Assessing GHG default emissions from biofuels in EU ...publications.jrc.ec.europa.eu/repository/bitstream/JRC76057/reqno... · Report EUR 25595 EN 2012 Robert Edwards Declan Mulligan

Report EUR 25595 EN

2012

Robert EdwardsDeclan Mulligan Jacopo Giuntoli Alessandro Agostini Aikaterini Boulamanti Renate Koeble Luisa Marelli Alberto Moro Monica Padella

Review of input database to calculate “Default GHG emissions”, following expert consultation 22-23 November 2011, Ispra (Italy)

Assessing GHG default emissions from biofuels in EU legislation

European Commission Joint Research Centre Institute for Energy and Transport Contact information Luisa Marelli Address: Joint Research Centre, Via Enrico Fermi 2749, TP 230, 21027 Ispra (VA), Italy E-mail: [email protected] Tel.: +39 0332 78 6332 Fax: +39 0332 78 5869 http://iet.jrc.ec.europa.eu/bf-ca/ http://iet.jrc.ec.europa.eu/alternative-fuels-alfa http://www.jrc.ec.europa.eu/ This publication is a Scientific and Policy Report by the Joint Research Centre of the European Commission. Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed.

A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu/. JRC76057 EUR 25595 EN ISBN 978-92-79-27363-6 (pdf) ISBN 978-92-79-27364-3 (print) ISSN 1831-9424 (online) ISSN 1018-5593 (print) doi: 10.2788/66442 Luxembourg: Publications Office of the European Union, 2012 © European Union, 2012 Reproduction is authorised provided the source is acknowledged.

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Contents PART ONE – INTRODUCTION AND EXPERT WORKSHOP.................................................. 1 1. Introduction............................................................................................................................. 1

1.1 Background........................................................................................................................... 1 1.2 Structure of the report. .......................................................................................................... 2

2. Consultation with experts ....................................................................................................... 4 2.1 Participants............................................................................................................................ 4 2.2 Structure of the meeting........................................................................................................ 5 2.3 Main outcomes of the discussions ........................................................................................ 6

PART TWO - GENERAL INPUT DATA AND COMMON PROCESSES ............................... 13 3. General input data for pathways ........................................................................................... 14

3.1. Fossil fuels provision ..................................................................................................... 14 3.2. Supply of process chemicals and pesticides .................................................................. 17 3.3. EU electricity mix .......................................................................................................... 32 3.4. N fertilizer manufacturing emissions calculation .......................................................... 42 3.5 Diesel, handling and storage, drying and plant protection use in cultivation. ................ 49

4. Soil emissions from biofuel crop cultivation........................................................................ 52 4.1 Background......................................................................................................................... 52 4.2 Pathways of N2O emission from managed soils ................................................................. 52 4.3 General approach to estimate soil N2O emissions from cultivation of potential biofuel crops.......................................................................................................................................... 53 4.4 Determining crop and site specific Fertilizer Induced Emissions (EF1i) ............................ 54 4.6 GNOC results and the JEC-WTW ...................................................................................... 59 4.7 Outlook for GNOC ............................................................................................................. 61 4.8. Manure calculation............................................................................................................. 64 4.9. Disaggregation between no-tillage and conventional agriculture data .............................. 64 4.10. Correction of IPCC method for estimating N2O emissions from leguminous crops....... 66 4.11 Comparison of the corrected IPCC method for soybean with site measurements............ 70 4.12. Emissions from acidification and liming methodology................................................... 72 4.13. Global crop specific calculation of CO2 emissions from agricultural lime application and fertilizer acidification................................................................................................................ 80 4.14 Lime application in the UK and Germany: Survey data vs. disaggregated country total lime consumption ...................................................................................................................... 87

5. Utilities & Auxiliary processes............................................................................................. 91 6. Transport processes............................................................................................................... 99

6.1 Road transportation............................................................................................................. 99 6.2 Maritime transportation .................................................................................................... 102 6.3 Inland water transportation: .............................................................................................. 109 6.4 Rail transportation............................................................................................................. 110 6.5 Pipeline transportation ...................................................................................................... 111

References for common input data ............................................................................................. 112 PART THREE – LIQUID BIOFUELS PROCESSES AND INPUT DATA............................. 117 7. Biofuels processes and input data ....................................................................................... 119

7.1 Wheat grain to Ethanol ..................................................................................................... 123

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7.2 Maize to Ethanol (made in EU) ........................................................................................ 128 7.3 Maize to Ethanol made in the US ..................................................................................... 133 7.4 Sugar beet to Ethanol ........................................................................................................ 138 7.5 Grain sorghum to Ethanol................................................................................................. 142 7.6 Sweet sorghum to Ethanol ................................................................................................ 145 7.7 Barley to Ethanol .............................................................................................................. 147 7.8 Sugarcane to ethanol ......................................................................................................... 152 7.9 Rye to Ethanol................................................................................................................... 158 7.10 Triticale to Ethanol ......................................................................................................... 163 7.11 Rapeseed to biodiesel...................................................................................................... 167 7.12 Sunflower to biodiesel .................................................................................................... 176 7.13 Soya to biodiesel ............................................................................................................. 182

A. No tillage and soybean import mix ................................................................................ 183 B. No-tillage and soya oil import mix ................................................................................ 193 C. Conventional agriculture and soybean import mix ........................................................ 201 D. Conventional agriculture and soya oil import mix......................................................... 204 E. National soy data ............................................................................................................ 208

7.14 Palm oil to biodiesel ....................................................................................................... 226 7.15 Jatropha to biodiesel ....................................................................................................... 234 7.16 Coconut to biodiesel ....................................................................................................... 241 7.17 Waste cooking oil to biodiesel........................................................................................ 244 7.18 Pure cottonseed oil.......................................................................................................... 246 7.19 Tallow oil ........................................................................................................................ 251 7.20 HVO................................................................................................................................ 253 7.21 Tall Oil ............................................................................................................................ 255 7.22 Black liquor..................................................................................................................... 258 7.23 Wood to FT diesel........................................................................................................... 266 7.24 Wood to Methanol .......................................................................................................... 268 7.25 Wood to DME................................................................................................................. 269 7.26 Organic waste to biogas .................................................................................................. 270

References for ethanol and biodiesel pathways .......................................................................... 271 PART FOUR – SOLID AND GASEOUS BIOFUELS PROCESSES AND INPUT DATA .... 281 8. Biogas processes and input data ......................................................................................... 282

8.1 Biogas from Maize............................................................................................................ 283 8.2 Biogas from Manure ......................................................................................................... 290 8.3 Co-Digestion..................................................................................................................... 296

9. Biomass and solid densified biomass pathways ................................................................. 298 9.1 Woodchips ........................................................................................................................ 304

A. Woodchips from forest residues (Pathway nr. 1)........................................................... 304 B. Woodchips from SRF (Pathway nr. 2) ........................................................................... 308 C. Woodchips from wood industry residues (Pathway nr. 3) ............................................. 311 D. Woodchips from roundwood (Pathway nr. 4)................................................................ 312

9.2 Pellets................................................................................................................................ 316 A. Pellets from forest residues and stumps (Pathway nr. 5) ............................................... 317 B. Pellets from SRF (Pathway nr. 6)................................................................................... 320 C. Pellets from wood industry residues (Pathway nr. 7)..................................................... 321

