operational expenditure. The socio-economic potential criterion encompasses three sub-criteria, i.e. direct employment creation potential I (DECP I: number of jobs providing a monthly income of less than R8 000), DECP II (number of jobs providing an income of R8 000-R24 000/month), and DECP III (number of jobs providing an income of more than R24 000/month). The last main criterion, ?Least environmental impact?, consists of two sub-criteria, namely ?Least local impact? and ?Least global impact?. The latter consists of another two sub-criteria, i.e. ?Lowest abiotic depletion potential? (ADP) and ?Lowest global warming potential? (GWP), while ?Lowest local environmental impact? was subdivided into ?Lowest acidification potential? (AP), ?Lowest eutrophication potential? (EP) and ?Lowest photochemical ozone creation potential? (POCP). Stellenbosch University http://scholar.sun.ac.za 195 Figure 62: Hierarchical value tree for the CWDM?s decision-making problem concerning choice of bioenergy system Notes: IRR Internal rate of return on capital investment CAPEX-conv. .Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operating expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operating expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential ? income of less than R8 000/month DECP II Direct employment creation potential ? income of R8 000-R24 000/month DECP III Direct employment creation potential ? income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential 7.4.2 Normalisation of LCA results In order to create consistency among the results, the absolute numeric values corresponding to the LBSs need to be translated into an interval scale of measurement (scores), i.e. a scale on which the Identifying the most viable/sustainable bioenergy system for the CWDM Financial-economic viability Best IRR Lowest cost of conversion technology Lowest CAPEX- conv. Lowest OPEX- conv. Lowest cost other than conversion technology Lowest CAPEX- other Lowest OPEX- other Socio-ecomomic potential (Direct Employment Creation Potential) Best DECP I Best DECP II Best DECP III Least environmental impact Least local impact Lowest AP Lowest EP Lowest POCP Least global impact Lowest ADP Lowest GWP Stellenbosch University http://scholar.sun.ac.za 196 difference between points is the important factor. Thus, to construct a scale, it is necessary to define two reference points and to allocate numerical values, e.g. 0-100, to these points. For the translation into standardised scores, the local scaling approach was selected, where the local scale is defined by the set of alternatives under consideration, i.e. the performances of the alternatives for a particular criterion are normalised to sum one. The alternative that does best on a particular criterion exhibits the greatest score, while the one that does the worst is represented by the lowest score. All other alternatives receive intermediate scores that reflect their performances proportionally. The local scaling method is preferred, since it is easy to implement, and since the set of alternatives allows for the consistent assessment of each of the selected criteria. Another approach, the ?global scaling approach?, is defined in reference to a wider set of possibilities, where the end points are defined by the ideal and the worst conceivable performances that could realistically occur. The latter approach however, requires more work than would be required on a local scale, and the definitions of the end points may be debatable in terms of what performances can realistically occur. One form of global scaling is the ?distance-to-target scoring approach?, where impact categories are evaluated according to the distance between the current level and a future target value (Sepp?l? and H?m?l?inen, 2001). Furthermore, to determine the ratio scale, consideration must be given to the direction of the original scale, which can be ? Monotonically increasing against the natural scale, i.e. the highest value is the most preferred, and the lowest is least the preferred, or vice versa; ? Monotonically decreasing against the natural scale; and ? Non-monotonic, i.e. an intermediate point on the scale defines the most preferred or least preferred point (often an indication that the proposed measure actually reflects two conflicting values). The directions of the original natural scales of the criteria applied in this study are either monotonically increasing or decreasing. However, to simplify interpretation of the scores, all local scales were converted to a monotonically increasing direction, i.e. the highest value is the most preferred, and the lowest is the least preferred. The original values for the sub-criterion ?Internal rate of return on capital investment? were expressed as a percentage and represent a measure of performance in terms of profitability (refer to section 5.3.1). Thus, the greater the IRR, the better the profitability of a particular alternative, indicating a monotonically increasing direction. The other financial-economic sub-criteria ?Cost of Stellenbosch University http://scholar.sun.ac.za 197 conversion technology? and ?Cost other than conversion technology? were each further subdivided into ?Capital expenditure? and ?Operational expenditure?, and were expressed in monetary terms representing the risk associated with the investment (refer to sections 5.3.2-5.3.3). The direction of the natural scale for these criteria is monotonically decreasing, i.e. the higher the cost, the less favourable it is as an alternative. However, during the normalisation process, the reciprocal value was used, resulting in ratio scales with monotonically increasing directions, i.e. the lower the respective cost for a particular alternative, the greater the score on the ratio scale (i.e. more favourable), and the higher the cost, the lower the score. Similar to the IRR, sub-criteria DECP I-III are also all moving in a monotonically increasing direction, i.e. the more employment is created, the more favourable the particular alternative is and the greater the score. The original results for the environmental impact criteria are monotonically decreasing, i.e. the lower the particular impact, the more preferable a particular alternative. Thus, similar to the cost- related criteria, by using the reciprocal values, the resulting ratio scales increase monotonically, favouring those alternatives with the least environmental impact. Table 52 shows the normalised, but unweighted performances/scores of the LBSs for biomass procurement area (BPA) I (refer also to Annexures 53-60). Figure 63, below, shows the normalised (to sum one) and aggregated, but unweighted scores for the set of 37 LBSs for biomass procurement area I. The ratio scale sums 100 and is subdivided into percentages according to the weighted scores of the LBSs. With a share of 3.76 percent, LBS 37 attains the highest aggregated, unweighted score, followed by LBS 29 (3.73%) and LBS 21 (3.71%), while LBSs 3, 12 and 11 show the weakest performances with 1.97, 1.87 and 1.84 percent respectively. Around 60% of LBS 37?s performance can be explained by its relatively low environmental impact (refer to green colour shades). Similar profiles are shared by top-ranked LBSs 5, 13, 21 and 29, which employ ? along with LBS 37 ? the same biomass upgrading and conversion technology. All of them use only bio-oil to generate electricity, while the by-product bio-char is used as a soil additive, which effectively acts as a carbon sink. This effect is covered by the ?Least global warming potential? criterion and contributes around one third of the aggregated unweighted score of the top-five-ranked LBSs. In comparison, the GWP score for LBS 23, which is ranked sixth in BPA I, is around 32 times lower than that of top-ranked LBS 37. Another major factor contributing to the overall performance of the top-five-ranked LBSs is their DECP III, with a relative share of 13-15 percent. Stellenbosch University http://scholar.sun.ac.za 198 Table 52: Normalised to sum one, but unweighted scores for BPA I LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (25 years) Cost of conversion technology Cost other than conversion technology Direct employment creation potential Local impact Global impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (25 yrs) OPEX (25 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 0.35% 0.31% 0.31% 0.23% 0.24% 0.09% 0.10% 0.00% 0.26% 0.18% 0.25% 0.25% 0.05% 2.63% 2 0.37% 0.40% 0.42% 0.29% 0.27% 0.10% 0.10% 0.00% 0.36% 0.12% 0.30% 0.27% 0.05% 3.05% 3 0.00% 0.13% 0.10% 0.11% 0.18% 0.23% 0.20% 0.29% 0.05% 0.31% 0.11% 0.19% 0.05% 1.97% 4 0.11% 0.01% 0.00% 0.16% 0.23% 0.27% 0.15% 0.29% 0.13% 0.26% 0.15% 0.25% 0.07% 2.09% 5 0.16% 0.00% 0.05% 0.04% 0.15% 0.38% 0.20% 0.49% 0.28% 0.17% 0.22% 0.20% 1.27% 3.60% 6 0.36% 0.31% 0.31% 0.25% 0.24% 0.03% 0.31% 0.10% 0.24% 0.18% 0.24% 0.21% 0.06% 2.83% 7 0.37% 0.40% 0.42% 0.30% 0.28% 0.04% 0.25% 0.10% 0.34% 0.12% 0.29% 0.24% 0.06% 3.20% 8 0.01% 0.13% 0.10% 0.16% 0.19% 0.13% 0.46% 0.39% 0.01% 0.31% 0.09% 0.13% 0.07% 2.19% 9 0.25% 0.31% 0.31% 0.17% 0.13% 0.35% 0.00% 0.00% 0.26% 0.18% 0.18% 0.17% 0.06% 2.37% 10 0.25% 0.40% 0.42% 0.25% 0.18% 0.32% 0.00% 0.00% 0.35% 0.12% 0.24% 0.20% 0.06% 2.79% 11 0.00% 0.13% 0.10% 0.11% 0.06% 0.59% 0.05% 0.29% 0.04% 0.32% 0.01% 0.07% 0.06% 1.84% 12 0.04% 0.01% 0.00% 0.15% 0.12% 0.58% 0.00% 0.29% 0.12% 0.27% 0.07% 0.14% 0.07% 1.87% 13 0.08% 0.00% 0.05% 0.00% 0.00% 0.79% 0.05% 0.49% 0.27% 0.17% 0.11% 0.06% 1.27% 3.35% 14 0.27% 0.31% 0.31% 0.22% 0.17% 0.27% 0.20% 0.10% 0.23% 0.18% 0.17% 0.12% 0.06% 2.61% 15 0.27% 0.40% 0.42% 0.27% 0.22% 0.24% 0.15% 0.10% 0.33% 0.12% 0.23% 0.16% 0.06% 2.97% 16 0.00% 0.13% 0.10% 0.13% 0.11% 0.48% 0.31% 0.39% 0.00% 0.32% 0.00% 0.00% 0.07% 2.04% 17 0.35% 0.31% 0.31% 0.21% 0.24% 0.07% 0.20% 0.00% 0.26% 0.18% 0.28% 0.27% 0.02% 2.71% 18 0.36% 0.40% 0.42% 0.27% 0.27% 0.08% 0.20% 0.00% 0.36% 0.12% 0.32% 0.29% 0.03% 3.12% 19 0.00% 0.13% 0.10% 0.08% 0.18% 0.20% 0.36% 0.29% 0.05% 0.31% 0.15% 0.22% 0.01% 2.09% 20 0.10% 0.01% 0.00% 0.14% 0.23% 0.24% 0.25% 0.29% 0.13% 0.26% 0.19% 0.27% 0.03% 2.16% 21 0.15% 0.00% 0.05% 0.01% 0.15% 0.34% 0.36% 0.49% 0.28% 0.17% 0.27% 0.23% 1.22% 3.71% 22 0.35% 0.31% 0.31% 0.23% 0.23% 0.00% 0.41% 0.10% 0.24% 0.18% 0.27% 0.23% 0.03% 2.90% 23 0.37% 0.40% 0.42% 0.28% 0.27% 0.02% 0.36% 0.10% 0.34% 0.12% 0.32% 0.25% 0.04% 3.27% 24 0.00% 0.13% 0.10% 0.12% 0.19% 0.10% 0.61% 0.39% 0.01% 0.31% 0.14% 0.16% 0.03% 2.29% 25 0.38% 0.31% 0.31% 0.27% 0.25% 0.10% 0.10% 0.00% 0.26% 0.18% 0.27% 0.25% 0.01% 2.71% 26 0.39% 0.40% 0.42% 0.32% 0.29% 0.10% 0.10% 0.00% 0.36% 0.12% 0.32% 0.28% 0.02% 3.12% 27 0.04% 0.13% 0.10% 0.20% 0.20% 0.23% 0.20% 0.29% 0.04% 0.32% 0.14% 0.19% 0.00% 2.10% 28 0.13% 0.01% 0.00% 0.25% 0.25% 0.27% 0.15% 0.29% 0.12% 0.27% 0.19% 0.25% 0.02% 2.20% 29 0.19% 0.00% 0.05% 0.13% 0.17% 0.38% 0.20% 0.49% 0.28% 0.17% 0.26% 0.20% 1.21% 3.73% 30 0.38% 0.31% 0.31% 0.30% 0.25% 0.03% 0.31% 0.10% 0.23% 0.18% 0.26% 0.21% 0.03% 2.90% 31 0.40% 0.40% 0.42% 0.33% 0.29% 0.04% 0.25% 0.10% 0.33% 0.12% 0.31% 0.24% 0.03% 3.26% 32 0.05% 0.13% 0.10% 0.23% 0.21% 0.13% 0.46% 0.39% 0.00% 0.32% 0.13% 0.13% 0.02% 2.30% 33 0.38% 0.31% 0.31% 0.33% 0.27% 0.00% 0.10% 0.00% 0.27% 0.18% 0.27% 0.28% 0.06% 2.76% 34 0.40% 0.40% 0.42% 0.37% 0.30% 0.01% 0.05% 0.00% 0.36% 0.12% 0.32% 0.30% 0.06% 3.11% 35 0.05% 0.13% 0.10% 0.28% 0.22% 0.09% 0.20% 0.29% 0.06% 0.31% 0.15% 0.24% 0.06% 2.18% 36 0.14% 0.01% 0.00% 0.30% 0.26% 0.15% 0.10% 0.29% 0.13% 0.26% 0.19% 0.30% 0.08% 2.22% 37 0.19% 0.00% 0.05% 0.20% 0.19% 0.22% 0.15% 0.49% 0.29% 0.16% 0.27% 0.26% 1.28% 3.76% Sum: 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 100.00% Stellenbosch University http://scholar.sun.ac.za 199 Figure 63: Aggregated, unweighted scores of LBSs for BPA I For LBS key, refer to Figure 11 Notes: IRR Internal rate of return on capital invested CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential As mentioned above, the first LBS not employing bioenergy conversion system (BCS) V is sixth- ranked LBS 23, which is characterised by its relatively strong financial-economic performance, contributing more than 50 percent to its overall performance, while almost 15% of its overall performance is contributed by the socio-economic potential criteria, and the remainder by the environmental impact criteria. Nearly 50 percent of the aggregated scores of bottom-ranked LBSs 11 and 12 are accounted for by the socio-economic criteria, followed by around 30 percent for the environmental impact criteria and around 20 percent for the financial-economic criteria. 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BPA I IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 200 Table 53: Ranking of LBSs based on unweighted scores Ranking BPA I BPA II BPA III BPA IV LBS AUS LBS AUS LBS AUS LBS AUS 1 37 3.889% 37 3.850% 37 3.854% 29 3.955% 2 21 3.833% 29 3.818% 21 3.823% 37 3.953% 3 29 3.831% 21 3.814% 29 3.819% 21 3.948% 4 5 3.724% 5 3.655% 5 3.647% 5 3.737% 5 23 3.525% 13 3.512% 13 3.461% 13 3.588% 6 31 3.507% 23 3.362% 31 3.377% 23 3.363% 7 7 3.463% 31 3.339% 23 3.350% 31 3.331% 8 13 3.453% 18 3.330% 26 3.317% 18 3.310% 9 18 3.381% 34 3.330% 34 3.302% 34 3.298% 10 34 3.370% 26 3.309% 18 3.291% 26 3.278% 11 26 3.365% 7 3.289% 7 3.284% 7 3.268% 12 2 3.313% 2 3.255% 2 3.224% 2 3.214% 13 15 3.220% 15 3.133% 15 3.104% 15 3.101% 14 10 3.037% 10 3.077% 30 3.033% 10 3.040% 15 22 2.975% 30 2.875% 10 3.026% 22 2.852% 16 30 2.967% 33 2.837% 22 2.910% 30 2.837% 17 6 2.906% 22 2.823% 17 2.794% 33 2.764% 18 33 2.850% 25 2.785% 6 2.782% 6 2.756% 19 17 2.788% 6 2.764% 25 2.778% 17 2.752% 20 25 2.780% 17 2.733% 33 2.774% 25 2.737% 21 1 2.717% 1 2.676% 1 2.668% 1 2.653% 22 14 2.680% 14 2.628% 14 2.613% 14 2.617% 23 9 2.441% 9 2.520% 9 2.482% 9 2.498% 24 36 2.071% 36 2.203% 36 2.162% 36 2.157% 25 28 2.032% 28 2.138% 20 2.120% 28 2.099% 26 20 2.000% 20 2.098% 28 2.094% 35 2.072% 27 24 1.992% 35 2.059% 32 2.080% 20 2.055% 28 32 1.987% 4 2.041% 35 2.044% 24 2.017% 29 4 1.932% 24 1.987% 24 2.037% 32 2.007% 30 8 1.893% 32 1.978% 4 1.993% 4 1.974% 31 35 1.887% 27 1.926% 27 1.960% 27 1.942% 32 27 1.789% 12 1.903% 19 1.942% 19 1.910% 33 19 1.787% 19 1.902% 8 1.913% 12 1.896% 34 16 1.721% 8 1.873% 12 1.822% 8 1.873% 35 12 1.698% 3 1.790% 3 1.761% 3 1.770% 36 3 1.673% 16 1.739% 16 1.727% 16 1.732% 37 11 1.523% 11 1.652% 11 1.631% 11 1.647% Notes: LBS Lignocellulosic bioenergy system BPA Biomass procurement area AUS Aggregated unweighted scores For LBS key, refer to Figure 11 Stellenbosch University http://scholar.sun.ac.za 201 The ranking of the LBSs in terms of normalised, but unweighted scores for each biomass procurement area is presented in Table 53. For all of the biomass procurement areas, LBSs 5, 13, 21, 29 and 37 are among the top-six-performing alternatives, exhibiting similar performances/score profiles to BPA I. In comparison, the other LBSs exhibit similar performance profiles for BPAs II- IV, as illustrated by similar patterns which can be found in Annexure 62-64, while the related data for the unweighted scores of the LBSs is presented in Annexure 59-61. 7.4.3 Discussion on thresholds In MCDA, thresholds often play an important role in assessing the performances of alternatives, using quantitative data. They can give ranges for which alternatives are perceived to be the same (indifference thresholds) or for which alternatives definitively outrank each other (preference thresholds ? refer in this regard to Mendoza and Martins (2006: 12)). However, while thresholds are appealing due to their intuitive logic, precise thresholds often do not reflect the whole ?truth? of a complex system, given that they attempt to quantify what are often ?fuzzy? perceptions on the part of the human decision makers involved, and therefore, they might create false certainties. The legitimacy of thresholds also depends on who sets them in first place. Thresholds can also be applied to eliminate the risk of the absolute substitution of one criterion with another. For instance, an alternative may outrank all the other competing ones because it is by far the most cost-efficient, but at the same time, it may create prohibitively large negative impacts on the environment. Thus, thresholds could be used to render alternatives invalid once a threshold for one criterion is violated (Buchholz et al., 2009: 490). In this study, for instance, by assuming the prerequisite of profitability, a threshold could be an IRR of greater than zero. Some of the LBSs exhibit IRRs of less than zero (refer to Figure 51 or Annexure 45), indicating non-profitability. These LBSs would have been excluded if IRR thresholds of greater than zero had been applied. However, they do show relatively high direct employment creation potentials (refer to section 5.4.1), resulting in a trade-off between IRR and DECP. While a private investor may be particularly interested in the profitability of the project, a public investor may be more interested in employment creation and may be willing to compensate for the resulting opportunity cost. Thus, no thresholds are applied in this study. 7.4.4 Expert panel workshop With the LCA results normalised into scores, the following step deals with the weighing of the criteria to elicit the relative preferences of the decision makers. In this study, a group of experts from various backgrounds was tasked with attaching weights to the predefined criteria during a Stellenbosch University http://scholar.sun.ac.za 202 workshop aimed at supporting the public decision makers in the CWDM. The workshop was held at the Department of Agricultural Economics at Stellenbosch University on 1 September 2011. Aimed at attaining a somewhat representative mix in terms of the selected criteria, the task group assembled members of the CWDM and representatives from the Department of Agriculture of the Western Cape, the banking sector, the farming community, environmental protection institutions, and resource and energy experts. A list of the names and respective backgrounds of the participants is given in Annexure 65. After familiarising participants with the background and aim of the study, the expert panel was introduced to the set of LBSs, their components, and the predefined criteria. Further, the original results, as well as the unweighted scores/performances of the LBSs for each of the biomass procurement areas (described in the previous section) were presented to the expert panel, serving as a starting point for the ?plenary session?, where discussion and assessment of the various alternatives led to ? after consensus had been reached ? the weighting of the criteria, resulting in a ranking of the LBSs in a consistent and transparent manner. The AHP-based Expert Choice software was used to establish the relative preferences in terms of pairwise comparisons, using a semantic scale for the weighting procedure (refer to Table 51). Prior to the workshop, the hierarchy value tree and the unweighted LBS scores were loaded into the Expert Choice software, allowing the LBSs to be ranked ? viewable in simple static visual displays ? immediately after the weighting procedure had been completed. This provided a powerful vehicle for reflecting back to the decision makers the information they had provided, the judgements they had made, and an initial attempt at synthesising these, giving direct feedback and testing of the decision makers? judgements. 7.4.5 Expert panel workshop ? outcome The assessment was conducted using the bottom-up approach, i.e. by assessing the relative weights within each criteria group to derive the vector indicating the contribution of the criterion relative to the group of criteria, which was then aggregated to the higher level. No serious conflicts in opinion between the participants were recorded during the discussions and the weighting procedure, resulting in consensus on a set of weights, which is presented in Table 54, below. Relative weights are assessed within families of criteria, i.e. the weights of criteria sharing the same parent are aggregated and normalised to sum 100. The cumulative weight of a criterion is the product of its relative weight compared with its siblings and the relative weight of its parent, parent?s parent, and so on to the top of the tree. The cumulative weights of all bottom-level criteria, by definition, sum Stellenbosch University http://scholar.sun.ac.za 203 100. The cumulative weight of a parent criterion is the total of the cumulative weights of its descendents. Table 54: Outcome of weighting procedure Goal Main criteria RW (%) Sub-criteria ? level 1 RW (%) Sub-criteria ? level 2 RW (%) CW (%) Identifying the most viable/ sustainable for the CWDM Financial- economic viability 59.36 Best IRR 72.48 - - 43.03 Lowest cost of conversion technology 15.04 Lowest CAPEX-conv. 75.00 6.70 Lowest OPEX-conv. 25.00 2.23 Lowest cost other than conversion technology 12.48 Lowest CAPEX-other 16.67 1.24 Lowest OPEX-other 83.33 6.17 Socio- economic potential 24.93 Best DECP I 73.06 - - 18.22 Best DECP II 18.84 - - 4.50 Best DECP III 8.10 - - 2.02 Environ- mental impact 15.71 Least local impact 83.33 Least AP 25.83 3.38 Least EP 63.70 8.34 Least POCP 10.47 1.37 Least global impact 16.67 Least ADP 85.71 2.24 Least GWP 14.29 0.37 Notes: IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential CW Cumulative weight expressed in percent RW Relative weight expressed in percent In terms of the findings of the expert group, the most important main criterion is ?Financial- economic viability?, representing almost 60 percent in relative weight ? more than twice as much as the second-ranked main criterion ?Socio-economic impact? (25%) and nearly four times as much as the third-ranked main criterion ?Least environmental impact? (16%). ?Best internal rate of return on capital investment? is the single most important sub-criterion with a cumulative weight of 43 percent. The experts justified this with the prerequisite of financial viability, i.e. if the LBS was not Stellenbosch University http://scholar.sun.ac.za 204 profitable, private investors would not be interested in investing in this particular alternative. However, it was also noted that from a private investor?s perspective, LBSs that are characterised by greater socio-economic potential but are less profitable could also be considered if private investors were compensated for their loss in profitability, in order to support socio-economic improvements. The relative weight of 75 percent for ?CAPEX-conv.? can be explained in terms of the risk for investors, as substantial financial resources are required initially for the setup of a conversion plant, which are expected to be carried by a single or a few investors such as banks or co-operatives. On the other hand, the costs other than for the upgrading and conversion technologies, and the related risks are carried by many stakeholders, such as land owners, farmers and contractors. Furthermore, given that the capital costs were proportionally lower, while the operational costs were proportionally higher, the experts allocated a relative weight of 83 percent to the ?Least OPEX-other? and 17 percent to ?Least CAPEX-other?. The second-ranked sub-criterion, based on its cumulative weight after IRR, representing more than 18 percent, is ?Direct employment creation potential I?. This translates into 73 percent in relative weight of the socio-economic potential main criterion, while ?Best DECP II? and ?Best DECP III? account for 18 and 8 percent respectively of this main criterion in relative weight. The relatively low weight for the main criterion ?environmental impact? can be explained by the perception of the experts that any of the given LBSs would represent an improvement compared with the current South African energy mix in terms of environmental impact. Particularly the cumulative weight of less than one percent for ?Least GWP? can be explained by this thinking, as more than 90% of the current energy mix is based on using coal as an energy source. Another reason for ?Least GWP? receiving a relatively low ranking was the financial compensation of the cleaner development mechanism (CDM, the so-called ?carbon credits?). Thus, those LBSs that do well in terms of GWP also benefit in financial-economic terms. Given the local character of the LBSs, great emphasis was given to the local environmental impact criteria, which account for more than 80 percent of the environmental impact main criterion. 7.4.6 Synthesising of information ? results The step following the weighting exercise encompassed multiplying the set of weights with the relative performance of each LBS against the said criteria to present a weighted score for the said LBS against the particular criteria. The aggregated final score for all criteria for each alternative facilitated a ranking of the LBSs. The detailed results thereof for each respective biomass procurement area can be found in Annexure 66-69. Stellenbosch University http://scholar.sun.ac.za 205 Figure 64: Aggregated, weighted scores of LBSs in BPA I For LBS key, refer to Figure 11 Notes: IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential Figure 64, above, shows the aggregated, weighted scores for the 37 LBSs in BPA I, suggesting that LBS 26 may be recommended to the public decision makers of the CWDM. LBS 26 exhibits the highest overall ranking on the ratio scale, with 3.94%; followed by LBS 31, with 3.91%; and by LBS 2, with 3.80%. The direct gasification of biomass (Bioenergy Conversion System II, refer to section 5.8.5) is common to the top-eight-ranked LBSs, which differ only in terms of the harvesting system they use. LBS 26 deploys a feller-buncher for harvesting and a forwarder for the primary transportation of the biomass to the roadside (harvesting system III, refer to section 5.3.3). There, 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BPA I IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 206 the biomass is comminuted (section 5.4.1) and then transported in containers to the central conversion unit. The bottom-ranked seven LBSs (LBSs 3, 8, 19, 24, 27, 32 and 35) each have an aggregated weighted score of less than 1.40 percent and have BCS III as the bioenergy system common to all of them, namely centralised pyrolysis combined with a combustion (boiler-steam-turbine) system (refer to section 5.8.6). The full ranking of the LBSs based on the weighted scores for all biomass procurement areas are to be found in Table 55, below. Almost 56 percent of LBS 26?s aggregated weighted score is accounted for by its internal rate of return, while the other financial-economic criteria contribute another 20% to its overall result. Thus, the financial-economic main criteria account for a total of around 75% of LBS 26?s aggregated weighted scores. Accordingly, it exhibits weighted scores in terms of its socio-economic potential and environmental impact criteria of 8 and 18 percent respectively. Similar profiles in terms of weighted scores were given to the second- to seventh-ranked LBSs, all having BCS II in common (refer to section 5.8.5). LBS 37, which performed best in terms of aggregated, unweighted scores, was ranked 23rd (2.63%) after the weighting of the scores, showing a distribution of scores for the main criteria ?Financial- economic viability?, ?Socio-economic potential? and ?Environmental impact? of 48, 28 and 24 percent respectively. In comparison, top-ranked LBS 26 in terms of weighted scores is ranked 11th in terms of unweighted scores, exhibiting a distribution of scores for the main criteria ?Financial- economic viability?, ?Socio-economic potential? and ?Least environmental impact? of 54, 6 and 40 percent respectively. A similar outcome as for biomass procurement area I can be seen in Figure 65, which illustrates the aggregated, weighted scores of the 37 LBSs for BPA II. Again, LBS 26 exhibits the best weighted performance, with a score of 3.75 percent, with more than 70 percent of its aggregated score being attributable to the financial-economic criteria. The socio-economic criteria contribute another 9 percent, while the environmental criteria contribute almost 20 percent. The single criterion contributing the most to LBS 26?s overall result is ?Best IRR?, accounting for nearly 53 percent. LBSs 2, 18 and 31 have similar weighted-score profiles, and perform similarly, with scores of 3.63, 3.62 and 3.59 percent respectively. Similar to BPA I, the top ten are dominated by alternatives that deploy the same biomass upgrading and conversion system, BCS II. Stellenbosch University http://scholar.sun.ac.za 207 Table 55: Ranking of LBSs based on experts? weighted scores Ranking BPA I BPA II BPA III BPA IV LBS AWS LBS AWS LBS AWS LBS AWS 1 26 3.935% 26 3.745% 13 3.592% 13 3.568% 2 31 3.912% 2 3.627% 26 3.507% 26 3.494% 3 2 3.800% 18 3.616% 31 3.414% 18 3.420% 4 7 3.785% 31 3.595% 2 3.404% 2 3.420% 5 34 3.776% 10 3.545% 10 3.386% 31 3.396% 6 18 3.770% 34 3.542% 18 3.379% 29 3.361% 7 23 3.752% 7 3.483% 15 3.337% 10 3.353% 8 15 3.542% 23 3.471% 7 3.312% 7 3.327% 9 25 3.513% 15 3.458% 34 3.303% 23 3.322% 10 30 3.493% 25 3.337% 23 3.287% 34 3.313% 11 10 3.478% 9 3.240% 29 3.275% 15 3.285% 12 1 3.375% 30 3.212% 25 3.184% 21 3.220% 13 6 3.360% 13 3.206% 9 3.172% 5 3.191% 14 33 3.359% 1 3.185% 30 3.156% 25 3.181% 15 17 3.353% 14 3.169% 14 3.139% 9 3.150% 16 22 3.333% 17 3.161% 21 3.119% 1 3.104% 17 14 3.269% 33 3.107% 5 3.118% 17 3.102% 18 9 3.205% 6 3.067% 17 3.075% 14 3.099% 19 13 3.030% 22 3.049% 1 3.070% 30 3.090% 20 29 2.952% 29 2.954% 22 3.012% 6 3.018% 21 5 2.773% 21 2.793% 6 3.000% 22 3.010% 22 21 2.745% 5 2.769% 33 2.941% 37 2.975% 23 37 2.631% 37 2.551% 37 2.904% 33 2.968% 24 28 2.069% 12 2.327% 12 2.393% 12 2.380% 25 12 2.042% 28 2.166% 28 2.193% 28 2.216% 26 4 1.918% 4 2.049% 4 2.100% 4 2.119% 27 20 1.881% 20 2.020% 20 2.091% 20 2.086% 28 36 1.849% 36 1.902% 36 1.960% 36 1.982% 29 11 1.776% 11 1.833% 11 1.886% 11 1.840% 30 16 1.674% 27 1.809% 16 1.865% 27 1.801% 31 27 1.399% 16 1.786% 27 1.840% 16 1.797% 32 32 1.378% 32 1.633% 32 1.733% 32 1.662% 33 35 1.223% 3 1.579% 3 1.606% 3 1.594% 34 19 1.193% 35 1.566% 35 1.605% 35 1.594% 35 3 1.173% 19 1.555% 19 1.596% 19 1.565% 36 8 1.166% 8 1.455% 8 1.542% 8 1.508% 37 24 1.116% 24 1.440% 24 1.505% 24 1.489% Notes: LBS Lignocellulosic bioenergy system BPA Biomass procurement area AWS Aggregated weighted scores For LBS key, refer to Figure 11 Stellenbosch University http://scholar.sun.ac.za 208 Figure 65: Aggregated, weighted scores of LBSs in BPA II For LBS key, refer to Figure 11 Notes: IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential With a score of 1.44%, lowest-ranked LBS 24 is more than 2.5 times less favourable than highest- ranked LBS 26. While LBS 24 performs relatively weakly in terms of the environmental impact criteria, which account for only seven percent of its overall performance, its socio-economic performance contributes 44 percent, and its financial-economic weighted scores contribute the remaining 50 percent to its overall result. Bioenergy conversion system III (refer to section 5.8.6) is common to all bottom-nine-ranked bioenergy system alternatives, including LBS 24. 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BPA II IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 209 Compared with the former two biomass procurement areas, for which LBS 26 is ranked top, the weighting of the scores for BPA III resulted in LBS 13 being the top-ranked alternative, as illustrated in Figure 66. With a weighted score of 3.59%, LBS 13 leads the ranking, followed by LBS 26 (3.51%), LBS 31 (3.41%) and LBS 2 (3.40%). Similar to biomass procurement area II, nine of the top-ten-ranked lignocellulosic bioenergy systems in BPA III deploy bioenergy conversion system II. Overall, 58 percent of LBS 13?s aggregated weighted score is accounted for by its socio-economic performance, while ?Financial-economic viability? and ?Least environmental impact? each contribute 21 percent to its overall score. A very different profile of the aggregated, weighted scores characterises second-ranked LBS 26, with 72% being contributed by its financial-economic viability, 10% by its socio-economic potential, and the remaining 18% by its performance in terms of the ?Least environmental impact? main criterion. This difference in profile between the first- and second-ranked LBSs becomes more apparent when comparing their respective absolute values in terms of ?IRR? and ?DECP I?. LBS 13 has an IRR of 1.76%, less than 6 times the IRR of LBS 26 (10.96%) (refer to Chapter 6). Yet, LBS 13 has a direct employment creation potential for category I of 403, compared with LBS 26?s 118, with this criterion contributing nearly 54 percent to the overall score for LBS 13 and only 6.6 percent to the overall result of second-ranked LBS 26. This can be explained, inter alia, by the relatively lower mean annual increments (MAI) of BPAs III and IV compared with the previously discussed biomass procurement areas (refer to section 4.3.2), resulting in more land being required to ensure its bioenergy feedstock supply and, thus, in more labour being required to establish and maintain the SRC plantations prior to harvesting. In addition to this, LBS 13?s labour-intensive motor-manual harvesting and manual loading and unloading of the logs used to generate bioenergy (refer to Harvesting System II, section 5.3.2) leads to a considerably higher employment potential, particularly for ?DECP I?. As illustrated in Figure 67, BPA IV?s LBS with the highest aggregated, weighted scores is LBS 13 (3.57%), followed by LBS 26 (3.49%), LBS 18 and LBS 2 (both with 3.42%). Similar to the previous biomass procurement area, the dominant main criterion is socio-economic potential, contributing nearly 53% to LBS 13?s overall result, with financial-economic viability and least environmental impact adding 21 and 22 percent respectively. Again, LBS 13 scores highest in terms of ?DECP I?, which contributes nearly 53 percent to LBS 13?s aggregated result. Of its overall, aggregated weighted score, 21% is determined by its financial-economic performance, 57% by its socio-economic performance, and 22% by its environmental impact performance. Second-ranked LBS 26 exhibits a considerably different Stellenbosch University http://scholar.sun.ac.za 210 profile, with 73% of its overall score being contributed by its financial-economic performance and the weighted scores for the socio-economic criteria and environmental impact criteria contributing 9% and 18% respectively. As with the other biomass procurement areas, with LBSs 2, 7, 10, 18, 23, 26, 31and 34 in the top-ten, BPA IV is dominated by alternatives deploying bioenergy conversion system II. Figure 66: Aggregated, weighted scores of LBSs in BPA III For LBS key, refer to Figure 11 Notes: IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BPA III IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 211 With aggregated scores of less than 1.84 percent, LBSs 3, 8, 11, 16, 19, 24, 27, 32 and 35 are least favourable, all having BCS III in common and featuring relatively weak performances in terms of financial-economic and environmental impacts. Figure 67: Aggregated, weighted scores of LBSs in BPA IV For LBS key, refer to Figure 11 Notes: IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential 7.5 Maximisation of main criteria/sensitivity analysis Although the expert group reached consensus on a set of weights for the selected criteria and resulting trade-offs, after having engaged in a discussion on the LBSs and their compositions, the 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BDA IV IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 212 final decision makers and other stakeholders may have differing interests and goals, which may result in a preference for a different set of weights. This section investigates the impact of maximising the relative weight of a particular main criterion, i.e. by omitting the other two main criteria, assuming that there is general agreement among the stakeholders on the relative weights for the sub-criteria provided by the expert group. The new rankings of the LBSs, based on the maximised main criterion, are then compared against the ?original? rankings of the LBSs, based on the set of weights provided by the expert group, taking all of the main criteria into account (refer to section 7.4.5). 