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D. Pellets from roundwood (Pathway nr. 8) ....................................................................... 322 9.3 Charcoal ............................................................................................................................ 323

A. Charcoal from forest residues (Pathway nr. 9) .............................................................. 323 B. Charcoal from SRF (Pathway nr. 10)............................................................................. 326

9.4 Other Raw Materials ......................................................................................................... 328 A. Agricultural residues with bulk density <0.2 tonne/m3 (Pathway nr. 11)...................... 328 B. Agricultural residues with bulk density >0.2 tonne/m3 (Pathway nr. 12) ...................... 328 C. Straw pellets (Pathway nr. 13) ....................................................................................... 330 D. Bagasse pellets / briquettes (Pathway nr. 14) ................................................................ 333 E. Miscanthus bales (Pathway nr. 15)................................................................................. 335 F. Palm kernel meal (Pathway nr. 16)................................................................................. 337

References for solid and gaseous biomass.................................................................................. 340 APPENDIX................................................................................................................................. 346 Appendix 1. Fuel/feedstock properties ....................................................................................... 347

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List of Tables Table 1: Emission factor: Diesel oil provision ............................................................................. 14 Table 2: Emission factor: Hard coal provision ............................................................................. 15 Table 3: Emission factor: Natural gas provision........................................................................... 15 Table 4: Emission factor: Heavy fuel oil provision ...................................................................... 16 Table 5: Supply of P2O5-fertilizer ............................................................................................... 17 Table 6: Supply of K2O-fertilizer................................................................................................. 18 Table 7: Supply of N-fertilizer...................................................................................................... 18 Table 8: Limestone mining ........................................................................................................... 19 Table 9: Limestone grinding and drying for the production of CaCO3 ....................................... 19 Table 10: CaO as a process chemical ........................................................................................... 20 Table 11: Supply of Hydrogen Chloride....................................................................................... 21 Table 12: Supply of Hydrogen via steam reforming of natural gas.............................................. 21 Table 13: Supply of Chlorine via membrane technology ............................................................. 22 Table 14: Supply of NaCl ............................................................................................................. 23 Table 15: Supply of Na2CO3 ....................................................................................................... 23 Table 16: Coke production from hard coal ................................................................................... 24 Table 17: Coke-oven gas combustion........................................................................................... 24 Table 18: Supply of NaOH ........................................................................................................... 24 Table 19: Supply of NH3.............................................................................................................. 25 Table 20: Supply of H2SO4.......................................................................................................... 25 Table 21 Supply of H3PO4........................................................................................................... 26 Table 22: Supply of cyclohexane.................................................................................................. 26 Table 23: Supply of lubricants...................................................................................................... 27 Table 24: Supply of alpha-amylase enzymes................................................................................ 27 Table 25: Supply of gluco-amylase enzymes ............................................................................... 27 Table 26: Supply of Sodium methoxide (CH3ONa)..................................................................... 28 Table 27: Supply of Sodium via molten-salt electrolysis ............................................................. 28 Table 28: Supply of methanol....................................................................................................... 28 Table 29: Supply of pesticides...................................................................................................... 29 Table 30: Emission factors for fossil fuels and main fertilizers. .................................................. 30 Table 31 – LHV adopted............................................................................................................... 32 Table 32 – Fuel consumption for electricity generation in 2009. ................................................. 34 Table 33 – Energy requirement and GHG emissions for the supply of fuels used for electricity generation...................................................................................................................................... 36 Table 34 – Primary energy consumption (including upstream) for a unit of gross electric energy production ..................................................................................................................................... 37 Table 35 – GHG emission factors – A.......................................................................................... 38 Table 36 – GHG emission factors – B .......................................................................................... 38 Table 37 – Electricity lost for own use of Power plants ............................................................... 39 Table 38 – Electricity lost for pumping ........................................................................................ 40 Table 39 – Electricity transmission losses in the High Voltage grid............................................ 41 Table 40 – Electricity distribution in the Medium Voltage grid................................................... 41

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Table 41 – Electricity distribution losses to Low Voltage (LV)................................................... 41 Table 42 Diesel use in cultivation derived from CAPRI data: ..................................................... 49 Table 43 CAPRI Handling & Storage and Drying data................................................................ 50 Table 44 CAPRI data on Primary Energy for inputs, used to convert CAPRI output to our input data................................................................................................................................................ 51 Table 45 Calculation of Pesticide use........................................................................................... 51 Table 46: Constant and effect values for calculating N2O emissions from agricultural fields after S&B............................................................................................................................................... 55 Table 47 Potential biofuel crops assignment to S&B vegetation classes ..................................... 55 Table 48 GNOC results................................................................................................................. 62 Table 49 Liming results ................................................................................................................ 78 Table 50: Limestone and dolomite consumption for the years 2000 as reported in the EDGARv4.1 database (EC-JRC/PBL) and share of limestone and dolomite applied to other land use/cover than cropland. ............................................................................................................... 83 Table 51 Lime application recommendations (Agricultural Lime Association, 2012). The values given are the amount of ground limestone (with a neutralizing value of 54 and 40% passing through a 150 micron mesh) required to achieve the target soil pH (explanation s ..................... 85 Table 52: Lime application at field level (DEFRA, 2001) and estimation of mean annual application rates on tillage crops in the year 2000 (explanation s. text)....................................... 88 Table 53: Lime application in the UK in the year 2000 based on this study................................ 89 Table 54: Process for a natural gas CHP to supply power and heat ............................................. 92 Table 55: Process for a natural gas boiler..................................................................................... 92 Table 56 H2 generation ................................................................................................................. 93 Table 57: Process for electricity production from a natural gas fuelled combined cycle (CCGT)....................................................................................................................................................... 93 Table 58 Process for a lignite fuelled CHP................................................................................... 93 Table 59: Process for a lignite fuelled power plant based on steam turbine technology.............. 94 Table 60: Process for a wood chips fuelled CHP ......................................................................... 95 Table 61: Process for a power plant fuelled with wood chips based on steam turbine technology....................................................................................................................................................... 96 Table 62: Process for an industrial wood pellet boiler ................................................................. 96 Table 63: Process for an industrial CHP based on ORC technology............................................ 97 Table 64: Process for an industrial wood pellet power plant based on steam turbine technology........................................................................................................................................................ 97 Table 65: Process for a light heating oil fuelled boiler ................................................................. 98 Table 66: Process for the energy consumption in an Ethanol or FAME depot ............................ 98 Table 67: Process for the energy consumption in a Ethanol / FAME filling station .................... 98 Table 68 Fuel consumption for a 40 t truck................................................................................ 100 Table 69 Fuel consumption for a 40 t truck, weighted average for sugar cane transport ........... 100 Table 70 Fuel consumption for a MB2213 dumpster truck used for filter mud cake................. 100 Table 71 Fuel consumption for a MB2318 truck used for seed cane transport .......................... 100 Table 72 Fuel consumption for a MB2318 Tanker truck used for vinasse transport ................. 101 Table 73 Fuel consumption for a 12 t truck................................................................................ 101 Table 74: Fuel consumption for a 20 t truck used for Jatropha seeds transport ......................... 102 Table 75 Fuel consumption for a bulk carrier for goods with bulk density > 0.6 t / m3............. 103 Table 76 Fuel consumption for a bulk carrier for wood chips.................................................... 103