7.5.1 Maximisation of financial-economic main criterion Although the expert group had already emphasised the importance of the financial-economic viability by allocating nearly 60% to this main criterion, a slightly different ranking results when maximising the relative weight of the financial-economic main criterion. Figure 68, below, shows the aggregated, weighted scores for the LBSs (refer also to Annexure 71), assuming a relative weight of 100% for the financial-economic main criterion, while retaining the relative weights of the sub-criteria (IRR: 72%; CAPEX-conv.: 11%; OPEX-conv.: 4%; CAPEX-other: 2%; OPEX- other: 10%). In terms of the maximised financial-economic main criterion, LBS 34 performs best across all four biomass procurement areas, followed by LBSs 31 and 26, while LBSs 11, 16 and 19 show the least favourable results (refer also to Annexure 74). In comparison, based on the ?original? ranking, LBS 34 comes 5th, 6th 9th and 10th in BPAs I-IV respectively, with LBS 26 and LBS 13 ranking top in BPAs I-II and II-IV respectively (refer to Table 55). Thus, in the case of BPAs I and II, if the final decision maker were to follow the expert group?s recommendation in selecting LBS 26 instead of LBS 34, the result would be lower efficiency in terms of financial-economic viability, greater environmental impact, and improved socio-economic potential, as is shown in Table 56. For instance, selecting LBS 26 would result in a relatively lower IRR of between one and two percent, depending on the biomass procurement area concerned. Taking the net present value (NPV) of the LBSs into consideration (refer to Annexure 47), selecting LBS 26 instead of LBS 34 would result in a loss of profitability of around R16 million over a period of 25 years in the case of BPA I (R331m vs R347m), and over 27 years in the case of BPA II (R380m vs R396m). While CAPEX-conv. and OPEX-conv. would remain the same, since both alternatives deploy the same biomass upgrading and conversion systems, LBS 26?s CAPEX-other is 20 and 66 percent higher than LBS 34?s CAPEX-other (for BPAs I and II respectively), and its OPEX-other is six and seven percent higher. Also, from an environmental impact point of view, the Stellenbosch University http://scholar.sun.ac.za 213 selection of LBS 26 instead of LBS 34 may have a negative effect, with an increased acidification and eutrophication potential of up to five percent, an increased photochemical ozone creation potential of around two percent, and an abiotic depletion potential of 23 to 35 percent. In BPA?s I and II, LBS 34 exhibits a global warming potential of around zero and 214 tonnes respectively of CO2-equivalent, compared with LBS 26?s global warming potential of 1 048 and 1 596 t CO2-equiv. From a socio-economic perspective, however, selecting LBS 26 would be favourable, creating 30 to 38 more jobs in terms of ?DECP I?, with one more jobs in terms of ?DECP II?, and the same number of jobs created for ?DECP III?. Figure 68: Aggregated weighted scores of LBSs, considering only financial-economic criteria For LBS key, refer to Figure 11 LBS 13, on the other hand, is positioned at the top of the ?original? rankings for BPAs III and IV, but when taking only the financial-economic criteria into account, it is positioned 33rd and 32nd respectively. With an IRR of 1.36% for BPA III and 1.42% for BPA IV, LBS 13 is around six to seven times less profitable than LBS 34?s 10.13% for BPA III and 8.25% for BPA IV. In absolute terms, for BPAs III and IV respectively, LBS 13 exhibits an NPV of minus R28 million and minus R26 million, compared with LBS 34?s NPV of R322 million and R291 million. LBS 13?s significantly lower profitability can be explained by its considerably higher costs. Its CAPEX- and 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 214 OPEX-conv., for instance, are more than six and four times respectively greater than the respective values for LBS 34. Also, LBS 13?s CAPEX- and OPEX-other are at least 2.5 times greater than those of LBS 34. Table 56: Comparison, top-ranked LBSs ? complete set of weights vs solely financial-economic criteria Biomass procurement area I II III IV I II III IV Ranking of LBSs based on: Complete set of weights Solely financial-economic criteria LBS 26 26 13 13 34 Weighted score ? complete set of weightsa 3.94 3.75 3.59 3.57 3.78 3.54 3.30 3.31 Position after ranking 1 5 6 9 10 Weighted score ? solely financial-economic criteriab 4.95 4.59 1.25 1.24 5.05 4.65 4.35 4.37 Position after ranking 3 2 33 32 1 Absolute and relative comparison based on selected criteria IRR (%)c 10.96 15.15 1.36 1.42 11.18 15.26 10.13 8.25 Loss/gain (%) 98% 99% 13% 17% 100% NPV (R million)d 331 380 -28 -26 347 396 322 291 Loss/gain (%) 95% 96% -9% -9% 100% CAPEX-conv. (R million)e 63 63 398 398 63 63 63 63 Loss/gain (%) 100% 100% 632% 632% 100% OPEX-conv. (R million)f 66 66 281 281 66 66 66 66 Loss/gain (%) 100% 100% 423% 423% 100% CAPEX-other (R million)g 138 59 113 66 115 35 42 22 Loss/gain (%) 120% 166% 272% 300% 100% OPEX-other (R million)h 231 246 736 750 218 230 279 291 Loss/gain (%) 106% 107% 263% 258% 100% DECP Ii 95 107 429 403 65 69 81 81 Loss/gain (%) 146% 155% 530% 498% 100% DECP IIi 5 4 3 3 4 3 3 3 Loss/gain (%) 125% 133% 100% 100% 100% DECP IIIi 2 2 7 7 2 2 2 2 Loss/gain (%) 100% 100% 350% 350% 100% AP (t SO2-equivalent) j 86 86 126 124 83 82 86 83 Loss/gain (%) 104% 105% 152% 151% 100% EP (t phosphate-equivalent)k 22 22 32 31 21 21 21 21 Loss/gain (%) 105% 105% 152% 148% 100% POCP (t ethene-equivalent)l 5.3 5.2 19.1 17.3 5.2 5.1 5.1 5.2 Loss/gain (%) 102% 102% 375% 333% 100% ADP fossil (gigajoule)m 20.1 19.4 69.9 62.6 16.3 14.3 14.8 14.6 Loss/gain (%) 123% 135% 473% 430% 100% GWP (t CO2-equivalent) n 1 048 1 596 -32 709 -32 884 1 214 936 958 Loss/gain (%) 104 800% 746% -3 495% -3 433% 100% Notes: a Aggregated, normalised (to sum one), relative weighted score based on set of weights by expert group b Aggregated, normalised (to sum one), relative weighted score based on maximisation of financial- economic main criterion only c Refer also to Annexure 45 d Refer also to Annexure 47 e Refer also to Annexure 48 f Refer also to Annexure 49 g Refer also to Annexure 50 Stellenbosch University http://scholar.sun.ac.za 215 h Refer also to Annexure 52 i Refer also to Annexure 53 j Refer also to Annexure 40 k Refer also to Annexure 41 l Refer also to Annexure 44 m Refer also to Annexure 39 n Refer also to Annexure 42 IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential A similar comparison can be made in terms of environmental impact, with LBS 13 showing an AP and EP of around 50 percent greater than LBS 34. In terms of environmental impact, LBS 13?s POCP and ADP are at least three and four times respectively greater. Only for global warming potential does LBS 13 show a significantly better result than LBS 34, with a GWP of around minus 32 000 t CO2-equivalent for LBS 13, compared with a GWP of around 950 t CO2-equivalent for LBS 34. Again, this can be explained by BCS V?s carbon storage and sink potential, which involves adding the biochar to soil. From a socio-economic perspective, however, LBS 13 exhibits significantly better results than LBS 34. With LBS 13?s more than 400 jobs created compared with LBS 34?s approximately 80, LBS 13?s performance in terms of ?DECP I? is around five times greater, and 3.5 times more employment is created in terms of ?DECP III?, while the same number of jobs created is expected for ?DECP II?. 7.5.2 Maximisation of socio-economic main criterion If the LBSs were to be assessed solely in terms of their socio-economic potential, or more specifically, in terms of their direct employment creation potential, while still maintaining the relative weights of the sub-criteria (?DECP I?: 73%, ?DECP II?: 19% and ?DECP III?: 8%), the ranking of the LBSs would be considerably different compared with the ?original? ranking, as is illustrated in Figure 69 (refer also to Annexure 72). Across all biomass procurement areas, LBS 13 leads the ranking, with relative scores of between 8.16 and 8.39 percent, followed by LBSs 11 (6.08-6.44%) and 12 (5.82-5.97%). These LBSs all entail motor-manual harvesting together with Stellenbosch University http://scholar.sun.ac.za 216 the manual loading and unloading of logs, as well as the manual feeding of the mobile chipping and pyrolysis units. LBSs 33 and 34, which are characterised by the relatively high levels of mechanisation of their harvesting systems, are bottom-ranked, showing the lowest weighted scores in terms of socio-economic potential (refer also to Annexure 75). LBS 26, the top-ranked LBS in terms of the expert group?s set of weights, is ranked 26 and 28 in BPAs I and II respectively. No comparison is required for BPA?s III and IV, since LBS 13 also leads the ranking in these areas, based on the full set of weighted scores. Table 57 compares LBS 26 and LBS 13 in terms of ranking, as well as in terms of the selected key criteria. Figure 69: Aggregated weighted scores of LBSs, considering only socio-economic criteria For LBS key, refer to Figure 11 LBS 26?s internal rate of return of 10.96% (BPA I) and 15.15% (BPA II), compared with LBS 13?s 1.76% and 3.54% respectively, make it considerably more profitable, which is further highlighted when comparing LBS 26?s NPVs for BPAs I and II of R331 and R380 million respectively, compared with LBS 13?s R2 and R116 million respectively. This is, inter alia, the result of the significantly lower capital required, as well as the lower operating costs. Furthermore, other than in respect of global warming potential, LBS 26 is more environmentally friendly than LBS 13, having a 30 percent lower AP and EP and around 60 to 70 percent lower POCP and ADP. Since LBS 13 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 217 uses only bio-oil to generate electricity, with the biochar being used as a soil additive, a considerably negative global warming potential of around 35 000 t CO2-equivalent is recorded for it, while LBS 26 has a GWP of 1 048 and 1 596 t CO2-equivalent. In terms of ?DECP I? and ?DECP III?, LBS 13 has a 3.5 times greater potential in BPAs I and II than LBS 26, while the latter has a 25% higher potential in terms of ?DECP II? than the former. Table 57: Comparison, top-ranked LBSs ? complete set of weights vs solely socio-economic criteria Biomass procurement area I II III IV I II III IV Ranking of LBSs based on: Complete set of weights Solely socio-economic criteria LBS 26 26 13 13 13 Weighted score ? complete set of weightsa 3.94 3.75 3.59 3.57 3.03 3.21 3.592 3.57 Position after ranking 1 19 13 1 1 Weighted score ? solely socio- economic criteriab 1.19 1.29 8.34 8.21 8.17 8.17 8.34 8.21 Position after ranking 26 28 1 1 1 Absolute and relative comparison based on selected criteria IRR (%)c 10.96 15.15 1.36 1.42 1.76 3.54 1.36 1.42 Loss/gain (%) 623% 428% 100% 100% 100% NPV (R million)d 331 380 -28 -26 2 116 -28 -26 Loss/gain (%) 15 168% 328% 100% 100% 100% CAPEX-conv. (R million)e 63 63 398 398 398 398 398 398 Loss/gain (%) 16% 16% 100% 100% 100% OPEX-conv. (R million)f 66 66 281 281 281 281 281 281 Loss/gain (%) 24% 24% 100% 100% 100% CAPEX-other (R million)g 138 59 113 66 289 96 113 66 Loss/gain (%) 48% 61% 100% 100% 100% OPEX-other (R million)h 231 246 736 750 527 604 736 750 Loss/gain (%) 44% 41% 100% 100% 100% DECP Ii 95 107 429 403 333 379 429 403 Loss/gain (%) 29% 28% 100% 100% 100% DECP IIi 5 4 3 3 4 3 3 3 Loss/gain (%) 125% 133% 100% 100% 100% DECP IIIi 2 2 7 7 7 7 7 7 Loss/gain (%) 29% 29% 100% 100% 100% AP (t SO2-equivalent) j 86 86 126 124 122 124 126 124 Loss/gain (%) 70% 69% 100% 100% 100% EP (t phosphate-equivalent)k 22 22 32 31 31 31 32 31 Loss/gain (%) 71% 71% 100% 100% 100% POCP (t Ethene-equivalent)l 5.3 5.2 19.1 17.3 16.2 17.6 19.1 17.3 Loss/gain (%) 33% 30% 100% 100% 100% ADP fossil (gigajoule)m 20.1 19.4 69.9 62.6 56.3 61.4 69.9 62.6 Loss/gain (%) 36% 32% 100% 100% 100% GWP (t CO2-equivalent) n 1 048 1 596 -32 709 -32 884 -36 178 -34 988 -32 709 -32 884 Loss/gain (%) -3% -5% 100% 100% 100% Notes: a Aggregated, normalised (to sum one), relative weighted score based on weights set by expert group b Aggregated, normalised (to sum one), relative weighted score based on maximisation of financial- economic main criterion only c Refer also to Annexure 45 d Refer also to Annexure 47 e Refer also to Annexure 48 Stellenbosch University http://scholar.sun.ac.za 218 f Refer also to Annexure 49 g Refer also to Annexure 50 h Refer also to Annexure 52 i Refer also to Annexure 53 j Refer also to Annexure 40 k Refer also to Annexure 41 l Refer also to Annexure 44 m Refer also to Annexure 39 n Refer also to Annexure 42 IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than of biomass upgrading and conversion technologies OPEX-other Operational expenditure other than of biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential 7.5.3 Maximisation of environmental impact main criterion The ranking of the LBSs changes, as with the other two main criteria, when assessing them solely against the third main criterion ?Least environmental impact? while maintaining the weights of their sub-criteria (AP: 22%; EP: 53%; POCP: 9%; ADP: 14%; and GWP: 2%). In BPAs I and II, LBS 34 is ranked top, with weighted scores of 4.50 and 4.52 percent respectively, followed by LBS 18 (4.47 and 4.39%) and LBS 26 (4.46 and 4.38%) (for the full ranking refer also to Annexure 76). The top eight LBSs all deploy BCS II (refer to section 5.8.5), which is characterised by its relatively good overall conversion efficiency, resulting in less biomass being required and consequently less land being required, with lower emissions at each stage of the bioenergy system. The relatively good overall harvesting efficiency, as well as the relatively high biomass productivity and the increase in carbon stock for the first two biomass procurement areas lead to the favourable performances of LBS 34. In BPAs III and IV, on the other hand, LBS 37 is ranked top, with weighted scores of 5.42 and 5.54 percent respectively, followed by LBS 21 (5.12 and 5.28%) and LBS 5 (5.11 and 5.25%). Including the fourth- and fifth-ranked LBS 5 and LBS 29, the top five-ranked LBSs employ, with BCS V, the same bioenergy conversion technology (refer to section 5.8.8), where only bio-oil is used to generate electricity, while the bio-char is sold as a soil additive. This leads to a significantly negative GWP, resulting in scores around six times greater than the LBS?s average GWP. As discussed in section 4.3.3.3, in general, it can be said that along with lower biomass productivity, Stellenbosch University http://scholar.sun.ac.za 219 the carbon storage capacity of the SRC plantations is also reduced. Thus, while in BPAs I and II, the carbon stock change contributes positively to all LBSs in terms of GWP, it has a lesser effect in BPAs III and IV. This emphasises the effect of applying bio-char to the soil, resulting in a change of the ranking of the LBSs, with a preference for the LBSs deploying BCS V. Across all biomass procurement areas, LBSs 3, 8, 11, 16, 19, 24, 27, 32 and 35 are the bottom-ranked alternatives, all having BCS III in common, which is characterised by its relatively low overall conversion efficiency, resulting in relatively higher emissions. Figure 70: Aggregated weighted scores of LBSs, considering only environmental impact criteria For LBS key, refer to Figure 11 A comparison of the ?expert's? top-ranked LBSs 13 and 26 with LBSs 34 and 37 is given in Table 58, below. Since LBS 34 is ranked top in BPAs I and II, not only from a ?Least environmental impact? perspective, but also from a financial-economic perspective, no further discussion on the comparison of LBS 26 with LBS 34 is necessary. In BPAs III and IV, however, in terms of ?Least environmental impact?, LBS 37 is ranked top, while LBS 13 is ranked top when taking all weighted criteria into consideration. With scores of 4.65 (BPA III) and 4.13% (BPA IV), LBS 37 has an IRR around three times greater than LBS 13 (1.36 and 1.42% respectively). LBS 37 generates an NPV of R209 million (BPA III) and R187 million (BPA IV), while LBS 13 exhibits a 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems Paarl Worcester Ashton Rural Cederberge Stellenbosch University http://scholar.sun.ac.za 220 negative NPV of R28 (BPA III) and R26 million (BPA IV). Since both alternatives deploy the same bioenergy conversion technologies, no differences are expected for ?CAPEX-conv.? and ?OPEX- conv?. Table 58: Comparison, top-ranked LBSs ? complete set of weights vs solely ?Least environmental impact? criteria Biomass procurement area I II III IV I II III IV Ranking of LBSs based on: Complete set of weights Solely least environmental impact criteria LBS 26 26 13 13 34 34 37 37 Weighted score ? complete set of weightsa 3.94 3.75 3.59 3.57 3.78 3.54 2.90 2.98 Position after ranking 1 5 6 23 22 Weighted score ? solely ?Least environmental impact? criteriab 4.39 4.38 4.44 4.62 4.50 4.52 5.42 5.54 Position after ranking 3 3 5 5 1 Absolute and relative comparison based on selected criteria IRR (%)c 10.96 15.15 1.36 1.42 11.18 15.26 4.65 4.13 Loss/gain (%) 98% 99% 29% 34% 100% NPV (R million)d 331 380 -28 -26 347 396 209 187 Loss/gain (%) 95% 96% -13% -14% 100% CAPEX-conv. (R million)e 63 63 398 398 63 63 398 398 Loss/gain (%) 100% 100% 100% 100% 100% OPEX-conv. (R million)f 66 66 281 281 66 66 281 281 Loss/gain (%) 100% 100% 100% 100% 100% CAPEX-other (R million)g 138 59 113 66 115 35 71 39 Loss/gain (%) 120% 166% 159% 167% 100% OPEX-other (R million)h 231 246 736 750 218 230 453 469 Loss/gain (%) 106% 107% 162% 160% 100% DECP Ii 95 107 429 403 65 69 167 167 Loss/gain (%) 146% 155% 257% 241% 100% DECP IIi 5 4 3 3 4 3 5 5 Loss/gain (%) 125% 133% 60% 60% 100% DECP IIIi 2 2 7 7 2 2 7 7 Loss/gain (%) 100% 100% 100% 100% 100% AP (t SO2-equivalent) j 86 86 126 124 83 82 116 115 Loss/gain (%) 104% 105% 109% 108% 100% EP (t phosphate-equivalent)k 22 22 32 31 21 21 30 29 Loss/gain (%) 105% 105% 107% 107% 100% POCP (t ethene-equivalent)l 5.3 5.2 19.1 17.3 5.2 5.1 8.1 8.1 Loss/gain (%) 102% 102% 236% 214% 100% ADP fossil (gigajoule)m 20.1 19.4 69.9 62.6 16.3 14.3 24.7 24.1 Loss/gain (%) 123% 135% 283% 260% 100% GWP (t CO2-equivalent) n 1 048 1 596 -32 709 -32 884 1 214 -34 258 -34 230 Loss/gain (%) 104 800% 746% 95% 96% 100% Notes: a Aggregated, normalised (to sum one), relative weighted score based on weights set by expert group b Aggregated, normalised (to sum one), relative weighted score based on maximisation of financial- economic main criterion only c Refer also to Annexure 45 d Refer also to Annexure 47 Stellenbosch University http://scholar.sun.ac.za 221 e Refer also to Annexure 48 f Refer also to Annexure 49 g Refer also to Annexure 50 h Refer also to Annexure 52 i Refer also to Annexure 53 j Refer also to Annexure 40 k Refer also to Annexure 41 l Refer also to Annexure 44 m Refer also to Annexure 39 n Refer also to Annexure 42 IRR Internal rate of return on capital investment CAPEX-conv.. Capital expenditure on biomass upgrading and conversion technologies OPEX-conv. Operational expenditure on biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than on biomass upgrading and conversion technologies OPEX-other Operational expenditure other than on biomass upgrading and conversion technologies DECP I Direct employment creation potential providing an income of less than R8 000/month DECP II Direct employment creation potential providing an income of R8 000-R24 000/month DECP III Direct employment creation potential providing an income of more than R24 000/month AP Acidification potential EP Eutrophication potential POCP Photochemical ozone creation potential ADP Abiotic depletion potential GWP Global warming potential The lower harvesting efficiency of LBS 13 compared with LBS 37, however, causes a higher cost of at least 60% in terms of ?CAPEX-other? and ?OPEX-other?. While LBS 13?s AP and EP are only slightly higher (around 10 percent), considerable differences were recorded for POCP and ADP, being around 2 times and 3.5 to 4 times greater than LBS 37?s respective values. For LBS 13, the five percent higher GWP can be explained by the loss of biomass during the motor-manual harvesting operation, resulting in more land being required and, thus, more energy being inputted to supply sufficient biomass. 7.6 Conclusions In the past, the ?success? of energy systems was driven mostly by financial drivers, leading to fossil fuels as a feedstock being the preferred choice. Major technical, social, political and economic challenges such as the need for security and diversification of energy supplies, the necessity for less reliance on fossil fuels, the uncertainty surrounding oil prices and the rising concerns over environmental degradation and climate change effects, however, have caused this approach to be viewed as unsustainable. This prompted not only the promotion of renewable energy sources but also a more sustainability driven approach in assessing such projects, necessitating more sophisticated measurements and the integration of a wider range of criteria in the decision-making process. Stellenbosch University http://scholar.sun.ac.za 222 Based on a case study for the Cape Winelands District Municipality (CWDM), Chapters 4 to 6 provide such sophisticated measurements of a wider range of criteria by using, inter alia, life-cycle assessment (LCA), geographic information systems (GIS) and multi-period budgeting (MPB). The nature of 37 defined lignocellulosic bioenergy systems (LBSs), which were assessed against five financial-economic, three socio-economic and five environmental key criteria, highlighted the complexity and the trade-offs between the different criteria, constituting a major barrier to the implementation of bioenergy projects. As shown in this chapter, multi-criteria decision analysis (MCDA) is an approach which can aid overcoming such a decision-making barrier by organising and synthesising the respective information, integrating mixed sets of data and assisting decision makers to place the problem in context and to determine the preferences of the potential stakeholders involved. The analytic hierarchy process (AHP), one of the commonly applied MCDA approaches, was used to identify the most sustainable lignocellulosic bioenergy system (LBS). The initial steps of the AHP included developing a hierarchy of criteria (criteria value tree) and translating and normalising the performances of the considered 37 LBS alternatives into a standardised common language of relative performance, i.e. in so-called scores. The aggregation of these scores for each LBS results in a ranking of the alternatives, with each criterion being equally important. Across all the four investigated biomass procurement areas, the ranking gives preference to those alternatives which deploy bioenergy conversion system V, which is characterised by a mobile pyrolysis system for biomass upgrading into bio-oil and bio-char. Only the former is used to generate electricity, while the latter is used as a soil additive, resulting in exceptionally high scores in terms of ?Least global warming potential?, but also in low scores in terms of financial-economic performance, due to the low overall conversion efficiency. Notwithstanding this, the decision-making problem persisted, as the conflicting nature of some of the criteria, the differing viewpoints of potential stakeholders and the resulting trade-offs were not considered in this phase of the MCDA, requiring an additional phase in which the stakeholder preferences were taken into account by attaching weights to the considered criteria. Hence, based on a discussion of the alternatives and their respective performances in terms of the predefined criteria, aimed at providing insight, a task team consisting of various experts, reflecting the broad spectrum of potential stakeholders, was requested to elicit the relative preferences for the criteria by means of pairwise comparisons using the AHP-based Expert Choice software. No serious conflicts in opinion between the participants were recorded during the discussions and the weighting procedure, resulting in consensus on a set of weights, where the main criterion ?financial-economic viability? Stellenbosch University http://scholar.sun.ac.za 223 received a preference of almost 60%, ?socio-economic potential? nearly 25% and ?least environmental impact? the remainder of almost 16%. The single most important sub-criteria are ?best IRR? and ?direct employment creation potential (DECP) I? with a cumulative weight of around 43 and 18 percent respectively. The aggregation of the weighted scores to a single indicator allowed a ranking of the lignocellulosic bioenergy systems (LBS), placing LBS 26 at the top in biomass procurement areas (BPA) I and II and second in BPA III and IV. Around 73-74 percent of its aggregated, weighted score derived from its ?Financial-economic viability?, around 8-9 percent from its ?Socio-economic potential? and 18- 19 percent from its ?Least environmental impact?. Similar profiles to that of LBS 26 are shown for most of the top-ten-ranked alternatives across all biomass procurement areas. With few exceptions, all encompass biomass upgrading and bioenergy conversion system (BCS) II, namely a parallel series of integrated 450Nm3/h gasifier-gas-turbine systems. Compared with the other bioenergy conversion systems, BCS II is characterised by relatively low capital and operational costs, as well as by good conversion efficiencies. The latter also has an effect on all upstream activities, as less biomass and, thus, less land is needed, resulting in fewer upstream activities and, therefore, in lower operational and capital costs, including for machinery and land. The results further suggest that the relatively immature fast-pyrolysis technology is currently not a viable option as part of a bio-electricity generation system. Particularly alternatives deploying bioenergy conversion system III (a centralised, stationary pyrolysis plant for biomass upgrading into bio-oil and bio-char combined with a boiler-steam turbine system), show poor results and are at the bottom of the ranking across all biomass procurement areas. This can be explained by its poor overall conversion efficiency, resulting in relatively greater upstream inputs and activities, as well as by its relatively high capital and operational costs for the conversion system. Similar reasons can be given for alternatives deploying bioenergy conversion systems IV and V, which both encompass mobile pyrolysis units for biomass upgrading. To generate electricity, the former uses a centralised, stationary boiler-steam turbine in which both pyrolysis products are combusted, while the latter uses only bio-oil in a direct injection gas turbine system; the bio-char is used as an additive to soil, which effectively works as a form of carbon capture and storage. The only exception represents the bioenergy conversion system V-driven LBS 13, which is ranked top for biomass procurement areas III and IV. In both BPAs, nearly 60% of LBS 13?s aggregated, weighted score is contributed by its socio-economic performance, with the remainder being equally contributed by its financial- economic and environmental performance. LBS 13?s relatively low harvesting and conversion efficiency causes such a significantly greater demand in terms of human capital, particularly in the Stellenbosch University http://scholar.sun.ac.za 224 low income category, that its aggregated, weighted score is slightly greater than the second-ranked LBS 26. Two aspects contribute to a different outcome for BPAs III and IV, namely (1) the relatively lower biomass productivity, resulting in more land being required to ensure sufficient biomass feedstock supply and, thus, in more material, machinery and human capital being required, and (2) the relatively lower land value, being one of the main cost factors, resulting in a lower impact in terms of costs on the financial-economic viability, i.e. the tendency of selecting alternatives with a relatively greater conversion efficiency becomes less important as increases in socio-economic potential compensate for a loss in financial-economic viability. However, similar to the first two biomass procurement areas, at least eight of the top-ten-ranked alternatives in BPA III and IV encompass bioenergy conversion system II, the aggregated, weighted score profiles of which represent a more valid reflection of the set of weights given by the expert group. Thus, despite the fact that the ranking of BPAs III and IV suggests that LBS 13 may be the preferred choice, the final decision maker may still select LBS 26, following the prerequisite of financial viability, i.e. if the LBS is not profitable, private investors will not be interested in investing in this particular alternative, which is the case for LBS 13 in BPAs III and IV (NPV of R-28m and R-26m respectively). However, as discussed in Chapter 3, MCDA does not provide the ?single right answer?, even within the context of the model used, as the concept of an optimum does not exist in a multi-criteria framework. This was illustrated during the sensitivity analysis of the MCDA results, where each of the main criteria was considered independently, while retaining the relative weights for their respective sub-criteria. When considering solely financial-economic criteria or environmental criteria in the decision-making process, LBS 34 may be the preferred choice ahead of LBS 26 in the case of biomass procurement areas I and II, as it encompasses a more efficient harvesting system (HS) (i.e. HS V) besides BCS II, resulting in greater cost efficiency and a lower environmental impact. However, LBS 34 is also characterised by a lower direct employment creation potential. If, on the other hand, solely socio-economic potential criteria were to be considered for BPAs I and II, LBS 13 would be the preferred choice in BPAs I and II, exhibiting a 3.5 times greater employment potential in the low and high income categories (DECP I and III) than LBS 26. However, this choice would also result in a considerably poorer financial-economic and environmental performance. As was shown in this chapter, MCDA proves to be an appealing and practical tool that organises and synthesises relevant information in a way that should lead decision makers to feel comfortable Stellenbosch University http://scholar.sun.ac.za 225 and confident about making a decision, as it minimises the potential post-decision regret by ensuring satisfaction that all criteria or factors have been properly taken into account. Particularly when seeking to implement bioenergy systems, it is well suited for integration with performance- data-generating methods such as LCA and complementary financial- and socio-economic assessment methods. It is, however, not the purpose of MCDA to solve a particular decision-making problem. Rather, its purpose is to produce insight in order to help decision makers make better decisions; learn about and understand the problem faced; understand own, other parties? and organisational priorities values and objectives; and through exploring these in the context of the problem, guide them in identifying a preferred course of action and to promote transparency. Stellenbosch University http://scholar.sun.ac.za 226 8 CHAPTER: CONCLUSIONS, SUMMARY AND RECOMMENDATIONS 8.1 Conclusions Some projections indicate an increase of more than 300% in global energy demand by 2035 ? by that time the daily extraction capacity of crude oil will have declined by 50 million barrels. Despite uncertainties about detail, it is now evident that the world faces the dawn of the second half of the age of oil, when this critical commodity, which plays such a fundamental role in the modern economy, heads into decline, due to natural depletion (Campell, 2012). Financially, this will add further pressure to the price per barrel of crude oil, which has already more than quintupled over the last ten years. In addition, the inability of the environment to maintain its sink function, i.e. the ability to maintain its assimilating capacity without the unacceptable degradation of its future waste-absorbing capacity or other important services, will force world economies to reduce their use of fossil energy sources and to reconsider current energy policies and management. However, the transition to decline threatens to be a time of great international tension. ?Petroleum man? will approach extinction this century and Homo sapiens will face major social, political and economic challenges in adapting to these losses. This has prompted not only a new energy paradigm, from fossil fuels to renewable energy sources, but also a demand for new ways of measuring the viability of energy systems. In the past, the ?success? of energy systems was mostly driven by financial considerations, using monetary assessment methods such as cost-benefit analyses. This led to fossil fuels being the preferred choice; however, the introduction of renewable energies has required a more sustainability-driven approach, necessitating more sophisticated measurements of a wider range of ?success? criteria. Technical efficiency, economic affordability, environmental friendliness and social acceptance, amongst others, are the driving sustainability factors determining the success of renewable energy systems. The resulting complexity, however, constitutes a major barrier to implementation, as much information of a complex and conflicting nature, often reflecting different viewpoints and often changing with time, needs to be processed. Confronted with such a decision-making problem, the public decision makers of the Cape Winelands District Municipality (CWDM) in the Western Cape, South Africa, were prompted to investigate possibilities for implementing local renewable energy systems. By using the CWDM as a case study area, this study aims to illustrate how to aid such a decision-making process by providing quantitative data using the life-cycle assessment (LCA) approach, geographic information systems (GIS), multi-period budgeting and other quantitative assessment methods, and by integrating various considerations using the multi-criteria decision analysis method. Stellenbosch University http://scholar.sun.ac.za 227 Against this background, bioenergy emerges as a solution that could not only meet a significant amount of the world?s constantly increasing energy demand, but that also represents ? compared with other available renewable energy sources, including hydro, solar, or wind ? the only carbon- based sustainable option for mitigating greenhouse gas emissions. Another advantage of using biomass to generate energy lies in its continuous availability. Unlike wind or solar energy, which depends on the availability of natural forces (wind and daylight), the production and consumption of biomass can be separated, i.e. biomass can be stored prior to its consumption. Considering the local land quality conditions of the CWDM, this study was limited to the production of lignocellulosic biomass, i.e. trees grown in a short-rotation coppice (SRC) system. Besides their capability of sustaining longer periods of frost or droughts, short-rotation crops such as willow, poplar or eucalyptus have turned out to be the biomass materials with the highest energy potential. Moreover, while intensive biofuel and bioenergy crops such as maize and canola are limited to highly productive land and, thus, compete with conventional agricultural activities for land, short- rotation coppice (SRC) plantations can be established on marginal and degraded land. As shown in this study, the life-cycle assessment (LCA) approach, originally developed as an environmental assessment tool, can support decision-making by providing environmental performance information in a structured and comprehensive way. It can be understood intuitively as a tool that captures environmental impacts along the entire life cycle (from its ?cradle? to its ?grave?) of a product or a service. There is broad agreement in the scientific community that LCA is one of the most effective methods for evaluating the environmental burdens associated with biofuel and bioenergy production. Due to its structured and systematic approach, LCA is well suited for integration with other complementary system assessment methods, such as multi-period budgeting (MPB) and geographic information systems (GIS). These widely recognised and applied methods generate additional performance data covering technical, financial-economic and socio-economic aspects along the bioenergy system?s life-cycle. By providing financial-economic, socio-economic and environmental performance data, the combined use of these assessment methods supports integrated decision-making in both a broad (public) context and a narrow (private) context. In a narrow context, it provides in-depth information for decision-making at an operational level, e.g. in a farming context. In a broad context, the provided information aids public decision-making by illustrating the trade-offs of the different alternatives, as well as the respective opportunity costs when selecting one alternative over another. In the latter case, however, additional support for the decision-making process of identifying the most sustainable solution is required, as the barrier to implementing bioenergy Stellenbosch University http://scholar.sun.ac.za 228 projects in terms of the multiple, and often conflicting objectives, persists. The multi-criteria decision analysis (MCDA) approach can aid decision makers to overcome such a decision-making barrier. It has gained recognition to support decision-making as a tool that organises and synthesises the respective information, which is capable of integrating mixed sets of data (qualitative and quantitative), and which assists the decision maker to place the problem in context and to determine the preferences of the stakeholders involved. In essence, based on a number of defined criteria, the goal of a decision maker is to identify an alternative solution that optimises all the criteria. However, in complex projects like bioenergy assessments, it is impossible to optimise in terms of all the criteria at the same time; therefore, a compromise solution needs to be sought by using subjective judgements of the considered criteria and by combining these as weighted scores to obtain an overall ranking of alternatives. Thus, MCDA aids decision-making processes by integrating objective measurement with value judgement, by making subjectivity explicit, and by managing this subjectivity in a transparent and reproducible manner. A number of MCDA studies integrating other assessment methods for providing performance data (e.g. LCA) to support decision-making processes concur that environmental, financial- and socio- economic criteria need to be included when seeking to determine the most sustainable energy project for implementation. However, a great deal of them fall short in their application, as they either consider only a single dimension (finance, social or environment) or they take a very limited number of other aspects into account (e.g. only one aspect for each dimension). The immaturity of complementary assessment methods, the data intensity, and the lengthy process of generating the respective information are given as explanations for omitting other sustainability indicators. In the early stage of the LCA, 37 lignocellulosic bioenergy systems (LBSs) were defined, encompassing different combinations of types of harvesting and primary transport in the SRC plantation, types of pretreatment (comminution, drying, fast pyrolysis) and locations thereof (roadside or landing of the central conversion plant), types of secondary transport from the roadside to the central conversion plant, and types of biomass upgrading and conversion into electricity. Each of these were assessed against a set of 13 key criteria, consisting of five financial-economic viability criteria, three socio-economic potential criteria and five environmental impact criteria. Four so-called biomass procurement areas (BPAs), namely Paarl, Worcester, Ashton and the Rural Cederberge, which differ, inter alia, in their biomass productivity (a mean annual increment of 27, 18, 9 and 5 tonnes of fresh biomass respectively), their availability of land and the resulting extent of the procurement area were used to determine the performance of the 37 LBSs in terms of the defined criteria. The quantitative performance data was then, as part of the MCDA process, Stellenbosch University http://scholar.sun.ac.za 229 translated into a standardised common language of relative performance. Taking the stakeholder preferences into account, weights were attached to the considered criteria by means of the analytical hierarchy process (AHP). The ?financial-economic viability? main criterion received a preference of almost 60%, ?socio-economic potential? nearly 25% and ?lowest environmental impact? the remainder of around 16%. With the results of this study, the first hypothesis was proven to be correct. This hypothesis states that life-cycle assessment and other complementary system assessment methods, including multi- period budgeting and geographic information systems, can be used as structured and comprehensive techniques for the detailed analysis of complex lignocellulosic bioenergy systems, to provide quantitative financial-economic, socio-economic and environmental performance data. The second hypothesis also proved to be correct, as this study confirms the applicability and suitability of multi- criteria decision analysis to aid decision-making processes, such as identifying ? by integrating and evaluating the provided performance data ? the most sustainable lignocellulosic bioenergy system for the Cape Winelands District Municipality. The following main facts and principles were derived from developing and implementing the methods employed. These can serve as guidelines for planning and developing sustainability-driven assessments of renewable energy systems in general and lignocellulosic bioenergy systems specifically, taking financial-economic, socio-economic and environmental criteria into consideration: (i) The outcome of the multi-criteria decision analysis for the introduction of lignocellulosic bioenergy systems (LBSs) in the Cape Winelands District Municipality (CWDM) showed that LBS 26 would be the preferred option in the Paarl and Worcester biomass procurement areas, and the next-best option, after LBS 13, in the Ashton and Rural Cederberge BPAs. However, LBS 26 would be preferred across all areas of the CWDM if the prerequisite of financial- economic viability were to be taken into consideration (i.e. if the alternative is not profitable, private investors will not be interested in investing in this particular alternative), despite various levels of productivity. LBS 26 comprises a feller-buncher for harvesting, a forwarder for the primary transportation of the biomass to the roadside, where the biomass is comminuted and then transported in containers to the central conversion site. For its conversion into electricity, the South African-designed and -made System Johansson Gas (SJG) producer, by Carbo Consult & Engineering (Pty) Ltd, was adopted. This system is based on parallel series of integrated gasifier-gas-turbine systems. The environmental impact and socio-economic potential Stellenbosch University http://scholar.sun.ac.za 230 of LBS 26 would be so similar across the four areas as to not cause a significant deviation from the financial-economical-dominated ranking. (ii) LBS 26 is expected to be profitable in all biomass procurement areas. A comparison of the four BPAs shows that the greatest ?financial-economic potential? can be expected for the Worcester area, since it has relatively the best ratio between biomass productivity and land cost. Therefore, it can be concluded, that it will not necessarily be the most productive land that will give the highest returns, but rather the area with the best balance between land productivity and land price, thereby recognising the equalising effect of the land market. (iii) Using the current South African energy mix as a reference system (more than 90% of South Africa?s electricity generation is based on fossil fuels such as coal or oil), all investigated lignocellulosic bioenergy systems are expected to perform better in terms of the environmental impact criteria. In terms of global warming potential, which is expressed in tonnes of carbon dioxide-equivalent emitted, even the worst performing LBS reaches levels of only eight percent of the SA energy mix. Only in terms of eutrophication potential do the LBSs? results exhibit similar outcomes to the current SA energy mix, which can be explained by the negative impact of applying fertilisers. Using the Worcester biomass procurement area as an example, from an environmental point of view, LBS 26 reaches abiotic depletion levels of 14% of those of the current South African energy mix, a global warming potential of 4%, an acidification and eutrophication potential of 16% and 92% respectively, and a photochemical ozone creation potential of 19%. Thus, generating electricity with a lignocellulosic bioenergy system represents a sustainable alternative to the current energy mix, contributing positively to the environmental balance of South Africa as a whole. (iv) Due to a lack of data for the SA energy mix, comparisons in terms of financial-economic and socio-economic performance were not possible. The national energy supplier, ESKOM, has various electricity tariffs, depending on types of consumers and their locations. Furthermore, ESKOM does not disclose detailed information on the employment it provides. Hence, comparisons with the assessed bioenergy systems were not possible. Nevertheless, ESKOM?s current electricity tariff of around R0.80/kWh for the private consumer in the Stellenbosch area is significantly lower than the R1.06/kWh at which ESKOM is supposed to purchase electricity produced from solid biomass, as proposed by the national energy regulator of South Africa (NERSA). However, increases in electricity tariffs in recent and forthcoming years are expected to improve the competitiveness of renewable energy systems in general. Income from trading certified emission reduction (CER) certificates can further improve the profitability of lignocellulosic bioenergy systems. Stellenbosch University http://scholar.sun.ac.za 231 (v) In contrast with the current energy mix of South Africa, this study of lignocellulosic bioenergy systems incorporates a variety of socio-economic and environmental considerations. MCDA was used to translate the generated performance data into a standardised common language of relative performance. This stands in contrast to single goal optimisation, and approaches using ?unifying units? to offset poor performances in terms of one criterion against another criterion. This latter approach is adopted in cost-benefit analyses (CBA) using monetary values assigned to parameters, allowing for substitution and comparability between criteria. However, environmental impacts such as ?eutrophication potential? (EP, expressed as t Phosphate- equivalent) or ?Photochemical ozone creation potential? (POCP, expressed as t Ethane- equivalent) cannot be expressed in monetary terms, and therefore, cannot be taken into account sufficiently when making decisions, giving rise to typical externalities. MCDA does not internalise these considerations by means of valuation methods expressing them in monetary terms on a cardinal scale, but accommodates them via scores and weights using ordinal scales. Hence, MCDA avoids the classical problem of not being able to internalise non-monetary considerations via monetarisation. (vi) As shown in this study, GIS can be a useful tool for providing a basis for life-cycle assessment, by accommodating resource quality and accessibility considerations. By means of GIS, around 175 000 hectares out of a total of 2.3 million hectares in the CWDM were identified, in a land availability assessment, as being suitable for producing lignocellulosic biomass in an SRC plantation system. Using available spatial information on land-use cover and precipitation patterns, amongst others, GIS helped to identify the extent and location of potential production areas, by excluding unsuitable areas such as urban developments, intensive agricultural/arable land, and environmentally and socially sensitive areas (e.g. biodiversity hotspots and buffer zones along waterbodies). (vii) In excluding environmentally and socially sensitive areas, GIS also aided the decision- making process, by limiting the number of criteria involved. Environmental concerns such as the impact on biodiversity or on water balance, or socio-economic concerns such as the competition between food and biomass production are often reasons for heated discussions, due to the difficulties in measuring these impacts, and are, therefore, difficult to quantify. Within the LCA approach, no recognised and established life-cycle impact assessment method covering these environmental or social-economic concerns is available for assessing bioenergy projects. Thus, GIS can help to minimise these impacts, by using spatial data to exclude sensitive areas. (viii) During the transport modelling assessment, GIS also helped to identify potential bioenergy conversion sites, taking various proximity aspects into consideration (e.g. proximity Stellenbosch University http://scholar.sun.ac.za 232 to electricity substations and major grid lines, to minimise feed-in costs; proximity to external customers, to whom potential excess heat/thermal energy could be sold; proximity to the road network, as the accessibility of the conversion sites affects feedstock transport efficiency and obviates the costs of additional infrastructure). This further highlighted the practicality and multi-functionality of GIS in aiding the assessment of renewable bioenergy systems. (ix) Changes in land use can potentially alter carbon stocks, by releasing or sequestering soil and vegetation carbon ? an issue often neglected in the assessment of bioenergy projects. In the context of the CWDM, the introduction of SRC plantations will result in an increase in carbon stock, particularly when substituting rain-fed intensive and extensive farmlands. The only exception is expected for irrigated, perennial crops in the drier parts of the CWDM. The correlation between biomass productivity and carbon sequestration capacity becomes apparent when comparing the biomass procurement areas against each other. SRC plantations in the Paarl BPA will nearly double the capacity of those in the low productivity Rural Cederberge BPA to store carbon. Hence, the results of this study confirm that primary productivity is a function of mean annual precipitation and temperature, as well as of organic matter inputs to the top soil tending to be greater in mesic than arid regions. Furthermore, they confirm that C storage capacity increases with an increase in lignocellulosic biomass. (x) One of the core elements of a life-cycle assessment of a product or a system is the functional unit, which provides a reference against which the input and output process data are normalised and a basis on which to present the final results. The functional unit used in this study is a dual time-product measurement, in terms of which the burdens calculated for an average year?s operation are normalised to the total electrical power of a 5 megawatt system produced per year: an annual electricity generation of 39 600 megawatt hours (MWh). For the financial-economic and the socio-economic assessment, however, a longer time boundary based on accounting principles was used: an economic business cycle of 20 years for the biomass upgrading and bioenergy conversion units, plus one rotation length to ensure the sustainable feedstock supply to the conversion plant, i.e. 25, 27, 30 and 35 years for the Paarl, Worcester, Ashton and Rural Cederberge BPAs respectively. This was done to capture the effects in terms of costs and profitability over the entire financial-economic life-cycle. The use of different functional units in this study is aimed at providing sufficient insight and a good understanding of the impacts and performances of the alternatives. MCDA provides the means to integrate these different ?languages?, by translating them into a standardised common language of relative performance. Stellenbosch University http://scholar.sun.ac.za 233 (xi) One of the reasons for LBS 26 being ranked top can be given as the overall conversion efficiency (OCE) of its biomass upgrading and bioenergy conversion system (BCS). In absolute terms, LBS 26 requires 52 937 tonnes of fresh woody biomass annually to ensure continuous electricity generation. LBS 24, which was ranked last across all biomass procurement areas, requires 87 272 tonnes per year to reach the same electrical output, around 65% more than LBS 26. This translates to an OCE of around 25% for LBS 26 and around 16% for LBS 24. Thus, the greater the OCE, the less bioenergy feedstock is required, resulting in fewer upstream activities and less land being required for biomass production. This makes the OCE the most decisive factor for the success of an entire bioenergy value chain, whether it be of a financial-economic, socio-economic or environmental nature. In terms of environmental impact, a greater OCE is desired, resulting in lower total emissions and, therefore, in lower impacts for each life-cycle impact category. Similarly, for the financial-economic viability, a greater OCE results in lower costs, both in terms of capital and operating expenditure, as well as in higher internal rates of return on the capital invested. The opposite picture may emerge for the socio-economic criteria, expressed in employment creation potential. The lower the OCE, the greater the employment creation potential, particularly for the unskilled and semi-skilled income categories, taking South African conditions into consideration. This relationship illustrates another important aspect, namely that environmental and financial-economic viability criteria are not necessarily contradictive and that the maximisation of one of them also has positive effects in terms of the other. The maximisation of direct employment as a socio-economic indicator, however, has a negative effect on financial-economic viability and environmental impact. (xii) A comparison of the rankings of the lignocellulosic bioenergy systems shows that most of the top-ten-ranked alternatives use the same biomass upgrading and bioenergy conversion system (BCS) as used by LBS 26. The next group in the rankings consists in most instances of alternatives comprising direct combustion integrated boiler-steam-turbine systems. In contrast, alternatives comprising the fast-pyrolysis technology as part of the BCS can be found at the lower end of the rankings. Therefore, based on the BCS technologies considered in this study, it could be said that with greater maturity of the BCSs used, lower capital costs and greater conversion efficiencies can be expected. While direct biomass combustion and gasification technologies are relatively well established and have been proven by their reliability, resulting in relatively lower capital costs and greater conversion efficiencies, the relatively immature fast- pyrolysis technologies used for biomass upgrading have not proven viable. This has even been the case in remote areas where the densification of the original feedstock by means of fast- pyrolysis into more valuable energy carrier bio-oil and bio-char, aimed at reducing secondary Stellenbosch University http://scholar.sun.ac.za 234 transport costs, was expected to play a potential role. The high capital and operational costs, as well as the relatively low OCE currently do not justify their application in the context of bio- electricity generation. However, this may change in future, when, for instance, the application of bio-char to soil as a means for carbon sequestration is accepted for the cleaner development mechanism (CDM), i.e. as a scheme for carbon credit trading, which could provide significant additional income and, thus, could make such an operation viable. (xiii) Another important consideration for the assessment of bioenergy systems is the efficiency of the harvesting system, which has an effect similar to the OCE. In the context of the CWDM, motor-manual harvesting has been found to be the most beneficial in terms of the creation of employment in the unskilled and semi-skilled income categories, while relatively greater harvesting efficiency is reached using the modified self-propelled forage harvester, involving felling and comminution of the trees occurring in a single operation, and resulting in relatively low environmental impacts and low production costs, in turn resulting in a positive impact on overall profitability. This harvesting system, however, is also characterised by relatively low direct employment creation potential. Thus, the greater the degree of mechanisation and automation of the harvesting system, the lower the environmental impact and the higher the cost-effectiveness and profitability, but also the lower the socio-economic benefits in terms of employment creation potential. (xiv) Various discussions and research projects in South Africa have also considered the use of invasive alien plant species (AIPs) to generate bioenergy. While no production costs are assumed for AIPs, the procurement costs are expected to be comparatively high, as AIPs are distributed over wide areas and, in many cases, in difficult terrain. The methods applied in this study provide a sound approach for evaluating the financial-economic, socio-economic and environmental performances, not only of SRC plantation-based bioenergy systems but also of other bioenergy options. In the case of AIPs, for instance, they can help decision makers in their decision-making processes, by providing accurate and reliable data to weigh up different aspects, such as production costs against procurement costs. 8.2 Summary Chapter 1 serves as a general introduction to the study, giving background information on the global and local issues driving renewable energies coming to the fore, and on the need for more sustainability-driven approaches to assessing energy system projects. Finite reserves, and the rapidly increasing demand for energy sources, expressed by the uncertainty surrounding oil prices, the need for security and diversification of energy supplies and the inability Stellenbosch University http://scholar.sun.ac.za 235 of the environment to maintain its sink function (maintaining its assimilative capacity without unacceptable degradation of its future waste absorptive capacity or other important services) are some of the major social, political and economic challenges that have prompted the international community to investigate alternative energy sources. This has resulted in a new energy paradigm, from fossil to renewable energy sources, including hydro, solar, wind and biomass-based energy systems, and coincides with a demand for new ways of measuring the viability of energy systems. In the past, financial and technical considerations were the main drivers of selecting a suitable energy carrier, leading to fossil fuels as the preferred choice. The introduction of renewable energies resulted in a more sustainability-driven approach, meaning the implementation of alternatives that are technically efficient, economically viable, environmentally sound and socially acceptable. This, however, necessitates more sophisticated measurements in terms of a wider range of criteria, covering not only financial-economic, but also environmental and socio-economic aspects. The life-cycle assessment (LCA) approach, originally developed as an environmental assessment tool, has gained recognition, as it provides environmental performance information in a structured and comprehensive way, to support decision-making in both the private and public sectors. LCA can be understood intuitively as a tool that captures the environmental impacts along the entire life cycle of a product or a service (from its ?cradle? to its ?grave?). It is regarded as one of the best methodologies for evaluating the environmental burdens of renewable energy systems. Further, LCA is believed to be well suited for integration with other, complementary assessment methods, such as multi-period budgeting (MPB) and geographic information systems (GIS). While LCA provides environmental performance data, these widely recognised and applied methods generate additional performance data, covering technical, financial-economic and socio-economic aspects along the product?s life cycle. However, the main problem in finding the most viable/most sustainable alternative in a decision environment with multiple, and often conflicting objectives persists. Multi-criteria decision analysis (MCDA) has come to the fore as a method that is capable of aiding decision-making by organising and synthesising the respective performance data, by integrating mixed sets of data (qualitative and quantitative), and by assisting the decision maker to place the problem in context and to determine the preferences of the stakeholders involved. Public and private decision makers of the Cape Winelands District Municipality (CWDM) in the Western Cape, South Africa, are confronted with such a decision-making problem, as they seek to implement viable/sustainable renewable energy systems in the CWDM. The resulting complexity, however, constitutes a major barrier to their implementation, as much information of a complex and Stellenbosch University http://scholar.sun.ac.za 236 conflicting nature needs to be processed. Adopting a case study approach, this dissertation aims to illustrate how to aid such a decision-making process by providing quantitative performance data and by integrating various considerations using the above-mentioned research methods. Chapter 2 provides the reader with additional background information on the study area and the resource baseline, as well as with a definition of the biomass and its properties considered as feedstock for generating bioenergy. Great variations in topology, climate and soil conditions characterise the 2.23 million hectares of the CWDM, which is located in the centre of the Western Cape Province and is shaped by a Mediterranean climate and a historically strong deterministic water supply (winter rainfall) from April to August. The south-western part of the CWDM is mainly frost free, but some areas towards the interior regularly experience periods of frost during winter and droughts during summer. One of the main concerns for public decision makers in the CWDM is the unemployment rate. Almost 21% in the CWDM (mostly in the unskilled to semi-skilled category) are unemployed, creating a variety of socio-economic problems. Biomass is considered to be one of the most promising alternatives to conventional fuels, as it is the only renewable source of fixed carbon, which can be converted to liquid, solid and gaseous fuels, in addition to heat and power. Bioenergy has an almost closed CO2 cycle, as the combustion of biomass releases the same amount of CO2 as was captured during its growth: only in the production stages are additional greenhouse gases (GHG) emitted by using external fossil fuel inputs to produce and harvest the feedstock, in processing and handling the biomass, in transporting the feedstock and in bioenergy plant operation. Some short-rotation coppice (SRC) crops such as willow, poplar, eucalyptus and other fast-growing tree species are not only frost and drought resistant, but have also turned out to be the biomass materials with the highest energy potential. Based on a land availability assessment, around 175 000 hectares were identified as being suitable for the production of energy wood in a short-rotation coppice system. With the aim of limiting the impact on the environment (e.g. biodiversity) and to avoid competition between food and biomass production, GIS was used to exclude non-suitable areas, most importantly areas with water limitations and ecologically sensitive areas, thereby decreasing the number of considerations to be handled during the multi-criteria decision analysis discussed further on. The biomass availability assessment indicated that about 1.4 million tons of fresh biomass lignocellulosic biomass could be supplied annually, assuming medium productivity. In general, indigenous species (e.g. Acacia karoo) are expected to produce higher yields in the interior, low production areas in the north-east of the CWDM, while exotic species (e.g. Eucalyptus cladocalyx) grow better in areas with higher production potential compared with indigenous species. Stellenbosch University http://scholar.sun.ac.za 237 The general characteristics and the chemical composition of suitable trees grown in an SRC system are important considerations for the assessment of bioenergy systems, influencing the various production stages. Besides the inherent calorific value, the moisture content of the bioenergy feedstock plays a particularly important role, as it affects, inter alia, handling, transport costs, and conversion efficiency. Aimed at familiarising the reader, Chapter 3 provides a comprehensive description and discussion of the methods applied in this study, including the origin, structure, and a discussion of recent applications of each method in the fields of agriculture, forestry and bioenergy. This is followed by a discussion of recent studies dealing with the combined use of both methods, highlighting the need for the expansion of existing research methods aimed at dealing with multi-criteria decision contexts. In recent years, an increasing number of LCA studies have dealt with environmental impacts in the primary sector, including conventional agricultural and forestry activities, as well as energy crop production and whole biofuel/bioenergy systems, often aimed at comparing the environmental impacts of a certain system to the environmental impacts of a reference system. In the case of bioenergy, this means that the selected bioenergy system is compared with a fossil reference system. In general, an LCA consists of four phases, namely goal and scope definition, inventory analysis, impact assessment, and interpretation of the results. Various types of LCA exist, but in general, a distinction is made between two types, namely accounting and change-oriented LCAs, with the former being comparative and retrospective (e.g. eco-labelling). Identification of the most sustainable bioenergy system can be assigned to the latter, which is comparative and prospective, and aimed at supporting decision-making. Similarly, with a heightened awareness of a more sustainable approach in decision-making in the public and private sector, MCDA has received increased attention in recent years, including for selecting the most viable agricultural, forestry and renewable energy systems. MCDA, which can be defined as ?formal approach that seeks to take explicit account of multiple criteria in helping individuals and groups explore decisions that matter?, stands in contrast to single goal optimisation, and approaches using ?unifying units? to offset poor performances in terms of one criterion by good performances in terms of another criterion. The latter approach is adopted in cost-benefit analyses using monetary values assigned to parameters, allowing for the substitution and comparability of criteria. MCDA in its use of interval scaling and weights, and in its focus on relative trade-offs within each dimension, avoids many of the problems associated with monetary evaluation techniques, while still permitting the assessment of potential trade-offs between criteria. Various Stellenbosch University http://scholar.sun.ac.za 238 MCDA methods exist, generally classified as value measurement models, goal aspiration and reverence-level models, and outranking models. In essence, the process of MCDA involves a number of defined criteria against which viable management alternatives are assessed in terms of scores. The goal of the decision maker is to identify an alternative solution that optimises all the criteria. However, the concept of an optimum does not exist in a multi-criteria framework; thus, a compromise solution needs to be actively sought by using subjective judgements of the considered criteria and by combining these as weighted scores to obtain an overall ranking of the alternatives. A variety of studies discuss the combined use of LCA and MCDA. However, while a variety of studies concur that environmental, financial- and socio-economic criteria need to be considered when seeking the most sustainable alternative, most of them fall short in their application, as they consider either only environmental aspects (in most instances solely LCA-based criteria) or they take a very limited number of financial and social aspects into account (e.g. only one for each aspect). The early development of complementary assessment methods, the data intensity, and the lengthy process of generating the respective information are given as explanations for omitting other sustainability indicators. This study provides a more comprehensive, more sustainability-driven approach in determining the most viable lignocellulosic bioenergy system for the CWDM, using LCA and other complementary assessment methods, including MPB and GIS, to provide financial-economic, socio-economic and environmental performance data. The analytical hierarchy process (AHP) ? one of the commonly applied and accepted MCDA approaches, characterised by its simplicity, and possessing the natural appeal of expressing relative importance by means of pairwise comparisons in ratio terms ? was applied to integrate and evaluate the generated performance data, resulting in an overall ranking of the alternatives. Along the lines of the LCA method, the first LCA phase (goal and scope definition) is covered in Chapter 4. This sets the foundation for this study by defining its goal and scope and by specifying the functional unit and the different dimensions of systems boundaries. The latter included the technical system boundaries, resulting in a set of 37 lignocellulosic bioenergy systems (LBSs), which are characterised by different combinations: type of harvesting and primary transport in the SRC plantation (the latter is also referred to as forwarding or extraction), type of pretreatment (comminution, drying, fast pyrolysis) and location thereof (roadside or landing of the central conversion plant, type of secondary transport from the roadside to the central conversion site, and type of biomass upgrading and conversion into electricity. For each production phase of the life cycle, general background information on trends and state-of-the-art technologies and systems was Stellenbosch University http://scholar.sun.ac.za 239 provided. The functional unit was defined as ?burdens calculated for an average year?s operation normalised to the electrical power produced per year?, i.e. the electricity generated annually by a 5- MW system over 330 days of full production. Four potential sites/biomass procurement areas (BPAs) within the CWDM were selected as geographical boundaries (Paarl, Worcester, Ashton and Rural Cederberge), based on their different site conditions and their different biomass productivity rates (relatively high, medium, low, very low respectively), which were estimated by experts taking available climate data into consideration. Other boundaries in relation to the natural system were specified by taking land-use change-related carbon stock changes for each of the BPAs into account. This is followed by Chapter 5, which entails a detailed life-cycle inventory (LCI) for each of the 37 lignocellulosic bioenergy systems. Information was gathered about all process-related inputs and outputs of the studied systems. For each process, qualitative and quantitative data, i.e. relating to machinery and equipment, was assumed, and the related productivity was specified, not only in terms of environmental input and output flows, which are typical for an LCI, but also by considering related financial-economic (capital and operational expenditures, income from selling electricity and related by-products such as thermal energy, bio-char or carbon credits) as well as socio-economic (direct employment creation potential) data. Various sources including the GaBi 4.4 database, the literature, and industrial data were used to calculate the environmental impacts and financial-economic performance, assuming best operating practices for each process. Each of the lignocellulosic bioenergy systems consist of five production phases, namely (i) primary production of biomass in short-rotation coppice (SRC) plantations; (ii) harvesting and primary transportation of the biomass from in-field to the roadside; (iii) pretreatment of the biomass, including comminution, drying and mobile fast pyrolysis; (iv) secondary transport of the bioenergy feedstock from the roadside to a central conversion plant; and (v) biomass upgrading and conversion into electricity. The first production phase, primary biomass production, is common to all alternatives and takes all the activities and processes in the establishment and maintenance of the SRC plantations into account. The second production phase, harvesting and primary transport, comprises five harvesting system modules, including three different harvesting technologies and three types of primary transportation. The harvesting technologies modelled are motor-manual machinery, mechanised forestry machinery, and modified agricultural machinery. A forwarder fitted with a crane; a tractor- pole-trailer combination loaded and unloaded, either manually or with a three-wheeler loader; and a tractor-container-trailer combination were assumed for the primary transportation. The third production phase, pretreatment of the biomass, entailed three types of activities, namely Stellenbosch University http://scholar.sun.ac.za 240 comminution, drying and mobile fast pyrolysis. Depending on the harvesting system applied, two locations for comminution were proposed, i.e. mobile comminution at the roadside and stationary comminution at the landing of the central conversion plant. Similarly, both the location of the stored biomass and the shape of the biomass (comminuted or uncomminuted) depend on the harvesting systems applied. In the case of four of the harvesting systems, uncomminuted biomass is stored in- field to air-dry for several weeks until the biomass has reached moisture content levels of around 40% (on a dry-matter basis). Once this level has been reached, the biomass is forwarded to the roadside for further processing. In the case of the remaining harvesting system, the trees are felled and comminuted in a single process, resulting in wood chips with moisture content levels of around 80% (dry basis). However, exhaust heat from the respective conversion system is used to reach the moisture content levels required for the upgrading and conversion process; no additional energy will be required to reach the stipulated moisture content levels of the bioenergy feedstock. Hence, no additional costs and emissions arise from the active drying process. Some of the alternatives use mobile fast pyrolysis, a process whereby the biomass is degraded in the absence of an oxidising agent, i.e. the volatile components of a solid carbonaceous feedstock are vaporised in primary reactions by heating, leaving a residue consisting of bio-char and ash. Pyrolysis always produces a gas vapour that can be collected as a liquid and as a solid char. Fast pyrolysis processes are designed and operated to maximise the liquid fraction by up to 75wt.% on a dry-biomass-feed basis. Thus, although fast pyrolysis can be understood as some form of pretreatment of the biomass, it also represents one of the possible pathways for upgrading low-bulk-density biomass into densified, more homogeneous energy carriers (bio-oil and bio-char). The fourth production phase encompasses the secondary transport of the bioenergy feedstock from the roadside to a central conversion plant. Uncomminuted biomass is assumed to be transported with a truck-pole-trailer combination, comminuted biomass and bio-char with a truck-container- trailer combination, and bio-oil from a mobile fast pyrolysis system by a dedicated truck-tanker- trailer combination. Only when transporting comminuted biomass from whole trees or bio-oil is the payload capacity limited by the volume; in all other instances, mass is the limiting factor. Various other factors had to be considered to determine the total mass transport rate, including fixed and variable time requirements. Fixed time includes the time for all non-travelling activities, i.e. loading and securing of the load prior to travelling, and once the destination has been reached, the clearing of the load (unloading and weighing prior to and after unloading). The calculations of the variable time requirements for secondary transport depend on a variety of considerations. The average transport distance, for instance, is a function of the supply and demand for bioenergy feedstock. The supply depends, inter alia, on the suitability of land for biomass production, the willingness of Stellenbosch University http://scholar.sun.ac.za 241 landowners to participate by offering their land for lignocellulosic biomass production, and the productivity rate of the respective areas. The demand component is driven mainly by the conversion efficiency of the respective bioenergy system, but also by feedstock losses during the procurement and pretreatment of the feedstock. The above-mentioned land and biomass availability assessment served as basis for a transport optimisation model using GIS and LINGO, where the weight-average transport distance in terms of each lignocellulosic bioenergy system and each biomass procurement area were calculated. In turn, this was used to determine the number of truck configurations required, taking the total number of shuttle trips for each truck configuration and the total transport time (variable and fixed) into account. Five configurations of bioenergy conversion systems (BCS) were assumed for the fifth production phase. The first bioenergy conversion system (BCS I) entails an integrated steam-turbine system, where the biomass at a maximum 20% moisture content (dry basis) is combusted to generate steam, which is then used in a steam turbine to generate electricity. The same moisture content (MC) is required for BCS II, an integrated gasifier-gas-turbine system, where the biomass is upgraded to bio-gas, which in turn, is fed into a gas turbine. BCS III consists of a stationary fast-pyrolysis plant converting biomass (10% MC) into bio-oil and bio-char. The upgraded products are then fed into an integrated boiler-steam-turbine system to generate electricity. An integrated steam-turbine system is also assumed for BCS IV, also using bio-oil and bio-char that is produced in a mobile fast-pyrolysis system at the roadside, close to the primary biomass production sites. The last bioenergy conversion system (BCS V) also encompasses mobile fast-pyrolysis systems, but differs in the final conversion step, where only bio-oil is used to generate electricity, by directly injecting the liquid into a gas turbine. Also transported to a central facility, the bio-char by-product is assumed to be sold to the fertilising industry, which uses it as an additive for soils. To some extent, this effectively works as a way of capturing and storing carbon. The first two bioenergy conversion systems are based on well-established commercial options for the production of electricity from wood, resulting in relatively good conversion efficiencies and low capital and operational costs. Only BCS II is South African-designed and -made, while all other systems are either from Europe or the United States, creating some uncertainty in terms of exchange rate and production capacities. Some of the data used is based on the conversion systems working at production capacities that differ from the stipulated 5MWel. In these instances, the so-called six- tenth factor rule was used to estimate the capital cost of the conversion plants. For each of the BCSs, an economic lifetime expectancy of 20 years was assumed. An electricity tariff of R1.06 per kilowatt hour, for which the generated electricity is sold, was based on the renewable energy feed-in Stellenbosch University http://scholar.sun.ac.za 242 tariff (REFIT), as stipulated by the national energy regulator of South Africa (NERSA). For the first three biomass procurement areas, it was assumed that the excess heat/thermal energy is sold to industrial consumers for heating/cooling purposes at a rate of R0.15/kWhth. Additional income was assumed for selling certified emission reduction (CER) certificates (also called carbon credits) into the carbon market at a rate of R100/t of CO2 emissions avoided, taking the current South African energy mix into account. Quantifying and determining the emissions caused by the conversion systems appeared to be particularly challenging, since no actual data using the assumed feedstock was available. In order to overcome this problem, a simplified approach was used, applying the so-called thermo-chemical equilibrium and theoretical or stoichiometric oxygen or air requirement, which assumes the complete combustion of the feedstock. The emissions are based on the amount of oxidant that is just sufficient to burn the carbon, hydrogen, and sulphur in a fuel to carbon dioxide, carbon monoxide, water vapour and sulphur dioxide. Thus, the gas ratios applied in this study represent a lower limit of emissions, but give some indication of the emissions to be expected. Sophisticated software packages could have been used to calculate more accurate emission estimates, requiring additional information on enthalpy and combustion conditions, but this would have entailed a study in itself, requiring going beyond the scope of this study. Potential water consumption was not included in the LCA, as closed water cycles were assumed for the conversion systems. The recirculation of ash for fertilisation purposes, the only ?waste? in lignocellulosic bioenergy systems, was considered, but since ash recirculation systems are not commercially available, it was assumed to be used for landfill or for construction material. Chapter 6 entails the third phase of the life-cycle assessment, the life-cycle impact assessment (LCIA). The purpose of the LCIA is to better understand the environmental significance of a product system?s life cycle by assessing its inventory results. This was achieved by translating the environmental loads from the inventory results of the 37 lignocellulosic bioenergy systems for each of the biomass procurement areas into environmental impacts, using the so-called CML 2001 method (normalisation factors from November 2009). CML 2001 is a collection of impact assessment methods that restricts quantitative modelling to the relatively early stages in the cause- effect chain, to limit uncertainties and to group LCI results into midpoint categories, according to themes. The environmental impact categories taken into account were abiotic depletion potential (ADP, measured in gigajoules), acidification potential (AP, t SO2-equivalent), eutrophication potential (EP, t phosphate-equivalent), global warming potential (GWP100years, t CO2-equivalent), and photochemical ozone creation potential (POCP, t ethene-equivalent). The toxicity impact Stellenbosch University http://scholar.sun.ac.za 243 categories (human toxicity potential, as well as terrestrial, freshwater aquatic and marine aquatic eco-toxicity potentials) were not included in this study, due to a lack of consistency in the field of hazardous substances and heavy metals, as well as a lack of inventory data for emissions, creating data gaps, potentially resulting in incorrect conclusions stemming from inconsistent data. Other important environmental impact assessment methods not included in the LCIA, such as the biodiversity intactness index and the water footprint were also discussed, but were not included in this study, since they are not included in the commonly accepted LCIA methods. In addition, both environmental impacts have been dealt with a priori in the land availability assessment, by means of GIS. Furthermore, using the LCA framework as a guideline, a set of financial-economic and socio- economic criteria was defined, against which the LBSs were assessed. By means of multi-period budgeting (MPB), financial-economic data was translated into key parameters describing the profitability and cost performance of each LBS, making them more comparable. Internal rate of return (IRR), expressed as a percentage, was used as a profitability indicator. Four cost indicators were considered important in terms of risk of investment: capital and operational costs of technology for biomass upgrading and conversion (CAPEXconv. and OPEXconv.). The establishment of a bioenergy conversion system represents a capital-intensive venture, characterised by significant risks (e.g. sufficient supply of feedstock, continuity of production, reliability of the conversion technology and all ancillary systems, and a guaranteed market for the products produced) that are carried by either a single or a few private investors, a public investor, or a joint venture between public and private sectors. Costs other than conversion technology (CAPEXother and OPEXother) include all expenses along the value chain prior to biomass upgrading and bioenergy conversion, i.e. from the land valuation, primary production of biomass, harvesting, forwarding, comminution and secondary transport, amongst others. In contrast with the costs of the conversion systems, the costs occurring during the other production phases are carried by a variety of investors, such as land owners and entrepreneurs (small business owners, contractors, etc.). ?Direct employment creation potential?, subdivided into three income categories, was used as the socio-economic indicator, based on the productivity data of each production phase used in the MPB and LCA models. DECP I comprises the number of jobs created for unskilled to semi-skilled labourers (earning an income of less than R8 000 per month), including farm and forest workers, chainsaw, tractor, three-wheeler loader, and conversion plant operators, as well as assistants to truck drivers during secondary transportation. DECP II (earning an income from R8 000-R24 000 per month) includes all skilled labourers, such as operators of combine harvesters, feller-bunchers, Stellenbosch University http://scholar.sun.ac.za 244 forwarders, service technicians for the stationary comminution units, and truck drivers. Highly skilled labourers earning a monthly income of more than R24 000, such as engineers and managers for the conversion plant as well as for the supply chain, are aggregated in the category DECP III. Similar to the environmental impacts biodiversity and water balance, food security, another socio- economic impact was also briefly discussed. The main driver for each criterion, whether it be of an environmental, financial-economic or socio- economic nature, is the overall conversion efficiency (OCE) of the biomass upgrading and bioenergy conversion system. The greater the OCE, the less biomass is required, resulting in fewer upstream activities and less land required for biomass production. In terms of the environmental impact of the lignocellulosic bioenergy systems, a greater OCE is desired, resulting in lower total emissions and, therefore, in lower impacts for each life-cycle impact category. Similarly, for the financial-economic viability of the LBSs, a greater OCE results in lower costs, both in terms of capital and operating expenditure, as well as in higher internal rates of return on the capital invested; the lower the OCE, the greater the direct employment creation potential, particularly for the unskilled to semi-skilled income category. Another important driver is the efficiency of the harvesting system, which has a similar effect to the OCE. The greater the degree of mechanisation and automation, the lower the environmental impact and the higher the cost-effectiveness and profitability, but also the lower the direct employment creation potential. In continuing the LCA approach, Chapter 7 encompasses the fourth LCA phase, the interpretation of the results using the multi-criteria decision analysis (MCDA) method. As illustrated in the previous chapter, the complexity of bioenergy systems constitutes a major barrier to the implementation of bioenergy systems, and hence the decision-making problem was highlighted by the trade-offs between the defined LBS alternatives. Aimed at identifying the most sustainable lignocellulosic