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Table 77 Fuel consumption for a bulk carrier for agri-residues with bulk density of 0.125 t/m3103 Table 78 Fuel consumption for a bulk carrier for agricultural-residues with bulk density of 0.3 t/m3.............................................................................................................................................. 104 Table 79 Fuel consumption for a Panamax bulk carrier for goods with bulk density > 0.6 t / m3 (weight-limited load) .................................................................................................................. 104 Table 80 Fuel consumption for a product tanker for ethanol transport ...................................... 106 Table 81 Fuel consumption for a product tanker for FAME transport ....................................... 106 Table 82 Fuel consumption for a product tanker for pure vegetable oil transport ..................... 107 Table 83 Fuel consumption for a product tanker for ethanol transport from US ....................... 108 Table 84: Fuel consumption for a bulk carrier for inland navigation ......................................... 109 Table 85: Fuel consumption for an oil carrier barge for inland navigation ................................ 109 Table 86: Fuel consumption for a freight train run on diesel fuel (US situation)....................... 110 Table 87: Fuel consumption for a freight train run on grid electricity ....................................... 110 Table 88: Fuel consumption for the pipeline distribution of FAME (5 km) .............................. 111 Table 89 Cultivation of wheat..................................................................................................... 124 Table 90 Process for drying of wheat grain ................................................................................ 125 Table 91 Handling and storage of wheat grain ........................................................................... 125 Table 92 Transport of wheat grain via 40 t truck (payload 27t) over a distance of 100 km (one way)............................................................................................................................................. 125 Table 93 Conversion of wheat grain to ethanol .......................................................................... 126 Table 94 Transport of ethanol to depot via 40 t truck over a distance of 150 km ...................... 127 Table 95 Ethanol depot ............................................................................................................... 127 Table 96 Ethanol filling station................................................................................................... 127 Table 97 Cultivation of maize in the EU .................................................................................... 129 Table 98 Drying of maize ........................................................................................................... 130 Table 99 Handling and storage of maize .................................................................................... 130 Table 100 Transport of maize via 40 t truck over a distance of 100 km (one way) ................... 130 Table 101 Conversion of maize to ethanol ................................................................................. 131 Table 102 Conversion of maize to ethanol (DDGS for biogas).................................................. 132 Table 103 Corn cultivation in the US ......................................................................................... 134 Table 104 Transport of corn via 40 t truck over a distance of 56 km (one way distance).......... 134 Table 105 Conversion of corn to ethanol (average US ethanol plant)........................................ 135 Table 106 Transportation of ethanol summary table .................................................................. 136 Table 107 Transport of ethanol via train over a distance of 1000 km (Indiana to Baltimore) ... 136 Table 108 Transport of ethanol via ship (payload 50,000 t) over a distance of 6800 km (Baltimore to Rotterdam)............................................................................................................ 136 Table 109 Transport of ethanol via 40 t truck over a distance of 150 km (one way) ................. 137 Table 110 Sugar beet cultivation ................................................................................................ 139 Table 111 Handling and storage of sugar beet............................................................................ 140 Table 112 Transport of sugar beet via 40 t truck over a distance of 30 km (one way) .............. 140 Table 113 Conversion to ethanol with no biogas from slops (SBET1a) .................................... 140 Table 114 Conversion to ethanol with biogas from slops (SBET1b) ......................................... 141 Table 115 Grain sorghum cultivation ......................................................................................... 143 Table 116 Transport of grain sorghum via 40 t truck over a distance of 100 km (one way)...... 143 Table 117 Conversion of grain sorghum to ethanol.................................................................... 144 Table 118 Sweet sorghum cultivation......................................................................................... 145

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Table 119 Transport of sweet sorghum via a 40 t (payload 27t) truck over a distance of 20 km (one way) .................................................................................................................................... 145 Table 120 Conversion of sweet sorghum to ethanol................................................................... 146 Table 121 Barley cultivation....................................................................................................... 148 Table 122 Drying of barley......................................................................................................... 149 Table 123 Handling and storage of barley.................................................................................. 149 Table 124 Transport of barley grain via 40 t truck over a distance of 100 km (one way).......... 149 Table 125 Conversion of barley to ethanol................................................................................. 150 Table 126 Sugar cane cultivation................................................................................................ 153 Table 127 Transportation of sugarcane summary table.............................................................. 154 Table 128 Transport of mud cake via dumpster truck MB2213 over a distance of 8 km (one way)..................................................................................................................................................... 154 Table 129 Transport of seeding material via MB2318 truck over a distance of 20 km (one way)..................................................................................................................................................... 154 Table 130 Transport of sugar cane via 40 t truck over a distance of 20 km (one way).............. 154 Table 131 Transport of vinasse summary table .......................................................................... 154 Table 132 Transport of vinasse via a tanker truck MB2318 over a distance of 7 km (one way)155 Table 133 Transport of vinasse via a tanker truck with water cannons over a distance of 14 km (one way) .................................................................................................................................... 155 Table 134 Transport of vinasse via water channels .................................................................... 155 Table 135 Conversion of sugar cane to ethanol (no heat export) SCETc................................... 156 Table 136 Summary transport table of sugarcane ethanol.......................................................... 157 Table 137 Transport of ethanol via a 40 t truck a distance of 700 km (one way) ...................... 157 Table 138 Maritime transport of ethanol via ship over a distance of 10,186 km (one way) ...... 157 Table 139 Rye cultivation........................................................................................................... 159 Table 140 Drying of rye grain .................................................................................................... 160 Table 141 Handling and storage of rye grain.............................................................................. 160 Table 142 Transport of rye grain via 40 t (payload 27t) truck over a distance of 100 km (one way)..................................................................................................................................................... 160 Table 143 Conversion of rye grain to ethanol (same as wheat).................................................. 161 Table 144 Triticale cultivation.................................................................................................... 164 Table 145 Transport of triticale grain via 40 t truck over a distance of 100 km (one way) ....... 164 Table 146 Conversion of triticale grain to ethanol (same as wheat)........................................... 165 Table 147 Rapeseed cultivation .................................................................................................. 168 Table 148 Rapeseed drying......................................................................................................... 169 Table 149 Transportation of rapeseed summary table................................................................ 169 Table 150 Transport of rapeseed over a distance of 163 km via 40 tonne truck (one way) ....... 169 Table 151 Maritime transport of rapeseed via Panamax ocean bulk carrier over a distance of 5000 km (one way) ..................................................................................................................... 170 Table 152 Transport of rapeseed over a distance of 376 km via inland ship (one way) ............ 170 Table 153 Transport of rapeseed over a distance of 309 km via train (one way)....................... 171 Table 154 Oil mill: extraction of vegetable oil from rapeseed ................................................... 171 Table 155 LHV of US rapeseed.................................................................................................. 172 Table 156 LHV of European rapeseed........................................................................................ 172 Table 157 LHV of dry rapeseed cake ......................................................................................... 172 Table 158 Refining of vegetable oil............................................................................................ 173