bioenergy system, the analytical hierarchy process (AHP), one of the commonly applied MCDA approaches, was applied to support decision makers in the CWDM in their attempt to overcome this decision-making barrier, by organising and synthesising the respective information, by integrating mixed sets of data, and by assisting decision makers to place the problem in context and to determine the preferences of the potential stakeholders involved. The initial steps of the AHP included the development of a hierarchy of criteria (criteria value tree) and the translation and normalisation of the performance data provided in the previous chapter into a standardised common language of relative performance, i.e. into so-called scores. The aggregation of these scores for each LBS resulted in a ranking of the alternatives, with each criterion being equally important. Consequently, the decision-making problem persisted, as the conflicting nature Stellenbosch University http://scholar.sun.ac.za 245 of some of the criteria, the differing viewpoints of potential stakeholders, and the resulting trade- offs were not considered in this phase of the MCDA, requiring an additional phase in which the stakeholder preferences were taken into consideration, by attaching weights to the considered criteria. Thus, aimed at providing insight, a task team of experts, reflecting the broad section of potential stakeholders, was introduced during a workshop to the decision-making problem at hand, including the alternatives and their respective performances in terms of the predefined criteria. The experts were then requested to express the relative preferences for the criteria by means of pairwise comparisons using the AHP-based ?Expert Choice? software. No serious conflicts of opinion between the participants were recorded during the discussions and the weighting procedure, resulting in consensus on a set of weights, where the main criterion ?financial-economic viability? received a preference of almost 60%, ?socio-economic potential?, nearly 25% and ?lowest environmental impact?, the remainder of almost 16%. The most important sub-criteria are ?best IRR? and ?direct employment creation potential (DECP) I?, with cumulative weights of around 43% and 18% respectively. The aggregation of the weighted scores into a single indicator allowed a ranking of the lignocellulosic bioenergy systems (LBS), placing LBS 26 at the top in biomass procurement areas (BPAs) I and II and second in BPAs III and IV. Around 73-74% of its aggregated, weighted score is derived from its ?financial-economic viability?, around 8-9% from its ?socio-economic potential? and 18-19% from its ?lowest environmental impact?. Similar profiles to that of LBS 26 are shown for most of the top-ten-ranked alternatives across all biomass procurement areas. With few exceptions, all encompass biomass upgrading and bioenergy conversion system (BCS) II, namely a parallel series of integrated 450Nm3/h gasifier-gas-turbine systems. Compared with the other bioenergy conversion systems, BCS II is characterised by relatively low capital and operating costs, as well as by good conversion efficiencies. This again highlights the importance of the overall conversion efficiency, as it also has an effect on all upstream activities, with less biomass and, thus, less land being required, resulting in fewer upstream activities and, therefore, in lower operational and capital costs, including for machinery and land. In contrast, the relatively poor overall conversion efficiency as well as the relatively high capital and operational costs of the conversion system are the main reasons why in commercial terms the relatively immature pyrolysis technology is currently not a viable option as part of a bio-electricity generating system. Particularly, alternatives deploying bioenergy conversion system III (a centralised, stationary fast-pyrolysis plant for biomass upgrading into bio-oil and bio-char, integrated with a boiler-steam-turbine system) showed poor results and were at the bottom of the Stellenbosch University http://scholar.sun.ac.za 246 ranking across all biomass procurement areas. Similar reasons can be given for alternatives deploying bioenergy conversion systems IV and V, which both encompass mobile fast-pyrolysis units for biomass upgrading. The only exception is the bioenergy conversion system V-driven LBS 13, which is ranked top in biomass procurement areas III and IV. Nearly 60% of LBS 13?s aggregated, weighted score is contributed by its socio-economic performance, with the remainder being equally contributed by its financial-economic and environmental performance. LBS 13?s relatively low harvesting and conversion efficiency causes such a significantly greater demand in terms of human capital, particularly in the low income category, that its aggregated, weighted score is slightly greater than the second-ranked LBS 26. However, the final decision maker may still select LBS 26, despite the fact that the ranking of BPAs III and IV gives preference to LBS 13, following the prerequisite of financial-economic viability, i.e. if the alternative is not profitable, private investors will not be interested in investing in this particular alternative. In both areas, LBS 13 is expected to generate a negative net present value. Two aspects contribute to a different outcome for BPAs III and IV, namely (i) the relatively lower biomass productivity, resulting in more land being required to ensure sufficient biomass feedstock supply and, thus, in more material, machinery and human capital being required, and (ii) the relatively lower land value, being one of the main cost factors, resulting in a lower impact in terms of costs on the financial-economic viability, i.e. the tendency to select alternatives with a relatively greater conversion efficiency becomes less important as increases in socio-economic potential compensate for a loss in financial-economic viability. However, similar to the first two biomass procurement areas, at least eight of the top-ten-ranked alternatives in BPAs III and IV encompass bioenergy conversion system II, the aggregated, weighted score profiles of which represent a more valid reflection of the set of weights given by the expert group. The application of MCDA proved to be an appealing and practical tool that organises and synthesises relevant information in a way that should lead decision makers to feel comfortable and confident about making a decision, as it minimises potential post-decision regret by ensuring satisfaction that all the criteria or factors have been properly taken into account. As was illustrated in the context of implementing bioenergy systems, MCDA is well suited to being integrated with performance-data-generating methods such as LCA and complementary financial- and socio- economic assessment methods. However, MCDA does not provide the ?single right answer?, even within the context of the model used, as the concept of an optimum does not exist in a multi-criteria framework. Rather, its purpose is to produce insight in order to help decision makers make better decisions; to learn and understand the problem faced; the stakeholder priorities, values and Stellenbosch University http://scholar.sun.ac.za 247 objectives; and through exploring these in the context of the problem, to guide them in identifying a preferred course of action, whilst promoting transparency. 8.3 Recommendations Following the outcome of this study, a number of recommendations can be put forward. The production of biomass for bioenergy in a short-ration coppice system is relatively new to South Africa. The availability of data related to this topic is limited and requires additional research and further validation of the current database in order to support similar studies in the future. Further, some recommendations deal with improvement to and application of the methods discussed in this study. ? In the context of land availability for biomass production, the combined use of geographic information systems and the biodiversity intactness index (BII), as well as the water footprint index (WF) could advance this study from a methodological point of view. The current approach limits the respective impacts due to land-use change by excluding sensitive areas, but does not quantify the respective impacts. The BII, for instance, is intended to provide a single, integrated measure of biodiversity, measuring the change in abundance across a wide range of well-known elements of biodiversity, relative to their levels in a chosen reference case. The application of the WF provides information on the volume of fresh water used for the production of a product (commodity, good or service) at the place where it was actually produced. ? In contrast to commercial forestry plantations, which are aimed at timber production and are characterised by long rotation cycles in order to produce high quality and dimension timber, short-rotation coppice (SRC) biomass production has far fewer requirements in terms of wood quality, and more emphasis is placed on the maximisation of volumetric production per time and area units. Hence, trees grown for bioenergy are to be harvested when reaching their maximum mean annual increment. Given the lack of experience of producing biomass in SRC systems in South Africa, additional research in terms of suitable tree species, their productivity rates, rotation lengths and other silvicultural parameters could improve the data quality, by providing sound and proven information. The use of improved genetic material/tree hybrids may, for instance, improve the growth rate, ease of cultivation, adaptability to site conditions, drought and frost resistance, or reduce the risk of invasion. ? In this context, another important consideration, for which additional research would present a useful and profound contribution, deals with the bulk densities of various types of comminuted and uncomminuted biomass at various moisture content levels from tree species grown for Stellenbosch University http://scholar.sun.ac.za 248 bioenergy. The assumptions on the bulk density of the bioenergy feedstock made in this study are based on the input from a group of forestry experts, but further research is recommended to validate the applied values. Sound assumptions on feedstock bulk densities are of crucial importance, as they affect a variety of bioenergy system components, such as handling, transport and storage. ? Another aspect pertaining to bioenergy feedstock, for which additional research would improve the data quality for assessing lignocellulosic bioenergy systems, is an approximation of the ratios of the basic components of the trees used for bioenergy production (e.g. bark, stem, branches, etc.), which in turn is affected by silvicultural aspects such as number of trees per hectare, and rotation length, amongst others. ? The chemical composition of the bioenergy feedstock represents another crucial aspect, particularly in terms of potential bioenergy conversion efficiencies and related emissions. A simplified approach to determining the magnitude of the emissions was used in this study, applying the so-called thermo-chemical equilibrium and theoretical or stoichiometric oxygen/air requirement, which assumes the complete combustion of the feedstock. This, however, represents the lower limit of emissions, giving some indication of the emissions to expect. By using software packages such as the NASA chemical equilibrium programme or ASPEN, more accurate emission estimates for the assessed bioenergy conversion systems could be provided, but this would have gone beyond the scope of this study. ? A previous study by the author has already dealt with the financial-economic viability of biomass production for bioenergy, from a farmer?s perspective. This was done by calculating the farm-gate price for biomass backwards, starting with the tariff paid by ESKOM, less the costs of bioenergy conversion, biomass upgrading, secondary transport, etc. (Von Doderer, 2009). Using the data and results from the present study to reassess the financial-economic impact on the various farm types found within the Cape Winelands District Municipality (CWDM) would provide additional information for a better understanding of this production system and for the implementation of such bioenergy systems. ? 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Stellenbosch University http://scholar.sun.ac.za 283 ANNEXURES Stellenbosch University http://scholar.sun.ac.za 284 Annexure 1: LCA results ?LBS 1 Paarl ? Primary production Forwarding Harvesting Comminution Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.43 0.61 0.14 0.22 0.19 0.00 0.27 ADP fossil [MJ] 24340734.13 6777362.08 2880466.74 5580608.67 3718611.61 0.00 5383685.03 AP [kg SO2-Equiv.] 125768.85 4155.63 1094.60 538.06 2133.49 115925.50 1921.57 EP [kg Phosphate-Equiv.] 31936.04 1209.89 224.59 29.69 477.40 29607.69 386.77 FAETP inf. [kg DCB-Equiv.] 4153.76 915.83 479.74 1274.57 613.39 0.00 870.23 GWP 100 years [kg CO2-Equiv.] 132794.05 -64309905.96 237653.74 154050.97 3476107.01 60120377.15 454511.16 HTP inf. [kg DCB-Equiv.] 611767.89 14149.37 8045.57 11556.80 12107.78 546596.71 19311.66 MAETP inf. [kg DCB-Equiv.] 80234139.97 21497245.58 9730829.71 18114892.40 12607743.19 0.00 18283429.09 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 9133.88 185.82 177.05 1733.50 267.09 6562.34 208.07 TETP inf. [kg DCB-Equiv.] 1369.29 334.51 157.48 391.76 199.92 0.00 285.61 Worcester (ADP elements) [kg Sb-Equiv.] 1.40 0.67 0.14 0.26 0.19 0.00 0.14 (ADP fossil) [MJ] 23740877.22 7760650.32 2880466.74 6577145.93 3718611.61 0.00 2804002.62 AP [kg SO2-Equiv.] 125637.76 4849.22 1094.60 634.14 2133.49 115925.50 1000.82 EP [kg Phosphate-Equiv.] 31963.41 1417.29 224.59 35.00 477.40 29607.69 201.44 FAETP inf. [kg DCB-Equiv.] 4090.16 1041.61 479.74 1502.17 613.39 0.00 453.25 GWP 100 years [kg CO2-Equiv.] 470765.64 -63781656.88 237653.74 181560.07 3476107.01 60120377.15 236724.56 HTP inf. [kg DCB-Equiv.] 606598.23 16169.50 8045.57 13620.51 12107.78 546596.71 10058.16 MAETP inf. [kg DCB-Equiv.] 77822517.12 24611630.30 9730829.71 21349694.61 12607743.19 0.00 9522619.32 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 9366.50 208.59 177.05 2043.05 267.09 6562.34 108.37 TETP inf. [kg DCB-Equiv.] 1349.95 382.07 157.48 461.72 199.92 0.00 148.76 Ashton (ADP elements) [kg Sb-Equiv.] 1.61 0.89 0.14 0.30 0.19 0.00 0.10 (ADP fossil) [MJ] 25506797.63 9467175.79 2880466.74 7533821.70 3718611.61 0.00 1906721.78 AP [kg SO2-Equiv.] 126238.52 5678.00 1094.60 726.38 2133.49 115925.50 680.56 EP [kg Phosphate-Equiv.] 32132.22 1645.46 224.59 40.09 477.40 29607.69 136.98 FAETP inf. [kg DCB-Equiv.] 4411.23 1289.22 479.74 1720.66 613.39 0.00 308.21 GWP 100 years [kg CO2-Equiv.] 1358009.50 -62845069.90 237653.74 207968.80 3476107.01 60120377.15 160972.70 HTP inf. [kg DCB-Equiv.] 609002.08 19810.80 8045.57 15601.68 12107.78 546596.71 6839.55 MAETP inf. [kg DCB-Equiv.] 83304513.10 30035454.34 9730829.71 24455104.73 12607743.19 0.00 6475381.14 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 9685.83 265.43 177.05 2340.22 267.09 6562.34 73.69 TETP inf. [kg DCB-Equiv.] 1456.07 468.64 157.48 528.88 199.92 0.00 101.15 Rural Cederberge (ADP elements) [kg Sb-Equiv.] 1.66 0.98 0.14 0.25 0.19 0.00 0.00 (ADP fossil) [MJ] 23950494 9054349 2880467 6278185 3718612 0 0 AP [kg SO2-Equiv.] 125460 4981 1095 605 2133 115926 0 EP [kg Phosphate-Equiv.] 31904 1416 225 33 477 29608 0 FAETP inf. [kg DCB-Equiv.] 4122 1268 480 1434 613 0 0 GWP 100 years [kg CO2-Equiv.] 1346380 -62831507 237654 173307 3476107 60120377 0 HTP inf. [kg DCB-Equiv.] 606103 19109 8046 13001 12108 546597 0 MAETP inf. [kg DCB-Equiv.] 78322292 28748179 9730830 20379254 12607743 0 0 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 9309 275 177 1950 267 6562 0 TETP inf. [kg DCB-Equiv.] 1358 453 157 441 200 0 0 Stellenbosch University http://scholar.sun.ac.za 285 Annexure 2: LCA results ?LBS 2 Paarl ? Primary production Forwarding Harvesting Comminution Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.21 0.52 0.12 0.19 0.16 0.00 0.21 ADP fossil [MJ] 20567400 5834261 2479637 4804042 3201150 0 4248311 AP [kg SO2-Equiv.] 83622 3577 942 463 1837 75287 1516 EP [kg Phosphate-Equiv.] 21092 1042 193 26 411 19116 305 FAETP inf. [kg DCB-Equiv.] 3513 788 413 1097 528 0 687 GWP 100 years [kg CO2-Equiv.] 81710 -55360890 204583 132614 2992391 51754353 358659 HTP inf. [kg DCB-Equiv.] 231311 12180 6926 9949 10423 176594 15239 MAETP inf. [kg DCB-Equiv.] 67757594 18505806 8376741 1559413 10853318 0 14427606 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 6476 160 152 1492 230 4277 164 TETP inf. [kg DCB-Equiv.] 1158 288 136 337 172 0 225 Worcester ADP elements [kg Sb-Equiv.] 1.20 0.58 0.12 0.23 0.16 0.00 0.12 ADP fossil [MJ] 20340674 6680721 2479637 5661906 3201150 0 2317260 AP [kg SO2-Equiv.] 83613 4174 942 546 1837 75287 827 EP [kg Phosphate-Equiv.] 21137 1220 193 30 411 19116 166 FAETP inf. [kg DCB-Equiv.] 3505 897 413 1293 528 0 375 GWP 100 years [kg CO2-Equiv.] 397105 -54906149 204583 156295 2992391 51754353 195632 HTP inf. [kg DCB-Equiv.] 227900 13919 6926 11725 10423 176594 8312 MAETP inf. [kg DCB-Equiv.] 66665260 21186810 8376741 18378788 10853318 0 7869603 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 6687 180 152 1759 230 4277 90 TETP inf. [kg DCB-Equiv.] 1157 329 136 397 172 0 123 Ashton ADP elements [kg Sb-Equiv.] 1.38 0.76 0.12 0.26 0.16 0.00 0.08 ADP fossil [MJ] 21860858 8149775 2479637 6485456 3201150 0 1544840 AP [kg SO2-Equiv.] 84130 4888 942 625 1837 75287 551 EP [kg Phosphate-Equiv.] 21282 1416 193 35 411 19116 111 FAETP inf. [kg DCB-Equiv.] 3782 1110 413 1481 528 0 250 GWP 100 years [kg CO2-Equiv.] 1160885 -54099893 204583 179029 2992391 51754353 130421 HTP inf. [kg DCB-Equiv.] 229969 17054 6926 13431 10423 176594 5541 MAETP inf. [kg DCB-Equiv.] 71384411 25855884 8376741 21052066 10853318 0 5246402 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 6962 228 152 2015 230 4277 60 TETP inf. [kg DCB-Equiv.] 1248 403 136 455 172 0 82 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.43 0.84 0.12 0.22 0.16 0.00 0.09 ADP fossil [MJ] 20714226 7794395 2479637 5404547 3201150 0 1834498 AP [kg SO2-Equiv.] 83529 4288 942 521 1837 75287 655 EP [kg Phosphate-Equiv.] 21099 1219 193 29 411 19116 132 FAETP inf. [kg DCB-Equiv.] 3564 1092 413 1234 528 0 297 GWP 100 years [kg CO2-Equiv.] 1167176 -54088217 204583 149191 2992391 51754353 154875 HTP inf. [kg DCB-Equiv.] 228166 16450 6926 11192 10423 176594 6580 MAETP inf. [kg DCB-Equiv.] 67751289 24747739 8376741 17543388 10853318 0 6230103 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 6645 236 152 1679 230 4277 71 TETP inf. [kg DCB-Equiv.] 1174 390 136 379 172 0 97 Stellenbosch University http://scholar.sun.ac.za 286 Annexure 3: LCA results ?LBS 3 Paarl ? Primary production Forwarding Harvesting Comminution Conversion Secondary transport ADP elements [kg Sb-Equiv.] 2.02 0.86 0.20 0.32 0.26 0.00 0.38 ADP fossil [MJ] 34385764 9618592 4088026 7920132 5277541 0 7481473 AP [kg SO2-Equiv.] 216723 5898 1553 764 3028 202810 2670 EP [kg Phosphate-Equiv.] 55235 1717 319 42 678 51942 537 FAETP inf. [kg DCB-Equiv.] 5869 1300 681 1809 871 0 1209 GWP 100 years [kg CO2-Equiv.] 97458 -91270137 337284 218633 4933373 85246691 631614 HTP inf. [kg DCB-Equiv.] 571633 20081 11418 16402 17184 479712 26837 MAETP inf. [kg DCB-Equiv.] 113329597 30509399 13810223 25709083 17893207 0 25407685 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 16418 264 251 2460 379 12775 289 TETP inf. [kg DCB-Equiv.] 1935 475 223 556 284 0 397 Worcester ADP elements [kg Sb-Equiv.] 1.99 0.95 0.20 0.37 0.26 0.00 0.20 ADP fossil [MJ] 33693614 11014098 4088026 9334441 5277541 0 3979507 AP [kg SO2-Equiv.] 216594 6882 1553 900 3028 202810 1420 EP [kg Phosphate-Equiv.] 55286 2011 319 50 678 51942 286 FAETP inf. [kg DCB-Equiv.] 5805 1478 681 2132 871 0 643 GWP 100 years [kg CO2-Equiv.] 590554 -90520434 337284 257674 4933373 85246691 335965 HTP inf. [kg DCB-Equiv.] 564867 22948 11418 19331 17184 479712 14275 MAETP inf. [kg DCB-Equiv.] 110447554 34929407 13810223 30299991 17893207 0 13514726 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 16755 296 251 2900 379 12775 154 TETP inf. [kg DCB-Equiv.] 1916 542 223 655 284 0 211 Ashton ADP elements [kg Sb-Equiv.] 2.29 1.26 0.20 0.43 0.26 0.00 0.14 ADP fossil [MJ] 36199849 13436039 4088026 10692178 5277541 0 2706065 AP [kg SO2-Equiv.] 217446 8058 1553 1031 3028 202810 966 EP [kg Phosphate-Equiv.] 55525 2335 319 57 678 51942 194 FAETP inf. [kg DCB-Equiv.] 6261 1830 681 2442 871 0 437 GWP 100 years [kg CO2-Equiv.] 1849752 -89191207 337284 295154 4933373 85246691 228456 HTP inf. [kg DCB-Equiv.] 568279 28116 11418 22142 17184 479712 9707 MAETP inf. [kg DCB-Equiv.] 118227732 42627026 13810223 34707262 17893207 0 9190014 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 17208 377 251 3321 379 12775 105 TETP inf. [kg DCB-Equiv.] 2066 665 223 751 284 0 144 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.36 1.39 0.20 0.36 0.26 0.00 0.15 ADP fossil [MJ] 34150287 12850146 4088026 8910148 5277541 0 3024425 AP [kg SO2-Equiv.] 216398 7069 1553 859 3028 202810 1079 EP [kg Phosphate-Equiv.] 55213 2009 319 47 678 51942 217 FAETP inf. [kg DCB-Equiv.] 5875 1800 681 2035 871 0 489 GWP 100 years [kg CO2-Equiv.] 1846685 -89171958 337284 245962 4933373 85246691 255333 HTP inf. [kg DCB-Equiv.] 564735 27120 11418 18452 17184 479712 10849 MAETP inf. [kg DCB-Equiv.] 111697435 40800094 13810223 28922718 17893207 0 10271192 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 16680 390 251 2768 379 12775 117 TETP inf. [kg DCB-Equiv.] 1936 643 223 625 284 0 160 Stellenbosch University http://scholar.sun.ac.za 287 Annexure 4: LCA results ?LBS 4 Paarl ? Prim. production Forwarding Harvesting Comminution Conversion Upgrading Transp. Transp. ADP elements [kg Sb-Equiv.] 1.52 0.74 0.17 0.27 0.23 0.00 0.00 0.028817438 0.082857162 ADP fossil [MJ] 25277401 8245734 3504544 6789694 4524280 0 0 571099 1642049 AP [kg SO2-Equiv.] 183359 5056 1332 655 2596 148246 24680 204 591 EP [kg Phosphate-Equiv.] 46819 1472 273 36 581 38171 6125 41 119 FAETP inf. [kg DCB-Equiv.] 4353 1114 584 1551 746 0 0 92 265 GWP 100 years [kg CO2-Equiv.] -270989 -78243187 289143 187427 4229235 69969071 3110473 48214 138633 HTP inf. [kg DCB-Equiv.] 472879 17215 9789 14061 14731 352463 56629 2049 5943 MAETP inf. [kg DCB-Equiv.] 82888884 26154804 11839096 22039636 15339317 0 0 1939499 5576532 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.000194505 0.00055925 POCP [kg Ethene-Equiv.] 13862 226 215 2109 325 9482 1418 22 64 TETP inf. [kg DCB-Equiv.] 1436 407 192 477 243 0 0 30 87 Worcester of bio-char of bio-oil ADP elements [kg Sb-Equiv.] 1.59 0.82 0.17 0.32 0.23 0.00 0.00 0.015328425 0.044072959 ADP fossil [MJ] 26650231 9442060 3504544 8002140 4524280 0 0 303776 873431 AP [kg SO2-Equiv.] 183947 5900 1332 772 2596 148246 24680 108 314 EP [kg Phosphate-Equiv.] 47002 1724 273 43 581 38171 6125 22 63 FAETP inf. [kg DCB-Equiv.] 4615 1267 584 1828 746 0 0 49 141 GWP 100 years [kg CO2-Equiv.] 317718 -77600488 289143 220897 4229235 69969071 3110473 25646 73741 HTP inf. [kg DCB-Equiv.] 474107 19673 9789 16572 14731 352463 56629 1090 3161 MAETP inf. [kg DCB-Equiv.] 87095533 29943946 11839096 25975285 15339317 0 0 1031648 2966241 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00010346 0.000297473 POCP [kg Ethene-Equiv.] 14226 254 215 2486 325 9482 1418 12 34 TETP inf. [kg DCB-Equiv.] 1524 465 192 562 243 0 0 16 46 Ashton ADP elements [kg Sb-Equiv.] 1.88 1.08 0.17 0.37 0.23 0.00 0.00 0.010423329 0.029969612 ADP fossil [MJ] 29513731 11518319 3504544 9166087 4524280 0 0 206568 593933 AP [kg SO2-Equiv.] 184933 6908 1332 884 2596 148246 24680 74 214 EP [kg Phosphate-Equiv.] 47259 2002 273 49 581 38171 6125 15 43 FAETP inf. [kg DCB-Equiv.] 5121 1569 584 2093 746 0 0 33 96 GWP 100 years [kg CO2-Equiv.] 1457551 -76460982 289143 253027 4229235 69969071 3110473 17439 50144 HTP inf. [kg DCB-Equiv.] 479587 24103 9789 18982 14731 352463 56629 741 2149 MAETP inf. [kg DCB-Equiv.] 96193373 36542888 11839096 29753508 15339317 0 0 701521 2017044 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 7.03529E-05 0.000202282 POCP [kg Ethene-Equiv.] 14642 323 215 2847 325 9482 1418 8 23 TETP inf. [kg DCB-Equiv.] 1691 570 192 643 243 0 0 11 32 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.94 1.19 0.17 0.31 0.23 0.00 0.00 0.011649603 0.033495448 ADP fossil [MJ] 27577957 11016049 3504544 7638406 4524280 0 0 230870 663807 AP [kg SO2-Equiv.] 183971 6060 1332 736 2596 148246 24680 82 239 EP [kg Phosphate-Equiv.] 46978 1722 273 41 581 38171 6125 17 48 FAETP inf. [kg DCB-Equiv.] 4762 1543 584 1745 746 0 0 37 107 GWP 100 years [kg CO2-Equiv.] 1439832 -76444480 289143 210856 4229235 69969071 3110473 19491 56043 HTP inf. [kg DCB-Equiv.] 475910 23249 9789 15818 14731 352463 56629 828 2402 MAETP inf. [kg DCB-Equiv.] 89988111 34976713 11839096 24794590 15339317 0 0 784053 2254343 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 7.86297E-05 0.00022608 POCP [kg Ethene-Equiv.] 14182 334 215 2373 325 9482 1418 9 26 TETP inf. [kg DCB-Equiv.] 1570 551 192 536 243 0 0 12 35 Stellenbosch University http://scholar.sun.ac.za 288 Annexure 5: LCA results ?LBS 5 Paarl ? Prim. production Forwarding Harvesting Comminution bio-char for sale Conversion Upgrading transport transport ADP elements [kg Sb-Equiv.] 2.02 0.98 0.23 0.36 0.30 0.00 0.00 0.00 0.041281032 0.118693034 ADP fossil [MJ] 33618583 10885596 4626519 8963406 5972722 0 0 0 818101 2352239 AP [kg SO2-Equiv.] 116761 6675 1758 864 3427 0 70317 32582 292 846 EP [kg Phosphate-Equiv.] 29481 1943 361 48 767 0 18047 8086 59 171 FAETP inf. [kg DCB-Equiv.] 5786 1471 771 2047 985 0 0 0 132 380 GWP 100 years [kg CO2-Equiv.] -36009946 -103292643 381712 247432 5583219 8929400 47766987 4106287 69067 198593 HTP inf. [kg DCB-Equiv.] 326526 22726 12923 18562 19447 0 166661 74759 2935 8513 MAETP inf. [kg DCB-Equiv.] 110270103 34528231 15629367 29095597 20250179 0 0 0 2778336 7988393 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.000278629 0.000801126 POCP [kg Ethene-Equiv.] 10536 298 284 2784 429 0 4745 1871 32 92 TETP inf. [kg DCB-Equiv.] 1909 537 253 629 321 0 0 0 43 125 Worcester of bio-char of bio-oil ADP elements [kg Sb-Equiv.] 2.11 1.08 0.23 0.42 0.30 0.00 0.00 0.00 0.021854664 0.062837489 ADP fossil [MJ] 35306595 12464924 4626519 10564015 5972722 0 0 0 433112 1245303 AP [kg SO2-Equiv.] 117494 7789 1758 1019 3427 0 70317 32582 155 448 EP [kg Phosphate-Equiv.] 29715 2276 361 56 767 0 18047 8086 31 90 FAETP inf. [kg DCB-Equiv.] 6113 1673 771 2413 985 0 0 0 70 201 GWP 100 years [kg CO2-Equiv.] -35243262 -102444185 381712 291616 5583219 8929400 47766987 4106287 36565 105137 HTP inf. [kg DCB-Equiv.] 327698 25971 12923 21877 19447 0 166661 74759 1554 4507 MAETP inf. [kg DCB-Equiv.] 115401281 39530463 15629367 34291239 20250179 0 0 0 1470884 4229149 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.000147509 0.000424126 POCP [kg Ethene-Equiv.] 11012 335 284 3281 429 0 4745 1871 17 49 TETP inf. [kg DCB-Equiv.] 2018 614 253 742 321 0 0 0 23 66 Ashton ADP elements [kg Sb-Equiv.] 2.51 1.42 0.23 0.49 0.30 0.00 0.00 0.00 0.02 0.06 ADP fossil [MJ] 39459822 15205894 4626519 12100599 5972722 0 0 0 401030 1153058 AP [kg SO2-Equiv.] 118928 9120 1758 1167 3427 0 70317 32582 143 415 EP [kg Phosphate-Equiv.] 30080 2643 361 64 767 0 18047 8086 29 84 FAETP inf. [kg DCB-Equiv.] 6841 2071 771 2764 985 0 0 0 65 186 GWP 100 years [kg CO2-Equiv.] -33707023 -100939867 381712 334033 5583219 8929400 47766987 4106287 33856 97349 HTP inf. [kg DCB-Equiv.] 336280 31820 12923 25059 19447 0 166661 74759 1439 4173 MAETP inf. [kg DCB-Equiv.] 128678457 48242046 15629367 39279056 20250179 0 0 0 1361929 3915879 ODP, steady state [kg R11-Equiv.] 0.05 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11575 426 284 3759 429 0 4745 1871 15 45 TETP inf. [kg DCB-Equiv.] 2259 753 253 849 321 0 0 0 21 61 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.56 1.57 0.23 0.40 0.30 0.00 0.00 0.00 0.02 0.04 ADP fossil [MJ] 36407004 14542824 4626519 10083832 5972722 0 0 0 304783 876324 AP [kg SO2-Equiv.] 117480 8000 1758 972 3427 0 70317 32582 109 315 EP [kg Phosphate-Equiv.] 29674 2274 361 54 767 0 18047 8086 22 64 FAETP inf. [kg DCB-Equiv.] 6287 2037 771 2303 985 0 0 0 49 142 GWP 100 years [kg CO2-Equiv.] -33772400 -100918083 381712 278361 5583219 8929400 47766987 4106287 25731 73986 HTP inf. [kg DCB-Equiv.] 329630 30693 12923 20882 19447 0 166661 74759 1093 3171 MAETP inf. [kg DCB-Equiv.] 118797690 46174463 15629367 32732547 20250179 0 0 0 1035066 2976068 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 10949 441 284 3132 429 0 4745 1871 12 34 TETP inf. [kg DCB-Equiv.] 2072 728 253 708 321 0 0 0 16 46 Stellenbosch University http://scholar.sun.ac.za 289 Annexure 6: LCA results ?LBS 6 Paarl ? Primary production Forwarding Harvesting Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.23 0.59 0.14 0.22 0.00 0.01 0.28 ADP fossil [MJ] 31676650 6569892 2792289 5409774 0 11411139 5493556 AP [kg SO2-Equiv.] 137612 4028 1061 522 115926 14115 1961 EP [kg Phosphate-Equiv.] 32047 1173 218 29 29608 625 395 FAETP inf. [kg DCB-Equiv.] 5107 888 465 1236 0 1631 888 GWP 100 years [kg CO2-Equiv.] -182953 -62341235 230379 149335 60120377 1194405 463787 HTP inf. [kg DCB-Equiv.] 938287 13716 7799 11203 546597 339266 19706 MAETP inf. [kg DCB-Equiv.] 3390850873 20839167 9432947 17560355 0 3324361844 18656560 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 9533 180 172 1680 6562 726 212 TETP inf. [kg DCB-Equiv.] 5025 324 153 380 0 3877 291 Worcester ADP elements [kg Sb-Equiv.] 1.19 0.65 0.14 0.26 0.00 0.01 0.14 ADP fossil [MJ] 30963540 7523079 2792289 6375805 0 11411139 2861227 AP [kg SO2-Equiv.] 137438 4701 1061 615 115926 14115 1021 EP [kg Phosphate-Equiv.] 32064 1374 218 34 29608 625 206 FAETP inf. [kg DCB-Equiv.] 5024 1010 465 1456 0 1631 462 GWP 100 years [kg CO2-Equiv.] 133561 -61829157 230379 176002 60120377 1194405 241556 HTP inf. [kg DCB-Equiv.] 932804 15675 7799 13204 546597 339266 10263 MAETP inf. [kg DCB-Equiv.] 3388066095 23858213 9432947 20696133 0 3324361844 9716958 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 9754 202 172 1981 6562 726 111 TETP inf. [kg DCB-Equiv.] 5000 370 153 448 0 3877 152 Ashton ADP elements [kg Sb-Equiv.] 1.39 0.86 0.14 0.29 0.00 0.01 0.10 ADP fossil [MJ] 32629622 9177364 2792289 7303195 0 11411139 1945634 AP [kg SO2-Equiv.] 138004 5504 1061 704 115926 14115 694 EP [kg Phosphate-Equiv.] 32224 1595 218 39 29608 625 140 FAETP inf. [kg DCB-Equiv.] 5328 1250 465 1668 0 1631 314 GWP 100 years [kg CO2-Equiv.] 989780 -60921241 230379 201602 60120377 1194405 164258 HTP inf. [kg DCB-Equiv.] 934970 19204 7799 15124 546597 339266 6979 MAETP inf. [kg DCB-Equiv.] 3393224803 29116002 9432947 23706479 0 3324361844 6607532 ODP, steady state [kg R11-Equiv.] 0.05 0.03 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 10062 257 172 2269 6562 726 75 TETP inf. [kg DCB-Equiv.] 5100 454 153 513 0 3877 103 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.44 0.95 0.14 0.24 0.00 0.01 0.10 ADP fossil [MJ] 31126682 8777175 2792289 6085995 0 11411139 2060084 AP [kg SO2-Equiv.] 137252 4828 1061 587 115926 14115 735 EP [kg Phosphate-Equiv.] 32003 1372 218 32 29608 625 148 FAETP inf. [kg DCB-Equiv.] 5048 1229 465 1390 0 1631 333 GWP 100 years [kg CO2-Equiv.] 978989 -60908093 230379 168002 60120377 1194405 173920 HTP inf. [kg DCB-Equiv.] 932179 18524 7799 12603 546597 339266 7390 MAETP inf. [kg DCB-Equiv.] 3388414533 27868133 9432947 19755399 0 3324361844 6996210 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 9697 266 172 1890 6562 726 80 TETP inf. [kg DCB-Equiv.] 5006 439 153 427 0 3877 109 Stellenbosch University http://scholar.sun.ac.za 290 Annexure 7: LCA results ? LBS 7 Paarl ? Primary production Forwarding Harvesting, Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.04 0.51 0.12 0.19 0.00 0.01 0.22 ADP fossil [MJ] 26874608 5655662 2403729 4656979 0 9823227 4335011 AP [kg SO2-Equiv.] 93815 3468 913 449 75287 12151 1547 EP [kg Phosphate-Equiv.] 21187 1010 187 25 19116 538 311 FAETP inf. [kg DCB-Equiv.] 4333 764 400 1064 0 1404 701 GWP 100 years [kg CO2-Equiv.] -190765 -53666169 198320 128554 51754353 1028198 365978 HTP inf. [kg DCB-Equiv.] 512365 11808 6714 9644 176594 292056 15550 MAETP inf. [kg DCB-Equiv.] 2917660197 17939302 8120310 15116752 0 2861761786 14722047 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 6819 155 148 1447 4277 625 168 TETP inf. [kg DCB-Equiv.] 4305 279 131 327 0 3338 230 Worcester ADP elements [kg Sb-Equiv.] 1.02 0.56 0.12 0.22 0.00 0.01 0.12 ADP fossil [MJ] 26556299 6476209 2403729 5488583 0 9823227 2364551 AP [kg SO2-Equiv.] 93770 4047 913 529 75287 12151 844 EP [kg Phosphate-Equiv.] 21223 1183 187 29 19116 538 170 FAETP inf. [kg DCB-Equiv.] 4309 869 400 1254 0 1404 382 GWP 100 years [kg CO2-Equiv.] 106658 -53225349 198320 151511 51754353 1028198 199624 HTP inf. [kg DCB-Equiv.] 508705 13493 6714 11366 176594 292056 8482 MAETP inf. [kg DCB-Equiv.] 2916266709 20538234 8120310 17816172 0 2861761786 8030208 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 7020 174 148 1705 4277 625 91 TETP inf. [kg DCB-Equiv.] 4299 319 131 385 0 3338 125 Ashton ADP elements [kg Sb-Equiv.] 1.20 0.74 0.12 0.25 0.00 0.01 0.08 ADP fossil [MJ] 27990538 7900292 2403729 6286922 0 9823227 1576368 AP [kg SO2-Equiv.] 94258 4738 913 606 75287 12151 563 EP [kg Phosphate-Equiv.] 21361 1373 187 33 19116 538 113 FAETP inf. [kg DCB-Equiv.] 4571 1076 400 1436 0 1404 255 GWP 100 years [kg CO2-Equiv.] 843730 -52443774 198320 173549 51754353 1028198 133083 HTP inf. [kg DCB-Equiv.] 510570 16532 6714 13019 176594 292056 5655 MAETP inf. [kg DCB-Equiv.] 2920707560 25064378 8120310 20407615 0 2861761786 5353472 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 7285 222 148 1953 4277 625 61 TETP inf. [kg DCB-Equiv.] 4385 391 131 441 0 3338 84 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.24 0.81 0.12 0.21 0.00 0.01 0.09 ADP fossil [MJ] 26893786 7555791 2403729 5239102 0 9823227 1871937 AP [kg SO2-Equiv.] 93680 4156 913 505 75287 12151 668 EP [kg Phosphate-Equiv.] 21185 1181 187 28 19116 538 134 FAETP inf. [kg DCB-Equiv.] 4362 1058 400 1197 0 1404 303 GWP 100 years [kg CO2-Equiv.] 851076 -52432455 198320 144624 51754353 1028198 158036 HTP inf. [kg DCB-Equiv.] 508875 15947 6714 10850 176594 292056 6715 MAETP inf. [kg DCB-Equiv.] 2917235845 23990155 8120310 17006346 0 2861761786 6357248 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 6979 229 148 1627 4277 625 72 TETP inf. [kg DCB-Equiv.] 4314 378 131 368 0 3338 99 Stellenbosch University http://scholar.sun.ac.za 291 Annexure 8: LCA results ? LBS 8 Paarl ? Primary production Forwarding Harvesting Upgrading Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.73 0.84 0.19 0.31 0.00 0.01 0.39 ADP fossil [MJ] 44793821 9324146 3962883 7677679 0 16194958 7634156 AP [kg SO2-Equiv.] 233530 5717 1506 740 202810 20032 2725 EP [kg Phosphate-Equiv.] 55392 1665 309 41 51942 887 548 FAETP inf. [kg DCB-Equiv.] 7222 1260 660 1754 0 2315 1234 GWP 100 years [kg CO2-Equiv.] -350931 -88476153 326959 211940 85246691 1695127 644504 HTP inf. [kg DCB-Equiv.] 1035025 19466 11069 15900 479712 481494 27384 MAETP inf. [kg DCB-Equiv.] 4811824007 29575437 13387461 24922070 0 4718012829 25926209 ODP, steady state [kg R11-Equiv.] 0.07 0.03 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 16985 256 244 2385 12775 1031 295 TETP inf. [kg DCB-Equiv.] 7124 460 217 539 0 5503 405 Worcester ADP elements [kg Sb-Equiv.] 1.69 0.92 0.19 0.36 0.00 0.01 0.20 ADP fossil [MJ] 43944187 10676932 3962883 9048693 0 16194958 4060721 AP [kg SO2-Equiv.] 233341 6671 1506 872 202810 20032 1449 EP [kg Phosphate-Equiv.] 55428 1950 309 48 51942 887 292 FAETP inf. [kg DCB-Equiv.] 7131 1433 660 2067 0 2315 656 GWP 100 years [kg CO2-Equiv.] 111986 -87749400 326959 249786 85246691 1695127 342822 HTP inf. [kg DCB-Equiv.] 1027825 22246 11069 18739 479712 481494 14566 MAETP inf. [kg DCB-Equiv.] 4808423406 33860139 13387461 29372440 0 4718012829 13790537 ODP, steady state [kg R11-Equiv.] 0.07 0.03 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 17304 287 244 2811 12775 1031 157 TETP inf. [kg DCB-Equiv.] 7096 526 217 635 0 5503 215 Ashton ADP elements [kg Sb-Equiv.] 1.98 1.22 0.19 0.42 0.00 0.01 0.14 ADP fossil [MJ] 46308729 13024732 3962883 10364866 0 16194958 2761290 AP [kg SO2-Equiv.] 234144 7812 1506 999 202810 20032 986 EP [kg Phosphate-Equiv.] 55656 2264 309 55 51942 887 198 FAETP inf. [kg DCB-Equiv.] 7562 1774 660 2367 0 2315 446 GWP 100 years [kg CO2-Equiv.] 1327151 -86460864 326959 286119 85246691 1695127 233119 HTP inf. [kg DCB-Equiv.] 1030899 27255 11069 21464 479712 481494 9905 MAETP inf. [kg DCB-Equiv.] 4815744767 41322117 13387461 33644795 0 4718012829 9377565 ODP, steady state [kg R11-Equiv.] 0.08 0.04 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 17741 365 244 3220 12775 1031 107 TETP inf. [kg DCB-Equiv.] 7238 645 217 728 0 5503 146 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.05 1.34 0.19 0.35 0.00 0.01 0.16 ADP fossil [MJ] 44338151 12456774 3962883 8637388 0 16194958 3086148 AP [kg SO2-Equiv.] 233134 6852 1506 833 202810 20032 1102 EP [kg Phosphate-Equiv.] 55354 1948 309 46 51942 887 222 FAETP inf. [kg DCB-Equiv.] 7191 1745 660 1973 0 2315 499 GWP 100 years [kg CO2-Equiv.] 1325550 -86442205 326959 238432 85246691 1695127 260544 HTP inf. [kg DCB-Equiv.] 1027522 26290 11069 17887 479712 481494 11070 MAETP inf. [kg DCB-Equiv.] 4809469539 39551112 13387461 28037329 0 4718012829 10480808 ODP, steady state [kg R11-Equiv.] 0.07242 0.03124 0.00138 0.00293 0.00000 0.03582 0.00105 POCP [kg Ethene-Equiv.] 17230 378 244 2683 12775 1031 119 TETP inf. [kg DCB-Equiv.] 7113 623 217 606 0 5503 164 Stellenbosch University http://scholar.sun.ac.za 292 Annexure 9: LCA results ? LBS 9 Paarl ? Primary production Harvesting Comminution Forwarding Conversion Secondary transport ADP elements [kg Sb-Equiv.] 2.15 0.87 0.66 0.14 0.19 0.00 0.29 ADP fossil [MJ] 38511285 9681946 16442865 2788959 3877350 0 5720165 AP [kg SO2-Equiv.] 129314 5937 1585 1600 2225 115926 2042 EP [kg Phosphate-Equiv.] 32690 1728 87 358 498 29608 411 FAETP inf. [kg DCB-Equiv.] 7088 1308 3755 460 640 0 925 GWP 100 years [kg CO2-Equiv.] 33665 -91871294 27578587 3398217 324860 60120377 482918 HTP inf. [kg DCB-Equiv.] 643085 20213 34051 9081 12625 546597 20519 MAETP inf. [kg DCB-Equiv.] 126112474 30710351 53374237 9455807 13145936 0 19426143 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 12635 265 5108 200 278 6562 221 TETP inf. [kg DCB-Equiv.] 2294 478 1154 150 208 0 303 Worcester ADP elements [kg Sb-Equiv.] 2.22 0.96 0.78 0.14 0.19 0.00 0.15 ADP fossil [MJ] 40160365 11086643 19379091 2788959 3877350 0 3028323 AP [kg SO2-Equiv.] 129627 6927 1868 1600 2225 115926 1081 EP [kg Phosphate-Equiv.] 32809 2025 103 358 498 29608 218 FAETP inf. [kg DCB-Equiv.] 7503 1488 4426 460 640 0 490 GWP 100 years [kg CO2-Equiv.] 642105 -91116653 27659641 3398217 324860 60120377 255663 HTP inf. [kg DCB-Equiv.] 642396 23099 40132 9081 12625 546597 10863 MAETP inf. [kg DCB-Equiv.] 130950995 35159472 62905350 9455807 13145936 0 10284429 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 13476 298 6020 200 278 6562 117 TETP inf. [kg DCB-Equiv.] 2425 546 1360 150 208 0 161 Ashton ADP elements [kg Sb-Equiv.] 2.63 1.27 0.89 0.14 0.19 0.00 0.14 ADP fossil [MJ] 45192716 13524537 22197868 2788959 3877350 0 2804003 AP [kg SO2-Equiv.] 131003 8111 2140 1600 2225 115926 1001 EP [kg Phosphate-Equiv.] 33134 2351 118 358 498 29608 201 FAETP inf. [kg DCB-Equiv.] 8464 1842 5070 460 640 0 453 GWP 100 years [kg CO2-Equiv.] 2038960 -89778671 27737452 3398217 324860 60120377 236725 HTP inf. [kg DCB-Equiv.] 652631 28301 45969 9081 12625 546597 10058 MAETP inf. [kg DCB-Equiv.] 147087374 42907792 72055219 9455807 13145936 0 9522619 ODP, steady state [kg R11-Equiv.] 0.05 0.04 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14424 379 6895 200 278 6562 108 TETP inf. [kg DCB-Equiv.] 2735 669 1558 150 208 0 149 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.60 1.40 0.74 0.14 0.19 0.00 0.12 ADP fossil [MJ] 40566838 12934784 18498223 2788959 3877350 0 2467522 AP [kg SO2-Equiv.] 129530 7115 1784 1600 2225 115926 881 EP [kg Phosphate-Equiv.] 32762 2022 98 358 498 29608 177 FAETP inf. [kg DCB-Equiv.] 7535 1812 4225 460 640 0 399 GWP 100 years [kg CO2-Equiv.] 1927801 -89759296 27635325 3398217 324860 60120377 208318 HTP inf. [kg DCB-Equiv.] 642760 27299 38308 9081 12625 546597 8851 MAETP inf. [kg DCB-Equiv.] 132096492 41068827 60046016 9455807 13145936 0 8379905 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 13275 392 5746 200 278 6562 95 TETP inf. [kg DCB-Equiv.] 2435 647 1299 150 208 0 131 Stellenbosch University http://scholar.sun.ac.za 293 Annexure 10: LCA results ? LBS 10 Paarl ? Primary production Harvesting Comminution Forwarding Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.84 0.75 0.57 0.12 0.17 0.00 0.24 ADP fossil [MJ] 32959159 8334659 14154765 2400862 3337799 0 4731073 AP [kg SO2-Equiv.] 86743 5111 1365 1377 1915 75287 1689 EP [kg Phosphate-Equiv.] 21756 1488 75 308 429 19116 340 FAETP inf. [kg DCB-Equiv.] 6070 1126 3233 396 551 0 765 GWP 100 years [kg CO2-Equiv.] 12678 -79086986 23740902 2925340 279654 51754353 399415 HTP inf. [kg DCB-Equiv.] 258963 17401 29313 7817 10868 176594 16971 MAETP inf. [kg DCB-Equiv.] 107907550 26436866 45946969 8139989 11316619 0 16067107 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 9497 229 4397 172 240 4277 183 TETP inf. [kg DCB-Equiv.] 1965 411 994 129 179 0 251 Worcester ADP elements [kg Sb-Equiv.] 1.91 0.82 0.67 0.12 0.17 0.00 0.13 ADP fossil [MJ] 34475316 9543887 16682402 2400862 3337799 0 2510365 AP [kg SO2-Equiv.] 87047 5963 1608 1377 1915 75287 896 EP [kg Phosphate-Equiv.] 21865 1743 89 308 429 19116 180 FAETP inf. [kg DCB-Equiv.] 6443 1281 3810 396 551 0 406 GWP 100 years [kg CO2-Equiv.] 544602 -78437356 23810676 2925340 279654 51754353 211935 HTP inf. [kg DCB-Equiv.] 258716 19885 34547 7817 10868 176594 9005 MAETP inf. [kg DCB-Equiv.] 112400668 30266872 54151785 8139989 11316619 0 8525404 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 10225 257 5182 172 240 4277 97 TETP inf. [kg DCB-Equiv.] 2083 470 1171 129 179 0 133 Ashton ADP elements [kg Sb-Equiv.] 2.27 1.09 0.77 0.12 0.17 0.00 0.12 ADP fossil [MJ] 38903944 11642536 19108933 2400862 3337799 0 2413813 AP [kg SO2-Equiv.] 88266 6983 1842 1377 1915 75287 862 EP [kg Phosphate-Equiv.] 22151 2024 102 308 429 19116 173 FAETP inf. [kg DCB-Equiv.] 7287 1585 4364 396 551 0 390 GWP 100 years [kg CO2-Equiv.] 1755229 -77285561 23877660 2925340 279654 51754353 203783 HTP inf. [kg DCB-Equiv.] 267873 24363 39572 7817 10868 176594 8659 MAETP inf. [kg DCB-Equiv.] 126619498 36936978 62028408 8139989 11316619 0 8197504 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11044 326 5936 172 240 4277 93 TETP inf. [kg DCB-Equiv.] 2354 576 1341 129 179 0 128 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.22 1.20 0.64 0.12 0.17 0.00 0.10 ADP fossil [MJ] 34728673 11134850 15924111 2400862 3337799 0 1931050 AP [kg SO2-Equiv.] 86929 6125 1535 1377 1915 75287 689 EP [kg Phosphate-Equiv.] 21817 1741 85 308 429 19116 139 FAETP inf. [kg DCB-Equiv.] 6455 1560 3637 396 551 0 312 GWP 100 years [kg CO2-Equiv.] 1643236 -77268882 23789744 2925340 279654 51754353 163027 HTP inf. [kg DCB-Equiv.] 258683 23500 32977 7817 10868 176594 6927 MAETP inf. [kg DCB-Equiv.] 113058864 35353913 51690340 8139989 11316619 0 6558003 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 10048 338 4946 172 240 4277 75 TETP inf. [kg DCB-Equiv.] 2086 557 1118 129 179 0 102 Stellenbosch University http://scholar.sun.ac.za 294 Annexure 11: LCA results ? LBS 11 Paarl ? Primary production Harvesting Comminution Forwarding Upgrading Secondary transport ADP elements [kg Sb-Equiv.] 3.05 1.23 0.94 0.20 0.28 0.00 0.41 ADP fossil [MJ] 54656124 13740846 23336102 3958156 5502826 0 8118194 AP [kg SO2-Equiv.] 221811 8425 2250 2271 3157 202810 2898 EP [kg Phosphate-Equiv.] 56317 2453 124 508 706 51942 583 FAETP inf. [kg DCB-Equiv.] 10059 1857 5330 653 908 0 1312 GWP 100 years [kg CO2-Equiv.] -29789 -130385910 39140182 4822830 461049 85246691 685369 HTP inf. [kg DCB-Equiv.] 616651 28687 48326 12888 17917 479712 29121 MAETP inf. [kg DCB-Equiv.] 178981802 43584855 75749977 13419905 18657023 0 27570041 ODP, steady state [kg R11-Equiv.] 0.05 0.04 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 21394 377 7249 284 395 12775 314 TETP inf. [kg DCB-Equiv.] 3256 678 1638 213 296 0 431 Worcester ADP elements [kg Sb-Equiv.] 3.15 1.36 1.10 0.20 0.28 0.00 0.22 ADP fossil [MJ] 56996539 15734426 27503263 3958156 5502826 0 4297867 AP [kg SO2-Equiv.] 222255 9832 2652 2271 3157 202810 1534 EP [kg Phosphate-Equiv.] 56486 2873 146 508 706 51942 309 FAETP inf. [kg DCB-Equiv.] 10649 2112 6282 653 908 0 695 GWP 100 years [kg CO2-Equiv.] 833723 -129314905 39255215 4822830 461049 85246691 362842 HTP inf. [kg DCB-Equiv.] 615672 32783 56956 12888 17917 479712 15417 MAETP inf. [kg DCB-Equiv.] 185848744 49899153 89276758 13419905 18657023 0 14595904 ODP, steady state [kg R11-Equiv.] 0.06 0.04 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 22587 423 8543 284 395 12775 166 TETP inf. [kg DCB-Equiv.] 3442 775 1931 213 296 0 228 Ashton ADP elements [kg Sb-Equiv.] 3.74 1.80 1.27 0.20 0.28 0.00 0.20 ADP fossil [MJ] 64138569 19194342 31503738 3958156 5502826 0 3979507 AP [kg SO2-Equiv.] 224208 11512 3037 2271 3157 202810 1420 EP [kg Phosphate-Equiv.] 56947 3336 168 508 706 51942 286 FAETP inf. [kg DCB-Equiv.] 12013 2614 7195 653 908 0 643 GWP 100 years [kg CO2-Equiv.] 2816172 -127416010 39365647 4822830 461049 85246691 335965 HTP inf. [kg DCB-Equiv.] 630197 40166 65241 12888 17917 479712 14275 MAETP inf. [kg DCB-Equiv.] 208749875 60895751 102262469 13419905 18657023 0 13514726 ODP, steady state [kg R11-Equiv.] 0.07 0.05 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 23932 538 9786 284 395 12775 154 TETP inf. [kg DCB-Equiv.] 3881 950 2212 213 296 0 211 Rural Cederberge ADP elements [kg Sb-Equiv.] 3.68 1.98 1.05 0.20 0.28 0.00 0.18 ADP fossil [MJ] 57573414 18357351 26253115 3958156 5502826 0 3501966 AP [kg SO2-Equiv.] 222117 10098 2531 2271 3157 202810 1250 EP [kg Phosphate-Equiv.] 56419 2870 140 508 706 51942 252 FAETP inf. [kg DCB-Equiv.] 10694 2571 5996 653 908 0 566 GWP 100 years [kg CO2-Equiv.] 2658413 -127388512 39220706 4822830 461049 85246691 295649 HTP inf. [kg DCB-Equiv.] 616189 38743 54367 12888 17917 479712 12562 MAETP inf. [kg DCB-Equiv.] 187474460 58285849 85218724 13419905 18657023 0 11892959 ODP, steady state [kg R11-Equiv.] 0.06 0.05 0.01 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 22302 557 8155 284 395 12775 135 TETP inf. [kg DCB-Equiv.] 3456 918 1843 213 296 0 186 Stellenbosch University http://scholar.sun.ac.za 295 Annexure 12: LCA results ? LBS 12 Paarl ? Prim. production Harvesting Comminution Forwarding Combustion Upgrading Transp. of bio-char Transp. of bio-oil ADP elements [kg Sb-Equiv.] 2.39 1.06 0.80 0.17 0.24 0.00 0.00 0.03 0.09 ADP fossil [MJ] 42297320 11779684 20005457 3393228 4717436 0 0 619707 1781808 AP [kg SO2-Equiv.] 187593 7223 1929 1947 2707 148246 24680 221 641 EP [kg Phosphate-Equiv.] 47721 2103 106 436 606 38171 6125 45 129 FAETP inf. [kg DCB-Equiv.] 7887 1592 4569 560 778 0 0 100 288 GWP 100 years [kg CO2-Equiv.] -410634 -111776582 33553900 4134491 395246 69969071 3110489 52318 150433 HTP inf. [kg DCB-Equiv.] 510194 24593 41429 11048 15360 352463 56629 2223 6448 MAETP inf. [kg DCB-Equiv.] 137957256 37364207 64938562 11504549 15994200 0 0 2104574 6051163 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 18113 323 6214 244 339 9482 1418 24 70 TETP inf. [kg DCB-Equiv.] 2549 581 1404 182 254 0 0 33 95 Worcester ADP elements [kg Sb-Equiv.] 2.58 1.16 0.95 0.17 0.24 0.00 0.00 0.02 0.05 ADP fossil [MJ] 46448644 13488730 23577860 3393228 4717436 0 0 328080 943310 AP [kg SO2-Equiv.] 188738 8428 2273 1947 2707 148246 24680 117 339 EP [kg Phosphate-Equiv.] 48018 2463 125 436 606 38171 6125 24 68 FAETP inf. [kg DCB-Equiv.] 8739 1810 5385 560 778 0 0 53 153 GWP 100 years [kg CO2-Equiv.] 510715 -110858436 33652515 4134491 395246 69969071 3110489 27698 79641 HTP inf. [kg DCB-Equiv.] 517023 28104 48827 11048 15360 352463 56629 1177 3414 MAETP inf. [kg DCB-Equiv.] 151128523 42777297 76534734 11504549 15994200 0 0 1114186 3203557 ODP, steady state [kg R11-Equiv.] 0.05 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 19219 363 7324 244 339 9482 1418 13 37 TETP inf. [kg DCB-Equiv.] 2823 664 1655 182 254 0 0 17 50 Ashton ADP elements [kg Sb-Equiv.] 3.09 1.54 1.08 0.17 0.24 0.00 0.00 0.02 0.04 ADP fossil [MJ] 52750074 16454830 27007367 3393228 4717436 0 0 303778 873435 AP [kg SO2-Equiv.] 190475 9869 2604 1947 2707 148246 24680 108 314 EP [kg Phosphate-Equiv.] 48426 2860 144 436 606 38171 6125 22 63 FAETP inf. [kg DCB-Equiv.] 9937 2241 6168 560 778 0 0 49 141 GWP 100 years [kg CO2-Equiv.] 2225310 -109230561 33747186 4134491 395246 69969071 3110489 25646 73742 HTP inf. [kg DCB-Equiv.] 530114 34433 55929 11048 15360 352463 56629 1090 3161 MAETP inf. [kg DCB-Equiv.] 171368124 52204406 87667059 11504549 15994200 0 0 1031654 2966257 ODP, steady state [kg R11-Equiv.] 0.06 0.05 0.01 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 20379 461 8389 244 339 9482 1418 12 34 TETP inf. [kg DCB-Equiv.] 3209 815 1896 182 254 0 0 16 46 Rural Cederberge ADP elements [kg Sb-Equiv.] 3.06 1.70 0.90 0.17 0.24 0.00 0.00 0.01 0.04 ADP fossil [MJ] 47390049 15737298 22506139 3393228 4717436 0 0 267324 768623 AP [kg SO2-Equiv.] 188778 8657 2170 1947 2707 148246 24680 95 276 EP [kg Phosphate-Equiv.] 47993 2461 120 436 606 38171 6125 19 56 FAETP inf. [kg DCB-Equiv.] 8850 2204 5140 560 778 0 0 43 124 GWP 100 years [kg CO2-Equiv.] 2112702 -109206988 33622931 4134491 395246 69969071 3110489 22569 64893 HTP inf. [kg DCB-Equiv.] 519063 33214 46608 11048 15360 352463 56629 959 2782 MAETP inf. [kg DCB-Equiv.] 154039794 49967002 73055882 11504549 15994200 0 0 907855 2610306 ODP, steady state [kg R11-Equiv.] 0.05 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 18991 477 6991 244 339 9482 1418 10 30 TETP inf. [kg DCB-Equiv.] 2858 787 1580 182 254 0 0 14 41 Stellenbosch University http://scholar.sun.ac.za 296 Annexure 13: LCA results ? LBS 13 Paarl ? Prim. production Harvesting Comminution Forwarding bio-char for sale Combustion Conversion Transp. of Transp. ADP elements [kg Sb-Equiv.] 3.17 1.40 1.06 0.22 0.31 0.00 0.00 0.00 0.05 0.13 ADP fossil [MJ] 56273597 15550851 26410037 4479542 6227683 0 0 0 930389 2675095 AP [kg SO2-Equiv.] 122418 9535 2546 2570 3573 0 70317 32582 332 962 EP [kg Phosphate-Equiv.] 30685 2776 141 575 800 0 18047 8086 67 194 FAETP inf. [kg DCB-Equiv.] 10482 2101 6032 739 1027 0 0 0 150 433 GWP 100 years [kg CO2-Equiv.] -36178053 -147560919 44295900 5458114 521780 8929400 47766987 4106287 78547 225850 HTP inf. [kg DCB-Equiv.] 376460 32466 54692 14585 20277 0 166661 74759 3337 9681 MAETP inf. [kg DCB-Equiv.] 183600901 49326045 85728099 15187634 21114609 0 0 0 3159676 9084839 ODP, steady state [kg R11-Equiv.] 0.06 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 16156 426 8204 322 447 0 4745 1871 36 105 TETP inf. [kg DCB-Equiv.] 3388 768 1854 241 335 0 0 0 49 142 Worcester bio-char of bio-oil ADP elements [kg Sb-Equiv.] 3.41 1.54 1.25 0.22 0.31 0.00 0.00 0.00 0.02 0.07 ADP fossil [MJ] 61443116 17807034 31126115 4479542 6227683 0 0 0 465194 1337548 AP [kg SO2-Equiv.] 123817 11127 3001 2570 3573 0 70317 32582 166 481 EP [kg Phosphate-Equiv.] 31056 3252 166 575 800 0 18047 8086 33 97 FAETP inf. [kg DCB-Equiv.] 11557 2390 7109 739 1027 0 0 0 75 216 GWP 100 years [kg CO2-Equiv.] -34987983 -146348836 44426086 5458114 521780 8929400 47766987 4106287 39273 112925 HTP inf. [kg DCB-Equiv.] 384352 37101 64459 14585 20277 0 166661 74759 1669 4841 MAETP inf. [kg DCB-Equiv.] 199933278 56472090 101036688 15187634 21114609 0 0 0 1579838 4542419 ODP, steady state [kg R11-Equiv.] 0.07 0.05 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 17603 479 9669 322 447 0 4745 1871 18 52 TETP inf. [kg DCB-Equiv.] 3733 877 2185 241 335 0 0 0 25 71 Ashton ADP elements [kg Sb-Equiv.] 4.09 2.03 1.43 0.22 0.31 0.00 0.00 0.00 0.02 0.07 ADP fossil [MJ] 69948385 21722706 35653549 4479542 6227683 0 0 0 481236 1383670 AP [kg SO2-Equiv.] 126177 13028 3438 2570 3573 0 70317 32582 172 498 EP [kg Phosphate-Equiv.] 31608 3776 190 575 800 0 18047 8086 35 100 FAETP inf. [kg DCB-Equiv.] 13169 2958 8143 739 1027 0 0 0 78 224 GWP 100 years [kg CO2-Equiv.] -32708731 -144199810 44551064 5458114 521780 8929400 47766987 4106287 40628 116819 HTP inf. [kg DCB-Equiv.] 402307 45456 73834 14585 20277 0 166661 74759 1726 5008 MAETP inf. [kg DCB-Equiv.] 227285755 68917209 115732933 15187634 21114609 0 0 0 1634315 4699055 ODP, steady state [kg R11-Equiv.] 0.08 0.06 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 19142 609 11075 322 447 0 4745 1871 19 54 TETP inf. [kg DCB-Equiv.] 4253 1075 2503 241 335 0 0 0 26 73 Rural Cederberge ADP elements [kg Sb-Equiv.] 4.04 2.24 1.19 0.22 0.31 0.00 0.00 0.00 0.02 0.05 ADP fossil [MJ] 62561576 20775462 29711291 4479542 6227683 0 0 0 352906 1014691 AP [kg SO2-Equiv.] 123826 11428 2865 2570 3573 0 70317 32582 126 365 EP [kg Phosphate-Equiv.] 31013 3248 158 575 800 0 18047 8086 25 74 FAETP inf. [kg DCB-Equiv.] 11683 2910 6786 739 1027 0 0 0 57 164 GWP 100 years [kg CO2-Equiv.] -32883630 -144168690 44387030 5458114 521780 8929400 47766987 4106287 29794 85667 HTP inf. [kg DCB-Equiv.] 386596 43847 61529 14585 20277 0 166661 74759 1266 3672 MAETP inf. [kg DCB-Equiv.] 203354344 65963519 96444111 15187634 21114609 0 0 0 1198498 3445973 ODP, steady state [kg R11-Equiv.] 0.07 0.05 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 17298 630 9229 322 447 0 4745 1871 14 40 TETP inf. [kg DCB-Equiv.] 3773 1039 2086 241 335 0 0 0 19 54 Stellenbosch University http://scholar.sun.ac.za 297 Annexure 14: LCA results ? LBS 14 Paarl ? Primary production Harvesting Forwarding Combustion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.97 0.84 0.64 0.19 0.00 0.01 0.29 ADP fossil [MJ] 46331770 9385560 15939512 3758655 0 11411139 5836903 AP [kg SO2-Equiv.] 141572 5755 1537 2156 115926 14115 2083 EP [kg Phosphate-Equiv.] 32895 1676 85 483 29608 625 419 FAETP inf. [kg DCB-Equiv.] 8103 1268 3640 620 0 1631 943 GWP 100 years [kg CO2-Equiv.] -202092 -89058908 26734345 314915 60120377 1194405 492774 HTP inf. [kg DCB-Equiv.] 971642 19595 33009 12238 546597 339266 20937 MAETP inf. [kg DCB-Equiv.] 3438438518 29770238 51740331 12743510 0 3324361844 19822595 ODP, steady state [kg R11-Equiv.] 0.06 0.03 0.01 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 12993 257 4951 270 6562 726 226 TETP inf. [kg DCB-Equiv.] 5971 463 1119 202 0 3877 310 Worcester ADP elements [kg Sb-Equiv.] 2.03 0.93 0.75 0.19 0.00 0.01 0.16 ADP fossil [MJ] 47793029 10747256 18785853 3758655 0 11411139 3090125 AP [kg SO2-Equiv.] 141826 6715 1811 2156 115926 14115 1103 EP [kg Phosphate-Equiv.] 33000 1963 100 483 29608 625 222 FAETP inf. [kg DCB-Equiv.] 8483 1442 4291 620 0 1631 499 GWP 100 years [kg CO2-Equiv.] 376127 -88327367 26812917 314915 60120377 1194405 260880 HTP inf. [kg DCB-Equiv.] 970481 22392 38903 12238 546597 339266 11084 MAETP inf. [kg DCB-Equiv.] 3442662506 34083161 60979676 12743510 0 3324361844 10494315 ODP, steady state [kg R11-Equiv.] 0.06 0.03 0.01 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 13803 289 5835 270 6562 726 119 TETP inf. [kg DCB-Equiv.] 6091 529 1319 202 0 3877 164 Ashton ADP elements [kg Sb-Equiv.] 2.43 1.23 0.86 0.19 0.00 0.01 0.14 ADP fossil [MJ] 52659883 13110520 21518341 3758655 0 11411139 2861227 AP [kg SO2-Equiv.] 143156 7863 2075 2156 115926 14115 1021 EP [kg Phosphate-Equiv.] 33314 2279 114 483 29608 625 206 FAETP inf. [kg DCB-Equiv.] 9413 1785 4915 620 0 1631 462 GWP 100 years [kg CO2-Equiv.] 1729255 -87030345 26888347 314915 60120377 1194405 241556 HTP inf. [kg DCB-Equiv.] 980361 27435 44562 12238 546597 339266 10263 MAETP inf. [kg DCB-Equiv.] 3458266047 41594288 69849447 12743510 0 3324361844 9716958 ODP, steady state [kg R11-Equiv.] 0.07 0.04 0.01 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 14721 368 6684 270 6562 726 111 TETP inf. [kg DCB-Equiv.] 6391 649 1511 202 0 3877 152 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.39 1.35 0.72 0.19 0.00 0.01 0.13 ADP fossil [MJ] 48158446 12538821 17931951 3758655 0 11411139 2517880 AP [kg SO2-Equiv.] 141722 6897 1729 2156 115926 14115 899 EP [kg Phosphate-Equiv.] 32952 1960 95 483 29608 625 181 FAETP inf. [kg DCB-Equiv.] 8510 1756 4096 620 0 1631 407 GWP 100 years [kg CO2-Equiv.] 1620049 -87011562 26789345 314915 60120377 1194405 212569 HTP inf. [kg DCB-Equiv.] 970731 26463 37135 12238 546597 339266 9032 MAETP inf. [kg DCB-Equiv.] 3443675768 39811618 58207873 12743510 0 3324361844 8550923 ODP, steady state [kg R11-Equiv.] 0.06 0.03 0.01 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 13607 380 5570 270 6562 726 97 TETP inf. [kg DCB-Equiv.] 6099 627 1259 202 0 3877 134 Stellenbosch University http://scholar.sun.ac.za 298 Annexure 15: LCA results ? LBS 15 Paarl ? Primary production Harvesting Forwarding Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.69 0.72 0.55 0.16 0.00 0.01 0.24 ADP fossil [MJ] 39687447 8079516 13721456 3235621 0 9823227 4827626 AP [kg SO2-Equiv.] 97294 4954 1323 1856 75287 12151 1723 EP [kg Phosphate-Equiv.] 21931 1442 73 415 19116 538 347 FAETP inf. [kg DCB-Equiv.] 6944 1092 3134 534 0 1404 780 GWP 100 years [kg CO2-Equiv.] -190606 -76665956 23014139 271093 51754353 1028198 407567 HTP inf. [kg DCB-Equiv.] 541785 16868 28416 10535 176594 292056 17317 MAETP inf. [kg DCB-Equiv.] 2959294989 25627574 44540429 10970192 0 2861761786 16395007 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 9805 222 4262 232 4277 625 187 TETP inf. [kg DCB-Equiv.] 5130 399 963 174 0 3338 256 Worcester ADP elements [kg Sb-Equiv.] 1.75 0.80 0.65 0.16 0.00 0.01 0.13 ADP fossil [MJ] 41043889 9251727 16171716 3235621 0 9823227 2561597 AP [kg SO2-Equiv.] 97548 5781 1559 1856 75287 12151 914 EP [kg Phosphate-Equiv.] 22029 1690 86 415 19116 538 184 FAETP inf. [kg DCB-Equiv.] 7287 1242 3693 534 0 1404 414 GWP 100 years [kg CO2-Equiv.] 315470 -76036213 23081778 271093 51754353 1028198 216260 HTP inf. [kg DCB-Equiv.] 541139 19276 33490 10535 176594 292056 9189 MAETP inf. [kg DCB-Equiv.] 2963265782 29340335 52494077 10970192 0 2861761786 8699392 ODP, steady state [kg R11-Equiv.] 0.06 0.03 0.01 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 10506 249 5023 232 4277 625 99 TETP inf. [kg DCB-Equiv.] 5238 455 1135 174 0 3338 136 Ashton ADP elements [kg Sb-Equiv.] 2.09 1.06 0.74 0.16 0.00 0.01 0.12 ADP fossil [MJ] 45332021 11286132 18523966 3235621 0 9823227 2463074 AP [kg SO2-Equiv.] 98728 6769 1786 1856 75287 12151 879 EP [kg Phosphate-Equiv.] 22306 1962 99 415 19116 538 177 FAETP inf. [kg DCB-Equiv.] 8103 1537 4231 534 0 1404 398 GWP 100 years [kg CO2-Equiv.] 1488621 -74919677 23146711 271093 51754353 1028198 207942 HTP inf. [kg DCB-Equiv.] 549998 23617 38361 10535 176594 292056 8835 MAETP inf. [kg DCB-Equiv.] 2977032611 35806254 60129579 10970192 0 2861761786 8364800 ODP, steady state [kg R11-Equiv.] 0.06 0.03 0.01 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 11300 316 5754 232 4277 625 95 TETP inf. [kg DCB-Equiv.] 5501 559 1300 174 0 3338 131 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.05 1.16 0.62 0.16 0.00 0.01 0.10 ADP fossil [MJ] 41259934 10793987 15436638 3235621 0 9823227 1970460 AP [kg SO2-Equiv.] 97423 5938 1488 1856 75287 12151 703 EP [kg Phosphate-Equiv.] 21981 1688 82 415 19116 538 142 FAETP inf. [kg DCB-Equiv.] 7294 1512 3526 534 0 1404 319 GWP 100 years [kg CO2-Equiv.] 1377977 -74903508 23061486 271093 51754353 1028198 166354 HTP inf. [kg DCB-Equiv.] 541001 22781 31967 10535 176594 292056 7068 MAETP inf. [kg DCB-Equiv.] 2963803451 34271650 50107983 10970192 0 2861761786 6691840 ODP, steady state [kg R11-Equiv.] 0.06 0.03 0.01 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 10333 327 4795 232 4277 625 76 TETP inf. [kg DCB-Equiv.] 5240 540 1084 174 0 3338 105 Stellenbosch University http://scholar.sun.ac.za 299 Annexure 16: LCA results ? LBS 16 Paarl ? Primary production Harvesting Forwarding Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 2.80 1.20 0.91 0.27 0.00 0.01 0.42 ADP fossil [MJ] 65755141 13320208 22621732 5334373 0 16194958 8283871 AP [kg SO2-Equiv.] 239207 8167 2181 3061 202810 20032 2957 EP [kg Phosphate-Equiv.] 56608 2378 120 685 51942 887 595 FAETP inf. [kg DCB-Equiv.] 11500 1800 5167 880 0 2315 1339 GWP 100 years [kg CO2-Equiv.] -364382 -126394505 37942013 446935 85246691 1695127 699356 HTP inf. [kg DCB-Equiv.] 1082946 27809 46847 17369 479712 481494 29715 MAETP inf. [kg DCB-Equiv.] 4879913139 42250625 73431100 18085890 0 4718012829 28132695 ODP, steady state [kg R11-Equiv.] 0.09 0.04 0.01 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 21902 365 7027 383 12775 1031 320 TETP inf. [kg DCB-Equiv.] 8475 657 1588 287 0 5503 439 Worcester ADP elements [kg Sb-Equiv.] 2.89 1.32 1.07 0.27 0.00 0.01 0.22 ADP fossil [MJ] 67828996 15252760 26661326 5334373 0 16194958 4385579 AP [kg SO2-Equiv.] 239569 9531 2571 3061 202810 20032 1565 EP [kg Phosphate-Equiv.] 56757 2786 142 685 51942 887 315 FAETP inf. [kg DCB-Equiv.] 12040 2047 6089 880 0 2315 709 GWP 100 years [kg CO2-Equiv.] 456241 -125356286 38053525 446935 85246691 1695127 370247 HTP inf. [kg DCB-Equiv.] 1081298 31779 55213 17369 479712 481494 15731 MAETP inf. [kg DCB-Equiv.] 4885907922 48371628 86543796 18085890 0 4718012829 14893780 ODP, steady state [kg R11-Equiv.] 0.09 0.04 0.01 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 23050 410 8282 383 12775 1031 169 TETP inf. [kg DCB-Equiv.] 8645 751 1872 287 0 5503 233 Ashton ADP elements [kg Sb-Equiv.] 3.45 1.74 1.23 0.27 0.00 0.01 0.20 ADP fossil [MJ] 74736150 18606760 30539338 5334373 0 16194958 4060721 AP [kg SO2-Equiv.] 241456 11160 2944 3061 202810 20032 1449 EP [kg Phosphate-Equiv.] 57203 3234 162 685 51942 887 292 FAETP inf. [kg DCB-Equiv.] 13360 2534 6975 880 0 2315 656 GWP 100 years [kg CO2-Equiv.] 2376632 -123515520 38160577 446935 85246691 1695127 342822 HTP inf. [kg DCB-Equiv.] 1095320 38936 63243 17369 479712 481494 14566 MAETP inf. [kg DCB-Equiv.] 4908052836 59031596 99131985 18085890 0 4718012829 13790537 ODP, steady state [kg R11-Equiv.] 0.10 0.05 0.01 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 24354 522 9486 383 12775 1031 157 TETP inf. [kg DCB-Equiv.] 9070 921 2144 287 0 5503 215 Rural Cederberge ADP elements [kg Sb-Equiv.] 3.40 1.92 1.02 0.27 0.00 0.01 0.18 ADP fossil [MJ] 68347604 17795391 25449448 5334373 0 16194958 3573435 AP [kg SO2-Equiv.] 239420 9789 2454 3061 202810 20032 1275 EP [kg Phosphate-Equiv.] 56689 2782 135 685 51942 887 257 FAETP inf. [kg DCB-Equiv.] 12077 2492 5812 880 0 2315 578 GWP 100 years [kg CO2-Equiv.] 2221645 -123488864 38020072 446935 85246691 1695127 301683 HTP inf. [kg DCB-Equiv.] 1081653 37557 52703 17369 479712 481494 12818 MAETP inf. [kg DCB-Equiv.] 4887345967 56501588 82609987 18085890 0 4718012829 12135673 ODP, steady state [kg R11-Equiv.] 0.09 0.04 0.01 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 22772 540 7905 383 12775 1031 138 TETP inf. [kg DCB-Equiv.] 8656 890 1787 287 0 5503 190 Stellenbosch University http://scholar.sun.ac.za 300 Annexure 17: LCA results ? LBS 17 Paarl ? Primary production Forwarding Comminution Harvesting Combustion Secondary transport ADP elements [kg Sb-Equiv.] 1.31 0.61 0.14 0.19 0.10 0.00 0.27 ADP fossil [MJ] 20890744 6777362 2880467 3718612 2130619 0 5383685 AP [kg SO2-Equiv.] 126137 4156 1095 2133 906 115926 1922 EP [kg Phosphate-Equiv.] 32097 1210 225 477 191 29608 387 FAETP inf. [kg DCB-Equiv.] 3234 916 480 613 354 0 870 GWP 100 years [kg CO2-Equiv.] 1006016 -64309906 237654 3476107 1027272 60120377 454511 HTP inf. [kg DCB-Equiv.] 606393 14149 8046 12108 6182 546597 19312 MAETP inf. [kg DCB-Equiv.] 69275248 21497246 9730830 12607743 7156001 0 18283429 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7488 186 177 267 88 6562 208 TETP inf. [kg DCB-Equiv.] 1096 335 157 200 119 0 286 Worcester ADP elements [kg Sb-Equiv.] 1.26 0.67 0.14 0.19 0.12 0.00 0.14 ADP fossil [MJ] 19674818 7760650 2880467 3718612 2511086 0 2804003 AP [kg SO2-Equiv.] 126071 4849 1095 2133 1067 115926 1001 EP [kg Phosphate-Equiv.] 32153 1417 225 477 225 29608 201 FAETP inf. [kg DCB-Equiv.] 3006 1042 480 613 418 0 453 GWP 100 years [kg CO2-Equiv.] 1614541 -63781657 237654 3476107 1325336 60120377 236725 HTP inf. [kg DCB-Equiv.] 600263 16170 8046 12108 7286 546597 10058 MAETP inf. [kg DCB-Equiv.] 64906680 24611630 9730830 12607743 8433858 0 9522619 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7427 209 177 267 104 6562 108 TETP inf. [kg DCB-Equiv.] 1028 382 157 200 140 0 149 Ashton ADP elements [kg Sb-Equiv.] 1.45 0.89 0.14 0.19 0.14 0.00 0.10 ADP fossil [MJ] 20849311 9467176 2880467 3718612 2876335 0 1906722 AP [kg SO2-Equiv.] 126735 5678 1095 2133 1223 115926 681 EP [kg Phosphate-Equiv.] 32350 1645 225 477 258 29608 137 FAETP inf. [kg DCB-Equiv.] 3169 1289 480 613 478 0 308 GWP 100 years [kg CO2-Equiv.] 2405564 -62845070 237654 3476107 1255524 60120377 160973 HTP inf. [kg DCB-Equiv.] 601746 19811 8046 12108 8345 546597 6840 MAETP inf. [kg DCB-Equiv.] 68510009 30035454 9730830 12607743 9660601 0 6475381 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7464 265 177 267 119 6562 74 TETP inf. [kg DCB-Equiv.] 1088 469 157 200 161 0 101 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.52 0.98 0.14 0.19 0.11 0.00 0.10 ADP fossil [MJ] 20069255 9054349 2880467 3718612 2396946 0 2018882 AP [kg SO2-Equiv.] 125874 4981 1095 2133 1019 115926 721 EP [kg Phosphate-Equiv.] 32085 1416 225 477 215 29608 145 FAETP inf. [kg DCB-Equiv.] 3086 1268 480 613 399 0 326 GWP 100 years [kg CO2-Equiv.] 1918460 -62831507 237654 3476107 745388 60120377 170442 HTP inf. [kg DCB-Equiv.] 600056 19109 8046 12108 6955 546597 7242 MAETP inf. [kg DCB-Equiv.] 65993538 28748179 9730830 12607743 8050501 0 6856286 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7458 275 177 267 99 6562 78 TETP inf. [kg DCB-Equiv.] 1051 453 157 200 134 0 107 Stellenbosch University http://scholar.sun.ac.za 301 Annexure 18: LCA results ? LBS 18 Paarl ? Primary production Forwarding Comminution Harvesting Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.11 0.52 0.12 0.16 0.09 0.00 0.21 ADP fossil [MJ] 17597492 5834261 2479637 3201150 1834133 0 4248311 AP [kg SO2-Equiv.] 83939 3577 942 1837 780 75287 1516 EP [kg Phosphate-Equiv.] 21231 1042 193 411 164 19116 305 FAETP inf. [kg DCB-Equiv.] 2721 788 413 528 305 0 687 GWP 100 years [kg CO2-Equiv.] 833419 -55360890 204583 2992391 884323 51754353 358659 HTP inf. [kg DCB-Equiv.] 226684 12180 6926 10423 5322 176594 15239 MAETP inf. [kg DCB-Equiv.] 58323682 18505806 8376741 10853318 6160211 0 14427606 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5059 160 152 230 76 4277 164 TETP inf. [kg DCB-Equiv.] 923 288 136 172 102 0 225 Worcester ADP elements [kg Sb-Equiv.] 1.08 0.58 0.12 0.16 0.10 0.00 0.12 ADP fossil [MJ] 16840425 6680721 2479637 3201150 2161657 0 2317260 AP [kg SO2-Equiv.] 83986 4174 942 1837 919 75287 827 EP [kg Phosphate-Equiv.] 21300 1220 193 411 194 19116 166 FAETP inf. [kg DCB-Equiv.] 2572 897 413 528 359 0 375 GWP 100 years [kg CO2-Equiv.] 1381719 -54906149 204583 2992391 1140909 51754353 195632 HTP inf. [kg DCB-Equiv.] 222446 13919 6926 10423 6272 176594 8312 MAETP inf. [kg DCB-Equiv.] 55546721 21186810 8376741 10853318 7260248 0 7869603 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5018 180 152 230 89 4277 90 TETP inf. [kg DCB-Equiv.] 880 329 136 172 121 0 123 Ashton ADP elements [kg Sb-Equiv.] 1.24 0.76 0.12 0.16 0.12 0.00 0.08 ADP fossil [MJ] 17851482 8149775 2479637 3201150 2476080 0 1544840 AP [kg SO2-Equiv.] 84557 4888 942 1837 1053 75287 551 EP [kg Phosphate-Equiv.] 21469 1416 193 411 222 19116 111 FAETP inf. [kg DCB-Equiv.] 2712 1110 413 528 412 0 250 GWP 100 years [kg CO2-Equiv.] 2062668 -54099893 204583 2992391 1080812 51754353 130421 HTP inf. [kg DCB-Equiv.] 223723 17054 6926 10423 7184 176594 5541 MAETP inf. [kg DCB-Equiv.] 58648630 25855884 8376741 10853318 8316284 0 5246402 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5050 228 152 230 102 4277 60 TETP inf. [kg DCB-Equiv.] 931 403 136 172 138 0 82 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.31 0.84 0.12 0.16 0.10 0.00 0.09 ADP fossil [MJ] 17373079 7794395 2479637 3201150 2063400 0 1834498 AP [kg SO2-Equiv.] 83885 4288 942 1837 877 75287 655 EP [kg Phosphate-Equiv.] 21255 1219 193 411 185 19116 132 FAETP inf. [kg DCB-Equiv.] 2672 1092 413 528 343 0 297 GWP 100 years [kg CO2-Equiv.] 1659649 -54088217 204583 2992391 641663 51754353 154875 HTP inf. [kg DCB-Equiv.] 222960 16450 6926 10423 5987 176594 6580 MAETP inf. [kg DCB-Equiv.] 57138138 24747739 8376741 10853318 6930237 0 6230103 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5052 236 152 230 85 4277 71 TETP inf. [kg DCB-Equiv.] 910 390 136 172 115 0 97 Stellenbosch University http://scholar.sun.ac.za 302 Annexure 19: LCA results ? LBS 19 Paarl ? Primary production Forwarding Comminution Harvesting Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.85 0.86 0.20 0.26 0.14 0.00 0.38 ADP fossil [MJ] 29489457 9618592 4088026 5277541 3023824 0 7481473 AP [kg SO2-Equiv.] 217245 5898 1553 3028 1285 202810 2670 EP [kg Phosphate-Equiv.] 55464 1717 319 678 271 51942 537 FAETP inf. [kg DCB-Equiv.] 4563 1300 681 871 503 0 1209 GWP 100 years [kg CO2-Equiv.] 1336755 -91270137 337284 4933373 1457929 85246691 631614 HTP inf. [kg DCB-Equiv.] 564005 20081 11418 17184 8773 479712 26837 MAETP inf. [kg DCB-Equiv.] 97776479 30509399 13810223 17893207 10155965 0 25407685 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14083 264 251 379 125 12775 289 TETP inf. [kg DCB-Equiv.] 1548 475 223 284 169 0 397 Worcester ADP elements [kg Sb-Equiv.] 1.79 0.95 0.20 0.26 0.17 0.00 0.20 ADP fossil [MJ] 27922966 11014098 4088026 5277541 3563793 0 3979507 AP [kg SO2-Equiv.] 217209 6882 1553 3028 1515 202810 1420 EP [kg Phosphate-Equiv.] 55556 2011 319 678 319 51942 286 FAETP inf. [kg DCB-Equiv.] 4266 1478 681 871 593 0 643 GWP 100 years [kg CO2-Equiv.] 2213828 -90520434 337284 4933373 1880948 85246691 335965 HTP inf. [kg DCB-Equiv.] 555877 22948 11418 17184 10340 479712 14275 MAETP inf. [kg DCB-Equiv.] 92117093 34929407 13810223 17893207 11969530 0 13514726 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14002 296 251 379 147 12775 154 TETP inf. [kg DCB-Equiv.] 1459 542 223 284 199 0 211 Ashton ADP elements [kg Sb-Equiv.] 2.05 1.26 0.20 0.26 0.19 0.00 0.14 ADP fossil [MJ] 29589835 13436039 4088026 5277541 4082163 0 2706065 AP [kg SO2-Equiv.] 218151 8058 1553 3028 1735 202810 966 EP [kg Phosphate-Equiv.] 55834 2335 319 678 366 51942 194 FAETP inf. [kg DCB-Equiv.] 4497 1830 681 871 679 0 437 GWP 100 years [kg CO2-Equiv.] 3336466 -89191207 337284 4933373 1781869 85246691 228456 HTP inf. [kg DCB-Equiv.] 557980 28116 11418 17184 11844 479712 9707 MAETP inf. [kg DCB-Equiv.] 97231023 42627026 13810223 17893207 13710553 0 9190014 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14055 377 251 379 169 12775 105 TETP inf. [kg DCB-Equiv.] 1544 665 223 284 228 0 144 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.16 1.39 0.20 0.26 0.16 0.00 0.15 ADP fossil [MJ] 28641941 12850146 4088026 5277541 3401802 0 3024425 AP [kg SO2-Equiv.] 216985 7069 1553 3028 1446 202810 1079 EP [kg Phosphate-Equiv.] 55470 2009 319 678 305 51942 217 FAETP inf. [kg DCB-Equiv.] 4406 1800 681 871 566 0 489 GWP 100 years [kg CO2-Equiv.] 2658595 -89171958 337284 4933373 1057872 85246691 255333 HTP inf. [kg DCB-Equiv.] 556153 27120 11418 17184 9870 479712 10849 MAETP inf. [kg DCB-Equiv.] 94200177 40800094 13810223 17893207 11425461 0 10271192 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14053 390 251 379 141 12775 117 TETP inf. [kg DCB-Equiv.] 1500 643 223 284 190 0 160 Stellenbosch University http://scholar.sun.ac.za 303 Annexure 20: LCA results ? LBS 20 Paarl ? Prim. production Forwarding Comminution Harvesting Combustion Conversion transport of bio-char transport of bio-oil ADP elements [kg Sb-Equiv.] 1.37 0.74 0.17 0.23 0.12 0.00 0.00 0.03 0.08 ADP fossil [MJ] 21079942 8245734 3504544 4524280 2592235 0 0 571099 1642049 AP [kg SO2-Equiv.] 183806 5056 1332 2596 1102 148246 24680 204 591 EP [kg Phosphate-Equiv.] 47015 1472 273 581 232 38171 6125 41 119 FAETP inf. [kg DCB-Equiv.] 3233 1114 584 746 431 0 0 92 265 GWP 100 years [kg CO2-Equiv.] 791423 -78243187 289143 4229235 1249840 69969071 3110473 48214 138633 HTP inf. [kg DCB-Equiv.] 466339 17215 9789 14731 7521 352463 56629 2049 5943 MAETP inf. [kg DCB-Equiv.] 69555656 26154804 11839096 15339317 8706408 0 0 1939499 5576532 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11860 226 215 325 107 9482 1418 22 64 TETP inf. [kg DCB-Equiv.] 1104 407 192 243 145 0 0 30 87 Worcester ADP elements [kg Sb-Equiv.] 1.42 0.82 0.17 0.23 0.15 0.00 0.00 0.02 0.04 ADP fossil [MJ] 21703225 9442060 3504544 4524280 3055134 0 0 303776 873431 AP [kg SO2-Equiv.] 184475 5900 1332 2596 1299 148246 24680 108 314 EP [kg Phosphate-Equiv.] 47234 1724 273 581 274 38171 6125 22 63 FAETP inf. [kg DCB-Equiv.] 3296 1267 584 746 508 0 0 49 141 GWP 100 years [kg CO2-Equiv.] 1709302 -77600488 289143 4229235 1612481 69969071 3110473 25646 73741 HTP inf. [kg DCB-Equiv.] 466399 19673 9789 14731 8864 352463 56629 1090 3161 MAETP inf. [kg DCB-Equiv.] 71381372 29943946 11839096 15339317 10261124 0 0 1031648 2966241 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11866 254 215 325 126 9482 1418 12 34 TETP inf. [kg DCB-Equiv.] 1133 465 192 243 170 0 0 16 46 Ashton ADP elements [kg Sb-Equiv.] 1.68 1.08 0.17 0.23 0.17 0.00 0.00 0.01 0.03 ADP fossil [MJ] 23847161 11518319 3504544 4524280 3499517 0 0 206568 593933 AP [kg SO2-Equiv.] 185537 6908 1332 2596 1488 148246 24680 74 214 EP [kg Phosphate-Equiv.] 47524 2002 273 581 314 38171 6125 15 43 FAETP inf. [kg DCB-Equiv.] 3610 1569 584 746 582 0 0 33 96 GWP 100 years [kg CO2-Equiv.] 2732067 -76460982 289143 4229235 1527544 69969071 3110473 17439 50144 HTP inf. [kg DCB-Equiv.] 470759 24103 9789 14731 10154 352463 56629 741 2149 MAETP inf. [kg DCB-Equiv.] 78193515 36542888 11839096 15339317 11753651 0 0 701521 2017044 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11939 323 215 325 145 9482 1418 8 23 TETP inf. [kg DCB-Equiv.] 1243 570 192 243 195 0 0 11 32 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.77 1.19 0.17 0.23 0.14 0.00 0.00 0.01 0.03 ADP fossil [MJ] 22855815 11016049 3504544 4524280 2916264 0 0 230870 663807 AP [kg SO2-Equiv.] 184474 6060 1332 2596 1240 148246 24680 82 239 EP [kg Phosphate-Equiv.] 47199 1722 273 581 261 38171 6125 17 48 FAETP inf. [kg DCB-Equiv.] 3502 1543 584 746 485 0 0 37 107 GWP 100 years [kg CO2-Equiv.] 2135858 -76444480 289143 4229235 906882 69969071 3110473 19491 56043 HTP inf. [kg DCB-Equiv.] 468553 23249 9789 14731 8461 352463 56629 828 2402 MAETP inf. [kg DCB-Equiv.] 74988230 34976713 11839096 15339317 9794709 0 0 784053 2254343 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11930 334 215 325 121 9482 1418 9 26 TETP inf. [kg DCB-Equiv.] 1196 551 192 243 163 0 0 12 35 Stellenbosch University http://scholar.sun.ac.za 304 Annexure 21: LCA results ? LBS 21 Paarl ? Prim. production Forwarding Comminution Harvesting bio-char for sale Combustion Upgrading transport transport ADP elements [kg Sb-Equiv.] 1.81 0.98 0.23 0.30 0.16 0.00 0.00 0.00 0.04 0.11 ADP fossil [MJ] 27828659 10885596 4626519 5972722 3422136 0 0 0 753936 2167749 AP [kg SO2-Equiv.] 117262 6675 1758 3427 1455 0 70317 32582 269 780 EP [kg Phosphate-Equiv.] 29722 1943 361 767 307 0 18047 8086 54 157 FAETP inf. [kg DCB-Equiv.] 4268 1471 771 985 569 0 0 0 122 350 GWP 100 years [kg CO2-Equiv.] -34628397 -103292643 381712 5583219 1649974 8929400 47766987 4106287 63650 183017 HTP inf. [kg DCB-Equiv.] 316995 22726 12923 19447 9929 0 166661 74759 2704 7845 MAETP inf. [kg DCB-Equiv.] 91823810 34528231 15629367 20250179 11493754 0 0 0 2560427 7361852 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7884 298 284 429 141 0 4745 1871 29 85 TETP inf. [kg DCB-Equiv.] 1457 537 253 321 191 0 0 0 40 115 Worcester of bio-char Of bio-oil ADP elements [kg Sb-Equiv.] 1.87 1.08 0.23 0.30 0.19 0.00 0.00 0.00 0.02 0.06 ADP fossil [MJ] 28651485 12464924 4626519 5972722 4033231 0 0 0 401030 1153058 AP [kg SO2-Equiv.] 118145 7789 1758 3427 1715 0 70317 32582 143 415 EP [kg Phosphate-Equiv.] 30011 2276 361 767 361 0 18047 8086 29 84 FAETP inf. [kg DCB-Equiv.] 4351 1673 771 985 671 0 0 0 65 186 GWP 100 years [kg CO2-Equiv.] -33416660 -102444185 381712 5583219 2128715 8929400 47766987 4106287 33856 97349 HTP inf. [kg DCB-Equiv.] 317074 25971 12923 19447 11702 0 166661 74759 1439 4173 MAETP inf. [kg DCB-Equiv.] 94234027 39530463 15629367 20250179 13546210 0 0 0 1361929 3915879 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7892 335 284 429 167 0 4745 1871 15 45 TETP inf. [kg DCB-Equiv.] 1495 614 253 321 225 0 0 0 21 61 Ashton ADP elements [kg Sb-Equiv.] 2.22 1.42 0.23 0.30 0.22 0.00 0.00 0.00 0.01 0.04 ADP fossil [MJ] 31481799 15205894 4626519 5972722 4619883 0 0 0 272700 784080 AP [kg SO2-Equiv.] 119547 9120 1758 3427 1964 0 70317 32582 97 282 EP [kg Phosphate-Equiv.] 30394 2643 361 767 414 0 18047 8086 20 57 FAETP inf. [kg DCB-Equiv.] 4766 2071 771 985 768 0 0 0 44 127 GWP 100 years [kg CO2-Equiv.] -32066458 -100939867 381712 5583219 2016585 8929400 47766987 4106287 23022 66198 HTP inf. [kg DCB-Equiv.] 322829 31820 12923 19447 13404 0 166661 74759 978 2838 MAETP inf. [kg DCB-Equiv.] 103227069 48242046 15629367 20250179 15516567 0 0 0 926112 2662798 ODP, steady state [kg R11-Equiv.] 0.05 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7988 426 284 429 191 0 4745 1871 11 31 TETP inf. [kg DCB-Equiv.] 1641 753 253 321 258 0 0 0 14 42 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.34 1.57 0.23 0.30 0.18 0.00 0.00 0.00 0.02 0.04 ADP fossil [MJ] 30173075 14542824 4626519 5972722 3849903 0 0 0 304783 876324 AP [kg SO2-Equiv.] 118144 8000 1758 3427 1637 0 70317 32582 109 315 EP [kg Phosphate-Equiv.] 29965 2274 361 767 345 0 18047 8086 22 64 FAETP inf. [kg DCB-Equiv.] 4624 2037 771 985 640 0 0 0 49 142 GWP 100 years [kg CO2-Equiv.] -32853542 -100918083 381712 5583219 1197219 8929400 47766987 4106287 25731 73986 HTP inf. [kg DCB-Equiv.] 319917 30693 12923 19447 11170 0 166661 74759 1093 3171 MAETP inf. [kg DCB-Equiv.] 98995616 46174463 15629367 20250179 12930473 0 0 0 1035066 2976068 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7976 441 284 429 159 0 4745 1871 12 34 TETP inf. [kg DCB-Equiv.] 1579 728 253 321 215 0 0 0 16 46 Stellenbosch University http://scholar.sun.ac.za 305 Annexure 22: LCA results ? LBS 22 Paarl ? Primary production Forwarding Harvesting Combustion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.11 0.59 0.14 0.10 0.00 0.01 0.28 ADP fossil [MJ] 28332272 6569892 2792289 2065396 0 11411139 5493556 AP [kg SO2-Equiv.] 137969 4028 1061 878 115926 14115 1961 EP [kg Phosphate-Equiv.] 32203 1173 218 185 29608 625 395 FAETP inf. [kg DCB-Equiv.] 4215 888 465 343 0 1631 888 GWP 100 years [kg CO2-Equiv.] 663537 -62341235 230379 995825 60120377 1194405 463787 HTP inf. [kg DCB-Equiv.] 933077 13716 7799 5993 546597 339266 19706 MAETP inf. [kg DCB-Equiv.] 3380227457 20839167 9432947 6936939 0 3324361844 18656560 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 7938 180 172 85 6562 726 212 TETP inf. [kg DCB-Equiv.] 4761 324 153 115 0 3877 291 Worcester ADP elements [kg Sb-Equiv.] 1.05 0.65 0.14 0.12 0.00 0.01 0.14 ADP fossil [MJ] 27021951 7523079 2792289 2434216 0 11411139 2861227 AP [kg SO2-Equiv.] 137858 4701 1061 1035 115926 14115 1021 EP [kg Phosphate-Equiv.] 32248 1374 218 218 29608 625 206 FAETP inf. [kg DCB-Equiv.] 3973 1010 465 405 0 1631 462 GWP 100 years [kg CO2-Equiv.] 1242323 -61829157 230379 1284764 60120377 1194405 241556 HTP inf. [kg DCB-Equiv.] 926663 15675 7799 7063 546597 339266 10263 MAETP inf. [kg DCB-Equiv.] 3375545641 23858213 9432947 8175678 0 3324361844 9716958 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 7874 202 172 101 6562 726 111 TETP inf. [kg DCB-Equiv.] 4688 370 153 136 0 3877 152 Ashton ADP elements [kg Sb-Equiv.] 1.23 0.86 0.14 0.13 0.00 0.01 0.10 ADP fossil [MJ] 28114711 9177364 2792289 2788284 0 11411139 1945634 AP [kg SO2-Equiv.] 138485 5504 1061 1185 115926 14115 694 EP [kg Phosphate-Equiv.] 32435 1595 218 250 29608 625 140 FAETP inf. [kg DCB-Equiv.] 4124 1250 465 464 0 1631 314 GWP 100 years [kg CO2-Equiv.] 2005266 -60921241 230379 1217089 60120377 1194405 164258 HTP inf. [kg DCB-Equiv.] 927935 19204 7799 8090 546597 339266 6979 MAETP inf. [kg DCB-Equiv.] 3378883192 29116002 9432947 9364868 0 3324361844 6607532 ODP, steady state [kg R11-Equiv.] 0.05 0.03 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 7908 257 172 115 6562 726 75 TETP inf. [kg DCB-Equiv.] 4743 454 153 156 0 3877 103 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.30 0.95 0.14 0.11 0.00 0.01 0.10 ADP fossil [MJ] 27364257 8777175 2792289 2323570 0 11411139 2060084 AP [kg SO2-Equiv.] 137653 4828 1061 988 115926 14115 735 EP [kg Phosphate-Equiv.] 32179 1372 218 208 29608 625 148 FAETP inf. [kg DCB-Equiv.] 4045 1229 465 386 0 1631 333 GWP 100 years [kg CO2-Equiv.] 1533557 -60908093 230379 722570 60120377 1194405 173920 HTP inf. [kg DCB-Equiv.] 926318 18524 7799 6742 546597 339266 7390 MAETP inf. [kg DCB-Equiv.] 3376463190 27868133 9432947 7804057 0 3324361844 6996210 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 7902 266 172 96 6562 726 80 TETP inf. [kg DCB-Equiv.] 4708 439 153 130 0 3877 109 Stellenbosch University http://scholar.sun.ac.za 306 Annexure 23: LCA results ? LBS 23 Paarl ? Primary production Forwarding Harvesting Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 0.95 0.51 0.12 0.08 0.00 0.01 0.24 ADP fossil [MJ] 24389707 5655662 2403729 1777986 0 9823227 4729103 AP [kg SO2-Equiv.] 94262 3468 913 756 75287 12151 1688 EP [kg Phosphate-Equiv.] 21350 1010 187 159 19116 538 340 FAETP inf. [kg DCB-Equiv.] 3629 764 400 296 0 1404 764 GWP 100 years [kg CO2-Equiv.] 571203 -53666169 198320 857252 51754353 1028198 399249 HTP inf. [kg DCB-Equiv.] 509294 11808 6714 5159 176594 292056 16964 MAETP inf. [kg DCB-Equiv.] 2909853446 17939302 8120310 5971633 0 2861761786 16060415 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5461 155 148 73 4277 625 183 TETP inf. [kg DCB-Equiv.] 4098 279 131 99 0 3338 251 Worcester ADP elements [kg Sb-Equiv.] 0.91 0.56 0.12 0.10 0.00 0.01 0.12 ADP fossil [MJ] 23261724 6476209 2403729 2095484 0 9823227 2463074 AP [kg SO2-Equiv.] 94167 4047 913 891 75287 12151 879 EP [kg Phosphate-Equiv.] 21389 1183 187 188 19116 538 177 FAETP inf. [kg DCB-Equiv.] 3420 869 400 348 0 1404 398 GWP 100 years [kg CO2-Equiv.] 1069448 -53225349 198320 1105984 51754353 1028198 207942 HTP inf. [kg DCB-Equiv.] 503772 13493 6714 6080 176594 292056 8835 MAETP inf. [kg DCB-Equiv.] 2905823125 20538234 8120310 7037996 0 2861761786 8364800 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5406 174 148 87 4277 625 95 TETP inf. [kg DCB-Equiv.] 4036 319 131 117 0 3338 131 Ashton ADP elements [kg Sb-Equiv.] 1.06 0.74 0.12 0.11 0.00 0.01 0.08 ADP fossil [MJ] 24202421 7900292 2403729 2400282 0 9823227 1674891 AP [kg SO2-Equiv.] 94707 4738 913 1020 75287 12151 598 EP [kg Phosphate-Equiv.] 21550 1373 187 215 19116 538 120 FAETP inf. [kg DCB-Equiv.] 3550 1076 400 399 0 1404 271 GWP 100 years [kg CO2-Equiv.] 1726225 -52443774 198320 1047726 51754353 1028198 141401 HTP inf. [kg DCB-Equiv.] 504868 16532 6714 6964 176594 292056 6008 MAETP inf. [kg DCB-Equiv.] 2908696241 25064378 8120310 8061704 0 2861761786 5688064 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5435 222 148 99 4277 625 65 TETP inf. [kg DCB-Equiv.] 