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Table 159 Esterification.............................................................................................................. 173 Table 160 Transportation of FAME summary table to the blending depot................................ 174 Table 161 Transport of FAME via 40 t truck over a distance of 305 km (one way).................. 174 Table 162 Maritime transport of FAME over a distance of 1118 km (one way) ....................... 174 Table 163 Transport of FAME over a distance of 153 km via inland ship (one way) ............... 175 Table 164 Transport of FAME over a distance of 381 km via train (one way).......................... 175 Table 165 FAME depot .............................................................................................................. 175 Table 166 FAME filling station.................................................................................................. 175 Table 167 Sunflower cultivation................................................................................................ 177 Table 168 Sunflower drying ....................................................................................................... 178 Table 169 Transportation of sunflower seed summary table...................................................... 178 Table 170 Transport of sunflower seed over a distance of 292 km via truck (one way)............ 178 Table 171 Transport of sunflower seed over a distance of 450 km via train (one way)............. 178 Table 172 Oil mill - Extraction of vegetable oil from sunflower seed ....................................... 179 Table 173 LHV of US sunflower................................................................................................ 179 Table 174 LHV of European sunflower...................................................................................... 180 Table 175 LHV of dry sunflower cake ....................................................................................... 180 Table 176 Refining of vegetable oil............................................................................................ 181 Table 177 Winterization of sunflower ........................................................................................ 181 Table 178 Soybean cultivation under no tillage ......................................................................... 184 Table 179 Soy Biodiesel made in EU......................................................................................... 185 Table 180 Total soy biodiesel used in EU .................................................................................. 185 Table 181 National fractions of no-tillage soy production ......................................................... 185 Table 182 Average diesel use weighted for imported soybean .................................................. 186 Table 183 Pesticide use............................................................................................................... 186 Table 184 Drying to 13% water content ..................................................................................... 187 Table 185 Transport of soybeans via 40 t truck over a distance of 447 km (one way) .............. 187 Table 186 Regional truck transport distances............................................................................. 187 Table 187 Transport of soybeans via diesel train over a distance of 99 km (one way).............. 188 Table 188 Regional train transport distances.............................................................................. 188 Table 189 Transport of soybeans via inland ship over a distance of 382 km (one way)............ 188 Table 190 Maritime transport of soybeans via ship over a distance of 13171 km (one way) .... 189 Table 191 Regional shipping and barge distances to Rotterdam................................................ 189 Table 192 Pre-drying at oil mill.................................................................................................. 189 Table 193 Oil mill....................................................................................................................... 190 Table 194 Refining of vegetable oil............................................................................................ 191 Table 195 Transport of FAME to depot via 40 t truck over a distance of 150 km..................... 191 Table 196 Soybean cultivation under no tillage ......................................................................... 194 Table 197 Soy Biodiesel made in EU......................................................................................... 195 Table 198 Total soy biodiesel used in EU .................................................................................. 195 Table 199 National fractions of no-tillage soy production ......................................................... 195 Table 200 Average diesel use weighted for imported soybean .................................................. 196 Table 201 Pesticide use............................................................................................................... 196 Table 202 Drying at 13% water content ..................................................................................... 196 Table 203 Transport of soybeans via 40 t truck over a distance of 373 km (one way) .............. 197 Table 204 Regional truck transport distances............................................................................. 197

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Table 205 Transport of soybeans via diesel train over a distance of 61 km (one way).............. 197 Table 206 Regional train transport distances.............................................................................. 198 Table 207 Transport of soy oil via inland ship over a distance of 529 km (one way)................ 198 Table 208 Maritime transport of soy oil via ship over a distance of 12986 km (one way) ........ 199 Table 209 Regional shipping and barge distances for soy oil to Rotterdam .............................. 199 Table 210 Soybean cultivation under no tillage ......................................................................... 202 Table 211 National fractions of conventional agriculture of soybeans ...................................... 203 Table 212 Average diesel use weighted for imported soybean .................................................. 203 Table 213 Pesticide use............................................................................................................... 203 Table 214 Soybean cultivation under no tillage ......................................................................... 205 Table 215 National fractions of conventional agriculture of soybeans ...................................... 206 Table 216 Average Diesel use weighted for imported soy oil.................................................... 206 Table 217 Pesticide use............................................................................................................... 206 Table 218 Soybean cultivation in Brazil..................................................................................... 208 Table 219 Soybean drying .......................................................................................................... 209 Table 220 Weighted average of transport of soybeans from Central West and South to Brazilian seaport ......................................................................................................................................... 210 Table 221 Truck.......................................................................................................................... 210 Table 222 Train........................................................................................................................... 210 Table 223 Waterway ................................................................................................................... 210 Table 224 Shipping (+barge) distances to Rotterdam................................................................. 211 Table 225 Maritime transport of soybeans via ship over a distance of 12719 km (one way) .... 211 Table 226 Soybean cultivation under no tillage in Brazil........................................................... 212 Table 227 Soybean cultivation under conventional agriculture (tilled) in Brazil....................... 213 Table 228 Soybean cultivation in Argentina .............................................................................. 215 Table 229 Soybean drying .......................................................................................................... 216 Table 230 Truck transport of soybeans....................................................................................... 216 Table 231 Shipping and barge distances to rotterdam................................................................ 216 Table 232 Maritime transport of soybeans via ship over a distance of 14701 km (one way) .... 217 Table 233 Soybean cultivation in Argentina under no tillage .................................................... 217 Table 234 Soybean cultivation in Argentina under no tillage .................................................... 218 Table 235 Soybean cultivation in USA ...................................................................................... 220 Table 236 Soybean drying .......................................................................................................... 221 Table 237 Transport of soybeans via 40 t truck over a distance of 80 km (one way) ................ 221 Table 238 Transport of soybeans seed via inland ship over a distance of 2161 km (one way).. 222 Table 239 Maritime transport of soybeans via ship over a distance of 14701 km (one way) .... 223 Table 240 Soybean cultivation in US under no tillage ............................................................... 223 Table 241 Soybean cultivation in US under conventional agriculture ....................................... 225 Table 242 Cultivation of oil palm tree (16% peat) ..................................................................... 227 Table 243 Cultivation of oil palm tree (mineral soil) ................................................................. 227 Table 244 Transport of fresh fruit bunches via 12 t truck (payload 7t) over a distance of 50 km (one way) .................................................................................................................................... 228 Table 245 Storage of fresh fruit bunches.................................................................................... 228 Table 246 Plant oil extraction from fresh fruit bunches ............................................................. 229 Table 247 LHV of palm oil......................................................................................................... 230 Table 248 Transport of palm oil summary table......................................................................... 231