4083 391 131 134 0 3338 89 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.12 0.81 0.12 0.10 0.00 0.01 0.09 ADP fossil [MJ] 23556396 7555791 2403729 2000235 0 9823227 1773414 AP [kg SO2-Equiv.] 93990 4156 913 850 75287 12151 633 EP [kg Phosphate-Equiv.] 21329 1181 187 179 19116 538 127 FAETP inf. [kg DCB-Equiv.] 3482 1058 400 333 0 1404 287 GWP 100 years [kg CO2-Equiv.] 1320155 -52432455 198320 622021 51754353 1028198 149718 HTP inf. [kg DCB-Equiv.] 503475 15947 6714 5803 176594 292056 6361 MAETP inf. [kg DCB-Equiv.] 2906612994 23990155 8120310 6718087 0 2861761786 6022656 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5430 229 148 83 4277 625 69 TETP inf. [kg DCB-Equiv.] 4053 378 131 112 0 3338 94 Stellenbosch University http://scholar.sun.ac.za 307 Annexure 24: LCA results ? LBS 24 Paarl ? Primary production Forwarding Harvesting Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.57 0.84 0.19 0.14 0.00 0.01 0.39 ADP fossil [MJ] 40209829 9324146 3962883 2931258 0 16194958 7796585 AP [kg SO2-Equiv.] 234094 5717 1506 1246 202810 20032 2783 EP [kg Phosphate-Equiv.] 55626 1665 309 263 51942 887 560 FAETP inf. [kg DCB-Equiv.] 5982 1260 660 487 0 2315 1260 GWP 100 years [kg CO2-Equiv.] 864140 -88476153 326959 1413299 85246691 1695127 658217 HTP inf. [kg DCB-Equiv.] 1028213 19466 11069 8505 479712 481494 27967 MAETP inf. [kg DCB-Equiv.] 4797298626 29575437 13387461 9845068 0 4718012829 26477831 ODP, steady state [kg R11-Equiv.] 0.06698 0.02607 0.00138 0.00104 0.00000 0.03582 0.00266 POCP [kg Ethene-Equiv.] 14728 256 244 121 12775 1031 301 TETP inf. [kg DCB-Equiv.] 6757 460 217 164 0 5503 414 Worcester ADP elements [kg Sb-Equiv.] 1.50 0.92 0.19 0.16 0.00 0.01 0.20 ADP fossil [MJ] 38350191 10676932 3962883 3454697 0 16194958 4060721 AP [kg SO2-Equiv.] 233937 6671 1506 1469 202810 20032 1449 EP [kg Phosphate-Equiv.] 55690 1950 309 310 51942 887 292 FAETP inf. [kg DCB-Equiv.] 5639 1433 660 575 0 2315 656 GWP 100 years [kg CO2-Equiv.] 1685567 -87749400 326959 1823368 85246691 1695127 342822 HTP inf. [kg DCB-Equiv.] 1019110 22246 11069 10023 479712 481494 14566 MAETP inf. [kg DCB-Equiv.] 4790654082 33860139 13387461 11603116 0 4718012829 13790537 ODP, steady state [kg R11-Equiv.] 0.07 0.03 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 14636 287 244 143 12775 1031 157 TETP inf. [kg DCB-Equiv.] 6653 526 217 193 0 5503 215 Ashton ADP elements [kg Sb-Equiv.] 1.75 1.22 0.19 0.19 0.00 0.01 0.14 ADP fossil [MJ] 39901062 13024732 3962883 3957199 0 16194958 2761290 AP [kg SO2-Equiv.] 234827 7812 1506 1682 202810 20032 986 EP [kg Phosphate-Equiv.] 55955 2264 309 355 51942 887 198 FAETP inf. [kg DCB-Equiv.] 5853 1774 660 658 0 2315 446 GWP 100 years [kg CO2-Equiv.] 2768354 -86460864 326959 1727322 85246691 1695127 233119 HTP inf. [kg DCB-Equiv.] 1020916 27255 11069 11481 479712 481494 9905 MAETP inf. [kg DCB-Equiv.] 4795390814 41322117 13387461 13290842 0 4718012829 9377565 ODP, steady state [kg R11-Equiv.] 0.08 0.04 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 14685 365 244 164 12775 1031 107 TETP inf. [kg DCB-Equiv.] 6732 645 217 221 0 5503 146 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.85 1.34 0.19 0.16 0.00 0.01 0.15 ADP fossil [MJ] 38835999 12456774 3962883 3297665 0 16194958 2923719 AP [kg SO2-Equiv.] 233645 6852 1506 1402 202810 20032 1044 EP [kg Phosphate-Equiv.] 55592 1948 309 296 51942 887 210 FAETP inf. [kg DCB-Equiv.] 5740 1745 660 548 0 2315 473 GWP 100 years [kg CO2-Equiv.] 2098892 -86442205 326959 1025488 85246691 1695127 246831 HTP inf. [kg DCB-Equiv.] 1018620 26290 11069 9568 479712 481494 10488 MAETP inf. [kg DCB-Equiv.] 4791956290 39551112 13387461 11075702 0 4718012829 9929187 ODP, steady state [kg R11-Equiv.] 0.07 0.03 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 14677 378 244 136 12775 1031 113 TETP inf. [kg DCB-Equiv.] 6682 623 217 184 0 5503 155 Stellenbosch University http://scholar.sun.ac.za 308 Annexure 25: LCA results ? LBS 25 Paarl ? Primary production Comminution Forwarding Harvesting Combustion Secondary transport ADP elements [kg Sb-Equiv.] 1.46 0.61 0.19 0.29 0.10 0.00 0.27 ADP fossil [MJ] 23818016 6777362 3718612 5807739 2130619 0 5383685 AP [kg SO2-Equiv.] 128384 4156 2133 3342 906 115926 1922 EP [kg Phosphate-Equiv.] 32621 1210 477 748 191 29608 387 FAETP inf. [kg DCB-Equiv.] 3713 916 613 959 354 0 870 GWP 100 years [kg CO2-Equiv.] 1254870 -64309906 3476107 486508 1027272 60120377 454511 HTP inf. [kg DCB-Equiv.] 617282 14149 12108 18935 6182 546597 19312 MAETP inf. [kg DCB-Equiv.] 79234390 21497246 12607743 19689972 7156001 0 18283429 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7737 186 267 425 88 6562 208 TETP inf. [kg DCB-Equiv.] 1251 335 200 312 119 0 286 Worcester ADP elements [kg Sb-Equiv.] 1.41 0.67 0.19 0.29 0.12 0.00 0.14 ADP fossil [MJ] 22602089 7760650 3718612 5807739 2511086 0 2804003 AP [kg SO2-Equiv.] 128318 4849 2133 3342 1067 115926 1001 EP [kg Phosphate-Equiv.] 32677 1417 477 748 225 29608 201 FAETP inf. [kg DCB-Equiv.] 3485 1042 613 959 418 0 453 GWP 100 years [kg CO2-Equiv.] 1863396 -63781657 3476107 486508 1325336 60120377 236725 HTP inf. [kg DCB-Equiv.] 611153 16170 12108 18935 7286 546597 10058 MAETP inf. [kg DCB-Equiv.] 74865822 24611630 12607743 19689972 8433858 0 9522619 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7675 209 267 425 104 6562 108 TETP inf. [kg DCB-Equiv.] 1183 382 200 312 140 0 149 Ashton ADP elements [kg Sb-Equiv.] 1.60 0.89 0.19 0.29 0.14 0.00 0.10 ADP fossil [MJ] 23776583 9467176 3718612 5807739 2876335 0 1906722 AP [kg SO2-Equiv.] 128982 5678 2133 3342 1223 115926 681 EP [kg Phosphate-Equiv.] 32873 1645 477 748 258 29608 137 FAETP inf. [kg DCB-Equiv.] 3648 1289 613 959 478 0 308 GWP 100 years [kg CO2-Equiv.] 2654419 -62845070 3476107 486508 1255524 60120377 160973 HTP inf. [kg DCB-Equiv.] 612635 19811 12108 18935 8345 546597 6840 MAETP inf. [kg DCB-Equiv.] 78469151 30035454 12607743 19689972 9660601 0 6475381 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7713 265 267 425 119 6562 74 TETP inf. [kg DCB-Equiv.] 1243 469 200 312 161 0 101 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.67 0.98 0.19 0.29 0.11 0.00 0.10 ADP fossil [MJ] 22996527 9054349 3718612 5807739 2396946 0 2018882 AP [kg SO2-Equiv.] 128121 4981 2133 3342 1019 115926 721 EP [kg Phosphate-Equiv.] 32609 1416 477 748 215 29608 145 FAETP inf. [kg DCB-Equiv.] 3566 1268 613 959 399 0 326 GWP 100 years [kg CO2-Equiv.] 2167314 -62831507 3476107 486508 745388 60120377 170442 HTP inf. [kg DCB-Equiv.] 610945 19109 12108 18935 6955 546597 7242 MAETP inf. [kg DCB-Equiv.] 75952680 28748179 12607743 19689972 8050501 0 6856286 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7706 275 267 425 99 6562 78 TETP inf. [kg DCB-Equiv.] 1206 453 200 312 134 0 107 Stellenbosch University http://scholar.sun.ac.za 309 Annexure 26: LCA results ? LBS 26 Paarl ? Primary production Comminution Forwarding Harvesting Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.24 0.52 0.16 0.25 0.09 0.00 0.21 ADP fossil [MJ] 20117421 5834261 3201150 4999565 1834133 0 4248311 AP [kg SO2-Equiv.] 85873 3577 1837 2877 780 75287 1516 EP [kg Phosphate-Equiv.] 21682 1042 411 644 164 19116 305 FAETP inf. [kg DCB-Equiv.] 3134 788 528 826 305 0 687 GWP 100 years [kg CO2-Equiv.] 1047644 -55360890 2992391 418808 884323 51754353 358659 HTP inf. [kg DCB-Equiv.] 236058 12180 10423 16300 5322 176594 15239 MAETP inf. [kg DCB-Equiv.] 66896964 18505806 10853318 16950023 6160211 0 14427606 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5273 160 230 366 76 4277 164 TETP inf. [kg DCB-Equiv.] 1057 288 172 269 102 0 225 Worcester ADP elements [kg Sb-Equiv.] 1.21 0.58 0.16 0.25 0.10 0.00 0.12 ADP fossil [MJ] 19360353 6680721 3201150 4999565 2161657 0 2317260 AP [kg SO2-Equiv.] 85920 4174 1837 2877 919 75287 827 EP [kg Phosphate-Equiv.] 21751 1220 411 644 194 19116 166 FAETP inf. [kg DCB-Equiv.] 2985 897 528 826 359 0 375 GWP 100 years [kg CO2-Equiv.] 1595944 -54906149 2992391 418808 1140909 51754353 195632 HTP inf. [kg DCB-Equiv.] 231820 13919 10423 16300 6272 176594 8312 MAETP inf. [kg DCB-Equiv.] 64120003 21186810 10853318 16950023 7260248 0 7869603 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5231 180 230 366 89 4277 90 TETP inf. [kg DCB-Equiv.] 1014 329 172 269 121 0 123 Ashton ADP elements [kg Sb-Equiv.] 1.37 0.76 0.16 0.25 0.12 0.00 0.08 ADP fossil [MJ] 20371411 8149775 3201150 4999565 2476080 0 1544840 AP [kg SO2-Equiv.] 86492 4888 1837 2877 1053 75287 551 EP [kg Phosphate-Equiv.] 21920 1416 411 644 222 19116 111 FAETP inf. [kg DCB-Equiv.] 3125 1110 528 826 412 0 250 GWP 100 years [kg CO2-Equiv.] 2276893 -54099893 2992391 418808 1080812 51754353 130421 HTP inf. [kg DCB-Equiv.] 233097 17054 10423 16300 7184 176594 5541 MAETP inf. [kg DCB-Equiv.] 67221912 25855884 10853318 16950023 8316284 0 5246402 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5263 228 230 366 102 4277 60 TETP inf. [kg DCB-Equiv.] 1065 403 172 269 138 0 82 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.44 0.84 0.16 0.25 0.10 0.00 0.09 ADP fossil [MJ] 19893008 7794395 3201150 4999565 2063400 0 1834498 AP [kg SO2-Equiv.] 85819 4288 1837 2877 877 75287 655 EP [kg Phosphate-Equiv.] 21706 1219 411 644 185 19116 132 FAETP inf. [kg DCB-Equiv.] 3085 1092 528 826 343 0 297 GWP 100 years [kg CO2-Equiv.] 1873874 -54088217 2992391 418808 641663 51754353 154875 HTP inf. [kg DCB-Equiv.] 232334 16450 10423 16300 5987 176594 6580 MAETP inf. [kg DCB-Equiv.] 65711420 24747739 10853318 16950023 6930237 0 6230103 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5265 236 230 366 85 4277 71 TETP inf. [kg DCB-Equiv.] 1044 390 172 269 115 0 97 Stellenbosch University http://scholar.sun.ac.za 310 Annexure 27: LCA results ? LBS 27 Paarl ? Primary production Comminution Forwarding Harvesting Conversion Secondary transport ADP elements [kg Sb-Equiv.] 2.06 0.86 0.26 0.41 0.14 0.00 0.38 ADP fossil [MJ] 33643910 9618592 5277541 8242480 3023824 0 7481473 AP [kg SO2-Equiv.] 220434 5898 3028 4743 1285 202810 2670 EP [kg Phosphate-Equiv.] 56207 1717 678 1062 271 51942 537 FAETP inf. [kg DCB-Equiv.] 5244 1300 871 1361 503 0 1209 GWP 100 years [kg CO2-Equiv.] 1689935 -91270137 4933373 690463 1457929 85246691 631614 HTP inf. [kg DCB-Equiv.] 579459 20081 17184 26873 8773 479712 26837 MAETP inf. [kg DCB-Equiv.] 111910728 30509399 17893207 27944473 10155965 0 25407685 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14435 264 379 603 125 12775 289 TETP inf. [kg DCB-Equiv.] 1768 475 284 443 169 0 397 Worcester ADP elements [kg Sb-Equiv.] 2.00 0.95 0.26 0.41 0.17 0.00 0.20 ADP fossil [MJ] 32077419 11014098 5277541 8242480 3563793 0 3979507 AP [kg SO2-Equiv.] 220398 6882 3028 4743 1515 202810 1420 EP [kg Phosphate-Equiv.] 56299 2011 678 1062 319 51942 286 FAETP inf. [kg DCB-Equiv.] 4946 1478 871 1361 593 0 643 GWP 100 years [kg CO2-Equiv.] 2567007 -90520434 4933373 690463 1880948 85246691 335965 HTP inf. [kg DCB-Equiv.] 571331 22948 17184 26873 10340 479712 14275 MAETP inf. [kg DCB-Equiv.] 106251343 34929407 17893207 27944473 11969530 0 13514726 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14355 296 379 603 147 12775 154 TETP inf. [kg DCB-Equiv.] 1679 542 284 443 199 0 211 Ashton ADP elements [kg Sb-Equiv.] 2.26 1.26 0.26 0.41 0.19 0.00 0.14 ADP fossil [MJ] 33744288 13436039 5277541 8242480 4082163 0 2706065 AP [kg SO2-Equiv.] 221340 8058 3028 4743 1735 202810 966 EP [kg Phosphate-Equiv.] 56577 2335 678 1062 366 51942 194 FAETP inf. [kg DCB-Equiv.] 5178 1830 871 1361 679 0 437 GWP 100 years [kg CO2-Equiv.] 3689646 -89191207 4933373 690463 1781869 85246691 228456 HTP inf. [kg DCB-Equiv.] 573435 28116 17184 26873 11844 479712 9707 MAETP inf. [kg DCB-Equiv.] 111365272 42627026 17893207 27944473 13710553 0 9190014 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14407 377 379 603 169 12775 105 TETP inf. [kg DCB-Equiv.] 1764 665 284 443 228 0 144 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.38 1.39 0.26 0.41 0.16 0.00 0.15 ADP fossil [MJ] 32796394 12850146 5277541 8242480 3401802 0 3024425 AP [kg SO2-Equiv.] 220175 7069 3028 4743 1446 202810 1079 EP [kg Phosphate-Equiv.] 56213 2009 678 1062 305 51942 217 FAETP inf. [kg DCB-Equiv.] 5086 1800 871 1361 566 0 489 GWP 100 years [kg CO2-Equiv.] 3011775 -89171958 4933373 690463 1057872 85246691 255333 HTP inf. [kg DCB-Equiv.] 571607 27120 17184 26873 9870 479712 10849 MAETP inf. [kg DCB-Equiv.] 108334426 40800094 17893207 27944473 11425461 0 10271192 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14405 390 379 603 141 12775 117 TETP inf. [kg DCB-Equiv.] 1720 643 284 443 190 0 160 Stellenbosch University http://scholar.sun.ac.za 311 Annexure 28: LCA results ? LBS 28 Paarl ? Prim .production Comminution Forwarding Harvesting Combustion Conversion Transp. of bio-char Transp. of bio-oil ADP elements [kg Sb-Equiv.] 1.55 0.74 0.23 0.35 0.12 0.00 0.00 0.03 0.08 ADP fossil [MJ] 24641564 8245779 4524304 7066072 2592249 0 0 571102 1642058 AP [kg SO2-Equiv.] 186540 5056 2596 4066 1102 148246 24680 204 591 EP [kg Phosphate-Equiv.] 47652 1472 581 910 232 38171 6125 41 119 FAETP inf. [kg DCB-Equiv.] 3817 1114 746 1167 431 0 0 92 265 GWP 100 years [kg CO2-Equiv.] 1093823 -78243608 4229258 591917 1249846 69969071 3110489 48215 138634 HTP inf. [kg DCB-Equiv.] 479588 17215 14731 23037 7521 352463 56629 2049 5943 MAETP inf. [kg DCB-Equiv.] 81672969 26154945 15339399 23956098 8706455 0 0 1939509 5576562 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 12162 226 325 517 107 9482 1418 22 64 TETP inf. [kg DCB-Equiv.] 1292 407 243 380 145 0 0 30 87 Worcester ADP elements [kg Sb-Equiv.] 1.60 0.82 0.23 0.35 0.15 0.00 0.00 0.02 0.04 ADP fossil [MJ] 25264851 9442111 4524304 7066072 3055151 0 0 303778 873435 AP [kg SO2-Equiv.] 187209 5900 2596 4066 1299 148246 24680 108 314 EP [kg Phosphate-Equiv.] 47871 1724 581 910 274 38171 6125 22 63 FAETP inf. [kg DCB-Equiv.] 3879 1267 746 1167 508 0 0 49 141 GWP 100 years [kg CO2-Equiv.] 2011707 -77600906 4229258 591917 1612490 69969071 3110489 25646 73742 HTP inf. [kg DCB-Equiv.] 479649 19673 14731 23037 8864 352463 56629 1090 3161 MAETP inf. [kg DCB-Equiv.] 83498694 29944108 15339399 23956098 10261179 0 0 1031654 2966257 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 12168 254 325 517 126 9482 1418 12 34 TETP inf. [kg DCB-Equiv.] 1321 465 243 380 170 0 0 16 46 Ashton ADP elements [kg Sb-Equiv.] 1.86 1.08 0.23 0.35 0.17 0.00 0.00 0.01 0.03 ADP fossil [MJ] 27408798 11518381 4524304 7066072 3499536 0 0 206569 593936 AP [kg SO2-Equiv.] 188271 6908 2596 4066 1488 148246 24680 74 214 EP [kg Phosphate-Equiv.] 48161 2002 581 910 314 38171 6125 15 43 FAETP inf. [kg DCB-Equiv.] 4193 1569 746 1167 582 0 0 33 96 GWP 100 years [kg CO2-Equiv.] 3034478 -76461393 4229258 591917 1527552 69969071 3110489 17439 50144 HTP inf. [kg DCB-Equiv.] 484008 24103 14731 23037 10154 352463 56629 741 2149 MAETP inf. [kg DCB-Equiv.] 90310875 36543084 15339399 23956098 11753714 0 0 701525 2017054 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 12241 323 325 517 145 9482 1418 8 23 TETP inf. [kg DCB-Equiv.] 1431 570 243 380 195 0 0 11 32 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.95 1.19 0.23 0.35 0.14 0.00 0.00 0.01 0.03 ADP fossil [MJ] 26417447 11016109 4524304 7066072 2916280 0 0 230871 663811 AP [kg SO2-Equiv.] 187208 6060 2596 4066 1240 148246 24680 82 239 EP [kg Phosphate-Equiv.] 47836 1722 581 910 261 38171 6125 17 48 FAETP inf. [kg DCB-Equiv.] 4086 1543 746 1167 485 0 0 37 107 GWP 100 years [kg CO2-Equiv.] 2438265 -76444891 4229258 591917 906887 69969071 3110489 19491 56044 HTP inf. [kg DCB-Equiv.] 481802 23250 14731 23037 8461 352463 56629 828 2402 MAETP inf. [kg DCB-Equiv.] 87105572 34976901 15339399 23956098 9794762 0 0 784057 2254355 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 12232 334 325 517 121 9482 1418 9 26 TETP inf. [kg DCB-Equiv.] 1385 551 243 380 163 0 0 12 35 Stellenbosch University http://scholar.sun.ac.za 312 Annexure 29: LCA results ? LBS 29 Paarl ? Prim. production Comminution Forwarding Harvesting bio-char for sale Combustion Conversion Transp. Transp. ADP elements [kg Sb-Equiv.] 2.05 0.98 0.30 0.47 0.16 0.00 0.00 0.00 0.04 0.11 ADP fossil [MJ] 32530355 10885596 5972722 9328216 3422136 0 0 0 753936 2167749 AP [kg SO2-Equiv.] 120871 6675 3427 5368 1455 0 70317 32582 269 780 EP [kg Phosphate-Equiv.] 30563 1943 767 1202 307 0 18047 8086 54 157 FAETP inf. [kg DCB-Equiv.] 5038 1471 985 1541 569 0 0 0 122 350 GWP 100 years [kg CO2-Equiv.] -34228695 -103292643 5583219 781414 1649974 8929400 47766987 4106287 63650 183017 HTP inf. [kg DCB-Equiv.] 334485 22726 19447 30412 9929 0 166661 74759 2704 7845 MAETP inf. [kg DCB-Equiv.] 107819886 34528231 20250179 31625442 11493754 0 0 0 2560427 7361852 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8282 298 429 683 141 0 4745 1871 29 85 TETP inf. [kg DCB-Equiv.] 1706 537 321 502 191 0 0 0 40 115 Worcester of bio-char of bio-oil ADP elements [kg Sb-Equiv.] 2.11 1.08 0.30 0.47 0.19 0.00 0.00 0.00 0.02 0.06 ADP fossil [MJ] 33353181 12464924 5972722 9328216 4033231 0 0 0 401030 1153058 AP [kg SO2-Equiv.] 121754 7789 3427 5368 1715 0 70317 32582 143 415 EP [kg Phosphate-Equiv.] 30852 2276 767 1202 361 0 18047 8086 29 84 FAETP inf. [kg DCB-Equiv.] 5121 1673 985 1541 671 0 0 0 65 186 GWP 100 years [kg CO2-Equiv.] -33016958 -102444185 5583219 781414 2128715 8929400 47766987 4106287 33856 97349 HTP inf. [kg DCB-Equiv.] 334564 25971 19447 30412 11702 0 166661 74759 1439 4173 MAETP inf. [kg DCB-Equiv.] 110230102 39530463 20250179 31625442 13546210 0 0 0 1361929 3915879 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8290 335 429 683 167 0 4745 1871 15 45 TETP inf. [kg DCB-Equiv.] 1744 614 321 502 225 0 0 0 21 61 Ashton ADP elements [kg Sb-Equiv.] 2.46 1.42 0.30 0.47 0.22 0.00 0.00 0.00 0.01 0.04 ADP fossil [MJ] 36183495 15205894 5972722 9328216 4619883 0 0 0 272700 784080 AP [kg SO2-Equiv.] 123156 9120 3427 5368 1964 0 70317 32582 97 282 EP [kg Phosphate-Equiv.] 31235 2643 767 1202 414 0 18047 8086 20 57 FAETP inf. [kg DCB-Equiv.] 5536 2071 985 1541 768 0 0 0 44 127 GWP 100 years [kg CO2-Equiv.] -31666756 -100939867 5583219 781414 2016585 8929400 47766987 4106287 23022 66198 HTP inf. [kg DCB-Equiv.] 340319 31820 19447 30412 13404 0 166661 74759 978 2838 MAETP inf. [kg DCB-Equiv.] 119223145 48242046 20250179 31625442 15516567 0 0 0 926112 2662798 ODP, steady state [kg R11-Equiv.] 0.05 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8387 426 429 683 191 0 4745 1871 11 31 TETP inf. [kg DCB-Equiv.] 1890 753 321 502 258 0 0 0 14 42 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.58 1.57 0.30 0.47 0.18 0.00 0.00 0.00 0.02 0.04 ADP fossil [MJ] 34874771 14542824 5972722 9328216 3849903 0 0 0 304783 876324 AP [kg SO2-Equiv.] 121754 8000 3427 5368 1637 0 70317 32582 109 315 EP [kg Phosphate-Equiv.] 30806 2274 767 1202 345 0 18047 8086 22 64 FAETP inf. [kg DCB-Equiv.] 5394 2037 985 1541 640 0 0 0 49 142 GWP 100 years [kg CO2-Equiv.] -32453840 -100918083 5583219 781414 1197219 8929400 47766987 4106287 25731 73986 HTP inf. [kg DCB-Equiv.] 337407 30693 19447 30412 11170 0 166661 74759 1093 3171 MAETP inf. [kg DCB-Equiv.] 114991691 46174463 20250179 31625442 12930473 0 0 0 1035066 2976068 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8374 441 429 683 159 0 4745 1871 12 34 TETP inf. [kg DCB-Equiv.] 1828 728 321 502 215 0 0 0 16 46 Stellenbosch University http://scholar.sun.ac.za 313 Annexure 30: LCA results ? LBS 30 Paarl ? Primary production Forwarding Harvesting Combustion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.25 0.59 0.28 0.10 0.00 0.01 0.28 ADP fossil [MJ] 31169933 6569892 5629951 2065396 0 11411139 5493556 AP [kg SO2-Equiv.] 140147 4028 3240 878 115926 14115 1961 EP [kg Phosphate-Equiv.] 32711 1173 725 185 29608 625 395 FAETP inf. [kg DCB-Equiv.] 4680 888 930 343 0 1631 888 GWP 100 years [kg CO2-Equiv.] 904774 -62341235 471615 995825 60120377 1194405 463787 HTP inf. [kg DCB-Equiv.] 943632 13716 18355 5993 546597 339266 19706 MAETP inf. [kg DCB-Equiv.] 3389881728 20839167 19087217 6936939 0 3324361844 18656560 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 8179 180 412 85 6562 726 212 TETP inf. [kg DCB-Equiv.] 4911 324 303 115 0 3877 291 Worcester ADP elements [kg Sb-Equiv.] 1.20 0.65 0.28 0.12 0.00 0.01 0.14 ADP fossil [MJ] 29859613 7523079 5629951 2434216 0 11411139 2861227 AP [kg SO2-Equiv.] 140037 4701 3240 1035 115926 14115 1021 EP [kg Phosphate-Equiv.] 32756 1374 725 218 29608 625 206 FAETP inf. [kg DCB-Equiv.] 4438 1010 930 405 0 1631 462 GWP 100 years [kg CO2-Equiv.] 1483559 -61829157 471615 1284764 60120377 1194405 241556 HTP inf. [kg DCB-Equiv.] 937218 15675 18355 7063 546597 339266 10263 MAETP inf. [kg DCB-Equiv.] 3385199911 23858213 19087217 8175678 0 3324361844 9716958 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 8114 202 412 101 6562 726 111 TETP inf. [kg DCB-Equiv.] 4838 370 303 136 0 3877 152 Ashton ADP elements [kg Sb-Equiv.] 1.38 0.86 0.28 0.13 0.00 0.01 0.10 ADP fossil [MJ] 30952373 9177364 5629951 2788284 0 11411139 1945634 AP [kg SO2-Equiv.] 140664 5504 3240 1185 115926 14115 694 EP [kg Phosphate-Equiv.] 32943 1595 725 250 29608 625 140 FAETP inf. [kg DCB-Equiv.] 4589 1250 930 464 0 1631 314 GWP 100 years [kg CO2-Equiv.] 2246503 -60921241 471615 1217089 60120377 1194405 164258 HTP inf. [kg DCB-Equiv.] 938491 19204 18355 8090 546597 339266 6979 MAETP inf. [kg DCB-Equiv.] 3388537463 29116002 19087217 9364868 0 3324361844 6607532 ODP, steady state [kg R11-Equiv.] 0.05 0.03 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 8149 257 412 115 6562 726 75 TETP inf. [kg DCB-Equiv.] 4893 454 303 156 0 3877 103 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.45 0.95 0.28 0.11 0.00 0.01 0.10 ADP fossil [MJ] 30201918 8777175 5629951 2323570 0 11411139 2060084 AP [kg SO2-Equiv.] 139831 4828 3240 988 115926 14115 735 EP [kg Phosphate-Equiv.] 32687 1372 725 208 29608 625 148 FAETP inf. [kg DCB-Equiv.] 4509 1229 930 386 0 1631 333 GWP 100 years [kg CO2-Equiv.] 1774793 -60908093 471615 722570 60120377 1194405 173920 HTP inf. [kg DCB-Equiv.] 936873 18524 18355 6742 546597 339266 7390 MAETP inf. [kg DCB-Equiv.] 3386117461 27868133 19087217 7804057 0 3324361844 6996210 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.03 0.00 POCP [kg Ethene-Equiv.] 8143 266 412 96 6562 726 80 TETP inf. [kg DCB-Equiv.] 4858 439 303 130 0 3877 109 Stellenbosch University http://scholar.sun.ac.za 314 Annexure 31: LCA results ? LBS 31 Paarl ? Primary production Forwarding Harvesting Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.08 0.51 0.24 0.08 0.00 0.01 0.24 ADP fossil [MJ] 26832495 5655662 4846517 1777986 0 9823227 4729103 AP [kg SO2-Equiv.] 96138 3468 2789 756 75287 12151 1688 EP [kg Phosphate-Equiv.] 21787 1010 624 159 19116 538 340 FAETP inf. [kg DCB-Equiv.] 4029 764 800 296 0 1404 764 GWP 100 years [kg CO2-Equiv.] 778870 -53666169 405987 857252 51754353 1028198 399249 HTP inf. [kg DCB-Equiv.] 518381 11808 15801 5159 176594 292056 16964 MAETP inf. [kg DCB-Equiv.] 2918164280 17939302 16431144 5971633 0 2861761786 16060415 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5668 155 355 73 4277 625 183 TETP inf. [kg DCB-Equiv.] 4228 279 261 99 0 3338 251 Worcester ADP elements [kg Sb-Equiv.] 1.03 0.56 0.24 0.10 0.00 0.01 0.12 ADP fossil [MJ] 25704512 6476209 4846517 2095484 0 9823227 2463074 AP [kg SO2-Equiv.] 96042 4047 2789 891 75287 12151 879 EP [kg Phosphate-Equiv.] 21826 1183 624 188 19116 538 177 FAETP inf. [kg DCB-Equiv.] 3820 869 800 348 0 1404 398 GWP 100 years [kg CO2-Equiv.] 1277115 -53225349 405987 1105984 51754353 1028198 207942 HTP inf. [kg DCB-Equiv.] 512859 13493 15801 6080 176594 292056 8835 MAETP inf. [kg DCB-Equiv.] 2914133960 20538234 16431144 7037996 0 2861761786 8364800 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5613 174 355 87 4277 625 95 TETP inf. [kg DCB-Equiv.] 4165 319 261 117 0 3338 131 Ashton ADP elements [kg Sb-Equiv.] 1.19 0.74 0.24 0.11 0.00 0.01 0.08 ADP fossil [MJ] 26645209 7900292 4846517 2400282 0 9823227 1674891 AP [kg SO2-Equiv.] 96582 4738 2789 1020 75287 12151 598 EP [kg Phosphate-Equiv.] 21987 1373 624 215 19116 538 120 FAETP inf. [kg DCB-Equiv.] 3950 1076 800 399 0 1404 271 GWP 100 years [kg CO2-Equiv.] 1933892 -52443774 405987 1047726 51754353 1028198 141401 HTP inf. [kg DCB-Equiv.] 513955 16532 15801 6964 176594 292056 6008 MAETP inf. [kg DCB-Equiv.] 2917007076 25064378 16431144 8061704 0 2861761786 5688064 ODP, steady state [kg R11-Equiv.] 0.05 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5642 222 355 99 4277 625 65 TETP inf. [kg DCB-Equiv.] 4212 391 261 134 0 3338 89 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.25 0.81 0.24 0.10 0.00 0.01 0.09 ADP fossil [MJ] 25999184 7555791 4846517 2000235 0 9823227 1773414 AP [kg SO2-Equiv.] 95865 4156 2789 850 75287 12151 633 EP [kg Phosphate-Equiv.] 21766 1181 624 179 19116 538 127 FAETP inf. [kg DCB-Equiv.] 3882 1058 800 333 0 1404 287 GWP 100 years [kg CO2-Equiv.] 1527822 -52432455 405987 622021 51754353 1028198 149718 HTP inf. [kg DCB-Equiv.] 512562 15947 15801 5803 176594 292056 6361 MAETP inf. [kg DCB-Equiv.] 2914923828 23990155 16431144 6718087 0 2861761786 6022656 ODP, steady state [kg R11-Equiv.] 0.04 0.02 0.00 0.00 0.00 0.02 0.00 POCP [kg Ethene-Equiv.] 5637 229 355 83 4277 625 69 TETP inf. [kg DCB-Equiv.] 4182 378 261 112 0 3338 94 Stellenbosch University http://scholar.sun.ac.za 315 Annexure 32: LCA results ? LBS 32 Paarl ? Primary production Forwarding Harvesting Conversion Comminution Secondary transport ADP elements [kg Sb-Equiv.] 1.78 0.84 0.40 0.14 0.00 0.01 0.39 ADP fossil [MJ] 44237105 9324146 7990159 2931258 0 16194958 7796585 AP [kg SO2-Equiv.] 237185 5717 4598 1246 202810 20032 2783 EP [kg Phosphate-Equiv.] 56346 1665 1029 263 51942 887 560 FAETP inf. [kg DCB-Equiv.] 6642 1260 1320 487 0 2315 1260 GWP 100 years [kg CO2-Equiv.] 1206508 -88476153 669327 1413299 85246691 1695127 658217 HTP inf. [kg DCB-Equiv.] 1043194 19466 26050 8505 479712 481494 27967 MAETP inf. [kg DCB-Equiv.] 4811000195 29575437 27089030 9845068 0 4718012829 26477831 ODP, steady state [kg R11-Equiv.] 0.07 0.03 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 15069 256 585 121 12775 1031 301 TETP inf. [kg DCB-Equiv.] 6970 460 430 164 0 5503 414 Worcester ADP elements [kg Sb-Equiv.] 1.70 0.92 0.40 0.16 0.00 0.01 0.20 ADP fossil [MJ] 42377467 10676932 7990159 3454697 0 16194958 4060721 AP [kg SO2-Equiv.] 237029 6671 4598 1469 202810 20032 1449 EP [kg Phosphate-Equiv.] 56410 1950 1029 310 51942 887 292 FAETP inf. [kg DCB-Equiv.] 6298 1433 1320 575 0 2315 656 GWP 100 years [kg CO2-Equiv.] 2027935 -87749400 669327 1823368 85246691 1695127 342822 HTP inf. [kg DCB-Equiv.] 1034091 22246 26050 10023 479712 481494 14566 MAETP inf. [kg DCB-Equiv.] 4804355651 33860139 27089030 11603116 0 4718012829 13790537 ODP, steady state [kg R11-Equiv.] 0.07 0.03 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 14978 287 585 143 12775 1031 157 TETP inf. [kg DCB-Equiv.] 6867 526 430 193 0 5503 215 Ashton ADP elements [kg Sb-Equiv.] 1.96 1.22 0.40 0.19 0.00 0.01 0.14 ADP fossil [MJ] 43928338 13024732 7990159 3957199 0 16194958 2761290 AP [kg SO2-Equiv.] 237919 7812 4598 1682 202810 20032 986 EP [kg Phosphate-Equiv.] 56676 2264 1029 355 51942 887 198 FAETP inf. [kg DCB-Equiv.] 6512 1774 1320 658 0 2315 446 GWP 100 years [kg CO2-Equiv.] 3110722 -86460864 669327 1727322 85246691 1695127 233119 HTP inf. [kg DCB-Equiv.] 1035898 27255 26050 11481 479712 481494 9905 MAETP inf. [kg DCB-Equiv.] 4809092383 41322117 27089030 13290842 0 4718012829 9377565 ODP, steady state [kg R11-Equiv.] 0.08 0.04 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 15026 365 585 164 12775 1031 107 TETP inf. [kg DCB-Equiv.] 6945 645 430 221 0 5503 146 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.06 1.34 0.40 0.16 0.00 0.01 0.15 ADP fossil [MJ] 42863275 12456774 7990159 3297665 0 16194958 2923719 AP [kg SO2-Equiv.] 236737 6852 4598 1402 202810 20032 1044 EP [kg Phosphate-Equiv.] 56312 1948 1029 296 51942 887 210 FAETP inf. [kg DCB-Equiv.] 6400 1745 1320 548 0 2315 473 GWP 100 years [kg CO2-Equiv.] 2441260 -86442205 669327 1025488 85246691 1695127 246831 HTP inf. [kg DCB-Equiv.] 1033601 26290 26050 9568 479712 481494 10488 MAETP inf. [kg DCB-Equiv.] 4805657858 39551112 27089030 11075702 0 4718012829 9929187 ODP, steady state [kg R11-Equiv.] 0.07 0.03 0.00 0.00 0.00 0.04 0.00 POCP [kg Ethene-Equiv.] 15018 378 585 136 12775 1031 113 TETP inf. [kg DCB-Equiv.] 6895 623 430 184 0 5503 155 Stellenbosch University http://scholar.sun.ac.za 316 Annexure 33: LCA results ? LBS 33 Paarl ? Primary production Harvesting Forwarding Combustion Secondary transport ADP elements [kg Sb-Equiv.] 1.22 0.61 0.17 0.09 0.00 0.35 ADP fossil [MJ] 18957852 6777362 3401391 1857218 0 6921881 AP [kg SO2-Equiv.] 124578 4156 960 1066 115926 2471 EP [kg Phosphate-Equiv.] 31732 1210 179 238 29608 497 FAETP inf. [kg DCB-Equiv.] 2945 916 604 306 0 1119 GWP 100 years [kg CO2-Equiv.] -23 -64309906 3449529 155605 60120377 584371 HTP inf. [kg DCB-Equiv.] 600370 14149 8747 6047 546597 24829 MAETP inf. [kg DCB-Equiv.] 62833539 21497246 11532234 6296793 0 23507266 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7659 186 510 133 6562 268 TETP inf. [kg DCB-Equiv.] 989 335 188 100 0 367 Worcester ADP elements [kg Sb-Equiv.] 1.12 0.67 0.17 0.09 0.00 0.18 ADP fossil [MJ] 16624406 7760650 3401391 1857218 0 3605146 AP [kg SO2-Equiv.] 124087 4849 960 1066 115926 1287 EP [kg Phosphate-Equiv.] 31701 1417 179 238 29608 259 FAETP inf. [kg DCB-Equiv.] 2535 1042 604 306 0 583 GWP 100 years [kg CO2-Equiv.] 248215 -63781657 3449529 155605 60120377 304360 HTP inf. [kg DCB-Equiv.] 590493 16170 8747 6047 546597 12932 MAETP inf. [kg DCB-Equiv.] 54684025 24611630 11532234 6296793 0 12243368 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7554 209 510 133 6562 139 TETP inf. [kg DCB-Equiv.] 861 382 188 100 0 191 Ashton ADP elements [kg Sb-Equiv.] 1.27 0.89 0.17 0.09 0.00 0.12 ADP fossil [MJ] 17177284 9467176 3401391 1857218 0 2451499 AP [kg SO2-Equiv.] 124504 5678 960 1066 115926 875 EP [kg Phosphate-Equiv.] 31847 1645 179 238 29608 176 FAETP inf. [kg DCB-Equiv.] 2596 1289 604 306 0 396 GWP 100 years [kg CO2-Equiv.] 1087406 -62845070 3449529 155605 60120377 206965 HTP inf. [kg DCB-Equiv.] 589996 19811 8747 6047 546597 8794 MAETP inf. [kg DCB-Equiv.] 56189972 30035454 11532234 6296793 0 8325490 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7566 265 510 133 6562 95 TETP inf. [kg DCB-Equiv.] 886 469 188 100 0 130 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.37 0.98 0.17 0.09 0.00 0.13 ADP fossil [MJ] 16908663 9054349 3401391 1857218 0 2595705 AP [kg SO2-Equiv.] 123859 4981 960 1066 115926 926 EP [kg Phosphate-Equiv.] 31627 1416 179 238 29608 186 FAETP inf. [kg DCB-Equiv.] 2598 1268 604 306 0 420 GWP 100 years [kg CO2-Equiv.] 1113144 -62831507 3449529 155605 60120377 219139 HTP inf. [kg DCB-Equiv.] 589811 19109 8747 6047 546597 9311 MAETP inf. [kg DCB-Equiv.] 55392431 28748179 11532234 6296793 0 8815225 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 7581 275 510 133 6562 100 TETP inf. [kg DCB-Equiv.] 878 453 188 100 0 138 Stellenbosch University http://scholar.sun.ac.za 317 Annexure 34 LCA results ? LBS 34 Paarl ? Primary production Harvesting Forwarding Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.05 0.52 0.15 0.08 0.00 0.30 ADP fossil [MJ] 16319781 5834261 2928072 1598778 0 5958670 AP [kg SO2-Equiv.] 82735 3577 827 917 75287 2127 EP [kg Phosphate-Equiv.] 20945 1042 154 205 19116 428 FAETP inf. [kg DCB-Equiv.] 2535 788 520 264 0 963 GWP 100 years [kg CO2-Equiv.] -20 -55360890 2969511 133952 51754353 503054 HTP inf. [kg DCB-Equiv.] 222884 12180 7530 5206 176594 21374 MAETP inf. [kg DCB-Equiv.] 54089966 18505806 9927471 5420566 0 20236123 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5221 160 439 115 4277 230 TETP inf. [kg DCB-Equiv.] 852 288 162 86 0 316 Worcester ADP elements [kg Sb-Equiv.] 0.96 0.58 0.15 0.08 0.00 0.16 ADP fossil [MJ] 14311044 6680721 2928072 1598778 0 3103474 AP [kg SO2-Equiv.] 82313 4174 827 917 75287 1108 EP [kg Phosphate-Equiv.] 20918 1220 154 205 19116 223 FAETP inf. [kg DCB-Equiv.] 2182 897 520 264 0 502 GWP 100 years [kg CO2-Equiv.] 213674 -54906149 2969511 133952 51754353 262007 HTP inf. [kg DCB-Equiv.] 214382 13919 7530 5206 176594 11132 MAETP inf. [kg DCB-Equiv.] 47074495 21186810 9927471 5420566 0 10539647 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5130 180 439 115 4277 120 TETP inf. [kg DCB-Equiv.] 741 329 162 86 0 165 Ashton ADP elements [kg Sb-Equiv.] 1.10 0.76 0.15 0.08 0.00 0.11 ADP fossil [MJ] 14786987 8149775 2928072 1598778 0 2110362 AP [kg SO2-Equiv.] 82672 4888 827 917 75287 753 EP [kg Phosphate-Equiv.] 21043 1416 154 205 19116 152 FAETP inf. [kg DCB-Equiv.] 2235 1110 520 264 0 341 GWP 100 years [kg CO2-Equiv.] 936089 -54099893 2969511 133952 51754353 178165 HTP inf. [kg DCB-Equiv.] 213954 17054 7530 5206 176594 7570 MAETP inf. [kg DCB-Equiv.] 48370882 25855884 9927471 5420566 0 7166960 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5141 228 439 115 4277 82 TETP inf. [kg DCB-Equiv.] 763 403 162 86 0 112 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.18 0.84 0.15 0.08 0.00 0.11 ADP fossil [MJ] 14555746 7794395 2928072 1598778 0 2234501 AP [kg SO2-Equiv.] 82116 4288 827 917 75287 798 EP [kg Phosphate-Equiv.] 20854 1219 154 205 19116 161 FAETP inf. [kg DCB-Equiv.] 2237 1092 520 264 0 361 GWP 100 years [kg CO2-Equiv.] 958245 -54088217 2969511 133952 51754353 188645 HTP inf. [kg DCB-Equiv.] 213795 16450 7530 5206 176594 8015 MAETP inf. [kg DCB-Equiv.] 47684322 24747739 9927471 5420566 0 7588546 ODP, steady state [kg R11-Equiv.] 0.02 0.02 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 5154 236 439 115 4277 86 TETP inf. [kg DCB-Equiv.] 756 390 162 86 0 119 Stellenbosch University http://scholar.sun.ac.za 318 Annexure 35: LCA results ? LBS 35 Paarl ? Primary production Harvesting Forwarding Conversion Secondary transport ADP elements [kg Sb-Equiv.] 1.73 0.86 0.24 0.13 0.00 0.50 ADP fossil [MJ] 26905431 9618592 4827335 2635808 0 9823697 AP [kg SO2-Equiv.] 215089 5898 1363 1512 202810 3506 EP [kg Phosphate-Equiv.] 54958 1717 254 338 51942 706 FAETP inf. [kg DCB-Equiv.] 4180 1300 858 435 0 1588 GWP 100 years [kg CO2-Equiv.] -77600 -91270137 4895653 220839 85246691 829354 HTP inf. [kg DCB-Equiv.] 556028 20081 12414 8582 479712 35238 MAETP inf. [kg DCB-Equiv.] 89174842 30509399 16366819 8936557 0 33362067 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14332 264 724 189 12775 380 TETP inf. [kg DCB-Equiv.] 1404 475 267 142 0 521 Worcester ADP elements [kg Sb-Equiv.] 1.58 0.95 0.24 0.13 0.00 0.26 ADP fossil [MJ] 23593749 11014098 4827335 2635808 0 5116509 AP [kg SO2-Equiv.] 214393 6882 1363 1512 202810 1826 EP [kg Phosphate-Equiv.] 54914 2011 254 338 51942 368 FAETP inf. [kg DCB-Equiv.] 3598 1478 858 435 0 827 GWP 100 years [kg CO2-Equiv.] 274705 -90520434 4895653 220839 85246691 431955 HTP inf. [kg DCB-Equiv.] 542010 22948 12414 8582 479712 18353 MAETP inf. [kg DCB-Equiv.] 77608860 34929407 16366819 8936557 0 17376077 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14182 296 724 189 12775 198 TETP inf. [kg DCB-Equiv.] 1222 542 267 142 0 271 Ashton ADP elements [kg Sb-Equiv.] 1.81 1.26 0.24 0.13 0.00 0.18 ADP fossil [MJ] 24378408 13436039 4827335 2635808 0 3479226 AP [kg SO2-Equiv.] 214985 8058 1363 1512 202810 1242 EP [kg Phosphate-Equiv.] 55120 2335 254 338 51942 250 FAETP inf. [kg DCB-Equiv.] 3684 1830 858 435 0 562 GWP 100 years [kg CO2-Equiv.] 1465705 -89191207 4895653 220839 85246691 293729 HTP inf. [kg DCB-Equiv.] 541304 28116 12414 8582 479712 12480 MAETP inf. [kg DCB-Equiv.] 79746134 42627026 16366819 8936557 0 11815732 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14200 377 724 189 12775 134 TETP inf. [kg DCB-Equiv.] 1258 665 267 142 0 185 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.95 1.39 0.24 0.13 0.00 0.19 ADP fossil [MJ] 23997174 12850146 4827335 2635808 0 3683886 AP [kg SO2-Equiv.] 214068 7069 1363 1512 202810 1315 EP [kg Phosphate-Equiv.] 54809 2009 254 338 51942 265 FAETP inf. [kg DCB-Equiv.] 3688 1800 858 435 0 595 GWP 100 years [kg CO2-Equiv.] 1502232 -89171958 4895653 220839 85246691 311008 HTP inf. [kg DCB-Equiv.] 541043 27120 12414 8582 479712 13214 MAETP inf. [kg DCB-Equiv.] 78614246 40800094 16366819 8936557 0 12510775 ODP, steady state [kg R11-Equiv.] 0.04 0.03 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 14221 390 724 189 12775 142 TETP inf. [kg DCB-Equiv.] 1247 643 267 142 0 195 Stellenbosch University http://scholar.sun.ac.za 319 Annexure 36: LCA results ? LBS 36 Paarl ? Primary production Harvesting Forwarding Conversion Upgrading transport of bio-char transport of bio-oil ADP elements [kg Sb-Equiv.] 1.17 0.74 0.21 0.11 0.00 0.00 0.03 0.08 ADP fossil [MJ] 16903993 8245779 4138353 2259612 0 0 583253 1676996 AP [kg SO2-Equiv.] 181258 5056 1169 1296 148246 24680 208 603 EP [kg Phosphate-Equiv.] 46440 1472 218 290 38171 6125 42 122 FAETP inf. [kg DCB-Equiv.] 2588 1114 735 373 0 0 94 271 GWP 100 years [kg CO2-Equiv.] -586982 -78243608 4196921 189319 69969071 3110489 49240 141584 HTP inf. [kg DCB-Equiv.] 452469 17215 10643 7357 352463 56629 2092 6069 MAETP inf. [kg DCB-Equiv.] 55522885 26154945 14030865 7661087 0 0 1980776 5695213 ODP, steady state [kg R11-Equiv.] 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11997 226 621 162 9482 1418 23 66 TETP inf. [kg DCB-Equiv.] 877 407 228 121 0 0 31 89 Worcester ADP elements [kg Sb-Equiv.] 1.19 0.82 0.21 0.11 0.00 0.00 0.02 0.04 ADP fossil [MJ] 17017290 9442111 4138353 2259612 0 0 303778 873435 AP [kg SO2-Equiv.] 181714 5900 1169 1296 148246 24680 108 314 EP [kg Phosphate-Equiv.] 46614 1724 218 290 38171 6125 22 63 FAETP inf. [kg DCB-Equiv.] 2565 1267 735 373 0 0 49 141 GWP 100 years [kg CO2-Equiv.] -35717 -77600906 4196921 189319 69969071 3110489 25646 73742 HTP inf. [kg DCB-Equiv.] 451016 19673 10643 7357 352463 56629 1090 3161 MAETP inf. [kg DCB-Equiv.] 55633970 29944108 14030865 7661087 0 0 1031654 2966257 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 11983 254 621 162 9482 1418 12 34 TETP inf. [kg DCB-Equiv.] 877 465 228 121 0 0 16 46 Ashton ADP elements [kg Sb-Equiv.] 1.44 1.08 0.21 0.11 0.00 0.00 0.01 0.03 ADP fossil [MJ] 18716851 11518381 4138353 2259612 0 0 206569 593936 AP [kg SO2-Equiv.] 182587 6908 1169 1296 148246 24680 74 214 EP [kg Phosphate-Equiv.] 46864 2002 218 290 38171 6125 15 43 FAETP inf. [kg DCB-Equiv.] 2806 1569 735 373 0 0 33 96 GWP 100 years [kg CO2-Equiv.] 1071992 -76461393 4196921 189319 69969071 3110489 17439 50144 HTP inf. [kg DCB-Equiv.] 454086 24103 10643 7357 352463 56629 741 2149 MAETP inf. [kg DCB-Equiv.] 60953615 36543084 14030865 7661087 0 0 701525 2017054 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 12037 323 621 162 9482 1418 8 23 TETP inf. [kg DCB-Equiv.] 963 570 228 121 0 0 11 32 Rural Cederberge ADP elements [kg Sb-Equiv.] 1.55 1.19 0.21 0.11 0.00 0.00 0.01 0.03 ADP fossil [MJ] 18261668 11016109 4138353 2259612 0 0 218720 628873 AP [kg SO2-Equiv.] 181755 6060 1169 1296 148246 24680 78 226 EP [kg Phosphate-Equiv.] 46588 1722 218 290 38171 6125 16 46 FAETP inf. [kg DCB-Equiv.] 2788 1543 735 373 0 0 35 102 GWP 100 years [kg CO2-Equiv.] 1092469 -76444891 4196921 189319 69969071 3110489 18465 53094 HTP inf. [kg DCB-Equiv.] 453402 23250 10643 7357 352463 56629 785 2276 MAETP inf. [kg DCB-Equiv.] 59547349 34976901 14030865 7661087 0 0 742791 2135705 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 12050 334 621 162 9482 1418 8 25 TETP inf. [kg DCB-Equiv.] 946 551 228 121 0 0 12 33 Stellenbosch University http://scholar.sun.ac.za 320 Annexure 37: LCA results ? LBS 37 Paarl ? Primary production Harvesting Forwarding bio-char for sale Conversion Upgrading Transp. of bio-char Transp. of bio-oil ADP elements [kg Sb-Equiv.] 1.55 0.98 0.27 0.15 0.00 0.00 0.00 0.04 0.11 ADP fossil [MJ] 22315665 10885596 5463213 2983008 0 0 0 769977 2213872 AP [kg SO2-Equiv.] 113899 6675 1543 1711 0 70317 32582 275 796 EP [kg Phosphate-Equiv.] 28963 1943 288 383 0 18047 8086 55 161 FAETP inf. [kg DCB-Equiv.] 3416 1471 971 492 0 0 0 124 358 GWP 100 years [kg CO2-Equiv.] -36447596 -103292643 5540530 249928 8929400 47766987 4106287 65004 186911 HTP inf. [kg DCB-Equiv.] 298683 22726 14050 9713 0 166661 74759 2762 8012 MAETP inf. [kg DCB-Equiv.] 73298072 34528231 18522729 10113720 0 0 0 2614904 7518487 ODP, steady state [kg R11-Equiv.] 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8065 298 819 214 0 4745 1871 30 87 TETP inf. [kg DCB-Equiv.] 1158 537 302 160 0 0 0 41 117 Worcester ADP elements [kg Sb-Equiv.] 1.58 1.08 0.27 0.15 0.00 0.00 0.00 0.02 0.06 ADP fossil [MJ] 22465232 12464924 5463213 2983008 0 0 0 401030 1153058 AP [kg SO2-Equiv.] 114500 7789 1543 1711 0 70317 32582 143 415 EP [kg Phosphate-Equiv.] 29193 2276 288 383 0 18047 8086 29 84 FAETP inf. [kg DCB-Equiv.] 3387 1673 971 492 0 0 0 65 186 GWP 100 years [kg CO2-Equiv.] -35719847 -102444185 5540530 249928 8929400 47766987 4106287 33856 97349 HTP inf. [kg DCB-Equiv.] 296765 25971 14050 9713 0 166661 74759 1439 4173 MAETP inf. [kg DCB-Equiv.] 73444720 39530463 18522729 10113720 0 0 0 1361929 3915879 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8046 335 819 214 0 4745 1871 15 45 TETP inf. [kg DCB-Equiv.] 1158 614 302 160 0 0 0 21 61 Ashton ADP elements [kg Sb-Equiv.] 1.90 1.42 0.27 0.15 0.00 0.00 0.00 0.01 0.04 ADP fossil [MJ] 24708894 15205894 5463213 2983008 0 0 0 272700 784080 AP [kg SO2-Equiv.] 115652 9120 1543 1711 0 70317 32582 97 282 EP [kg Phosphate-Equiv.] 29523 2643 288 383 0 18047 8086 20 57 FAETP inf. [kg DCB-Equiv.] 3704 2071 971 492 0 0 0 44 127 GWP 100 years [kg CO2-Equiv.] -34257515 -100939867 5540530 249928 8929400 47766987 4106287 23022 66198 HTP inf. [kg DCB-Equiv.] 300818 31820 14050 9713 0 166661 74759 978 2838 MAETP inf. [kg DCB-Equiv.] 80467405 48242046 18522729 10113720 0 0 0 926112 2662798 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8118 426 819 214 0 4745 1871 11 31 TETP inf. [kg DCB-Equiv.] 1271 753 302 160 0 0 0 14 42 Rural Cederberge ADP elements [kg Sb-Equiv.] 