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Table 249 Transport of palm oil via a 40 t truck over a distance of 120 km (one way)............. 231 Table 250 Maritime transport of palm oil via ship over a distance of 10,186 km (one way)..... 231 Table 251 Refining of vegetable oil from oil palm..................................................................... 232 Table 252 Esterification.............................................................................................................. 232 Table 253 Transport of FAME via 40 t truck over a distance of 150 km................................... 233 Table 254 Cultivation of Jatropha seed / plantation – IFEU (mechanised cultivation).............. 235 Table 255 N2O from Jatropha cultivation calculation:............................................................... 236 Table 256 Transport System of "jatropha capsules" via 20 t truck (payload 10 t) over a distance of 190 km (one way) ................................................................................................................... 237 Table 257 Extraction of vegetable oil from Jatropha.................................................................. 237 Table 258 Conversion of Jatropha seed to plant oil via expeller (allocation by energy)............ 238 Table 259 Transport of Jatropha oil summary table ................................................................... 238 Table 260 Transport of Jatropha oil via a 40 t truck over a distance of 150 km (one way) ....... 238 Table 261 Maritime transport of jatropha oil via ship over a distance of 6,332 nautical miles (one way)............................................................................................................................................. 239 Table 262 Refining of vegetable oil from Jatropha .................................................................... 239 Table 263 Transport of FAME via 40 t truck over a distance of 150 km................................... 240 Table 264 Coconut cultivation (Indonesia)................................................................................. 241 Table 265 Transport of copra via 12 t truck (payload 6.35t) over a distance of 200 km (one way)..................................................................................................................................................... 242 Table 266 Extraction of vegetable oil from Coconut.................................................................. 242 Table 267 Transport of coconut oil via ship (payload 22,560 t) from port to biodiesel plant over a distance of 15,000 km (one way)................................................................................................ 243 Table 268 Transport of waste oil via 40 t truck over a distance of 100 km................................ 244 Table 269 Maritime transport of waste cookng oil via ship over a distance of 10,000 nautical miles (18 500 km) ....................................................................................................................... 244 Table 270 Transesterification of animal fat & used cooking oil to FAME ................................ 245 Table 271 By-products used for allocation................................................................................. 245 Table 272 Pure cottonseed oil cultivation................................................................................... 247 Table 273 Transport of cottonseed by 40 t truck to processing plant km (one way).................. 247 Table 274 Cottonseed and products composition (ref 5) ............................................................ 248 Table 275 LHV of cottonseed..................................................................................................... 248 Table 276 LHV of Cotton seed meal de-oiled and partly peeled ............................................... 248 Table 277 LHV of Cotton Lint ................................................................................................... 249 Table 278 Product ratios for allocation by wet-LHV at cotton oil mill...................................... 249 Table 279 Transport of cotton oil via 40 t truck over a distance of 50 km (one way)................ 250 Table 280 Animal fat processing from carcass (biodiesel)......................................................... 251 Table 281 Transport of carcass via 40 t truck over a distance of 30 km (one way) ................... 251 Table 282 Rendering................................................................................................................... 252 Table 283 Transport of tallow via 40 t truck over a distance of 150 km (one way)................... 252 Table 284 Hydrotreating of vegetable oil via NExBTL process (generation of a BTL like fuel):..................................................................................................................................................... 253 Table 285 Hydrotreating of palm oil via NExBTL process (generation of a BTL like fuel) ..... 253 Table 286 Transport of BTL like fuel via a 40 t truck over a distance of 150 km (one way) .... 254 Table 287 BTL depot .................................................................................................................. 254 Table 288 BTL filling station ..................................................................................................... 254

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Table 289 Cultivation of roundwood (mainly pine) ................................................................... 255 Table 290 Wood chipping........................................................................................................... 255 Table 291 Transport of wood chips via a 40 t truck over a distance of 50 km (one way).......... 255 Table 292 Crude Tall oil production (Liquid biofuel) ................................................................ 255 Table 293 Market pulp mill (Inputs)........................................................................................... 256 Table 294 Market pulp mil (Outputs) ......................................................................................... 256 Table 295 Tall oil results ............................................................................................................ 257 Table 296 Cultivation of roundwood (mainly pine) ................................................................... 258 Table 297 Wood chipping........................................................................................................... 258 Table 298 Transport of wood chips via a 40 t truck over a distance of 50 km (one way).......... 258 Table 299 Forestry residues collection including stump harvesting and chipping..................... 259 Table 300 Liquid biofuel production .......................................................................................... 259 Table 301 Black liquor gasification to methanol (Inputs) .......................................................... 260 Table 302 Black liquor gasification to methanol (Outputs)........................................................ 261 Table 303 Methanol results......................................................................................................... 261 Table 304 Black liquor gasification to DME (Inputs) ................................................................ 262 Table 305 Black liquor gasification to DME (Outputs).............................................................. 262 Table 306 DME results ............................................................................................................... 263 Table 307 Black liquor gasification to FT liquids (Inputs)......................................................... 264 Table 308 Black liquor gasification to FT liquids (Outputs)...................................................... 264 Table 309 FT diesel plant ........................................................................................................... 266 Table 310 Transport of FT diesel via a 40 t truck over a distance of 150 km (one way) ........... 266 Table 311 FT diesel depot........................................................................................................... 266 Table 312 FT diesel filling station .............................................................................................. 267 Table 313 methanol production (gasification, synthesis) ........................................................... 268 Table 314 Transport of methanol via a 40 t truck over a distance of 150 km (one way) ........... 268 Table 315 Methanol filling station.............................................................................................. 268 Table 316 DME production (gasification, synthesis) ................................................................. 269 Table 317 Transport of DME via a 40 t truck over a distance of 150 km (one way) ................. 269 Table 318 Methanol filling station.............................................................................................. 269 Table 319 Transport to the filling station via local (10 km) NG pipeline .................................. 270 Table 320 CNG filling station..................................................................................................... 270 Table 321: Process for cultivation of maize whole plant............................................................ 284 Table 322: UPDATED Transport distance for maize to biogas plant ........................................ 285 Table 323: UPDATED process for anaerobic digestion of maize whole plant .......................... 285 Table 324: Process for open tank storage of digestate from maize ............................................ 287 Table 325: Process for electricity generation via a biogas fuelled gas engine CHP................... 288 Table 326: Process for physical upgrading without combustion of the off-gas ......................... 289 Table 327: Process for chemical upgrading with combustion of the off-gas ............................. 289 Table 328: Transport distance for manure to biogas plant ......................................................... 291 Table 329: UPDATED Process for anaerobic digestion of manure ........................................... 291 Table 330: Process for open tank storage of digestate from manure.......................................... 292 Table 331: Transport scheme for all the solid biomass pathways. Distances are to final destination................................................................................................................................... 300 Table 332: Transportation scheme for wood chips pathways..................................................... 304 Table 333: Process for forest residues collection. ...................................................................... 305

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Table 334: Process for forest residues bundles seasoning at forest roadside. ............................ 305 Table 335: Process for wood chipping........................................................................................ 306 Table 336: Transport distances via a 40 t truck of woodchips to final destination .................... 307 Table 337: Transport distances via bulk carrier of woodchips to final destination .................... 307 Table 338: Transport distances via freight train of woodchips to port ....................................... 307 Table 339: Process for Cultivation of Eucalyptus ...................................................................... 309 Table 340: Process for wood chipping........................................................................................ 310 Table 341: Transport of wood chips from roadside to terminal ................................................. 311 Table 342: Storage and seasoning of wood chips at terminal..................................................... 311 Table 343: Process for cultivation and harvesting of roundwood .............................................. 313 Table 344: Process for seasoning of roundwood at central terminal .......................................... 314 Table 345: Transportation scheme for pellets pathways............................................................. 316 Table 346: Transport distance via a 40 t truck for wood chips to pellet mill ............................. 317 Table 346: Transport distance via a 40 t truck for wood pellets to final destination.................. 318 Table 347: Transport distance via a bulk carrier for wood pellets to final destination .............. 318 Table 348: Transport distance via a freight train for wood pellets to port ................................. 318 Table 350: UPDATED Process for the production of pellets from fresh woodchips................. 319 Table 351: Process for the production of pellets from a mix of wet and dry residues ............... 321 Table 352: Process for log splitter .............................................................................................. 324 Table 353: Process for the production of charcoal from wood logs. .......................................... 325 Table 354: Transport distances via a 40 t truck for charcoal to final destination....................... 325 Table 355: Transport distance via a bulk carrier for charcoal to final destination ..................... 325 Table 356: Process for agri-residues pre processing................................................................... 329 Table 357: Transport distances via a 40 t truck of agri-residues to final destination ................. 329 Table 358: Transport distances via a bulk carrier of agri-residues to final destination.............. 329 Table 359: Transport distance via a freight train of agri-residues to port .................................. 329 Table 360: Process for the production of pellets from straw bales............................................. 330 Table 361: Transport distances via a 40 t ttruck of straw bales to pellet mill ............................ 331 Table 362: Transport distances via a 40 t truck for straw pellets to final destination or port..... 331 Table 363: Transport distances via a bulk carrier for straw pellets to final destination ............. 331 Table 364: Transport distances via a freight train for straw pellets to port ................................ 331 Table 365: Process for bagasse CHP. ......................................................................................... 333 Table 366: Process for electricity generation by bagasse steam turbine .................................... 334 Table 367: Transport distances via a 40 t truck for bagasse pellets / briquettes to final destination..................................................................................................................................................... 334 Table 368: Transport distances via a bulk carrier for bagasse pellets / briquettes to final destination................................................................................................................................... 334 Table 369: Process for the cultivation of Miscanthus................................................................. 335 Table 370: Process for the production of Miscanthus bales ....................................................... 336 Table 371: Allocation to co-products of palm oil extraction from FFB..................................... 338 Table 372: FFB cultivation emissions allocated by energy to all co-products. .......................... 338 Table 373: Transport of PKM via a 40 t truck over 700 km....................................................... 339 Table 374: Maritime transport of PKM via a bulk carrier over 13000 km................................. 339

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Glossary AOAC Association of Official Analytical Chemists CA Conventional Agriculture CAPRI Common Agricultural Policy Regional Impact Analysis CENBIO Centro Nacional de Referência em Biomassa CHP - Combined Heat and Power DDGS Dried Distillers Grains with Solubles DG CLIMA Directorate-General for Climate Action DG ENER Directorate-General for Energy DNDC - DeNitrification DeComposition model EDGAR Emission Database for Global Atmospheric Research ENTSO European Network of Transmission System Operators for Electricity EPA US Environmental Protection Agency ETS Emissions Trading Scheme FAO Food and Agriculture Organization of the United Nations FIE - Fertilizer Induced Emissions GEMIS Globales Emissions-Modell Integrierter Systeme GHG Greenhouse Gases GNOC Global crop and site specific Nitrous Oxide emission Calculator GREET Greenhouse Gases, Regulated Emissions, and Energy use in Transportation HVO Hydrotreated Vegetable Oil IEA International Energy Agency IFA International Fertilizer Organisation IFEU Institute for Energy and Environmental Research IMO International Maritime Organization JEC JRC-EUCAR-CONCAWE consortium JRC European Commission Joint Research Centre JRC IET JRC Institute of Energy and Transport JRC IES JRC Institute for the Environment and Sustainability LCA Life Cycle Assessment LHV Lower Heating Value LPG Liquefied Petroleum Gas NG Natural-Gas NT No-Tillage NUTS - Nomenclature of Territorial Units for Statistics POME Palm Oil Milling Effluent RED Renewable Energy Directive WTT Well-To-Tank report WTW Well to Wheels

1

PART ONE – INTRODUCTION AND EXPERT WORKSHOP

1. Introduction

1.1 Background

The EU legislation contains a set of mandatory targets specific for the EU transport sector which aims at achieving the overall objective of a European sustainably fuelled transport system. In particular, Directives 2009/28/EC Renewable Energy Directive (RED) and 2009/30/EC Fuel Quality Directive (FQD) fix a threshold of 35% savings of GHG emissions for biofuels and bioliquids and set out the rules for calculating the greenhouse impact of biofuels, bioliquids and their fossil fuels comparators. Further, the Commission is working on a report and possible legislative proposal that could have similar or the same provisions for other bioenergy sources (biogas and biomass). To help economic operators in calculating GHG emission savings, default and typical values are also listed in the Annexes of the RED and FQD directives. These were calculated on the basis of the JEC (JRC-EUCAR-CONCAWE consortium) database of input data1 following a consultation process with stakeholders launched by the Commission. According to article 19.7 in the RED and 7d.7 in the FQD, the Commission can also adapt default values to technical and scientific progress by updating the existing values and by adding additional pathways. On request of the European Commission’s Directorate-General for Energy (DG ENER), the JRC has worked on the update of the existing input database, and the current list of pathways in the Directives are expected to be modified accordingly. This report aims at describing the JRC assumptions made to construct the update dataset used for the calculation of default GHG emissions for the different pathways in annexes of the Directives.

1 version of 31 July 2008

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1.2 Structure of the report. Within the first part of this report, chapter 2 describes the outcomes of the expert workshop held in November 2011 and highlights the changes made to the pathways related to those outcomes. The second part (Chapters 3, 4, 5 and 6) describes the data that are used in numerous pathways. These data include: • Fossil fuel provision. • Supply of chemical fertilizers, pesticides and process chemicals. • EU electricity mix. • Soil Nitrous Oxide (N2O) emissions from biofuel crop cultivation. • Auxiliary plant processes (such as a natural gas boiler) • Fuel consumption for different means of transportation. • Diesel, handling and storage, drying and plant protection use in cultivation The third part (Chapter 7) describes the specific input data used in the processes that make up the liquid biofuel pathways. The pathways also identify which common data are used. The fourth part (Chapters 8) describes the specific input data used in the processes that make up the biogas processes input data and biomass and solid densified biomass pathways.

3

4

2. Consultation with experts In order to guarantee both transparency and the use of the most updated scientific information and data, the JRC consulted with recognised selected experts on the best way to assess the input data used for calculating default GHG emissions, the processes for future updates, and to discuss and resolve methodological issues which may arise. This expert consultation, organised by the JRC’s Institute of Energy and Transport (IET) at JRC Ispra on 22nd and 23rd November 2011, had the following objectives: • Discuss input data used in the latest JRC calculations of default values for biofuels, bioliquids, biomass and biogas (to be updated in Annexes of the relevant directives). The aim of these discussions was to collect inputs and comments from the experts on the data presented, to verify the quality of the data and that the data sources were the most updated available. • Discuss the need of standardization activities and of harmonization of the used conversion factors and input values for GHG calculations. The main outcomes of the discussions with the experts are summarised in Section 2.3.