2.05 1.57 0.27 0.15 0.00 0.00 0.00 0.01 0.04 ADP fossil [MJ] 24107987 14542824 5463213 2983008 0 0 0 288741 830202 AP [kg SO2-Equiv.] 114554 8000 1543 1711 0 70317 32582 103 299 EP [kg Phosphate-Equiv.] 29158 2274 288 383 0 18047 8086 21 60 FAETP inf. [kg DCB-Equiv.] 3680 2037 971 492 0 0 0 47 134 GWP 100 years [kg CO2-Equiv.] -34230482 -100918083 5540530 249928 8929400 47766987 4106287 24377 70092 HTP inf. [kg DCB-Equiv.] 299915 30693 14050 9713 0 166661 74759 1036 3005 MAETP inf. [kg DCB-Equiv.] 78610934 46174463 18522729 10113720 0 0 0 980589 2819433 ODP, steady state [kg R11-Equiv.] 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 POCP [kg Ethene-Equiv.] 8135 441 819 214 0 4745 1871 11 32 TETP inf. [kg DCB-Equiv.] 1249 728 302 160 0 0 0 15 44 Stellenbosch University http://scholar.sun.ac.za 321 Annexure 38: LCA results ? current power grid mix of South African Life-Cycle Impact Assessment categories based on CML 2001 ?Nov. 2009 Unit South African Power grid mix1 ADP elements kg Sb-Equiv. 0.26 ADP fossil GJ 429458 AP t SO2-Equiv. 531 EP t Phosphate-Equiv. 24 FAETP inf. t DCB-Equiv. 61 GWP 100 years t CO2-Equiv. 44951 HTP inf. t DCB-Equiv. 12768 MAETP inf. t DCB-Equiv. 125112384 ODP, steady state kg R11-Equiv. 0.949984761 POCP t Ethene-Equiv. 27 TETP inf. t DCB-Equiv. 146 Note: 1 Based on the functional unit of 39 600 MWhel/a Stellenbosch University http://scholar.sun.ac.za 322 Annexure 39: Abiotic Depletion Potential per LBS and BPA LBS Abiotic Depletion (ADP fossil) [GJ] Paarl Worcester Ashton R. Cederberge 1 24 341 23 741 25 507 23 950 2 20 567 20 341 21 861 20 714 3 34 386 33 694 36 200 34 150 4 25 277 26 650 29 514 27 578 5 33 619 35 307 39 460 36 407 6 31 677 30 964 32 630 31 127 7 26 875 26 556 27 991 26 894 8 44 794 43 944 46 309 44 338 9 38 511 40 160 45 193 40 567 10 32 959 34 475 38 904 34 729 11 54 656 56 997 64 139 57 573 12 42 297 46 449 52 750 47 390 13 56 274 61 443 69 948 62 562 14 46 332 47 793 52 660 48 158 15 39 687 41 044 45 332 41 260 16 65 755 67 829 74 736 68 348 17 20 891 19 675 20 849 20 069 18 17 597 16 840 17 851 17 373 19 29 489 27 923 29 590 28 642 20 21 080 21 703 23 847 22 856 21 27 829 28 651 31 482 30 173 22 28 332 27 022 28 115 27 364 23 24 390 23 262 24 202 23 556 24 40 210 38 350 39 901 38 836 25 23 818 22 602 23 777 22 997 26 20 117 19 360 20 371 19 893 27 33 644 32 077 33 744 32 796 28 24 642 25 265 27 409 26 417 29 32 530 33 353 36 183 34 875 30 31 170 29 860 30 952 30 202 31 26 832 25 705 26 645 25 999 32 44 237 42 377 43 928 42 863 33 18 958 16 624 17 177 16 909 34 16 320 14 311 14 787 14 556 35 26 905 23 594 24 378 23 997 36 16 904 17 017 18 717 18 262 37 22 316 22 465 24 709 24 108 Minimum 65 755 67 829 74 736 68 348 Maximum 16 320 14 311 14 787 14 556 Average 31 519 31 498 34 101 32 121 Stellenbosch University http://scholar.sun.ac.za 323 Annexure 40: Acidification Potential per LBS and BPA LBS Acidification Potential (AP) [t SO2-Equiv.] Paarl Worcester Ashton R. Cederberge 1 126 126 126 125 2 84 84 84 84 3 217 217 217 216 4 183 184 185 184 5 117 117 119 117 6 138 137 138 137 7 94 94 94 94 8 234 233 234 233 9 129 130 131 130 10 87 87 88 87 11 222 222 224 222 12 188 189 190 189 13 122 124 126 124 14 142 142 143 142 15 97 98 99 97 16 239 240 241 239 17 126 126 127 126 18 84 84 85 84 19 217 217 218 217 20 184 184 186 184 21 117 118 120 118 22 138 138 138 138 23 94 94 95 94 24 234 234 235 234 25 128 128 129 128 26 86 86 86 86 27 220 220 221 220 28 187 187 188 187 29 121 122 123 122 30 140 140 141 140 31 96 96 97 96 32 237 237 238 237 33 125 124 125 124 34 83 82 83 82 35 215 214 215 214 36 181 182 183 182 37 114 114 116 115 Minimum 239 240 241 239 Maximum 83 82 83 82 Average 150 150 151 150 Stellenbosch University http://scholar.sun.ac.za 324 Annexure 41: Eutrophication Potential per LBS and BPA LBS Eutrophication Potential (EP) [t Phosphate-Equiv.] Paarl Worcester Ashton R. Cederberge 1 32 32 32 32 2 21 21 21 21 3 55 55 56 55 4 47 47 47 47 5 29 30 30 30 6 32 32 32 32 7 21 21 21 21 8 55 55 56 55 9 33 33 33 33 10 22 22 22 22 11 56 56 57 56 12 48 48 48 48 13 31 31 32 31 14 33 33 33 33 15 22 22 22 22 16 57 57 57 57 17 32 32 32 32 18 21 21 21 21 19 55 56 56 55 20 47 47 48 47 21 30 30 30 30 22 32 32 32 32 23 21 21 22 21 24 56 56 56 56 25 33 33 33 33 26 22 22 22 22 27 56 56 57 56 28 48 48 48 48 29 31 31 31 31 30 33 33 33 33 31 22 22 22 22 32 56 56 57 56 33 32 32 32 32 34 21 21 21 21 35 55 55 55 55 36 46 47 47 47 37 29 29 30 29 Minimum 57 57 57 57 Maximum 21 21 21 21 Average 37 37 37 37 Stellenbosch University http://scholar.sun.ac.za 325 Annexure 42: Global Warming Potential per LBS and BPA LBS Global Warming Potential (GWP 100 years) [t CO2-Equiv.] Paarl Worcester Ashton R. Cederberge 1 133 471 1 358 1 346 2 82 397 1 161 1 167 3 97 591 1 850 1 847 4 -271 318 1 458 1 440 5 -36 010 -35 243 -33 707 -33 772 6 -183 134 990 979 7 -191 107 844 851 8 -351 112 1 327 1 326 9 34 642 2 039 1 928 10 13 545 1 755 1 643 11 -30 834 2 816 2 658 12 -411 511 2 225 2 113 13 -36 178 -34 988 -32 709 -32 884 14 -202 376 1 729 1 620 15 -191 315 1 489 1 378 16 -364 456 2 377 2 222 17 1 006 1 615 2 406 1 918 18 833 1 382 2 063 1 660 19 1 337 2 214 3 336 2 659 20 791 1 709 2 732 2 136 21 -34 628 -33 417 -32 066 -32 854 22 664 1 242 2 005 1 534 23 571 1 069 1 726 1 320 24 864 1 686 2 768 2 099 25 1 255 1 863 2 654 2 167 26 1 048 1 596 2 277 1 874 27 1 690 2 567 3 690 3 012 28 1 094 2 012 3 034 2 438 29 -34 229 -33 017 -31 667 -32 454 30 905 1 484 2 247 1 775 31 779 1 277 1 934 1 528 32 1 207 2 028 3 111 2 441 33 0 248 1 087 1 113 34 0 214 936 958 35 -78 275 1 466 1 502 36 -587 -36 1 072 1 092 37 -36 448 -35 720 -34 258 -34 230 Minimum 1 690 2 567 3 690 3 012 Maximum -36 448 -35 720 -34 258 -34 230 Average -4 485 -3 841 -2 715 -2 985 Stellenbosch University http://scholar.sun.ac.za 326 Annexure 43: Avoided net CO2-equivalent emissions per LBS and BPA LBS Avoided CO2-eqivalent emissions in tonnes/year Paarl Worcester Ashton R. Cederberge 1 39 701 39 815 39 760 39 809 2 39 935 40 012 39 964 39 991 3 39 095 39 245 39 167 39 224 4 39 714 39 332 39 570 39 633 5 39 228 39 208 39 018 39 139 6 38 916 39 037 38 987 39 034 7 39 260 39 342 39 299 39 324 8 37 981 38 141 38 070 38 124 9 39 235 39 286 39 092 39 245 10 39 519 39 556 39 381 39 528 11 38 421 38 495 38 219 38 436 12 39 164 39 062 38 812 38 978 13 38 487 38 376 38 032 38 274 14 38 392 38 452 38 264 38 414 15 38 794 38 838 38 669 38 813 16 37 225 37 311 37 045 37 257 17 38 814 38 656 38 694 39 222 18 39 172 39 014 39 047 39 486 19 37 837 37 600 37 655 38 391 20 38 636 38 282 38 274 38 919 21 37 823 37 356 37 345 38 197 22 38 057 37 913 37 954 38 465 23 38 491 38 367 38 402 38 842 24 36 750 36 546 36 604 37 329 25 38 580 38 421 38 459 38 987 26 38 970 38 811 38 844 39 284 27 37 504 37 266 37 321 38 058 28 38 350 37 995 37 988 38 633 29 37 446 36 978 36 967 37 819 30 37 829 37 685 37 726 38 237 31 38 294 38 171 38 206 38 646 32 36 426 36 223 36 281 37 006 33 39 817 40 011 39 998 40 015 34 40 006 40 173 40 161 40 176 35 39 248 39 523 39 505 39 529 36 39 995 40 005 39 910 39 941 37 39 618 39 631 39 506 39 546 Stellenbosch University http://scholar.sun.ac.za 327 Annexure 44: Photochemical Ozone Creation Potential per LBS and BPA LBS Photochem. Ozone Creation Potential (POCP) [t Ethene-Equivalent] Paarl Worcester Ashton R. Cederberge 1 9.1 9.4 9.7 9.3 2 6.5 6.7 7.0 6.6 3 16.4 16.8 17.2 16.7 4 13.9 14.2 14.6 14.2 5 10.5 11.0 11.6 10.9 6 9.5 9.8 10.1 9.7 7 6.8 7.0 7.3 7.0 8 17.0 17.3 17.7 17.2 9 12.6 13.5 14.4 13.3 10 9.5 10.2 11.0 10.0 11 21.4 22.6 23.9 22.3 12 18.1 19.2 20.4 19.0 13 16.2 17.6 19.1 17.3 14 13.0 13.8 14.7 13.6 15 9.8 10.5 11.3 10.3 16 21.9 23.1 24.4 22.8 17 7.5 7.4 7.5 7.5 18 5.1 5.0 5.0 5.1 19 14.1 14.0 14.1 14.1 20 11.9 11.9 11.9 11.9 21 7.9 7.9 8.0 8.0 22 7.9 7.9 7.9 7.9 23 5.5 5.4 5.4 5.4 24 14.7 14.6 14.7 14.7 25 7.7 7.7 7.7 7.7 26 5.3 5.2 5.3 5.3 27 14.4 14.4 14.4 14.4 28 12.2 12.2 12.2 12.2 29 8.3 8.3 8.4 8.4 30 8.2 8.1 8.1 8.1 31 5.7 5.6 5.6 5.6 32 15.1 15.0 15.0 15.0 33 7.7 7.6 7.6 7.6 34 5.2 5.1 5.1 5.2 35 14.3 14.2 14.2 14.2 36 12.0 12.0 12.0 12.1 37 8.1 8.0 8.1 8.1 Minimum 21.9 23.1 24.4 22.8 Maximum 5.1 5.0 5.0 5.1 Average 11.1 11.4 11.7 11.3 Stellenbosch University http://scholar.sun.ac.za 328 Annexure 45: IRR per LBS and BPA ? including land value LBS Internal Rate of Return (IRR) on capital investment (%) Paarl Worcester Ashton R. Cederberge 1 9.86% 13.15% 9.10% 7.61% 2 10.33% 14.26% 9.37% 7.70% 3 -a 1.04% -0.84% -0.55% 4 2.58% 4.00% 2.38% 2.28% 5 3.98% 5.71% 3.70% 3.38% 6 9.96% 13.24% 9.30% 7.71% 7 10.46% 14.19% 9.52% 7.83% 8 -0.34% 1.81% -0.25% 0.06% 9 6.80% 10.06% 6.24% 5.32% 10 6.80% 10.57% 6.17% 5.17% 11 - a -2.00% -4.31% -2.83% 12 0.62% 2.12% 0.25% 0.52% 13 1.76% 3.54% 1.36% 1.42% 14 7.35% 10.62% 6.85% 5.73% 15 7.41% 11.07% 6.77% 5.55% 16 - -0.38% -2.84% -1.78% 17 9.66% 12.85% 8.89% 7.49% 18 10.09% 13.96% 9.11% 7.55% 19 - a 0.57% -1.26% -0.87% 20 2.40% 3.75% 2.15% 2.10% 21 3.79% 5.45% 3.48% 3.23% 22 9.77% 12.94% 9.09% 7.59% 23 10.21% 13.88% 9.26% 7.68% 24 -0.71% 1.36% -0.64% -0.24% 25 10.49% 14.07% 9.77% 8.04% 26 10.96% 15.15% 9.95% 8.12% 27 0.36% 2.85% 0.74% 0.62% 28 3.25% 4.86% 3.13% 2.84% 29 4.82% 6.82% 4.70% 4.12% 30 10.60% 14.04% 9.97% 8.14% 31 11.09% 15.07% 10.11% 8.25% 32 0.67% 3.15% 1.02% 0.97% 33 10.64% 14.07% 9.82% 8.14% 34 11.18% 15.26% 10.13% 8.25% 35 0.86% 3.22% 1.16% 1.01% 36 3.48% 5.05% 3.33% 3.05% 37 4.89% 6.77% 4.65% 4.13% Minimum - a -2.00% -4.31% -2.83% Maximum 11.18% 15.26% 10.13% 8.25% Average 6.24% 8.06% 4.90% 4.20% Notes: a IRR that unfavourable, that no result was shown in spread sheet based MPB model Stellenbosch University http://scholar.sun.ac.za 329 Annexure 46: IRR per LBS and BPA ? excluding land value LBS Internal Rate of Return (IRR) on capital investment excluding land value (%) Paarl Worcester Ashton R. Cederberge 1 20.41% 16.47% 11.55% 8.28% 2 24.72% 18.69% 12.23% 8.44% 3 3.31% 2.23% 0.11% -0.29% 4 5.84% 4.91% 3.24% 2.56% 5 8.43% 6.99% 4.86% 3.74% 6 20.18% 16.50% 11.74% 8.38% 7 24.37% 18.44% 12.35% 8.57% 8 4.07% 3.03% 0.73% 0.34% 9 17.10% 13.19% 8.41% 5.90% 10 20.01% 14.48% 8.64% 5.80% 11 0.69% -0.87% -3.52% -2.61% 12 4.31% 3.11% 1.10% 0.78% 13 6.59% 4.88% 2.46% 1.74% 14 17.73% 13.80% 9.11% 6.34% 15 20.74% 15.01% 9.30% 6.20% 16 2.25% 0.88% -1.94% -1.53% 17 19.98% 16.07% 11.28% 8.15% 18 24.09% 18.27% 11.90% 8.28% 19 2.74% 1.69% -0.36% -0.62% 20 5.60% 4.63% 2.99% 2.37% 21 8.15% 6.69% 4.61% 3.58% 22 19.76% 16.10% 11.48% 8.24% 23 23.75% 18.02% 12.02% 8.41% 24 3.53% 2.53% 0.29% 0.03% 25 21.96% 17.77% 12.44% 8.75% 26 26.56% 19.98% 13.01% 8.90% 27 5.51% 4.35% 1.94% 0.94% 28 6.81% 5.88% 4.11% 3.14% 29 9.80% 8.31% 6.06% 4.52% 30 21.70% 17.62% 12.63% 8.85% 31 26.17% 19.73% 13.13% 9.02% 32 5.69% 4.62% 2.22% 1.30% 33 21.93% 17.63% 12.42% 8.85% 34 26.57% 20.00% 13.16% 9.03% 35 5.99% 4.69% 2.34% 1.33% 36 7.03% 6.06% 4.30% 3.36% 37 9.71% 8.19% 5.93% 4.52% Minimum 0.69% -0.87% -3.52% -2.61% Maximum 26.57% 20.00% 13.16% 9.03% Average 13.62% 10.56% 6.71% 4.69% Stellenbosch University http://scholar.sun.ac.za 330 Annexure 47: Net Present Value per LBS and BPA LBS Biomass procurement areas Paarl Worcester Ashton R. Cederberge 1 R389 047 553 R444 187 194 R358 086 396 R326 318 210 2 R307 193 416 R355 827 319 R281 882 136 R257 141 806 3 R-104 490 634 R-20 969 563 R-91 378 248 R-91 587 549 4 R50 712 243 R115 302 881 R36 166 690 R32 744 163 5 R162 747 067 R239 715 364 R135 215 146 R123 205 558 6 R393 205 218 R447 136 520 R368 221 348 R329 762 641 7 R310 950 667 R353 053 514 R287 306 355 R261 764 773 8 R-80 297 493 R2 317 983 R-72 312 618 R-69 061 742 9 R273 429 890 R354 058 134 R240 571 073 R220 729 749 10 R205 734 966 R276 469 249 R179 426 968 R164 574 835 11 R-226 770 029 R-117 805 363 R-221 043 890 R-202 435 581 12 R-72 128 261 R20 384 754 R-88 910 703 R-79 442 294 13 R2 179 515 R115 880 559 R-28 095 088 R-26 448 008 14 R306 013 410 R383 541 857 R278 087 605 R249 387 963 15 R233 320 237 R296 934 734 R207 817 691 R184 978 904 16 R-183 260 121 R-71 322 588 R-178 509 348 R-163 239 711 17 R378 777 663 R431 314 110 R345 874 323 R317 600 859 18 R297 486 757 R345 719 703 R270 207 305 R248 818 618 19 R-118 033 666 R-35 050 246 R-104 728 121 R-103 028 958 20 R39 997 427 R102 357 562 R23 409 134 R21 723 524 21 R148 493 363 R223 270 055 R119 597 689 R111 059 771 22 R383 060 687 R434 411 958 R356 151 610 R321 156 682 23 R301 303 023 R343 063 269 R275 744 170 R253 542 524 24 R-93 702 039 R-11 574 944 R-85 484 247 R-80 352 513 25 R419 758 133 R475 959 717 R390 855 753 R353 844 604 26 R330 584 012 R379 748 273 R304 567 688 R278 212 551 27 R-53 075 823 R33 975 407 R-36 114 571 R-46 149 743 28 R91 283 151 R157 560 741 R78 651 165 R66 853 956 29 R223 663 797 R303 313 949 R203 492 130 R180 378 012 30 R423 611 657 R475 912 376 R400 761 502 R357 051 342 31 R354 057 841 R376 733 885 R309 783 442 R282 652 330 32 R-41 771 908 R44 244 167 R-26 218 902 R-32 048 383 33 R436 431 277 R492 230 909 R406 090 882 R367 550 968 34 R346 515 506 R395 726 719 R321 826 563 R291 345 006 35 R-35 383 231 R48 581 485 R-22 541 267 R-31 765 927 36 R107 206 701 R172 812 915 R92 857 565 R81 665 670 37 R236 291 763 R313 899 137 R209 042 136 R186 855 512 Minimum R-226 770 029 R-117 805 363 R-221 043 890 R-202 435 581 Maximum R436 431 277 R492 230 909 R406 090 882 R367 550 968 Average R166 057 939 R235 106 046 R149 361 013 R133 658 382 Stellenbosch University http://scholar.sun.ac.za 331 Annexure 48: CAPEX of bioenergy conversion systems per LBS and BPA LBS Capital Expenditure (CAPEX) of bioenergy conversion systems including biomass upgrading (ZAR) Paarl Worcester Ashton R. Cederberge 1 R137 754 846 R137 754 846 R137 754 846 R137 754 846 2 R62 922 227 R62 922 227 R62 922 227 R62 922 227 3 R286 292 420 R286 292 420 R286 292 420 R286 292 420 4 R386 570 062 R386 570 062 R386 570 062 R386 570 062 5 R397 982 082 R397 982 082 R397 982 082 R397 982 082 6 R137 754 846 R137 754 846 R137 754 846 R137 754 846 7 R62 922 227 R62 922 227 R62 922 227 R62 922 227 8 R286 292 420 R286 292 420 R286 292 420 R286 292 420 9 R137 754 846 R137 754 846 R137 754 846 R137 754 846 10 R62 922 227 R62 922 227 R62 922 227 R62 922 227 11 R286 292 420 R286 292 420 R286 292 420 R286 292 420 12 R386 570 062 R386 570 062 R386 570 062 R386 570 062 13 R397 982 082 R397 982 082 R397 982 082 R397 982 082 14 R137 754 846 R137 754 846 R137 754 846 R137 754 846 15 R62 922 227 R62 922 227 R62 922 227 R62 922 227 16 R286 292 420 R286 292 420 R286 292 420 R286 292 420 17 R137 754 846 R137 754 846 R137 754 846 R137 754 846 18 R62 922 227 R62 922 227 R62 922 227 R62 922 227 19 R286 292 420 R286 292 420 R286 292 420 R286 292 420 20 R386 570 062 R386 570 062 R386 570 062 R386 570 062 21 R397 982 082 R397 982 082 R397 982 082 R397 982 082 22 R137 754 846 R137 754 846 R137 754 846 R137 754 846 23 R62 922 227 R62 922 227 R62 922 227 R62 922 227 24 R286 292 420 R286 292 420 R286 292 420 R286 292 420 25 R137 754 846 R137 754 846 R137 754 846 R137 754 846 26 R62 922 227 R62 922 227 R62 922 227 R62 922 227 27 R286 292 420 R286 292 420 R286 292 420 R286 292 420 28 R386 570 062 R386 570 062 R386 570 062 R386 570 062 29 R397 982 082 R397 982 082 R397 982 082 R397 982 082 30 R137 754 846 R137 754 846 R137 754 846 R137 754 846 31 R62 922 227 R62 922 227 R62 922 227 R62 922 227 32 R286 292 420 R286 292 420 R286 292 420 R286 292 420 33 R137 754 846 R137 754 846 R137 754 846 R137 754 846 34 R62 922 227 R62 922 227 R62 922 227 R62 922 227 35 R286 292 420 R286 292 420 R286 292 420 R286 292 420 36 R386 570 062 R386 570 062 R386 570 062 R386 570 062 37 R397 982 082 R397 982 082 R397 982 082 R397 982 082 Stellenbosch University http://scholar.sun.ac.za 332 Annexure 49: OPEX of BCSs over period of 20 years per LBS and BPA LBS Operational Expenditure (OPEX) of bioenergy conversion systems including biomass upgrading [ZAR] Paarl Worcester Ashton R. Cederberge 1 R127 631 680 R127 631 680 R127 631 680 R127 631 680 2 R66 464 060 R66 464 060 R66 464 060 R66 464 060 3 R250 952 560 R250 952 560 R250 952 560 R250 952 560 4 R309 957 780 R309 957 780 R309 957 780 R309 957 780 5 R281 112 520 R281 112 520 R281 112 520 R281 112 520 6 R127 631 680 R127 631 680 R127 631 680 R127 631 680 7 R66 464 060 R66 464 060 R66 464 060 R66 464 060 8 R250 952 560 R250 952 560 R250 952 560 R250 952 560 9 R127 631 680 R127 631 680 R127 631 680 R127 631 680 10 R66 464 060 R66 464 060 R66 464 060 R66 464 060 11 R250 952 560 R250 952 560 R250 952 560 R250 952 560 12 R309 957 780 R309 957 780 R309 957 780 R309 957 780 13 R281 112 520 R281 112 520 R281 112 520 R281 112 520 14 R127 631 680 R127 631 680 R127 631 680 R127 631 680 15 R66 464 060 R66 464 060 R66 464 060 R66 464 060 16 R250 952 560 R250 952 560 R250 952 560 R250 952 560 17 R127 631 680 R127 631 680 R127 631 680 R127 631 680 18 R66 464 060 R66 464 060 R66 464 060 R66 464 060 19 R250 952 560 R250 952 560 R250 952 560 R250 952 560 20 R309 957 780 R309 957 780 R309 957 780 R309 957 780 21 R281 112 520 R281 112 520 R281 112 520 R281 112 520 22 R127 631 680 R127 631 680 R127 631 680 R127 631 680 23 R66 464 060 R66 464 060 R66 464 060 R66 464 060 24 R250 952 560 R250 952 560 R250 952 560 R250 952 560 25 R127 631 680 R127 631 680 R127 631 680 R127 631 680 26 R66 464 060 R66 464 060 R66 464 060 R66 464 060 27 R250 952 560 R250 952 560 R250 952 560 R250 952 560 28 R309 957 780 R309 957 780 R309 957 780 R309 957 780 29 R281 112 520 R281 112 520 R281 112 520 R281 112 520 30 R127 631 680 R127 631 680 R127 631 680 R127 631 680 31 R66 464 060 R66 464 060 R66 464 060 R66 464 060 32 R250 952 560 R250 952 560 R250 952 560 R250 952 560 33 R127 631 680 R127 631 680 R127 631 680 R127 631 680 34 R66 464 060 R66 464 060 R66 464 060 R66 464 060 35 R250 952 560 R250 952 560 R250 952 560 R250 952 560 36 R309 957 780 R309 957 780 R309 957 780 R309 957 780 37 R281 112 520 R281 112 520 R281 112 520 R281 112 520 Stellenbosch University http://scholar.sun.ac.za 333 Annexure 50: CAPEX other than bioenergy conversion systems per LBS and BPA LBS Capital Expenditure (CAPEX) other than bioenergy conversion systems [ZAR] Paarl Worcester Ashton R. Cederberge 1 R181 176 016 R87 573 075 R94 046 857 R72 947 167 2 R153 480 570 R73 839 972 R80 238 501 R60 392 505 3 R235 768 893 R124 412 750 R132 136 085 R107 348 605 4 R212 034 845 R108 762 338 R117 940 645 R92 469 988 5 R271 107 186 R135 947 090 R145 979 454 R114 124 762 6 R169 772 525 R79 538 157 R85 505 365 R66 052 772 7 R147 800 692 R69 337 995 R74 334 424 R56 036 304 8 R215 963 731 R104 309 845 R113 919 377 R87 831 146 9 R208 266 229 R67 217 482 R78 697 555 R46 569 453 10 R172 984 042 R58 560 965 R67 648 893 R40 550 881 11 R238 581 820 R80 469 832 R93 649 608 R54 207 453 12 R218 360 115 R72 328 986 R85 462 636 R49 146 924 13 R288 682 829 R96 332 421 R113 127 919 R65 992 366 14 R187 088 021 R59 462 089 R69 943 797 R39 279 341 15 R164 076 414 R53 465 764 R61 344 816 R36 314 902 16 R225 508 661 R68 826 657 R82 566 084 R44 328 151 17 R190 762 432 R102 289 774 R108 538 839 R82 308 866 18 R163 066 986 R83 426 388 R94 955 200 R69 978 921 19 R250 485 592 R138 904 732 R146 403 351 R121 840 587 20 R221 396 544 R123 254 320 R132 207 911 R106 961 970 21 R285 374 452 R155 344 639 R165 152 286 R128 167 311 22 R179 358 941 R94 254 857 R99 997 348 R75 414 472 23 R157 387 108 R78 924 411 R89 051 124 R65 622 720 24 R230 680 431 R118 801 828 R128 186 643 R102 323 129 25 R160 040 170 R72 557 512 R79 301 577 R52 081 604 26 R137 939 724 R58 794 126 R70 817 938 R45 346 659 27 R194 029 699 R83 438 839 R91 432 458 R65 879 694 28 R172 899 282 R75 747 058 R85 195 649 R58 959 708 29 R229 888 559 R100 868 746 R111 666 393 R73 691 418 30 R148 636 679 R64 027 595 R70 760 086 R45 187 210 31 R132 259 846 R54 292 149 R64 913 862 R40 990 458 32 R182 864 538 R71 975 935 R82 350 750 R55 497 236 33 R134 194 762 R40 367 104 R46 616 169 R25 741 196 34 R115 064 507 R35 424 909 R41 598 721 R21 977 442 35 R158 681 731 R47 100 871 R54 599 490 R30 036 726 36 R146 558 874 R43 061 650 R52 015 241 R26 769 300 37 R196 633 876 R61 249 063 R71 056 710 R39 426 735 Stellenbosch University http://scholar.sun.ac.za 334 Annexure 51: Land value per LBS and BPA LBS Total cost for land [ZAR] Paarl Worcester Ashton R. Cederberge 1 R119 900 000 R28 776 000 R35 965 000 R12 946 000 2 R103 200 000 R24 768 000 R30 960 000 R11 145 000 3 R144 000 000 R34 560 000 R43 200 000 R15 552 000 4 R132 600 000 R31 824 000 R39 775 000 R14 319 000 5 R175 050 000 R42 008 000 R52 510 000 R18 903 000 6 R116 200 000 R27 888 000 R34 860 000 R12 550 000 7 R100 050 000 R24 008 000 R30 010 000 R10 804 000 8 R139 600 000 R33 504 000 R41 880 000 R15 076 000 9 R179 250 000 R41 104 000 R51 375 000 R18 495 000 10 R147 450 000 R35 384 000 R44 225 000 R15 921 000 11 R205 750 000 R49 376 000 R61 715 000 R22 218 000 12 R189 450 000 R45 464 000 R56 825 000 R20 456 000 13 R250 050 000 R60 016 000 R75 015 000 R27 005 000 14 R166 000 000 R39 840 000 R49 800 000 R17 928 000 15 R142 950 000 R34 304 000 R42 875 000 R15 434 000 16 R199 450 000 R47 864 000 R59 830 000 R21 538 000 17 R119 900 000 R28 776 000 R35 965 000 R12 946 000 18 R103 200 000 R24 768 000 R30 960 000 R11 145 000 19 R144 000 000 R34 560 000 R43 200 000 R15 552 000 20 R132 600 000 R31 824 000 R39 775 000 R14 319 000 21 R175 050 000 R42 008 000 R52 510 000 R18 903 000 22 R116 200 000 R27 888 000 R34 860 000 R12 550 000 23 R100 050 000 R24 008 000 R30 010 000 R10 804 000 24 R139 600 000 R33 504 000 R41 880 000 R15 076 000 25 R119 900 000 R28 776 000 R35 965 000 R12 946 000 26 R103 200 000 R24 768 000 R30 960 000 R11 145 000 27 R144 000 000 R34 560 000 R43 200 000 R15 552 000 28 R132 600 000 R31 824 000 R39 775 000 R14 319 000 29 R175 050 000 R42 008 000 R52 510 000 R18 903 000 30 R116 200 000 R27 888 000 R34 860 000 R12 550 000 31 R100 050 000 R24 008 000 R30 010 000 R10 804 000 32 R139 600 000 R33 504 000 R41 880 000 R15 076 000 33 R119 900 000 R28 776 000 R35 965 000 R12 946 000 34 R103 200 000 R24 768 000 R30 960 000 R11 145 000 35 R144 000 000 R34 560 000 R43 200 000 R15 552 000 36 R132 600 000 R31 824 000 R39 775 000 R14 319 000 37 R175 050 000 R42 008 000 R52 510 000 R18 903 000 Stellenbosch University http://scholar.sun.ac.za 335 Annexure 52: OPEX other than BCS per LBS and BPA LBS Operational Expenditure (OPEX) other than bioenergy conversion systems [ZAR] Paarl Worcester Ashton R. Cederberge 1 R280 615 803 R297 419 784 R357 995 801 R367 331 105 2 R244 303 932 R258 856 864 R311 098 989 R318 950 593 3 R338 753 946 R361 109 135 R425 334 129 R444 381 926 4 R289 022 888 R320 238 289 R390 365 380 R398 399 158 5 R371 453 208 R420 409 326 R506 657 810 R516 698 156 6 R282 716 526 R296 851 865 R346 688 685 R364 503 199 7 R242 807 589 R263 196 706 R305 094 187 R313 299 622 8 R326 132 547 R345 204 100 R414 509 377 R425 842 358 9 R388 093 906 R423 428 972 R515 077 835 R524 174 805 10 R337 656 786 R367 289 244 R446 947 823 R454 761 992 11 R460 336 691 R512 388 716 R614 556 560 R624 777 288 12 R407 113 165 R458 885 228 R563 836 766 R570 827 793 13 R527 367 757 R603 501 843 R735 637 094 R750 435 924 14 R353 587 185 R385 281 593 R464 908 900 R482 402 077 15 R304 600 057 R339 336 250 R407 714 157 R423 653 365 16 R411 743 691 R451 238 147 R557 086 468 R567 328 945 17 R284 343 349 R301 902 332 R362 806 164 R371 968 513 18 R247 537 848 R262 656 053 R315 241 815 R322 939 016 19 R343 340 387 R366 497 971 R431 105 771 R450 034 443 20 R293 180 242 R325 199 855 R395 682 773 R403 604 706 21 R377 033 547 R427 065 853 R513 801 779 R523 570 077 22 R286 357 617 R301 194 878 R351 352 064 R368 995 688 23 R245 947 541 R266 875 831 R309 110 055 R317 169 949 24 R330 583 624 R350 427 260 R420 107 335 R431 325 899 25 R265 200 139 R282 902 842 R344 261 165 R353 411 539 26 R231 037 494 R246 274 574 R299 262 198 R306 975 728 27 R320 236 120 R343 560 746 R408 763 830 R427 673 819 28 R271 877 724 R304 077 146 R375 092 606 R383 005 614 29 R348 923 970 R399 203 203 R486 650 476 R496 405 602 30 R267 782 602 R282 783 197 R333 373 718 R351 018 619 31 R229 926 617 R251 012 051 R293 620 137 R301 679 329 32 R308 157 810 R328 190 876 R398 437 721 R409 633 722 33 R250 069 015 R264 007 197 R321 292 426 R334 962 350 34 R218 020 967 R230 060 126 R279 493 874 R291 115 429 35 R301 994 905 R320 832 884 R381 186 973 R405 428 944 36 R255 135 648 R283 155 463 R349 692 769 R362 526 353 37 R326 845 424 R371 593 429 R453 083 568 R469 459 265 Stellenbosch University http://scholar.sun.ac.za 336 Annexure 53: Employment potential subdivided in income categories per LBS and BPA Employment potential subdivided in income categories LBS < R8 000/month R8 000 ? R24 000 > R24 000/month BPA BPA BPA I II III IV I II III IV I II III IV 1 93 108 122 120 5 4 4 4 2 2 2 2 2 94 106 119 117 5 4 4 4 2 2 2 2 3 140 161 180 179 7 6 5 6 5 5 5 5 4 153 172 188 186 6 6 5 5 5 5 5 5 5 192 215 238 235 7 6 6 6 7 7 7 7 6 70 74 88 87 9 6 6 6 4 4 4 4 7 74 77 89 88 8 5 5 5 4 4 4 4 8 106 112 132 130 12 8 8 8 7 7 7 7 9 181 210 241 225 3 2 2 2 2 2 2 2 10 170 194 221 207 3 2 2 2 2 2 2 2 11 265 306 350 327 4 3 3 3 5 5 5 5 12 260 296 333 314 3 3 2 3 5 5 5 5 13 333 379 429 403 4 3 3 3 7 7 7 7 14 153 171 201 186 7 4 4 4 4 4 4 4 15 145 160 186 173 6 3 3 3 4 4 4 4 16 224 250 293 271 9 5 5 5 7 7 7 7 17 86 98 111 112 7 6 7 6 2 2 2 2 18 87 98 110 110 7 6 6 6 2 2 2 2 19 129 148 165 166 10 9 9 9 5 5 5 5 20 143 160 175 175 8 8 8 7 5 5 5 5 21 179 200 221 221 10 10 10 10 7 7 7 7 22 62 65 78 78 11 8 9 8 4 4 4 4 23 67 69 80 80 10 7 7 7 4 4 4 4 24 95 100 118 118 15 11 11 11 7 7 7 7 25 94 108 122 121 5 5 5 4 2 2 2 2 26 95 107 119 118 5 4 5 4 2 2 2 2 27 141 161 180 180 7 6 6 6 5 5 5 5 28 154 172 188 186 6 6 5 5 5 5 5 5 29 192 215 238 236 7 7 7 7 7 7 7 7 30 70 75 88 87 9 7 7 6 4 4 4 4 31 74 77 89 88 8 5 6 5 4 4 4 4 32 107 113 132 130 12 8 9 8 7 7 7 7 33 61 66 78 78 5 4 3 3 2 2 2 2 34 65 69 81 81 4 3 3 3 2 2 2 2 35 93 101 119 120 7 6 5 6 5 5 5 5 36 112 119 134 134 5 5 4 4 5 5 5 5 37 137 146 167 167 6 5 5 5 7 7 7 7 Stellenbosch University http://scholar.sun.ac.za 337 Annexure 54: Normalised, un-weighted scores for BPA I LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria IRR (25 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (25 yrs) OPEX (25 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 4.61% 4.05% 3.90% 2.98% 3.11% 1.21% 1.32% 0.00% 3.94% 3.41% 3.37% 3.19% 2.28% 2 4.81% 5.22% 5.45% 3.75% 3.56% 1.25% 1.32% 0.00% 5.41% 4.91% 4.06% 3.48% 2.29% 3 0.00% 1.74% 1.38% 1.47% 2.37% 2.99% 2.65% 3.80% 0.78% 0.19% 1.47% 2.42% 2.29% 4 1.43% 0.18% 0.69% 2.13% 3.00% 3.49% 1.99% 3.80% 1.94% 1.35% 2.14% 3.12% 2.32% 5 2.04% 0.00% 0.00% 0.49% 1.96% 4.96% 2.65% 6.33% 0.30% 3.75% 1.46% 2.48% 6.03% 6 4.65% 4.05% 3.90% 3.30% 3.08% 0.34% 3.97% 1.27% 3.53% 3.40% 3.26% 2.63% 2.31% 7 4.87% 5.22% 5.45% 3.91% 3.58% 0.49% 3.31% 1.27% 5.05% 4.90% 3.97% 3.00% 2.31% 8 0.16% 1.74% 1.38% 2.02% 2.53% 1.71% 5.96% 5.06% 0.20% 0.17% 1.33% 1.62% 2.32% 9 3.27% 4.05% 3.90% 2.23% 1.75% 4.55% 0.00% 0.00% 3.82% 3.31% 2.46% 2.10% 2.29% 10 3.27% 5.22% 5.45% 3.21% 2.39% 4.13% 0.00% 0.00% 5.30% 4.82% 3.27% 2.53% 2.29% 11 0.00% 1.74% 1.38% 1.39% 0.84% 7.73% 0.66% 3.80% 0.60% 0.04% 0.18% 0.86% 2.30% 12 0.58% 0.18% 0.69% 1.95% 1.51% 7.54% 0.00% 3.80% 1.79% 1.23% 1.03% 1.81% 2.33% 13 1.08% 0.00% 0.00% 0.00% 0.00% 10.31% 0.66% 6.33% 0.11% 3.58% 0.00% 0.73% 6.04% 14 3.51% 4.05% 3.90% 2.82% 2.19% 3.49% 2.65% 1.27% 8.26% 3.28% 4.45% 3.84% 0.00% 15 3.54% 5.22% 5.45% 3.46% 2.80% 3.18% 1.99% 1.27% 4.93% 4.79% 3.19% 2.01% 2.31% 16 0.00% 1.74% 1.38% 1.75% 1.46% 6.18% 3.97% 5.06% 0.00% 0.00% 0.05% 0.00% 2.32% 17 4.52% 4.05% 3.90% 2.72% 3.06% 0.95% 2.65% 0.00% 3.93% 3.39% 3.79% 3.46% 2.21% 18 4.71% 5.22% 5.45% 3.49% 3.52% 0.99% 2.65% 0.00% 5.40% 4.89% 4.43% 3.71% 2.22% 19 0.00% 1.74% 1.38% 1.06% 2.32% 2.58% 4.64% 3.80% 0.76% 0.16% 2.08% 2.80% 2.18% 20 1.36% 0.18% 0.69% 1.87% 2.95% 3.11% 3.31% 3.80% 1.93% 1.33% 2.66% 3.44% 2.23% 21 1.96% 0.00% 0.00% 0.09% 1.89% 4.47% 4.64% 6.33% 0.28% 3.72% 2.15% 2.92% 5.91% 22 4.57% 4.05% 3.90% 3.03% 3.03% 0.04% 5.30% 1.27% 3.52% 3.37% 3.68% 2.89% 2.24% 23 4.76% 5.22% 5.45% 3.64% 3.54% 0.23% 4.64% 1.27% 5.04% 4.87% 4.32% 3.19% 2.24% 24 0.00% 1.74% 1.38% 1.61% 2.48% 1.29% 7.95% 5.06% 0.18% 0.14% 1.91% 1.97% 2.22% 25 4.88% 4.05% 3.90% 3.57% 3.30% 1.25% 1.32% 0.00% 3.85% 3.32% 3.73% 3.23% 2.19% 26 5.08% 5.22% 5.45% 4.18% 3.73% 1.29% 1.32% 0.00% 5.33% 4.83% 4.37% 3.52% 2.20% 27 0.47% 1.74% 1.38% 2.63% 2.61% 3.03% 2.65% 3.80% 0.65% 0.06% 1.99% 2.48% 2.15% 28 1.73% 0.18% 0.69% 3.21% 3.22% 3.52% 1.99% 3.80% 1.83% 1.24% 2.58% 3.17% 2.20% 29 2.41% 0.00% 0.00% 1.63% 2.25% 4.96% 2.65% 6.33% 0.16% 3.60% 2.05% 2.56% 5.87% 30 4.93% 4.05% 3.90% 3.89% 3.27% 0.34% 3.97% 1.27% 3.44% 3.30% 3.61% 2.67% 2.22% 31 5.14% 5.22% 5.45% 4.34% 3.74% 0.49% 3.31% 1.27% 4.97% 4.81% 4.27% 3.00% 2.23% 32 0.60% 1.74% 1.38% 2.94% 2.76% 1.74% 5.96% 5.06% 0.07% 0.04% 1.82% 1.66% 2.19% 33 4.95% 4.05% 3.90% 4.29% 3.49% 0.00% 1.32% 0.00% 3.98% 3.44% 3.75% 3.61% 2.29% 34 5.18% 5.22% 5.45% 4.82% 3.89% 0.15% 0.66% 0.00% 5.44% 4.93% 4.38% 3.81% 2.29% 35 0.68% 1.74% 1.38% 3.61% 2.84% 1.21% 2.65% 3.80% 0.84% 0.23% 2.02% 3.00% 2.30% 36 1.83% 0.18% 0.69% 3.95% 3.43% 1.93% 1.32% 3.80% 2.01% 1.41% 2.62% 3.77% 2.34% 37 2.44% 0.00% 0.00% 2.56% 2.52% 2.88% 1.99% 6.33% 0.40% 3.82% 2.10% 3.35% 6.06% sum 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Stellenbosch University http://scholar.sun.ac.za 338 Annexure 55: Normalised, un-weighted scores for BPA II LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria IRR (27 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (27 yrs) OPEX (27 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 4.07% 4.05% 3.90% 2.51% 3.15% 1.41% 1.52% 0.00% 4.15% 3.97% 3.36% 3.28% 0.76% 2 4.37% 5.22% 5.45% 3.02% 3.55% 1.34% 1.52% 0.00% 5.68% 5.46% 4.00% 3.53% 0.79% 3 0.82% 1.74% 1.38% 1.14% 2.49% 3.14% 3.03% 3.80% 0.84% 0.77% 1.61% 2.54% 0.72% 4 1.61% 0.18% 0.69% 1.72% 2.92% 3.50% 3.03% 3.80% 2.03% 1.91% 2.21% 3.06% 0.82% 5 2.07% 0.00% 0.00% 0.72% 1.88% 4.91% 3.03% 6.33% 0.30% 0.18% 1.57% 2.42% 16.50% 6 4.10% 4.05% 3.90% 2.80% 3.16% 0.29% 3.03% 1.27% 3.72% 3.96% 3.27% 2.74% 0.89% 7 4.35% 5.22% 5.45% 3.18% 3.50% 0.39% 2.27% 1.27% 5.31% 5.45% 3.92% 3.07% 0.90% 8 1.02% 1.74% 1.38% 1.89% 2.66% 1.54% 4.55% 5.06% 0.23% 0.75% 1.48% 1.78% 0.89% 9 3.24% 4.05% 3.90% 3.26% 1.85% 4.75% 0.00% 0.00% 4.01% 3.85% 2.39% 2.06% 0.70% 10 3.38% 5.22% 5.45% 3.58% 2.43% 4.23% 0.00% 0.00% 5.56% 5.36% 3.16% 2.48% 0.74% 11 0.00% 1.74% 1.38% 2.77% 0.94% 7.89% 0.76% 3.80% 0.63% 0.60% 0.22% 0.81% 0.63% 12 1.11% 0.18% 0.69% 3.07% 1.49% 7.57% 0.76% 3.80% 1.85% 1.77% 1.02% 1.59% 0.75% 13 1.49% 0.00% 0.00% 2.18% 0.00% 10.28% 0.76% 6.33% 0.07% 0.00% 0.00% 0.48% 16.41% 14 3.39% 4.05% 3.90% 3.55% 2.25% 3.47% 1.52% 1.27% 3.56% 3.83% 2.31% 1.49% 0.80% 15 3.51% 5.22% 5.45% 3.77% 2.72% 3.11% 0.76% 1.27% 5.18% 5.34% 3.09% 1.99% 0.82% 16 0.43% 1.74% 1.38% 3.20% 1.57% 6.06% 2.27% 5.06% 0.00% 0.57% 0.11% 0.00% 0.77% 17 3.99% 4.05% 3.90% 1.96% 3.10% 1.08% 3.03% 0.00% 4.14% 3.95% 3.83% 3.58% 0.35% 18 4.29% 5.22% 5.45% 2.66% 3.51% 1.08% 3.03% 0.00% 5.67% 5.44% 4.40% 3.79% 0.43% 19 0.69% 1.74% 1.38% 0.61% 2.44% 2.72% 5.30% 3.80% 0.82% 0.73% 2.26% 2.97% 0.13% 20 1.55% 0.18% 0.69% 1.19% 2.86% 3.11% 4.55% 3.80% 2.01% 1.87% 2.77% 3.43% 0.31% 21 2.00% 0.00% 0.00% 0.00% 1.82% 4.42% 6.06% 6.33% 0.28% 0.14% 2.31% 2.91% 15.83% 22 4.01% 4.05% 3.90% 2.26% 3.11% 0.00% 4.55% 1.27% 3.71% 3.93% 3.72% 3.04% 0.48% 23 4.27% 5.22% 5.45% 2.83% 3.46% 0.13% 3.79% 1.27% 5.30% 5.42% 4.31% 3.32% 0.55% 24 0.90% 1.74% 1.38% 1.35% 2.60% 1.15% 6.82% 5.06% 0.21% 0.71% 2.11% 2.19% 0.32% 25 4.32% 4.05% 3.90% 3.06% 3.30% 1.41% 2.27% 0.00% 4.06% 3.87% 3.77% 3.36% 0.26% 26 4.61% 5.22% 5.45% 3.57% 3.68% 1.38% 1.52% 0.00% 5.60% 5.37% 4.35% 3.61% 0.35% 27 1.30% 1.74% 1.38% 2.66% 2.68% 3.14% 3.03% 3.80% 0.70% 0.63% 2.18% 2.66% 0.00% 28 1.84% 0.18% 0.69% 2.94% 3.08% 3.50% 3.03% 3.80% 1.91% 1.79% 2.70% 3.17% 0.20% 29 2.37% 0.00% 0.00% 2.02% 2.10% 4.91% 3.79% 6.33% 0.15% 0.03% 2.21% 2.56% 15.69% 30 4.31% 4.05% 3.90% 3.38% 3.30% 0.33% 3.79% 1.27% 3.63% 3.86% 3.66% 2.82% 0.39% 31 4.59% 5.22% 5.45% 3.74% 3.63% 0.39% 2.27% 1.27% 5.23% 5.36% 4.26% 3.13% 0.47% 32 1.38% 1.74% 1.38% 3.08% 2.83% 1.57% 4.55% 5.06% 0.09% 0.61% 2.03% 1.89% 0.20% 33 4.32% 4.05% 3.90% 4.25% 3.49% 0.03% 1.52% 0.00% 4.21% 4.01% 3.80% 3.81% 0.84% 34 4.64% 5.22% 5.45% 4.44% 3.84% 0.13% 0.76% 0.00% 5.73% 5.49% 4.37% 3.98% 0.86% 35 1.40% 1.74% 1.38% 4.00% 2.91% 1.18% 3.03% 3.80% 0.92% 0.82% 2.22% 3.29% 0.83% 36 1.89% 0.18% 0.69% 4.15% 3.30% 1.77% 2.27% 3.80% 2.11% 1.96% 2.74% 3.78% 0.95% 37 2.36% 0.00% 0.00% 3.48% 2.39% 2.65% 2.27% 6.33% 0.41% 0.26% 2.27% 3.37% 16.67% sum 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Stellenbosch University http://scholar.sun.ac.za 339 Annexure 56: Normalised, un-weighted scores for BPA III LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria IRR (30 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (30 yrs) OPEX (30 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 3.93% 4.05% 3.83% 2.61% 3.14% 1.33% 1.54% 0.00% 4.15% 3.97% 3.27% 0.85% 3.31% 2 4.01% 5.22% 5.35% 3.11% 3.53% 1.24% 1.54% 0.00% 5.67% 5.43% 3.52% 0.92% 3.89% 3 1.02% 1.74% 1.36% 1.21% 2.58% 3.08% 2.31% 3.80% 0.86% 0.80% 2.56% 0.67% 1.69% 4 1.96% 0.18% 0.68% 1.73% 2.87% 3.32% 2.31% 3.80% 2.04% 1.92% 3.01% 0.81% 2.24% 5 2.35% 0.00% 0.00% 0.70% 1.90% 4.82% 3.08% 6.33% 0.32% 0.21% 2.35% 16.36% 1.63% 6 3.99% 4.05% 3.83% 2.92% 3.23% 0.30% 3.08% 1.27% 3.73% 3.95% 2.80% 0.98% 3.23% 7 4.06% 5.22% 5.35% 3.33% 3.58% 0.33% 2.31% 1.27% 5.30% 5.42% 3.11% 1.04% 3.82% 8 1.19% 1.74% 1.36% 1.88% 2.67% 1.63% 4.62% 5.06% 0.26% 0.78% 1.89% 0.86% 1.58% 9 3.09% 4.05% 3.83% 3.17% 1.83% 4.91% 0.00% 0.00% 3.98% 3.83% 1.96% 0.60% 2.29% 10 3.08% 5.22% 5.35% 3.58% 2.40% 4.31% 0.00% 0.00% 5.52% 5.32% 2.38% 0.70% 3.02% 11 0.00% 1.74% 1.36% 2.62% 1.01% 8.20% 0.77% 3.80% 0.62% 0.61% 0.70% 0.32% 0.25% 12 1.34% 0.18% 0.68% 2.92% 1.43% 7.69% 0.00% 3.80% 1.84% 1.76% 1.46% 0.53% 1.01% 13 1.66% 0.00% 0.00% 1.91% 0.00% 10.58% 0.77% 6.33% 0.05% 0.00% 0.32% 15.99% 0.00% 14 3.28% 4.05% 3.83% 3.49% 2.25% 3.71% 1.54% 1.27% 3.54% 3.81% 1.47% 0.71% 2.23% 15 3.25% 5.22% 5.35% 3.81% 2.73% 3.26% 0.77% 1.27% 5.14% 5.30% 1.96% 0.80% 2.96% 16 0.43% 1.74% 1.36% 3.03% 1.48% 6.48% 2.31% 5.06% 0.00% 0.57% 0.00% 0.48% 0.15% 17 3.87% 4.05% 3.83% 2.08% 3.10% 0.99% 3.85% 0.00% 4.13% 3.94% 3.58% 0.47% 3.79% 18 3.94% 5.22% 5.35% 2.57% 3.49% 0.96% 3.08% 0.00% 5.65% 5.41% 3.78% 0.59% 4.30% 19 0.89% 1.74% 1.36% 0.69% 2.53% 2.62% 5.38% 3.80% 0.84% 0.76% 3.00% 0.13% 2.37% 20 1.90% 0.18% 0.68% 1.21% 2.83% 2.92% 4.62% 3.80% 2.01% 1.88% 3.38% 0.35% 2.82% 21 2.29% 0.00% 0.00% 0.00% 1.84% 4.31% 6.15% 6.33% 0.29% 0.16% 2.88% 15.76% 2.40% 22 3.93% 4.05% 3.83% 2.39% 3.19% 0.00% 5.38% 1.27% 3.71% 3.92% 3.10% 0.61% 3.69% 23 3.98% 5.22% 5.35% 2.79% 3.55% 0.06% 3.85% 1.27% 5.28% 5.40% 3.36% 0.72% 4.22% 24 1.08% 1.74% 1.36% 1.36% 2.62% 1.21% 6.92% 5.06% 0.24% 0.74% 2.32% 0.34% 2.23% 25 4.13% 4.05% 3.83% 3.15% 3.25% 1.33% 2.31% 0.00% 4.05% 3.86% 3.39% 0.38% 3.73% 26 4.19% 5.22% 5.35% 3.46% 3.63% 1.24% 2.31% 0.00% 5.58% 5.35% 3.62% 0.51% 4.26% 27 1.48% 1.74% 1.36% 2.70% 2.72% 3.08% 3.08% 3.80% 0.72% 0.66% 2.73% 0.00% 2.29% 28 2.18% 0.18% 0.68% 2.93% 3.00% 3.32% 2.31% 3.80% 1.92% 1.80% 3.15% 0.24% 2.76% 29 2.64% 0.00% 0.00% 1.96% 2.07% 4.82% 3.85% 6.33% 0.16% 0.05% 2.56% 15.61% 2.31% 30 4.19% 4.05% 5.66% 3.46% 3.34% 0.30% 3.85% 1.27% 3.63% 3.86% 2.91% 0.53% 3.64% 31 4.23% 5.22% 5.35% 3.68% 3.67% 0.33% 3.08% 1.27% 5.22% 5.34% 3.20% 0.64% 4.18% 32 1.56% 1.74% 1.36% 3.04% 2.80% 1.63% 5.38% 5.06% 0.13% 0.64% 2.05% 0.21% 2.16% 33 4.15% 4.05% 3.83% 4.35% 3.44% 0.00% 0.77% 0.00% 4.21% 4.00% 3.83% 0.95% 3.76% 34 4.24% 5.22% 5.35% 4.53% 3.79% 0.09% 0.77% 0.00% 5.72% 5.47% 3.99% 1.00% 4.28% 35 1.60% 1.74% 1.36% 4.05% 2.95% 1.24% 2.31% 3.80% 0.95% 0.85% 3.35% 0.81% 2.34% 36 2.24% 0.18% 0.68% 4.15% 3.21% 1.69% 1.54% 3.80% 2.12% 1.97% 3.73% 0.95% 2.80% 37 2.63% 0.00% 0.00% 3.45% 2.35% 2.68% 2.31% 6.33% 0.43% 0.28% 3.33% 16.56% 2.37% sum 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Stellenbosch University http://scholar.sun.ac.za 340 Annexure 57: Normalised, un-weighted scores for BPA IV LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria IRR (35 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (35 yrs) OPEX (35 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 4.02% 4.05% 3.90% 2.33% 3.16% 1.33% 1.60% 0.00% 4.16% 3.97% 3.31% 0.64% 3.39% 2 4.05% 5.22% 5.45% 2.85% 3.56% 1.24% 1.60% 0.00% 5.68% 5.45% 3.55% 0.71% 4.03% 3 0.88% 1.74% 1.38% 0.88% 2.52% 3.21% 3.20% 3.80% 0.84% 0.77% 2.55% 0.45% 1.59% 4 1.97% 0.18% 0.69% 1.50% 2.90% 3.43% 2.40% 3.80% 2.02% 1.90% 3.04% 0.61% 2.20% 5 2.39% 0.00% 0.00% 0.59% 1.93% 4.99% 3.20% 6.33% 0.30% 0.18% 2.38% 17.07% 1.54% 6 4.05% 4.05% 3.90% 2.62% 3.18% 0.29% 3.20% 1.27% 3.73% 3.96% 2.78% 0.78% 3.29% 7 4.10% 5.22% 5.45% 3.04% 3.60% 0.32% 2.40% 1.27% 5.31% 5.44% 3.09% 0.83% 3.95% 8 1.11% 1.74% 1.38% 1.70% 2.67% 1.65% 4.80% 5.06% 0.23% 0.75% 1.79% 0.65% 1.46% 9 3.13% 4.05% 3.90% 3.44% 1.86% 4.67% 0.00% 0.00% 4.01% 3.85% 2.07% 0.42% 2.42% 10 3.08% 5.22% 5.45% 3.69% 2.44% 4.10% 0.00% 0.00% 5.56% 5.36% 2.51% 0.53% 3.21% 11 0.00% 1.74% 1.38% 3.11% 1.04% 7.91% 0.80% 3.80% 0.63% 0.61% 0.80% 0.14% 0.23% 12 1.29% 0.18% 0.69% 3.33% 1.48% 7.50% 0.80% 3.80% 1.85% 1.76% 1.56% 0.35% 1.03% 13 1.63% 0.00% 0.00% 2.62% 0.00% 10.32% 0.80% 6.33% 0.07% 0.00% 0.43% 16.73% 0.00% 14 3.29% 4.05% 3.90% 3.74% 2.21% 3.43% 1.60% 1.27% 3.56% 3.83% 1.51% 0.54% 2.34% 15 3.22% 5.22% 5.45% 3.87% 2.69% 3.02% 0.80% 1.27% 5.18% 5.33% 2.02% 0.63% 3.14% 16 0.40% 1.74% 1.38% 3.53% 1.51% 6.13% 2.40% 5.06% 0.00% 0.57% 0.00% 0.30% 0.11% 17 3.97% 4.05% 3.90% 1.93% 3.12% 1.08% 3.20% 0.00% 4.14% 3.95% 3.60% 0.42% 3.84% 18 3.99% 5.22% 5.45% 2.45% 3.52% 1.02% 3.20% 0.00% 5.67% 5.43% 3.80% 0.52% 4.42% 19 0.75% 1.74% 1.38% 0.27% 2.48% 2.80% 5.60% 3.80% 0.82% 0.74% 2.96% 0.14% 2.23% 20 1.90% 0.18% 0.69% 0.89% 2.86% 3.08% 4.00% 3.80% 2.00% 1.87% 3.39% 0.34% 2.75% 21 2.33% 0.00% 0.00% 0.00% 1.87% 4.54% 6.40% 6.33% 0.27% 0.14% 2.85% 16.72% 2.27% 22 4.01% 4.05% 3.90% 2.22% 3.14% 0.00% 4.80% 1.27% 3.71% 3.93% 3.06% 0.57% 3.73% 23 4.04% 5.22% 5.45% 2.63% 3.57% 0.06% 4.00% 1.27% 5.30% 5.42% 3.34% 0.65% 4.33% 24 1.00% 1.74% 1.38% 1.09% 2.63% 1.27% 7.20% 5.06% 0.21% 0.72% 2.20% 0.35% 2.08% 25 4.18% 4.05% 3.90% 3.20% 3.27% 1.37% 1.60% 0.00% 4.06% 3.87% 3.38% 0.33% 3.78% 26 4.21% 5.22% 5.45% 3.49% 3.65% 1.27% 1.60% 0.00% 5.60% 5.37% 3.62% 0.44% 4.37% 27 1.33% 1.74% 1.38% 2.62% 2.66% 3.24% 3.20% 3.80% 0.70% 0.63% 2.65% 0.00% 2.15% 28 2.18% 0.18% 0.69% 2.91% 3.03% 3.43% 2.40% 3.80% 1.90% 1.78% 3.13% 0.22% 2.67% 29 2.67% 0.00% 0.00% 2.29% 2.09% 5.02% 4.00% 6.33% 0.14% 0.03% 2.50% 16.56% 2.17% 30 4.22% 4.05% 3.90% 3.49% 3.29% 0.29% 3.20% 1.27% 3.63% 3.86% 2.85% 0.48% 3.67% 31 4.26% 5.22% 5.45% 3.67% 3.70% 0.32% 2.40% 1.27% 5.23% 5.36% 3.16% 0.57% 4.28% 32 1.46% 1.74% 1.38% 3.06% 2.81% 1.65% 4.80% 5.06% 0.10% 0.62% 1.90% 0.22% 2.00% 33 4.22% 4.05% 3.90% 4.31% 3.42% 0.00% 0.80% 0.00% 4.21% 4.01% 3.84% 0.73% 3.81% 34 4.26% 5.22% 5.45% 4.47% 3.78% 0.10% 0.80% 0.00% 5.74% 5.49% 4.01% 0.79% 4.40% 35 1.48% 1.74% 1.38% 4.13% 2.84% 1.33% 3.20% 3.80% 0.92% 0.83% 3.31% 0.58% 2.19% 36 2.26% 0.18% 0.69% 4.27% 3.20% 1.78% 1.60% 3.80% 2.10% 1.95% 3.74% 0.74% 2.72% 37 2.68% 0.00% 0.00% 3.74% 2.32% 2.83% 2.40% 6.33% 0.40% 0.25% 3.30% 17.25% 2.23% sum 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Stellenbosch University http://scholar.sun.ac.za 341 Annexure 58: Normalised to sum one, but un-weighted scores for BPA I LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (25 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (25 yrs) OPEX (25 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 0.35% 0.31% 0.30% 0.23% 0.24% 0.09% 0.10% 0.00% 0.30% 0.26% 0.26% 0.25% 0.18% 2.78% 2 0.37% 0.40% 0.42% 0.29% 0.27% 0.10% 0.10% 0.00% 0.42% 0.38% 0.31% 0.27% 0.18% 3.23% 3 0.00% 0.13% 0.11% 0.11% 0.18% 0.23% 0.20% 0.29% 0.06% 0.01% 0.11% 0.19% 0.18% 2.08% 4 0.11% 0.01% 0.05% 0.16% 0.23% 0.27% 0.15% 0.29% 0.15% 0.10% 0.16% 0.24% 0.18% 2.26% 5 0.16% 0.00% 0.00% 0.04% 0.15% 0.38% 0.20% 0.49% 0.02% 0.29% 0.11% 0.19% 0.46% 2.51% 6 0.36% 0.31% 0.30% 0.25% 0.24% 0.03% 0.31% 0.10% 0.27% 0.26% 0.25% 0.20% 0.18% 2.96% 7 0.37% 0.40% 0.42% 0.30% 0.28% 0.04% 0.25% 0.10% 0.39% 0.38% 0.31% 0.23% 0.18% 3.37% 8 0.01% 0.13% 0.11% 0.16% 0.19% 0.13% 0.46% 0.39% 0.02% 0.01% 0.10% 0.12% 0.18% 2.29% 9 0.25% 0.31% 0.30% 0.17% 0.13% 0.35% 0.00% 0.00% 0.29% 0.25% 0.19% 0.16% 0.18% 2.51% 10 0.25% 0.40% 0.42% 0.25% 0.18% 0.32% 0.00% 0.00% 0.41% 0.37% 0.25% 0.19% 0.18% 2.96% 11 0.00% 0.13% 0.11% 0.11% 0.06% 0.59% 0.05% 0.29% 0.05% 0.00% 0.01% 0.07% 0.18% 1.94% 12 0.04% 0.01% 0.05% 0.15% 0.12% 0.58% 0.00% 0.29% 0.14% 0.09% 0.08% 0.14% 0.18% 2.03% 13 0.08% 0.00% 0.00% 0.00% 0.00% 0.79% 0.05% 0.49% 0.01% 0.28% 0.00% 0.06% 0.46% 2.26% 14 0.27% 0.31% 0.30% 0.22% 0.17% 0.27% 0.20% 0.10% 0.64% 0.25% 0.34% 0.30% 0.00% 3.11% 15 0.27% 0.40% 0.42% 0.27% 0.22% 0.24% 0.15% 0.10% 0.38% 0.37% 0.25% 0.15% 0.18% 3.14% 16 0.00% 0.13% 0.11% 0.13% 0.11% 0.48% 0.31% 0.39% 0.00% 0.00% 0.00% 0.00% 0.18% 2.13% 17 0.35% 0.31% 0.30% 0.21% 0.24% 0.07% 0.20% 0.00% 0.30% 0.26% 0.29% 0.27% 0.17% 2.88% 18 0.36% 0.40% 0.42% 0.27% 0.27% 0.08% 0.20% 0.00% 0.42% 0.38% 0.34% 0.29% 0.17% 3.32% 19 0.00% 0.13% 0.11% 0.08% 0.18% 0.20% 0.36% 0.29% 0.06% 0.01% 0.16% 0.22% 0.17% 2.23% 20 0.10% 0.01% 0.05% 0.14% 0.23% 0.24% 0.25% 0.29% 0.15% 0.10% 0.20% 0.26% 0.17% 2.36% 21 0.15% 0.00% 0.00% 0.01% 0.15% 0.34% 0.36% 0.49% 0.02% 0.29% 0.17% 0.22% 0.45% 2.67% 22 0.35% 0.31% 0.30% 0.23% 0.23% 0.00% 0.41% 0.10% 0.27% 0.26% 0.28% 0.22% 0.17% 3.05% 23 0.37% 0.40% 0.42% 0.28% 0.27% 0.02% 0.36% 0.10% 0.39% 0.37% 0.33% 0.25% 0.17% 3.46% 24 0.00% 0.13% 0.11% 0.12% 0.19% 0.10% 0.61% 0.39% 0.01% 0.01% 0.15% 0.15% 0.17% 2.43% 25 0.38% 0.31% 0.30% 0.27% 0.25% 0.10% 0.10% 0.00% 0.30% 0.26% 0.29% 0.25% 0.17% 2.88% 26 0.39% 0.40% 0.42% 0.32% 0.29% 0.10% 0.10% 0.00% 0.41% 0.37% 0.34% 0.27% 0.17% 3.32% 27 0.04% 0.13% 0.11% 0.20% 0.20% 0.23% 0.20% 0.29% 0.05% 0.00% 0.15% 0.19% 0.17% 2.26% 28 0.13% 0.01% 0.05% 0.25% 0.25% 0.27% 0.15% 0.29% 0.14% 0.10% 0.20% 0.24% 0.17% 2.41% 29 0.19% 0.00% 0.00% 0.13% 0.17% 0.38% 0.20% 0.49% 0.01% 0.28% 0.16% 0.20% 