2.1 Participants - 15 invited experts on Life Cycle Assessment (LCA) and Greenhouse Gas (GHG) emissions of biofuels from various EU scientific institutions, National Ministers or regulatory boards. - In addition, following their request of participation, several representatives of industries were also invited (EBB2, ePure, Malaysian Palm Oil Board, Novozymes, BP Biofuels, Neste Oil, Shell). UNICA (Brazilian Sugar cane Association) also expressed an interest to participate, but they did not follow up our invitation. - DG ENER and the Directorate-General for Climate Action (DG CLIMA) - 8 JRC experts, from both JRC Petten and JRC Ispra

2 EBB, European Biodiesel Board ePure, The European renewable ethanol industry MPOB Malaysian Palm Oil Board,

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2.2 Structure of the meeting To facilitate the discussions and to help experts in preparing their comments for the meeting, the input data for all solid, gaseous and liquid biofuels prepared by the JRC and used for GHG calculations were distributed one week prior to the meeting. The presentation of the data followed the following structure: 1. General overview of input data common for all pathways: fossil fuel comparator and crude mixes, transport processes, chemicals and fertilizers. Fuel properties (e.g. Lower Heating Values (LHV), yield, moisture content) were distributed in advance and not discussed again during the meeting 2. Presentation of biogas pathways input data, resulting from the combination of two feedstocks (manure and maize), two outputs (biomethane and electricity) and two groups of upgrading technologies. 3. Presentation of biomass pathways input data: 13 pathways from several feedstocks (e.g. forest or industrial residues, short rotation forestry, roundwood, agricultural residues etc.) through different process chains (used for power and heat production) were discussed. 4. Presentation of new JRC methodology for calculation of global N2O emissions from cultivation, developed in collaboration with the “Climate Change Unit” of JRC’s Institute for the Environment and Sustainability (IES). 5. Presentation of liquid biofuels input data. These included the update of existing input data (e.g. rapeseed, soybean, palm oil, sugar and cereal crops) and the development of new pathways.

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2.3 Main outcomes of the discussions General issues The main issues raised at the workshop are described below (JRC responses are shown in italics):

- JRC values for flaring emissions are increasing in this new set of data compared to the previous version (from the Well to Wheels (WTW) report version 2 – 2008 dataset), while flaring emissions are observed to decrease in the recent years. However, the reason for this “apparent” increase in JRC data is a more detailed and precise methodology to calculate flaring emissions which caused the numbers to increase.

- It was suggested to use differentiated emission factors for fossil fuels based on different crude oil mixes for different world regions (instead of using the common EU Averaged value), making use of e.g. US Environmental Protection Agency (EPA) or International Energy Agency (IEA) inventory databases. JRC will investigate if this is feasible.

- Shipping emissions: the JRC considered that the return journey of the means of transport was empty, but it was argued that the return trip is often used to transport other goods. However, this may apply to container ships, not to the chemical tankers or grain carriers because these are specialist ships which will have trouble to find a suitable export commodity from EU for a return journey. Updated ship data based on International Maritime Organization (IMO) data has been used for crop, vegetable oil and ethanol shipping. Sugar cane ethanol, palm oil and soya have also been adjusted.

- JRC is using as a source for many input data Öko Institute’s3 Globales Emissions-Modell Integrierter Systeme (GEMIS) database v. 4.5 and 4.6. More updated versions are now available (4.7 released in September 2011 and 4.8 expected for December 2011). New GEMIS data has been taken into account and updated in the relevant pathways. GEMIS 4.8 was not available at the time of the updates4.

3 http://www.oeko.de 4 This was last checked in October 2012

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- Bonn University’s Common Agricultural Policy Regional Impact Analysis (CAPRI) database provide a number of relevant input data for EU cultivation processes; in particular data on diesel use that may be useful to supplement the JRC dataset. CAPRI data on diesel use in cultivation, drying and pesticide use has been included in many of the pathways.

- It was proposed to the JRC to create and make available a specific database for the emissions deriving from the production of fertilizers in use (not only Ammonium Nitrate and urea), built up on International Fertilizer Association (IFA) data. - Fertilizers: if producers can claim emissions from a specific fertilizer factory, these may have already been “traded away” under ETS. Nevertheless, it is allowed according to DG ENER. - More information desirable on sources of EU fertilizer imports. - The JRC was asked to clarify how LHV data for feedstocks (e.g. Wood, Dried Distillers Grains with Solubles (DDGS), etc.) are calculated.

Table of LHV values included in this report.

- Hydrotreated Vegetable Oil (HVO) fuel properties were not included in the database distributed at the workshop. HVO data was subsequently distributed and is included in this report.

Comments on Biogas pathways - Transport distances of wet manure and silage maize have to be checked and if necessary updated (additional data will be provided by Öko Institute) - A new pathway on “Biogas from grass” should be added to the list, because this might be relevant as grass is more and more often used in co-digestion.

The JRC will consider including this pathway. Supporting data might be provided by the University of Cork.

- Data for the digestion process need to be verified and improved. In particular they should be differentiated for the different feedstocks. - Concerns were raised about the Directive’s indication of NOT considering emissions from the fuel in use; this would affect in particular the emissions of methane from biogas engines. The JRC already raised this issue in a note recently sent to DG ENER.

8

Updated and included in this report. Comments on biomass pathways

- The Swedish Ministry of Energy commented that the consumption of Electricity in the pellet mill used by the JRC appears to be too high, and offered to provide data from Swedish industries. - It was suggested to consider also the need and use of additives in pellets. However, for the current market of pellets from wood, this is unlikely to be necessary. - Eucalyptus pathway: JRC values for yields and N-fertilizer input need to be checked against additional literature data. - Diesel consumption for roundwood logging: JRC used data from Sweden, but it was argued that the numbers could be higher for operations in Germany or other parts of the EU. Additional data (e.g. reports from the University of Hamburg) can be provided to the JRC. - Charcoal is more relevant for technology purposes rather than power and heat. The current data on charcoal production may reflect small-scale non-industrial production, but more efficient processes would be used if the fuel were to be exported for energy purposes. It was thus argued that, since this pathway is not used for power and heat in Europe, it should not be considered. - Suggestion was raised to provide a pathway for torrefied biomass and in particular pelletized torrefied biomass. The JRC will consider this. - Data on straw pellet production are based on GEMIS and are relative to small-scale potential use, but not to commercial or real operations. A comment was raised that transport distances of straw bales (to pellet mill) could be shortened. The JRC will evaluate this issue if relevant data will be made available. - Bagasse pellets pathways need to be checked: the feed to bagasse CHP are the raw bagasse and not the pellets as inadvertently reported in JRC slide. - Miscanthus: GEMIS will remove its Miscanthus data from version 4.8 because they were “potential” values rather than “real values”. Therefore, there is the need to update the literature sources to get more reliable data for this pathway; otherwise it should be removed for the moment.

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Comments on calculation of global N2O emissions - The approach to calculate soil N2O emissions got positive feedback, especially the transparency of the methodology and the obtained results. However, it has to be stressed that the Stehfest & Bouwman statistical approach allows the calculation of soil N2O emission for crop groups only, the individual biofuel crops have to be assigned to the corresponding group.

New chapter on N2O methodology included in this report Corrected N2O calculation and liming emissions.

Comments on liquid biofuel pathways Biodiesel: - It was argued that emissions attributed to methanol input, should be excused the 40% “conservatism factor” in biodiesel processing emissions , since the amount of methanol is fixed stoichiometry, and will not vary from plant to plant. However, the emissions associated with different processes for methanol production can vary greatly. - A Greenpeace report analyses sources of soy biodiesel in EU, useful for making a weighted average of EU suppliers.

The Greenpeace report does not say anything about sources of oils, only the mix of oils used in different EU countries.

- More up-to-date data on Brazilian soy-biodiesel cultivation and processing can be obtained from Centro Nacional de Referência em Biomassa (CENBIO) or Campinas University. More up-to-date data was found and used, but not from these sources (these data are described on the soybean pathways). - Misprint in soy winterization yield

Corrected. - Operational data now available for NESTE5 HVO process, and other Swedish HVO process. The experts have agreed to provide JRC with these data.