0.45% 2.69% 30 0.38% 0.31% 0.30% 0.30% 0.25% 0.03% 0.31% 0.10% 0.26% 0.25% 0.28% 0.21% 0.17% 3.06% 31 0.40% 0.40% 0.42% 0.33% 0.29% 0.04% 0.25% 0.10% 0.38% 0.37% 0.33% 0.23% 0.17% 3.45% 32 0.05% 0.13% 0.11% 0.23% 0.21% 0.13% 0.46% 0.39% 0.01% 0.00% 0.14% 0.13% 0.17% 2.44% 33 0.38% 0.31% 0.30% 0.33% 0.27% 0.00% 0.10% 0.00% 0.31% 0.26% 0.29% 0.28% 0.18% 2.91% 34 0.40% 0.40% 0.42% 0.37% 0.30% 0.01% 0.05% 0.00% 0.42% 0.38% 0.34% 0.29% 0.18% 3.29% 35 0.05% 0.13% 0.11% 0.28% 0.22% 0.09% 0.20% 0.29% 0.06% 0.02% 0.16% 0.23% 0.18% 2.29% 36 0.14% 0.01% 0.05% 0.30% 0.26% 0.15% 0.10% 0.29% 0.15% 0.11% 0.20% 0.29% 0.18% 2.38% 37 0.19% 0.00% 0.00% 0.20% 0.19% 0.22% 0.15% 0.49% 0.03% 0.29% 0.16% 0.26% 0.47% 2.66% sum 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 100.00% Stellenbosch University http://scholar.sun.ac.za 342 Annexure 59: Normalised to sum one, but un-weighted scores for BPA II LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (27 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (27 yrs) OPEX (27 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 0.31% 0.31% 0.30% 0.19% 0.24% 0.11% 0.12% 0.00% 0.32% 0.31% 0.26% 0.25% 0.06% 2.78% 2 0.34% 0.40% 0.42% 0.23% 0.27% 0.10% 0.12% 0.00% 0.44% 0.42% 0.31% 0.27% 0.06% 3.38% 3 0.06% 0.13% 0.11% 0.09% 0.19% 0.24% 0.23% 0.29% 0.06% 0.06% 0.12% 0.20% 0.06% 1.85% 4 0.12% 0.01% 0.05% 0.13% 0.22% 0.27% 0.23% 0.29% 0.16% 0.15% 0.17% 0.24% 0.06% 2.11% 5 0.16% 0.00% 0.00% 0.06% 0.14% 0.38% 0.23% 0.49% 0.02% 0.01% 0.12% 0.19% 1.27% 3.07% 6 0.32% 0.31% 0.30% 0.22% 0.24% 0.02% 0.23% 0.10% 0.29% 0.30% 0.25% 0.21% 0.07% 2.86% 7 0.33% 0.40% 0.42% 0.24% 0.27% 0.03% 0.17% 0.10% 0.41% 0.42% 0.30% 0.24% 0.07% 3.41% 8 0.08% 0.13% 0.11% 0.15% 0.20% 0.12% 0.35% 0.39% 0.02% 0.06% 0.11% 0.14% 0.07% 1.92% 9 0.25% 0.31% 0.30% 0.25% 0.14% 0.37% 0.00% 0.00% 0.31% 0.30% 0.18% 0.16% 0.05% 2.62% 10 0.26% 0.40% 0.42% 0.28% 0.19% 0.33% 0.00% 0.00% 0.43% 0.41% 0.24% 0.19% 0.06% 3.20% 11 0.00% 0.13% 0.11% 0.21% 0.07% 0.61% 0.06% 0.29% 0.05% 0.05% 0.02% 0.06% 0.05% 1.71% 12 0.09% 0.01% 0.05% 0.24% 0.11% 0.58% 0.06% 0.29% 0.14% 0.14% 0.08% 0.12% 0.06% 1.97% 13 0.11% 0.00% 0.00% 0.17% 0.00% 0.79% 0.06% 0.49% 0.01% 0.00% 0.00% 0.04% 1.26% 2.92% 14 0.26% 0.31% 0.30% 0.27% 0.17% 0.27% 0.12% 0.10% 0.27% 0.29% 0.18% 0.11% 0.06% 2.72% 15 0.27% 0.40% 0.42% 0.29% 0.21% 0.24% 0.06% 0.10% 0.40% 0.41% 0.24% 0.15% 0.06% 3.25% 16 0.03% 0.13% 0.11% 0.25% 0.12% 0.47% 0.17% 0.39% 0.00% 0.04% 0.01% 0.00% 0.06% 1.78% 17 0.31% 0.31% 0.30% 0.15% 0.24% 0.08% 0.23% 0.00% 0.32% 0.30% 0.29% 0.28% 0.03% 2.84% 18 0.33% 0.40% 0.42% 0.20% 0.27% 0.08% 0.23% 0.00% 0.44% 0.42% 0.34% 0.29% 0.03% 3.46% 19 0.05% 0.13% 0.11% 0.05% 0.19% 0.21% 0.41% 0.29% 0.06% 0.06% 0.17% 0.23% 0.01% 1.97% 20 0.12% 0.01% 0.05% 0.09% 0.22% 0.24% 0.35% 0.29% 0.15% 0.14% 0.21% 0.26% 0.02% 2.18% 21 0.15% 0.00% 0.00% 0.00% 0.14% 0.34% 0.47% 0.49% 0.02% 0.01% 0.18% 0.22% 1.22% 3.24% 22 0.31% 0.31% 0.30% 0.17% 0.24% 0.00% 0.35% 0.10% 0.29% 0.30% 0.29% 0.23% 0.04% 2.92% 23 0.33% 0.40% 0.42% 0.22% 0.27% 0.01% 0.29% 0.10% 0.41% 0.42% 0.33% 0.26% 0.04% 3.48% 24 0.07% 0.13% 0.11% 0.10% 0.20% 0.09% 0.52% 0.39% 0.02% 0.05% 0.16% 0.17% 0.02% 2.04% 25 0.33% 0.31% 0.30% 0.24% 0.25% 0.11% 0.17% 0.00% 0.31% 0.30% 0.29% 0.26% 0.02% 2.89% 26 0.35% 0.40% 0.42% 0.27% 0.28% 0.11% 0.12% 0.00% 0.43% 0.41% 0.33% 0.28% 0.03% 3.44% 27 0.10% 0.13% 0.11% 0.20% 0.21% 0.24% 0.23% 0.29% 0.05% 0.05% 0.17% 0.20% 0.00% 1.99% 28 0.14% 0.01% 0.05% 0.23% 0.24% 0.27% 0.23% 0.29% 0.15% 0.14% 0.21% 0.24% 0.02% 2.22% 29 0.18% 0.00% 0.00% 0.16% 0.16% 0.38% 0.29% 0.49% 0.01% 0.00% 0.17% 0.20% 1.21% 3.24% 30 0.33% 0.31% 0.30% 0.26% 0.25% 0.03% 0.29% 0.10% 0.28% 0.30% 0.28% 0.22% 0.03% 2.98% 31 0.35% 0.40% 0.42% 0.29% 0.28% 0.03% 0.17% 0.10% 0.40% 0.41% 0.33% 0.24% 0.04% 3.46% 32 0.11% 0.13% 0.11% 0.24% 0.22% 0.12% 0.35% 0.39% 0.01% 0.05% 0.16% 0.15% 0.02% 2.03% 33 0.33% 0.31% 0.30% 0.33% 0.27% 0.00% 0.12% 0.00% 0.32% 0.31% 0.29% 0.29% 0.06% 2.94% 34 0.36% 0.40% 0.42% 0.34% 0.30% 0.01% 0.06% 0.00% 0.44% 0.42% 0.34% 0.31% 0.07% 3.45% 35 0.11% 0.13% 0.11% 0.31% 0.22% 0.09% 0.23% 0.29% 0.07% 0.06% 0.17% 0.25% 0.06% 2.12% 36 0.15% 0.01% 0.05% 0.32% 0.25% 0.14% 0.17% 0.29% 0.16% 0.15% 0.21% 0.29% 0.07% 2.28% 37 0.18% 0.00% 0.00% 0.27% 0.18% 0.20% 0.17% 0.49% 0.03% 0.02% 0.17% 0.26% 1.28% 3.27% sum 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 100.00% Stellenbosch University http://scholar.sun.ac.za 343 Annexure 60: Normalised to sum one, but un-weighted scores for BPA III LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (30 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (30 yrs) OPEX (30 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 0.30% 0.31% 0.29% 0.20% 0.24% 0.10% 0.12% 0.00% 0.32% 0.31% 0.25% 0.07% 0.25% 2.77% 2 0.31% 0.40% 0.41% 0.24% 0.27% 0.10% 0.12% 0.00% 0.44% 0.42% 0.27% 0.07% 0.30% 3.34% 3 0.08% 0.13% 0.10% 0.09% 0.20% 0.24% 0.18% 0.29% 0.07% 0.06% 0.20% 0.05% 0.13% 1.82% 4 0.15% 0.01% 0.05% 0.13% 0.22% 0.26% 0.18% 0.29% 0.16% 0.15% 0.23% 0.06% 0.17% 2.07% 5 0.18% 0.00% 0.00% 0.05% 0.15% 0.37% 0.24% 0.49% 0.02% 0.02% 0.18% 1.26% 0.13% 3.08% 6 0.31% 0.31% 0.29% 0.22% 0.25% 0.02% 0.24% 0.10% 0.29% 0.30% 0.22% 0.08% 0.25% 2.87% 7 0.31% 0.40% 0.41% 0.26% 0.28% 0.03% 0.18% 0.10% 0.41% 0.42% 0.24% 0.08% 0.29% 3.39% 8 0.09% 0.13% 0.10% 0.14% 0.21% 0.13% 0.36% 0.39% 0.02% 0.06% 0.15% 0.07% 0.12% 1.96% 9 0.24% 0.31% 0.29% 0.24% 0.14% 0.38% 0.00% 0.00% 0.31% 0.29% 0.15% 0.05% 0.18% 2.58% 10 0.24% 0.40% 0.41% 0.28% 0.18% 0.33% 0.00% 0.00% 0.42% 0.41% 0.18% 0.05% 0.23% 3.14% 11 0.00% 0.13% 0.10% 0.20% 0.08% 0.63% 0.06% 0.29% 0.05% 0.05% 0.05% 0.02% 0.02% 1.69% 12 0.10% 0.01% 0.05% 0.22% 0.11% 0.59% 0.00% 0.29% 0.14% 0.14% 0.11% 0.04% 0.08% 1.89% 13 0.13% 0.00% 0.00% 0.15% 0.00% 0.81% 0.06% 0.49% 0.00% 0.00% 0.02% 1.23% 0.00% 2.89% 14 0.25% 0.31% 0.29% 0.27% 0.17% 0.29% 0.12% 0.10% 0.27% 0.29% 0.11% 0.05% 0.17% 2.70% 15 0.25% 0.40% 0.41% 0.29% 0.21% 0.25% 0.06% 0.10% 0.40% 0.41% 0.15% 0.06% 0.23% 3.22% 16 0.03% 0.13% 0.10% 0.23% 0.11% 0.50% 0.18% 0.39% 0.00% 0.04% 0.00% 0.04% 0.01% 1.78% 17 0.30% 0.31% 0.29% 0.16% 0.24% 0.08% 0.30% 0.00% 0.32% 0.30% 0.28% 0.04% 0.29% 2.90% 18 0.30% 0.40% 0.41% 0.20% 0.27% 0.07% 0.24% 0.00% 0.43% 0.42% 0.29% 0.05% 0.33% 3.41% 19 0.07% 0.13% 0.10% 0.05% 0.19% 0.20% 0.41% 0.29% 0.06% 0.06% 0.23% 0.01% 0.18% 2.01% 20 0.15% 0.01% 0.05% 0.09% 0.22% 0.22% 0.36% 0.29% 0.15% 0.14% 0.26% 0.03% 0.22% 2.20% 21 0.18% 0.00% 0.00% 0.00% 0.14% 0.33% 0.47% 0.49% 0.02% 0.01% 0.22% 1.21% 0.18% 3.26% 22 0.30% 0.31% 0.29% 0.18% 0.25% 0.00% 0.41% 0.10% 0.29% 0.30% 0.24% 0.05% 0.28% 3.01% 23 0.31% 0.40% 0.41% 0.21% 0.27% 0.00% 0.30% 0.10% 0.41% 0.42% 0.26% 0.06% 0.32% 3.46% 24 0.08% 0.13% 0.10% 0.10% 0.20% 0.09% 0.53% 0.39% 0.02% 0.06% 0.18% 0.03% 0.17% 2.09% 25 0.32% 0.31% 0.29% 0.24% 0.25% 0.10% 0.18% 0.00% 0.31% 0.30% 0.26% 0.03% 0.29% 2.88% 26 0.32% 0.40% 0.41% 0.27% 0.28% 0.10% 0.18% 0.00% 0.43% 0.41% 0.28% 0.04% 0.33% 3.44% 27 0.11% 0.13% 0.10% 0.21% 0.21% 0.24% 0.24% 0.29% 0.06% 0.05% 0.21% 0.00% 0.18% 2.03% 28 0.17% 0.01% 0.05% 0.23% 0.23% 0.26% 0.18% 0.29% 0.15% 0.14% 0.24% 0.02% 0.21% 2.17% 29 0.20% 0.00% 0.00% 0.15% 0.16% 0.37% 0.30% 0.49% 0.01% 0.00% 0.20% 1.20% 0.18% 3.26% 30 0.32% 0.31% 0.44% 0.27% 0.26% 0.02% 0.30% 0.10% 0.28% 0.30% 0.22% 0.04% 0.28% 3.13% 31 0.33% 0.40% 0.41% 0.28% 0.28% 0.03% 0.24% 0.10% 0.40% 0.41% 0.25% 0.05% 0.32% 3.49% 32 0.12% 0.13% 0.10% 0.23% 0.22% 0.13% 0.41% 0.39% 0.01% 0.05% 0.16% 0.02% 0.17% 2.14% 33 0.32% 0.31% 0.29% 0.33% 0.26% 0.00% 0.06% 0.00% 0.32% 0.31% 0.29% 0.07% 0.29% 2.87% 34 0.33% 0.40% 0.41% 0.35% 0.29% 0.01% 0.06% 0.00% 0.44% 0.42% 0.31% 0.08% 0.33% 3.42% 35 0.12% 0.13% 0.10% 0.31% 0.23% 0.10% 0.18% 0.29% 0.07% 0.07% 0.26% 0.06% 0.18% 2.10% 36 0.17% 0.01% 0.05% 0.32% 0.25% 0.13% 0.12% 0.29% 0.16% 0.15% 0.29% 0.07% 0.22% 2.23% 37 0.20% 0.00% 0.00% 0.27% 0.18% 0.21% 0.18% 0.49% 0.03% 0.02% 0.26% 1.27% 0.18% 3.29% sum 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 100.00% Stellenbosch University http://scholar.sun.ac.za 344 Annexure 61: Normalised to sum one, but un-weighted scores for BPA IV LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (35 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (35 yrs) OPEX (35 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 0.31% 0.31% 0.30% 0.18% 0.24% 0.10% 0.12% 0.00% 0.32% 0.31% 0.25% 0.05% 0.26% 2.76% 2 0.31% 0.40% 0.42% 0.22% 0.27% 0.10% 0.12% 0.00% 0.44% 0.42% 0.27% 0.05% 0.31% 3.34% 3 0.07% 0.13% 0.11% 0.07% 0.19% 0.25% 0.25% 0.29% 0.06% 0.06% 0.20% 0.03% 0.12% 1.83% 4 0.15% 0.01% 0.05% 0.12% 0.22% 0.26% 0.18% 0.29% 0.16% 0.15% 0.23% 0.05% 0.17% 2.05% 5 0.18% 0.00% 0.00% 0.05% 0.15% 0.38% 0.25% 0.49% 0.02% 0.01% 0.18% 1.31% 0.12% 3.15% 6 0.31% 0.31% 0.30% 0.20% 0.24% 0.02% 0.25% 0.10% 0.29% 0.30% 0.21% 0.06% 0.25% 2.85% 7 0.32% 0.40% 0.42% 0.23% 0.28% 0.02% 0.18% 0.10% 0.41% 0.42% 0.24% 0.06% 0.30% 3.39% 8 0.09% 0.13% 0.11% 0.13% 0.21% 0.13% 0.37% 0.39% 0.02% 0.06% 0.14% 0.05% 0.11% 1.92% 9 0.24% 0.31% 0.30% 0.26% 0.14% 0.36% 0.00% 0.00% 0.31% 0.30% 0.16% 0.03% 0.19% 2.60% 10 0.24% 0.40% 0.42% 0.28% 0.19% 0.32% 0.00% 0.00% 0.43% 0.41% 0.19% 0.04% 0.25% 3.16% 11 0.00% 0.13% 0.11% 0.24% 0.08% 0.61% 0.06% 0.29% 0.05% 0.05% 0.06% 0.01% 0.02% 1.71% 12 0.10% 0.01% 0.05% 0.26% 0.11% 0.58% 0.06% 0.29% 0.14% 0.14% 0.12% 0.03% 0.08% 1.97% 13 0.13% 0.00% 0.00% 0.20% 0.00% 0.79% 0.06% 0.49% 0.01% 0.00% 0.03% 1.29% 0.00% 2.99% 14 0.25% 0.31% 0.30% 0.29% 0.17% 0.26% 0.12% 0.10% 0.27% 0.29% 0.12% 0.04% 0.18% 2.71% 15 0.25% 0.40% 0.42% 0.30% 0.21% 0.23% 0.06% 0.10% 0.40% 0.41% 0.16% 0.05% 0.24% 3.22% 16 0.03% 0.13% 0.11% 0.27% 0.12% 0.47% 0.18% 0.39% 0.00% 0.04% 0.00% 0.02% 0.01% 1.78% 17 0.31% 0.31% 0.30% 0.15% 0.24% 0.08% 0.25% 0.00% 0.32% 0.30% 0.28% 0.03% 0.30% 2.86% 18 0.31% 0.40% 0.42% 0.19% 0.27% 0.08% 0.25% 0.00% 0.44% 0.42% 0.29% 0.04% 0.34% 3.44% 19 0.06% 0.13% 0.11% 0.02% 0.19% 0.22% 0.43% 0.29% 0.06% 0.06% 0.23% 0.01% 0.17% 1.98% 20 0.15% 0.01% 0.05% 0.07% 0.22% 0.24% 0.31% 0.29% 0.15% 0.14% 0.26% 0.03% 0.21% 2.13% 21 0.18% 0.00% 0.00% 0.00% 0.14% 0.35% 0.49% 0.49% 0.02% 0.01% 0.22% 1.29% 0.17% 3.36% 22 0.31% 0.31% 0.30% 0.17% 0.24% 0.00% 0.37% 0.10% 0.29% 0.30% 0.24% 0.04% 0.29% 2.95% 23 0.31% 0.40% 0.42% 0.20% 0.27% 0.00% 0.31% 0.10% 0.41% 0.42% 0.26% 0.05% 0.33% 3.48% 24 0.08% 0.13% 0.11% 0.08% 0.20% 0.10% 0.55% 0.39% 0.02% 0.06% 0.17% 0.03% 0.16% 2.07% 25 0.32% 0.31% 0.30% 0.25% 0.25% 0.11% 0.12% 0.00% 0.31% 0.30% 0.26% 0.03% 0.29% 2.85% 26 0.32% 0.40% 0.42% 0.27% 0.28% 0.10% 0.12% 0.00% 0.43% 0.41% 0.28% 0.03% 0.34% 3.41% 27 0.10% 0.13% 0.11% 0.20% 0.20% 0.25% 0.25% 0.29% 0.05% 0.05% 0.20% 0.00% 0.17% 2.01% 28 0.17% 0.01% 0.05% 0.22% 0.23% 0.26% 0.18% 0.29% 0.15% 0.14% 0.24% 0.02% 0.21% 2.18% 29 0.21% 0.00% 0.00% 0.18% 0.16% 0.39% 0.31% 0.49% 0.01% 0.00% 0.19% 1.27% 0.17% 3.37% 30 0.32% 0.31% 0.30% 0.27% 0.25% 0.02% 0.25% 0.10% 0.28% 0.30% 0.22% 0.04% 0.28% 2.94% 31 0.33% 0.40% 0.42% 0.28% 0.28% 0.02% 0.18% 0.10% 0.40% 0.41% 0.24% 0.04% 0.33% 3.45% 32 0.11% 0.13% 0.11% 0.24% 0.22% 0.13% 0.37% 0.39% 0.01% 0.05% 0.15% 0.02% 0.15% 2.06% 33 0.32% 0.31% 0.30% 0.33% 0.26% 0.00% 0.06% 0.00% 0.32% 0.31% 0.30% 0.06% 0.29% 2.87% 34 0.33% 0.40% 0.42% 0.34% 0.29% 0.01% 0.06% 0.00% 0.44% 0.42% 0.31% 0.06% 0.34% 3.42% 35 0.11% 0.13% 0.11% 0.32% 0.22% 0.10% 0.25% 0.29% 0.07% 0.06% 0.25% 0.04% 0.17% 2.13% 36 0.17% 0.01% 0.05% 0.33% 0.25% 0.14% 0.12% 0.29% 0.16% 0.15% 0.29% 0.06% 0.21% 2.23% 37 0.21% 0.00% 0.00% 0.29% 0.18% 0.22% 0.18% 0.49% 0.03% 0.02% 0.25% 1.33% 0.17% 3.36% sum 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 7.69% 100.00% Stellenbosch University http://scholar.sun.ac.za 345 Annexure 62: Aggregated, unweighted scores of LBSs for BPA II Notes: IRR Internal Rate of Return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than biomass upgrading and conversion technologies OPEX-other Operational expenditure other than biomass upgrading and conversion technologies DECP I Direct Employment Creation Potential with income less than R8 000/month DECP II Direct Employment Creation Potential with income between R8 000 ? R24 000/month DECP III Direct Employment Creation Potential with income more than R24 000/month AP Acidification Potential EP Eutrophication Potential POCP Photochemical Ozone Creation Potential ADP Abiotic Depletion Potential GWP Global Warming Potential 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BPA II IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 346 Annexure 63: Aggregated, unweighted scores of LBSs for BPA III Notes: IRR Internal Rate of Return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than biomass upgrading and conversion technologies OPEX-other Operational expenditure other than biomass upgrading and conversion technologies DECP I Direct Employment Creation Potential with income less than R8 000/month DECP II Direct Employment Creation Potential with income between R8 000 ? R24 000/month DECP III Direct Employment Creation Potential with income more than R24 000/month AP Acidification Potential EP Eutrophication Potential POCP Photochemical Ozone Creation Potential ADP Abiotic Depletion Potential GWP Global Warming Potential 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BPA III IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 347 Annexure 64: Aggregated, unweighted scores of LBSs for BPA IV Notes: IRR Internal Rate of Return on capital investment CAPEX-conv.. Capital expenditure of biomass upgrading and conversion technologies OPEX-conv. Operational expenditure of biomass upgrading and conversion technologies CAPEX-other Capital expenditure other than biomass upgrading and conversion technologies OPEX-other Operational expenditure other than biomass upgrading and conversion technologies DECP I Direct Employment Creation Potential with income less than R8 000/month DECP II Direct Employment Creation Potential with income between R8 000 ? R24 000/month DECP III Direct Employment Creation Potential with income more than R24 000/month AP Acidification Potential EP Eutrophication Potential POCP Photochemical Ozone Creation Potential ADP Abiotic Depletion Potential GWP Global Warming Potential 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Lignocellulosic bioenergy systems in BPA IV IRR CAPEX-conv. OPEX-conv. CAPEX-other OPEX-other DECP I DECP II DECP III AP EP POCP ADP GWP Stellenbosch University http://scholar.sun.ac.za 348 Annexure 65: Participants of MCDA workshop Name Organisation/Company Position/background 1 Mr J. Buckle South African National Biodiversity Institute (SANBI) Freshwater programme: Eastern Cape Provincial coordinator 2 Mr R. Burger Department of Economics, Stellenbosch University Senior lecturer/labour economics, technical expert 3 Mrs G. Daniels Cape Winelands District Municipality Manager: local development 4 Mr G. Forsyth Council for Scientific and Industrial Research (CSIR)/ AHP expert, MCDA facilitator 4 Dr W. De Lange Council for Scientific and Industrial Research (CSIR)/Natural Resources and the Environment Unit Senior environmental economist/Specialist in MCDA 5 Prof J. Du Toit Institute of Futures Research (IFR), Stellenbosch University Former member of IFR, pensioner, energy expert 6 Ms H. Fourie Department of Agriculture: Western Cape Senior agricultural economist: resource economics 7 Mr C Goosen Department of Agriculture: Western Cape Agricultural economist: Production economics 8 Mr A. Greeff Standardbank Head of agriculture unit 9 Mr J.D. Kirsten Farmer and owner, entrepreneur 10 Prof TE Kleynhans Department of Agricultural Economics, Stellenbosch University Associate Professor, Resource Economist, Co- facilitator 11 Ms N. Peacock Cape Winelands District Municipality Assistant manager: local development 12 Mr T. Roos Rust en Vrede grape farm Farmer and owner 13 Dr A. Rozanov Department of Soil Science, Stellenbosch University Soil scientist, technical expert 14 Mr. D. Roussow Nedbank Head of agriculture unit 15 Mr C. Von Doderer Department of Agricultural Economics, Stellenbosch University PhD student, facilitator Stellenbosch University http://scholar.sun.ac.za 349 Annexure 66: Normalised, weighted scores for BPA I LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (25 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (25 yrs) OPEX (25 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 1.98% 0.27% 0.09% 0.04% 0.19% 0.22% 0.06% 0.00% 0.13% 0.28% 0.05% 0.07% 0.01% 3.29% 2 2.07% 0.35% 0.12% 0.05% 0.22% 0.23% 0.06% 0.00% 0.18% 0.41% 0.06% 0.08% 0.01% 3.54% 3 0.00% 0.12% 0.03% 0.02% 0.15% 0.55% 0.12% 0.08% 0.03% 0.02% 0.02% 0.05% 0.01% 1.48% 4 0.62% 0.01% 0.02% 0.03% 0.19% 0.64% 0.09% 0.08% 0.07% 0.11% 0.03% 0.07% 0.01% 2.10% 5 0.88% 0.00% 0.00% 0.01% 0.12% 0.90% 0.12% 0.13% 0.01% 0.31% 0.02% 0.06% 0.02% 2.60% 6 2.00% 0.27% 0.09% 0.04% 0.19% 0.06% 0.19% 0.03% 0.12% 0.28% 0.04% 0.06% 0.01% 3.28% 7 2.09% 0.35% 0.12% 0.05% 0.22% 0.09% 0.16% 0.03% 0.17% 0.41% 0.05% 0.07% 0.01% 3.53% 8 0.07% 0.12% 0.03% 0.02% 0.16% 0.31% 0.28% 0.10% 0.01% 0.01% 0.02% 0.04% 0.01% 1.47% 9 1.41% 0.27% 0.09% 0.03% 0.11% 0.83% 0.00% 0.00% 0.13% 0.28% 0.03% 0.05% 0.01% 3.13% 10 1.41% 0.35% 0.12% 0.04% 0.15% 0.75% 0.00% 0.00% 0.18% 0.40% 0.04% 0.06% 0.01% 3.23% 11 0.00% 0.12% 0.03% 0.02% 0.05% 1.41% 0.03% 0.08% 0.02% 0.00% 0.00% 0.02% 0.01% 2.10% 12 0.25% 0.01% 0.02% 0.02% 0.09% 1.37% 0.00% 0.08% 0.06% 0.10% 0.01% 0.04% 0.01% 2.24% 13 0.46% 0.00% 0.00% 0.00% 0.00% 1.88% 0.03% 0.13% 0.00% 0.30% 0.00% 0.02% 0.02% 2.88% 14 1.51% 0.27% 0.09% 0.03% 0.14% 0.64% 0.12% 0.03% 0.28% 0.27% 0.06% 0.09% 0.00% 3.25% 15 1.52% 0.35% 0.12% 0.04% 0.17% 0.58% 0.09% 0.03% 0.17% 0.40% 0.04% 0.05% 0.01% 3.30% 16 0.00% 0.12% 0.03% 0.02% 0.09% 1.13% 0.19% 0.10% 0.00% 0.00% 0.00% 0.00% 0.01% 2.00% 17 1.94% 0.27% 0.09% 0.03% 0.19% 0.17% 0.12% 0.00% 0.13% 0.28% 0.05% 0.08% 0.01% 3.27% 18 2.02% 0.35% 0.12% 0.04% 0.22% 0.18% 0.12% 0.00% 0.18% 0.41% 0.06% 0.08% 0.01% 3.51% 19 0.00% 0.12% 0.03% 0.01% 0.14% 0.47% 0.22% 0.08% 0.03% 0.01% 0.03% 0.06% 0.01% 1.50% 20 0.58% 0.01% 0.02% 0.02% 0.18% 0.57% 0.16% 0.08% 0.07% 0.11% 0.04% 0.08% 0.01% 2.06% 21 0.84% 0.00% 0.00% 0.00% 0.12% 0.81% 0.22% 0.13% 0.01% 0.31% 0.03% 0.07% 0.02% 2.58% 22 1.96% 0.27% 0.09% 0.04% 0.19% 0.01% 0.25% 0.03% 0.12% 0.28% 0.05% 0.06% 0.01% 3.25% 23 2.05% 0.35% 0.12% 0.04% 0.22% 0.04% 0.22% 0.03% 0.17% 0.41% 0.06% 0.07% 0.01% 3.50% 24 0.00% 0.12% 0.03% 0.02% 0.15% 0.23% 0.37% 0.10% 0.01% 0.01% 0.03% 0.04% 0.01% 1.43% 25 2.10% 0.27% 0.09% 0.04% 0.20% 0.23% 0.06% 0.00% 0.13% 0.28% 0.05% 0.07% 0.01% 3.44% 26 2.19% 0.35% 0.12% 0.05% 0.23% 0.23% 0.06% 0.00% 0.18% 0.40% 0.06% 0.08% 0.01% 3.69% 27 0.20% 0.12% 0.03% 0.03% 0.16% 0.55% 0.12% 0.08% 0.02% 0.00% 0.03% 0.06% 0.01% 1.72% 28 0.74% 0.01% 0.02% 0.04% 0.20% 0.64% 0.09% 0.08% 0.06% 0.10% 0.04% 0.07% 0.01% 2.26% 29 1.04% 0.00% 0.00% 0.02% 0.14% 0.90% 0.12% 0.13% 0.01% 0.30% 0.03% 0.06% 0.02% 2.80% 30 2.12% 0.27% 0.09% 0.05% 0.20% 0.06% 0.19% 0.03% 0.12% 0.28% 0.05% 0.06% 0.01% 3.42% 31 2.21% 0.35% 0.12% 0.05% 0.23% 0.09% 0.16% 0.03% 0.17% 0.40% 0.06% 0.07% 0.01% 3.66% 32 0.26% 0.12% 0.03% 0.04% 0.17% 0.32% 0.28% 0.10% 0.00% 0.00% 0.03% 0.04% 0.01% 1.70% 33 2.13% 0.27% 0.09% 0.05% 0.22% 0.00% 0.06% 0.00% 0.13% 0.29% 0.05% 0.08% 0.01% 3.27% 34 2.23% 0.35% 0.12% 0.06% 0.24% 0.03% 0.03% 0.00% 0.18% 0.41% 0.06% 0.09% 0.01% 3.51% 35 0.29% 0.12% 0.03% 0.04% 0.18% 0.22% 0.12% 0.08% 0.03% 0.02% 0.03% 0.07% 0.01% 1.52% 36 0.79% 0.01% 0.02% 0.05% 0.21% 0.35% 0.06% 0.08% 0.07% 0.12% 0.04% 0.08% 0.01% 2.02% 37 1.05% 0.00% 0.00% 0.03% 0.16% 0.52% 0.09% 0.13% 0.01% 0.32% 0.03% 0.08% 0.02% 2.45% Sum 43.03% 6.69% 2.23% 1.23% 6.17% 18.22% 4.70% 2.02% 3.38% 8.34% 1.37% 2.24% 0.37% 100.00% Stellenbosch University http://scholar.sun.ac.za 350 Annexure 67: Normalised, weighted scores for BPA II LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (27 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (27 yrs) OPEX (27 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 1.75% 0.27% 0.09% 0.03% 0.19% 0.26% 0.07% 0.00% 0.14% 0.33% 0.05% 0.07% 0.00% 3.26% 2 1.88% 0.35% 0.12% 0.04% 0.22% 0.24% 0.07% 0.00% 0.19% 0.46% 0.05% 0.08% 0.00% 3.71% 3 0.35% 0.12% 0.03% 0.01% 0.15% 0.57% 0.14% 0.08% 0.03% 0.06% 0.02% 0.06% 0.00% 1.63% 4 0.69% 0.01% 0.02% 0.02% 0.18% 0.64% 0.14% 0.08% 0.07% 0.16% 0.03% 0.07% 0.00% 2.11% 5 0.89% 0.00% 0.00% 0.01% 0.12% 0.89% 0.14% 0.13% 0.01% 0.02% 0.02% 0.05% 0.06% 2.34% 6 1.76% 0.27% 0.09% 0.03% 0.19% 0.05% 0.14% 0.03% 0.13% 0.33% 0.04% 0.06% 0.00% 3.14% 7 1.87% 0.35% 0.12% 0.04% 0.22% 0.07% 0.11% 0.03% 0.18% 0.45% 0.05% 0.07% 0.00% 3.56% 8 0.44% 0.12% 0.03% 0.02% 0.16% 0.28% 0.21% 0.10% 0.01% 0.06% 0.02% 0.04% 0.00% 1.50% 9 1.39% 0.27% 0.09% 0.04% 0.11% 0.87% 0.00% 0.00% 0.14% 0.32% 0.03% 0.05% 0.00% 3.31% 10 1.45% 0.35% 0.12% 0.04% 0.15% 0.77% 0.00% 0.00% 0.19% 0.45% 0.04% 0.06% 0.00% 3.62% 11 0.00% 0.12% 0.03% 0.03% 0.06% 1.44% 0.04% 0.08% 0.02% 0.05% 0.00% 0.02% 0.00% 1.88% 12 0.48% 0.01% 0.02% 0.04% 0.09% 1.38% 0.04% 0.08% 0.06% 0.15% 0.01% 0.04% 0.00% 2.39% 13 0.64% 0.00% 0.00% 0.03% 0.00% 1.87% 0.04% 0.13% 0.00% 0.00% 0.00% 0.01% 0.06% 2.78% 14 1.46% 0.27% 0.09% 0.04% 0.14% 0.63% 0.07% 0.03% 0.12% 0.32% 0.03% 0.03% 0.00% 3.24% 15 1.51% 0.35% 0.12% 0.05% 0.17% 0.57% 0.04% 0.03% 0.17% 0.44% 0.04% 0.04% 0.00% 3.54% 16 0.19% 0.12% 0.03% 0.04% 0.10% 1.10% 0.11% 0.10% 0.00% 0.05% 0.00% 0.00% 0.00% 1.83% 17 1.72% 0.27% 0.09% 0.02% 0.19% 0.20% 0.14% 0.00% 0.14% 0.33% 0.05% 0.08% 0.00% 3.23% 18 1.85% 0.35% 0.12% 0.03% 0.22% 0.20% 0.14% 0.00% 0.19% 0.45% 0.06% 0.09% 0.00% 3.70% 19 0.30% 0.12% 0.03% 0.01% 0.15% 0.50% 0.25% 0.08% 0.03% 0.06% 0.03% 0.07% 0.00% 1.61% 20 0.66% 0.01% 0.02% 0.01% 0.18% 0.57% 0.21% 0.08% 0.07% 0.16% 0.04% 0.08% 0.00% 2.08% 21 0.86% 0.00% 0.00% 0.00% 0.11% 0.81% 0.28% 0.13% 0.01% 0.01% 0.03% 0.07% 0.06% 2.37% 22 1.73% 0.27% 0.09% 0.03% 0.19% 0.00% 0.21% 0.03% 0.13% 0.33% 0.05% 0.07% 0.00% 3.12% 23 1.84% 0.35% 0.12% 0.03% 0.21% 0.02% 0.18% 0.03% 0.18% 0.45% 0.06% 0.07% 0.00% 3.55% 24 0.39% 0.12% 0.03% 0.02% 0.16% 0.21% 0.32% 0.10% 0.01% 0.06% 0.03% 0.05% 0.00% 1.49% 25 1.86% 0.27% 0.09% 0.04% 0.20% 0.26% 0.11% 0.00% 0.14% 0.32% 0.05% 0.08% 0.00% 3.41% 26 1.98% 0.35% 0.12% 0.04% 0.23% 0.25% 0.07% 0.00% 0.19% 0.45% 0.06% 0.08% 0.00% 3.83% 27 0.56% 0.12% 0.03% 0.03% 0.17% 0.57% 0.14% 0.08% 0.02% 0.05% 0.03% 0.06% 0.00% 1.86% 28 0.79% 0.01% 0.02% 0.04% 0.19% 0.64% 0.14% 0.08% 0.06% 0.15% 0.04% 0.07% 0.00% 2.23% 29 1.02% 0.00% 0.00% 0.02% 0.13% 0.89% 0.18% 0.13% 0.00% 0.00% 0.03% 0.06% 0.06% 2.53% 30 1.86% 0.27% 0.09% 0.04% 0.20% 0.06% 0.18% 0.03% 0.12% 0.32% 0.05% 0.06% 0.00% 3.28% 31 1.97% 0.35% 0.12% 0.05% 0.22% 0.07% 0.11% 0.03% 0.18% 0.45% 0.06% 0.07% 0.00% 3.67% 32 0.60% 0.12% 0.03% 0.04% 0.17% 0.29% 0.21% 0.10% 0.00% 0.05% 0.03% 0.04% 0.00% 1.68% 33 1.86% 0.27% 0.09% 0.05% 0.22% 0.01% 0.07% 0.00% 0.14% 0.33% 0.05% 0.09% 0.00% 3.18% 34 2.00% 0.35% 0.12% 0.05% 0.24% 0.02% 0.04% 0.00% 0.19% 0.46% 0.06% 0.09% 0.00% 3.62% 35 0.60% 0.12% 0.03% 0.05% 0.18% 0.21% 0.14% 0.08% 0.03% 0.07% 0.03% 0.07% 0.00% 1.62% 36 0.81% 0.01% 0.02% 0.05% 0.20% 0.32% 0.11% 0.08% 0.07% 0.16% 0.04% 0.08% 0.00% 1.96% 37 1.01% 0.00% 0.00% 0.04% 0.15% 0.48% 0.11% 0.13% 0.01% 0.02% 0.03% 0.08% 0.06% 2.13% Sum 43.03% 6.69% 2.23% 1.23% 6.17% 18.22% 4.70% 2.02% 3.38% 8.34% 1.37% 2.24% 0.37% 100.00% Stellenbosch University http://scholar.sun.ac.za 351 Annexure 68: Normalised, weighted scores for BPA III LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (30 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (30 yrs) OPEX (30 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 1.69% 0.27% 0.09% 0.03% 0.19% 0.24% 0.07% 0.00% 0.14% 0.33% 0.04% 0.02% 0.01% 3.14% 2 1.73% 0.35% 0.12% 0.04% 0.22% 0.23% 0.07% 0.00% 0.19% 0.45% 0.05% 0.02% 0.01% 3.48% 3 0.44% 0.12% 0.03% 0.01% 0.16% 0.56% 0.11% 0.08% 0.03% 0.07% 0.04% 0.02% 0.01% 1.66% 4 0.84% 0.01% 0.02% 0.02% 0.18% 0.60% 0.11% 0.08% 0.07% 0.16% 0.04% 0.02% 0.01% 2.16% 5 1.01% 0.00% 0.00% 0.01% 0.12% 0.88% 0.14% 0.13% 0.01% 0.02% 0.03% 0.37% 0.01% 2.72% 6 1.72% 0.27% 0.09% 0.04% 0.20% 0.05% 0.14% 0.03% 0.13% 0.33% 0.04% 0.02% 0.01% 3.06% 7 1.75% 0.35% 0.12% 0.04% 0.22% 0.06% 0.11% 0.03% 0.18% 0.45% 0.04% 0.02% 0.01% 3.38% 8 0.51% 0.12% 0.03% 0.02% 0.16% 0.30% 0.22% 0.10% 0.01% 0.07% 0.03% 0.02% 0.01% 1.59% 9 1.33% 0.27% 0.09% 0.04% 0.11% 0.90% 0.00% 0.00% 0.13% 0.32% 0.03% 0.01% 0.01% 3.24% 10 1.32% 0.35% 0.12% 0.04% 0.15% 0.79% 0.00% 0.00% 0.19% 0.44% 0.03% 0.02% 0.01% 3.46% 11 0.00% 0.12% 0.03% 0.03% 0.06% 1.49% 0.04% 0.08% 0.02% 0.05% 0.01% 0.01% 0.00% 1.94% 12 0.58% 0.01% 0.02% 0.04% 0.09% 1.40% 0.00% 0.08% 0.06% 0.15% 0.02% 0.01% 0.00% 2.45% 13 0.72% 0.00% 0.00% 0.02% 0.00% 1.93% 0.04% 0.13% 0.00% 0.00% 0.00% 0.36% 0.00% 3.20% 14 1.41% 0.27% 0.09% 0.04% 0.14% 0.68% 0.07% 0.03% 0.12% 0.32% 0.02% 0.02% 0.01% 3.20% 15 1.40% 0.35% 0.12% 0.05% 0.17% 0.59% 0.04% 0.03% 0.17% 0.44% 0.03% 0.02% 0.01% 3.41% 16 0.19% 0.12% 0.03% 0.04% 0.09% 1.18% 0.11% 0.10% 0.00% 0.05% 0.00% 0.01% 0.00% 1.91% 17 1.67% 0.27% 0.09% 0.03% 0.19% 0.18% 0.18% 0.00% 0.14% 0.33% 0.05% 0.01% 0.01% 3.14% 18 1.69% 0.35% 0.12% 0.03% 0.22% 0.18% 0.14% 0.00% 0.19% 0.45% 0.05% 0.01% 0.02% 3.45% 19 0.38% 0.12% 0.03% 0.01% 0.16% 0.48% 0.25% 0.08% 0.03% 0.06% 0.04% 0.00% 0.01% 1.65% 20 0.82% 0.01% 0.02% 0.01% 0.17% 0.53% 0.22% 0.08% 0.07% 0.16% 0.05% 0.01% 0.01% 2.15% 21 0.98% 0.00% 0.00% 0.00% 0.11% 0.79% 0.29% 0.13% 0.01% 0.01% 0.04% 0.35% 0.01% 2.73% 22 1.69% 0.27% 0.09% 0.03% 0.20% 0.00% 0.25% 0.03% 0.13% 0.33% 0.04% 0.01% 0.01% 3.08% 23 1.71% 0.35% 0.12% 0.03% 0.22% 0.01% 0.18% 0.03% 0.18% 0.45% 0.05% 0.02% 0.02% 3.36% 24 0.46% 0.12% 0.03% 0.02% 0.16% 0.22% 0.33% 0.10% 0.01% 0.06% 0.03% 0.01% 0.01% 1.55% 25 1.78% 0.27% 0.09% 0.04% 0.20% 0.24% 0.11% 0.00% 0.14% 0.32% 0.05% 0.01% 0.01% 3.25% 26 1.80% 0.35% 0.12% 0.04% 0.22% 0.23% 0.11% 0.00% 0.19% 0.45% 0.05% 0.01% 0.02% 3.58% 27 0.64% 0.12% 0.03% 0.03% 0.17% 0.56% 0.14% 0.08% 0.02% 0.05% 0.04% 0.00% 0.01% 1.89% 28 0.94% 0.01% 0.02% 0.04% 0.18% 0.60% 0.11% 0.08% 0.06% 0.15% 0.04% 0.01% 0.01% 2.25% 29 1.14% 0.00% 0.00% 0.02% 0.13% 0.88% 0.18% 0.13% 0.01% 0.00% 0.04% 0.35% 0.01% 2.88% 30 1.80% 0.27% 0.13% 0.04% 0.21% 0.05% 0.18% 0.03% 0.12% 0.32% 0.04% 0.01% 0.01% 3.22% 31 1.82% 0.35% 0.12% 0.05% 0.23% 0.06% 0.14% 0.03% 0.18% 0.45% 0.04% 0.01% 0.02% 3.49% 32 0.67% 0.12% 0.03% 0.04% 0.17% 0.30% 0.25% 0.10% 0.00% 0.05% 0.03% 0.00% 0.01% 1.78% 33 1.78% 0.27% 0.09% 0.05% 0.21% 0.00% 0.04% 0.00% 0.14% 0.33% 0.05% 0.02% 0.01% 3.01% 34 1.82% 0.35% 0.12% 0.06% 0.23% 0.02% 0.04% 0.00% 0.19% 0.46% 0.05% 0.02% 0.02% 3.38% 35 0.69% 0.12% 0.03% 0.05% 0.18% 0.23% 0.11% 0.08% 0.03% 0.07% 0.05% 0.02% 0.01% 1.66% 36 0.96% 0.01% 0.02% 0.05% 0.20% 0.31% 0.07% 0.08% 0.07% 0.16% 0.05% 0.02% 0.01% 2.02% 37 1.13% 0.00% 0.00% 0.04% 0.14% 0.49% 0.11% 0.13% 0.01% 0.02% 0.05% 0.37% 0.01% 2.51% Sum 43.03% 6.69% 2.23% 1.23% 6.17% 18.22% 4.70% 2.02% 3.38% 8.34% 1.37% 2.24% 0.37% 100.00% Stellenbosch University http://scholar.sun.ac.za 352 Annexure 69: Normalised, weighted scores for BPA IV LBS Financial-economic criteria Socio-economic criteria Environmental impact criteria Sum IRR (35 years) Cost of Conversion technology Cost other than conversion technology Direct Employment Creation Potential Local Impact Global Impact CAPEX (20 yrs) OPEX (20 yrs) CAPEX (35 yrs) OPEX (35 yrs) DECP I DECP II DECP III AP EP POCP ADP fossil GWP 100 years 1 1.73% 0.27% 0.09% 0.03% 0.19% 0.24% 0.08% 0.00% 0.14% 0.33% 0.05% 0.01% 0.01% 3.17% 2 1.74% 0.35% 0.12% 0.04% 0.22% 0.23% 0.08% 0.00% 0.19% 0.45% 0.05% 0.02% 0.02% 3.50% 3 0.38% 0.12% 0.03% 0.01% 0.16% 0.58% 0.15% 0.08% 0.03% 0.06% 0.03% 0.01% 0.01% 1.65% 4 0.85% 0.01% 0.02% 0.02% 0.18% 0.62% 0.11% 0.08% 0.07% 0.16% 0.04% 0.01% 0.01% 2.18% 5 1.03% 0.00% 0.00% 0.01% 0.12% 0.91% 0.15% 0.13% 0.01% 0.02% 0.03% 0.38% 0.01% 2.79% 6 1.74% 0.27% 0.09% 0.03% 0.20% 0.05% 0.15% 0.03% 0.13% 0.33% 0.04% 0.02% 0.01% 3.08% 7 1.76% 0.35% 0.12% 0.04% 0.22% 0.06% 0.11% 0.03% 0.18% 0.45% 0.04% 0.02% 0.01% 3.40% 8 0.48% 0.12% 0.03% 0.02% 0.17% 0.30% 0.23% 0.10% 0.01% 0.06% 0.02% 0.01% 0.01% 1.55% 9 1.35% 0.27% 0.09% 0.04% 0.12% 0.85% 0.00% 0.00% 0.14% 0.32% 0.03% 0.01% 0.01% 3.22% 10 1.32% 0.35% 0.12% 0.05% 0.15% 0.75% 0.00% 0.00% 0.19% 0.45% 0.03% 0.01% 0.01% 3.43% 11 0.00% 0.12% 0.03% 0.04% 0.06% 1.44% 0.04% 0.08% 0.02% 0.05% 0.01% 0.00% 0.00% 1.89% 12 0.55% 0.01% 0.02% 0.04% 0.09% 1.37% 0.04% 0.08% 0.06% 0.15% 0.02% 0.01% 0.00% 2.44% 13 0.70% 0.00% 0.00% 0.03% 0.00% 1.88% 0.04% 0.13% 0.00% 0.00% 0.01% 0.38% 0.00% 3.16% 14 1.42% 0.27% 0.09% 0.05% 0.14% 0.62% 0.08% 0.03% 0.12% 0.32% 0.02% 0.01% 0.01% 3.16% 15 1.39% 0.35% 0.12% 0.05% 0.17% 0.55% 0.04% 0.03% 0.18% 0.44% 0.03% 0.01% 0.01% 3.36% 16 0.17% 0.12% 0.03% 0.04% 0.09% 1.12% 0.11% 0.10% 0.00% 0.05% 0.00% 0.01% 0.00% 1.84% 17 1.71% 0.27% 0.09% 0.02% 0.19% 0.20% 0.15% 0.00% 0.14% 0.33% 0.05% 0.01% 0.01% 3.17% 18 1.72% 0.35% 0.12% 0.03% 0.22% 0.19% 0.15% 0.00% 0.19% 0.45% 0.05% 0.01% 0.02% 3.50% 19 0.32% 0.12% 0.03% 0.00% 0.15% 0.51% 0.26% 0.08% 0.03% 0.06% 0.04% 0.00% 0.01% 1.62% 20 0.82% 0.01% 0.02% 0.01% 0.18% 0.56% 0.19% 0.08% 0.07% 0.16% 0.05% 0.01% 0.01% 2.14% 21 1.00% 0.00% 0.00% 0.00% 0.12% 0.83% 0.30% 0.13% 0.01% 0.01% 0.04% 0.38% 0.01% 2.82% 22 1.72% 0.27% 0.09% 0.03% 0.19% 0.00% 0.23% 0.03% 0.13% 0.33% 0.04% 0.01% 0.01% 3.08% 23 1.74% 0.35% 0.12% 0.03% 0.22% 0.01% 0.19% 0.03% 0.18% 0.45% 0.05% 0.01% 0.02% 3.40% 24 0.43% 0.12% 0.03% 0.01% 0.16% 0.23% 0.34% 0.10% 0.01% 0.06% 0.03% 0.01% 0.01% 1.54% 25 1.80% 0.27% 0.09% 0.04% 0.20% 0.25% 0.08% 0.00% 0.14% 0.32% 0.05% 0.01% 0.01% 3.25% 26 1.81% 0.35% 0.12% 0.04% 0.23% 0.23% 0.08% 0.00% 0.19% 0.45% 0.05% 0.01% 0.02% 3.57% 27 0.57% 0.12% 0.03% 0.03% 0.16% 0.59% 0.15% 0.08% 0.02% 0.05% 0.04% 0.00% 0.01% 1.85% 28 0.94% 0.01% 0.02% 0.04% 0.19% 0.62% 0.11% 0.08% 0.06% 0.15% 0.04% 0.00% 0.01% 2.27% 29 1.15% 0.00% 0.00% 0.03% 0.13% 0.91% 0.19% 0.13% 0.00% 0.00% 0.03% 0.37% 0.01% 2.96% 30 1.82% 0.27% 0.09% 0.04% 0.20% 0.05% 0.15% 0.03% 0.12% 0.32% 0.04% 0.01% 0.01% 3.16% 31 1.83% 0.35% 0.12% 0.05% 0.23% 0.06% 0.11% 0.03% 0.18% 0.45% 0.04% 0.01% 0.02% 3.47% 32 0.63% 0.12% 0.03% 0.04% 0.17% 0.30% 0.23% 0.10% 0.00% 0.05% 0.03% 0.00% 0.01% 1.71% 33 1.82% 0.27% 0.09% 0.05% 0.21% 0.00% 0.04% 0.00% 0.14% 0.33% 0.05% 0.02% 0.01% 3.04% 34 1.83% 0.35% 0.12% 0.06% 0.23% 0.02% 0.04% 0.00% 0.19% 0.46% 0.06% 0.02% 0.02% 3.39% 35 0.63% 0.12% 0.03% 0.05% 0.18% 0.24% 0.15% 0.08% 0.03% 0.07% 0.05% 0.01% 0.01% 1.65% 36 0.97% 0.01% 0.02% 0.05% 0.20% 0.32% 0.08% 0.08% 0.07% 0.16% 0.05% 0.02% 0.01% 2.04% 37 1.15% 0.00% 0.00% 0.05% 0.14% 0.51% 0.11% 0.13% 0.01% 0.02% 0.05% 0.39% 0.01% 2.57% Sum 43.03% 6.69% 2.23% 1.23% 6.17% 18.22% 4.70% 2.02% 3.38% 8.34% 1.37% 2.24% 0.37% 100.00% Stellenbosch University http://scholar.sun.ac.za 353 Annexure 70: Comparison of the top-ten ranked LBSs across all four BPAs Paarl biomass procurement area Worcester biomass procurement area Ranking LBS1 relative weight of main criteria BCS5 HS6 Ranking LBS2 relative weight of main criteria BCS5 HS6 Fin.-Econ.2 Soc.-econ.3 Environ.4 Fin.-Econ.2 Soc.-econ.3 Environ.4 1 26 75 8 18 2 4 1 26 73 9 19 2 4 2 31 76 7 17 2 4 2 2 72 9 19 2 1 3 2 74 8 19 2 1 3 18 71 9 20 2 3 4 7 75 7 18 2 1 4 31 76 6 19 2 4 5 34 79 2 19 2 5 5 10 60 22 19 2 2 6 18 73 8 19 2 3 6 34 78 2 20 2 5 7 23 74 8 18 2 3 7 7 75 6 20 2 1 8 15 62 20 18 2 2 8 23 74 7 20 2 3 9 25 77 8 15 1 4 9 15 64 18 18 2 2 10 30 78 8 14 1 4 10 25 74 11 15 1 4 average: 74.3 8.4 17.5 1.8 3.1 average: 71.7 9.9 18.9 1.9 2.9 median: 75 8 18 2 3.5 median: 73.5 9 19 2 3 Ashton biomass procurement area Rural Cederberge biomass procurement area Ranking LBS1 relative weight of main criteria BCS5 HS6 Ranking LBS2 relative weight of main criteria BCS5 HS6 Fin.-Econ.2 Soc.-econ.3 Environ.4 Fin.-Econ.2 Soc.-econ.3 Environ.4 1 13 21 58 21 5 2 1 13 21 57 22 5 2 2 26 72 10 18 2 4 2 26 73 9 18 2 4 3 31 75 7 18 2 4 3 18 71 10 19 2 3 4 2 72 9 19 2 1 4 2 72 9 19 2 1 5 10 59 23 18 2 2 5 31 76 6 18 2 4 6 18 71 9 19 2 3 6 29 39 37 25 5 4 7 15 62 20 18 2 2 7 10 59 22 18 2 2 8 7 75 6 19 2 1 8 7 75 6 19 2 1 9 34 78 2 20 2 5 9 23 74 7 19 2 3 10 23 74 7 19 2 3 10 34 78 2 20 2 5 average: 65.9 15.1 18.9 2.3 2.7 average: 63.8 16.5 19.7 2.6 2.9 median: 72 9 19 2 2.5 median: 72.5 9 19 2 3 Notes: 1 Lignocellulosic bioenergy system 2 Financial-economic viability 3 Socio-economic potential 4 Least environmental impact 5 Bioenergy conversion system 6 Harvesting system Stellenbosch University http://scholar.sun.ac.za 354 Annexure 71: Maximisation of financial-economic main criterion LBS Biomass procurement areas Paarl Worcester Ashton R. Cederberge 1 4.333% 3.934% 3.833% 3.891% 2 4.727% 4.394% 4.130% 4.159% 3 0.523% 1.124% 1.278% 1.163% 4 1.415% 1.554% 1.803% 1.804% 5 1.720% 1.713% 1.916% 1.944% 6 4.368% 3.958% 3.893% 3.927% 7 4.773% 4.377% 4.172% 4.204% 8 0.668% 1.305% 1.428% 1.366% 9 3.210% 3.213% 3.101% 3.140% 10 3.479% 3.568% 3.343% 3.353% 11 0.362% 0.403% 0.406% 0.421% 12 0.638% 1.066% 1.225% 1.204% 13 0.804% 1.125% 1.245% 1.238% 14 3.441% 3.370% 3.282% 3.298% 15 3.720% 3.701% 3.508% 3.490% 16 0.433% 0.792% 0.778% 0.771% 17 4.259% 3.859% 3.773% 3.843% 18 4.642% 4.323% 4.060% 4.104% 19 0.508% 1.014% 1.173% 1.057% 20 1.348% 1.489% 1.739% 1.736% 21 1.644% 1.640% 1.849% 1.883% 22 4.298% 3.884% 3.833% 3.880% 23 4.685% 4.306% 4.103% 4.150% 24 0.537% 1.202% 1.329% 1.266% 25 4.564% 4.141% 3.999% 4.040% 26 4.952% 4.590% 4.272% 4.297% 27 0.909% 1.525% 1.661% 1.542% 28 1.672% 1.763% 2.002% 2.002% 29 2.038% 1.979% 2.172% 2.203% 30 4.602% 4.143% 4.128% 4.077% 31 4.998% 4.574% 4.314% 4.342% 32 1.029% 1.609% 1.736% 1.663% 33 4.646% 4.187% 4.054% 4.108% 34 5.052% 4.649% 4.349% 4.370% 35 1.111% 1.650% 1.801% 1.699% 36 1.782% 1.848% 2.090% 2.107% 37 2.108% 2.029% 2.221% 2.259% sum 100.000% 100.000% 100.000% 100.000% max 0.362% 0.403% 0.406% 0.421% average 5.052% 4.649% 4.349% 4.370% Stellenbosch University http://scholar.sun.ac.za 355 Annexure 72: Maximisation of socio-economic main criterion LBSs Biomass procurement areas Paarl Worcester Ashton R. Cederberge 1 1.135% 1.315% 1.259% 1.276% 2 1.163% 1.267% 1.193% 1.207% 3 2.994% 3.176% 2.989% 3.254% 4 3.229% 3.439% 3.165% 3.266% 5 4.638% 4.673% 4.616% 4.759% 6 1.100% 0.889% 0.902% 0.914% 7 1.086% 0.818% 0.780% 0.787% 8 2.779% 2.391% 2.469% 2.521% 9 3.322% 3.470% 3.590% 3.412% 10 3.018% 3.087% 3.150% 2.994% 11 6.080% 6.218% 6.444% 6.237% 12 5.817% 5.979% 5.924% 5.936% 13 8.168% 8.170% 8.389% 8.206% 14 3.149% 2.925% 3.102% 2.911% 15 2.802% 2.519% 2.626% 2.458% 16 5.671% 5.266% 5.581% 5.342% 17 1.191% 1.361% 1.451% 1.392% 18 1.219% 1.361% 1.285% 1.346% 19 3.063% 3.293% 3.238% 3.405% 20 3.202% 3.437% 3.314% 3.312% 21 4.653% 4.885% 4.822% 5.037% 22 1.128% 0.959% 1.117% 1.007% 23 1.142% 0.912% 0.871% 0.902% 24 2.848% 2.532% 2.595% 2.695% 25 1.163% 1.457% 1.404% 1.299% 26 1.191% 1.291% 1.338% 1.230% 27 3.021% 3.176% 3.134% 3.278% 28 3.257% 3.439% 3.165% 3.266% 29 4.638% 4.816% 4.761% 4.933% 30 1.100% 1.055% 1.047% 0.914% 31 1.086% 0.818% 0.924% 0.787% 32 2.806% 2.415% 2.614% 2.521% 33 0.250% 0.309% 0.145% 0.151% 34 0.236% 0.238% 0.211% 0.220% 35 1.692% 1.740% 1.645% 1.885% 36 1.969% 2.028% 1.831% 1.909% 37 2.991% 2.879% 2.908% 3.030% sum 100.000% 100.000% 100.000% 100.000% max 0.236% 0.238% 0.145% 0.151% average 8.168% 8.170% 8.389% 8.206% Stellenbosch University http://scholar.sun.ac.za 356 Annexure 73: Maximisation of least environmental impact main criterion LBS Biomass procurement areas Paarl Worcester Ashton R. Cederberge 1 3.281% 3.286% 3.039% 3.012% 2 4.374% 4.360% 4.014% 3.993% 3 0.826% 0.864% 0.883% 0.827% 4 1.776% 1.760% 1.700% 1.662% 5 3.682% 3.626% 5.112% 5.249% 6 3.141% 3.155% 2.920% 2.880% 7 4.254% 4.247% 3.912% 3.879% 8 0.627% 0.678% 0.714% 0.640% 9 2.869% 2.833% 2.607% 2.612% 10 4.016% 3.970% 3.640% 3.657% 11 0.238% 0.221% 0.270% 0.264% 12 1.279% 1.212% 1.193% 1.186% 13 3.022% 2.903% 4.447% 4.621% 14 2.717% 2.692% 2.473% 2.462% 15 3.884% 3.849% 3.524% 3.528% 16 0.021% 0.021% 0.079% 0.051% 17 3.387% 3.399% 3.037% 3.034% 18 4.465% 4.457% 4.012% 4.011% 19 0.976% 1.024% 0.880% 0.858% 20 1.904% 1.898% 1.697% 1.688% 21 3.856% 3.810% 5.121% 5.283% 22 3.243% 3.265% 2.918% 2.901% 23 4.334% 4.339% 3.908% 3.900% 24 0.769% 0.834% 0.711% 0.674% 25 3.293% 3.309% 2.935% 2.925% 26 4.385% 4.379% 3.925% 3.918% 27 0.843% 0.896% 0.735% 0.703% 28 1.791% 1.788% 1.573% 1.556% 29 3.706% 3.665% 4.957% 5.109% 30 3.153% 3.177% 2.819% 2.796% 31 4.256% 4.264% 3.822% 3.809% 32 0.640% 0.709% 0.571% 0.524% 33 3.441% 3.477% 3.211% 3.175% 34 4.504% 4.523% 4.160% 4.135% 35 1.049% 1.135% 1.127% 1.062% 36 2.007% 2.012% 1.926% 1.880% 37 3.992% 3.961% 5.423% 5.537% sum 100.000% 100.000% 100.000% 100.000% min 2.189% 0.021% 0.079% 0.051% max 3.085% 4.523% 5.423% 5.537% average 2.703% 2.703% 2.703% 2.703% Stellenbosch University http://scholar.sun.ac.za 357 Annexure 74: Ranking of LBSs based on maximised financial-economic criterion Ranking Biomass procurement area Paarl Worcester Ashton R. Cederberge LBS Weighted score LBS Weighted score LBS Weighted score LBS Weighted score 1 34 5.052% 34 4.649% 34 4.349% 34 4.370% 2 31 4.998% 26 4.590% 31 4.314% 31 4.342% 3 26 4.952% 31 4.574% 26 4.272% 26 4.297% 4 7 4.773% 2 4.394% 7 4.172% 7 4.204% 5 2 4.727% 7 4.377% 2 4.130% 2 4.159% 6 23 4.685% 18 4.323% 30 4.128% 23 4.150% 7 33 4.646% 23 4.306% 23 4.103% 33 4.108% 8 18 4.642% 33 4.187% 18 4.060% 18 4.104% 9 30 4.602% 30 4.143% 33 4.054% 30 4.077% 10 25 4.564% 25 4.141% 25 3.999% 25 4.040% 11 6 4.368% 6 3.958% 6 3.893% 6 3.927% 12 1 4.333% 1 3.934% 22 3.833% 1 3.891% 13 22 4.298% 22 3.884% 1 3.833% 22 3.880% 14 17 4.259% 17 3.859% 17 3.773% 17 3.843% 15 15 3.720% 15 3.701% 15 3.508% 15 3.490% 16 10 3.479% 10 3.568% 10 3.343% 10 3.353% 17 14 3.441% 14 3.370% 14 3.282% 14 3.298% 18 9 3.210% 9 3.213% 9 3.101% 9 3.140% 19 37 2.108% 37 2.029% 37 2.221% 37 2.259% 20 29 2.038% 29 1.979% 29 2.172% 29 2.203% 21 36 1.782% 36 1.848% 36 2.090% 36 2.107% 22 5 1.720% 28 1.763% 28 2.002% 28 2.002% 23 28 1.672% 5 1.713% 5 1.916% 5 1.944% 24 21 1.644% 35 1.650% 21 1.849% 21 1.883% 25 4 1.415% 21 1.640% 4 1.803% 4 1.804% 26 20 1.348% 32 1.609% 35 1.801% 20 1.736% 27 35 1.111% 4 1.554% 20 1.739% 35 1.699% 28 32 1.029% 27 1.525% 32 1.736% 32 1.663% 29 27 0.909% 20 1.489% 27 1.661% 27 1.542% 30 13 0.804% 8 1.305% 8 1.428% 8 1.366% 31 8 0.668% 24 1.202% 24 1.329% 24 1.266% 32 12 0.638% 13 1.125% 3 1.278% 13 1.238% 33 24 0.537% 3 1.124% 13 1.245% 12 1.204% 34 3 0.523% 12 1.066% 12 1.225% 3 1.163% 35 19 0.508% 19 1.014% 19 1.173% 19 1.057% 36 16 0.433% 16 0.792% 16 0.778% 16 0.771% 37 11 0.362% 11 0.403% 11 0.406% 11 0.421% Stellenbosch University http://scholar.sun.ac.za 358 Annexure 75: Ranking of LBSs based on maximised socio-economic criterion Ranking Biomass procurement area Paarl Worcester Ashton R. Cederberge LBS Weighted score LBS Weighted score LBS Weighted score LBS Weighted score 1 13 8.168% 13 8.170% 13 8.389% 13 8.206% 2 11 6.080% 11 6.218% 11 6.444% 11 6.237% 3 12 5.817% 12 5.979% 12 5.924% 12 5.936% 4 16 5.671% 16 5.266% 16 5.581% 16 5.342% 5 21 4.653% 21 4.885% 21 4.822% 21 5.037% 6 5 4.638% 29 4.816% 29 4.761% 29 4.933% 7 29 4.638% 5 4.673% 5 4.616% 5 4.759% 8 9 3.322% 9 3.470% 9 3.590% 9 3.412% 9 28 3.257% 4 3.439% 20 3.314% 19 3.405% 10 4 3.229% 28 3.439% 19 3.238% 20 3.312% 11 20 3.202% 20 3.437% 4 3.165% 27 3.278% 12 14 3.149% 19 3.293% 28 3.165% 4 3.266% 13 19 3.063% 3 3.176% 10 3.150% 28 3.266% 14 27 3.021% 27 3.176% 27 3.134% 3 3.254% 15 10 3.018% 10 3.087% 14 3.102% 37 3.030% 16 3 2.994% 14 2.925% 3 2.989% 10 2.994% 17 37 2.991% 37 2.879% 37 2.908% 14 2.911% 18 24 2.848% 24 2.532% 15 2.626% 24 2.695% 19 32 2.806% 15 2.519% 32 2.614% 8 2.521% 20 15 2.802% 32 2.415% 24 2.595% 32 2.521% 21 8 2.779% 8 2.391% 8 2.469% 15 2.458% 22 36 1.969% 36 2.028% 36 1.831% 36 1.909% 23 35 1.692% 35 1.740% 35 1.645% 35 1.885% 24 18 1.219% 25 1.457% 17 1.451% 17 1.392% 25 17 1.191% 17 1.361% 25 1.404% 18 1.346% 26 26 1.191% 18 1.361% 26 1.338% 25 1.299% 27 2 1.163% 1 1.315% 18 1.285% 1 1.276% 28 25 1.163% 26 1.291% 1 1.259% 26 1.230% 29 23 1.142% 2 1.267% 2 1.193% 2 1.207% 30 1 1.135% 30 1.055% 22 1.117% 22 1.007% 31 22 1.128% 22 0.959% 30 1.047% 6 0.914% 32 6 1.100% 23 0.912% 31 0.924% 30 0.914% 33 30 1.100% 6 0.889% 6 0.902% 23 0.902% 34 7 1.086% 7 0.818% 23 0.871% 7 0.787% 35 31 1.086% 31 0.818% 7 0.780% 31 0.787% 36 33 0.250% 33 0.309% 34 0.211% 34 0.220% 37 34 0.236% 34 0.238% 33 0.145% 33 0.151% Stellenbosch University http://scholar.sun.ac.za 359 Annexure 76: Ranking of LBSs based on maximised environmental impact criterion Ranking Biomass procurement area Paarl Worcester Ashton R. Cederberge LBS Weighted score LBS Weighted score LBS Weighted score LBS Weighted score 1 34 4.504% 34 4.523% 37 5.423% 37 5.537% 2 18 4.465% 18 4.457% 21 5.121% 21 5.283% 3 26 4.385% 26 4.379% 5 5.112% 5 5.249% 4 2 4.374% 2 4.360% 29 4.957% 29 5.109% 5 23 4.334% 23 4.339% 13 4.447% 13 4.621% 6 31 4.256% 31 4.264% 34 4.160% 34 4.135% 7 7 4.254% 7 4.247% 2 4.014% 18 4.011% 8 10 4.016% 10 3.970% 18 4.012% 2 3.993% 9 37 3.992% 37 3.961% 26 3.925% 26 3.918% 10 15 3.884% 15 3.849% 7 3.912% 23 3.900% 11 21 3.856% 21 3.810% 23 3.908% 7 3.879% 12 29 3.706% 29 3.665% 31 3.822% 31 3.809% 13 5 3.682% 5 3.626% 10 3.640% 10 3.657% 14 33 3.441% 33 3.477% 15 3.524% 15 3.528% 15 17 3.387% 17 3.399% 33 3.211% 33 3.175% 16 25 3.293% 25 3.309% 1 3.039% 17 3.034% 17 1 3.281% 1 3.286% 17 3.037% 1 3.012% 18 22 3.243% 22 3.265% 25 2.935% 25 2.925% 19 30 3.153% 30 3.177% 6 2.920% 22 2.901% 20 6 3.141% 6 3.155% 22 2.918% 6 2.880% 21 13 3.022% 13 2.903% 30 2.819% 30 2.796% 22 9 2.869% 9 2.833% 9 2.607% 9 2.612% 23 14 2.717% 14 2.692% 14 2.473% 14 2.462% 24 36 2.007% 36 2.012% 36 1.926% 36 1.880% 25 20 1.904% 20 1.898% 4 1.700% 20 1.688% 26 28 1.791% 28 1.788% 20 1.697% 4 1.662% 27 4 1.776% 4 1.760% 28 1.573% 28 1.556% 28 12 1.279% 12 1.212% 12 1.193% 12 1.186% 29 35 1.049% 35 1.135% 35 1.127% 35 1.062% 30 19 0.976% 19 1.024% 3 0.883% 19 0.858% 31 27 0.843% 27 0.896% 19 0.880% 3 0.827% 32 3 0.826% 3 0.864% 27 0.735% 27 0.703% 33 24 0.769% 24 0.834% 8 0.714% 24 0.674% 34 32 0.640% 32 0.709% 24 0.711% 8 0.640% 35 8 0.627% 8 0.678% 32 0.571% 32 0.524% 36 11 0.238% 11 0.221% 11 0.270% 11 0.264% 37 16 0.021% 16 0.021% 16 0.079% 16 0.051% Stellenbosch University http://scholar.sun.ac.za