These data has been included in the update 5 http://www.nesteoil.com/

10

- Institute for Energy and Environmental Research (IFEU) should be able to provide new data on biodiesel from Jatropha seeds. IFEU data on mechanized pathway in Tanzania taken into account as this gives the most realistic yields.

- The representative of NESTE OIL offered to provide data concerning a possible new pathway for biodiesel from Camelina. Neste provided a mix of future sources. JRC have not prepared a Camelina pathway but a note on Camelina is given in Chapter 7.

- EBB offered to provide data concerning soy bean crushing. No data received from EBB. Argentina and US crushing data used by JRC, See soybean pathway for more details. Palm oil:

- The Malaysian Palm Oil Board (MPOB) said there was no decomposition of palm fruit before processing as it is carried out within 24h in Malaysia. - Another point is whether empty fruit bunches might form methane when used as a mulch. - NO DATA TO INDICATE THESE EMISSIONS. - Suggestion to separate palm-kernel-oil processing from palm oil process (easy by allocation to the kernels). - Various palm oil data received in paper published by MPOB staff. Especially diesel use needs to be checked. - Methane capture from palm oil crushing effluent is only ~85% effective and only few oil mills in Malaysia are actually yet equipped with such technology. Cottonseed oil: More data are now available and representatives from EBB offered to provide them to JRC. Animal fat: This needs to be specified whether the new default pathway applies only to category .3 animal fats. Cat. 1 and 2 should be classified as residues according to RED annex V.

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Ethanol pathways - - Natural-Gas combined heat and power (NG-CHP) process for “steam” should maybe refer to “heat” output- This needs to be checked.

- Electricity and steam use data in ethanol process need to be checked: latest processes may be better by 10-15%. - Summary of comparison could be included in the JEC’s Well-To-Tank (WTT) report. - No straw-fired ethanol plants in EU, but in Sweden they are fired by wood chips.

Straw fired ethanol plants replaced by wood chip fired plants

- Argonne National Laboratory in the US made updated survey of fuel used in US corn-ethanol plant. - We should not confuse Argonne National Laboratory’s Californian “Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation” (GREET) data with US-average GREET data, for which there was an update in Sept 2011.

Changes included in updated US corn (maize) to ethanol pathway - There was a more detailed data in review of US dry mill ethanol production by Steffen Müller (2008).

The US corn to ethanol pathway has been updated using data from the Müller paper and GREET 2011. (See Section 7.3 in this report)

- Transportation of corn-ethanol to EU is suggested to happen by barge via the Mississippi river rather than by train to Baltimore as it is considered at present. This needs to be checked in JRC pathways. Update: The general trend is still for trains to east coast of the United states (See Section 7.3 in this report).

- It was noted that in Brazil, limestone (CaCO3) is used, not Calcium oxide (CaO). Note: possible confusion here: JRC’s “CaO for fertilizer” is ~85% limestone, and only has about 10% of the emissions of CaO as a process chemical.

12

CaCO3 has been updated in all liquid biofuel pathways. - Sweet sorghum -ethanol data from Thailand can be provided by IFEU. There is also data on cultivation trials in Spain. However, at the moment there seems to be no use of ethanol from sweet sorghum in EU.

Insufficient new data to update the pathway.

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PART TWO - GENERAL INPUT DATA AND COMMON PROCESSES

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3. General input data for pathways This section includes the processes with the input data used for the production and supply of fossil fuels and for the European electricity mix. The total emission factor for the whole supply chain is indicated in the comments of the tables and summarized in Table 30. 3.1. Fossil fuels provision Diesel oil provision:

• Based on JEC-WTW v4 (as yet unpublished) data, with higher flaring emissions estimate. • Includes marginal refinery emissions for making a bit more diesel. These are higher than the average emissions for all petroleum product slate. • Based on existing EU crude mix • Current “marginal crude” would have lower upstream emissions (as excess production capacity only exists in middle east) • For simplicity, EU figures are taken for all the world

Table 1: Emission factor: Diesel oil provision

I/O Unit Amount

Diesel oil Output MJ 1

Emissions

CO2 Output g/MJ 14.30

CH4 Output g/MJ 0.04

N2O Output g/MJ 0.00003

Comments: - The total emission factor for the supply of 1 MJ of diesel oil is: 15.4 gCO2 eq/MJ. - The emission factor for combustion of 1 MJ of diesel oil is: 73.3 gCO2 eq/MJ. - The emissions from diesel and gasoline are considered to be the same and they are the result of a weighted average between 2/3 of emissions from diesel and 1/3 from gasoline.

Source: To be completed

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Hard Coal Provision Table 2: Emission factor: Hard coal provision

I/O Unit Amount

Hard coal Output MJ 1

Emissions




Comments: - The total emission factor for the supply of 1 MJ of hard coal is: 16.1 gCO2 eq/MJ. - The emission factor for combustion of 1 MJ of hard coal is: 96.1 gCO2 eq/MJ.

Source: To be completed Natural Gas Provision Table 3: Emission factor: Natural gas provision

I/O Unit Amount

Natural gas Output MJ 1

Emissions




Comments: - The total emission factor for the supply of 1 MJ of natural gas is: 12.75 gCO2 eq/MJ. - The emission factor for combustion of 1 MJ of natural gas is: 55.0 gCO2 eq/MJ.


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Heavy Fuel Oil Provision Table 4: Emission factor: Heavy fuel oil provision

I/O Unit Amount

Heavy Fuel Oil Output MJ 1

Emissions


Comments: - The total emission factor for the supply of 1 MJ of heavy fuel oil is: 12.75 gCO2 eq/MJ. - The emission factor for combustion of 1 MJ of heavy fuel oil is: 80.56 gCO2 eq/MJ.


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3.2. Supply of process chemicals and pesticides This section includes the processes with the input data used for the production and supply of various chemicals, fertilizers and pesticides used in the pathways. Many processes are linked in a “supply chain” in order to provide the final product. However, for ease of use, the total emission factor for the whole supply chain is indicated in the comments of the tables. 3.2.1 Chemical Fertilizers Phosphorus pentoxide (P2O5) fertilizer supply Table 5: Supply of P2O5-fertilizer

I/O Unit Amount

Hard Coal Input MJ/kg 0.57

Diesel Oil Input MJ/kg 1.12

Electricity Input MJ/kg 1.602

Heavy Fuel Oil (1.8% S)

Input MJ/kg 5.00

NG Input MJ/kg 3.15

P2O5-fertilizer Output kg 1.0

Emissions

CO2 - g/kg 700

CH4 - g/kg 0.023

N2O - g/kg 0.042

Comment: - The total emission factor including upstream emissions to produce 1 kg of P2O5 fertilizer is: 1029.85 gCO2 eq./kgP2O5.

Source: 1 Kaltschmitt and Reinhardt, 1997.

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Potassium oxide (K2O) fertilizer supply Table 6: Supply of K2O-fertilizer

I/O Unit Amount Comment

Diesel Oil Input MJ/kg 0.54 To be connected with diesel supply

Electricity Input MJ/kg 0.22 To be connected with electricity supply

NG Input MJ/kg 7.5 Tot be connected with NG supply

K2O-fertilizer Output kg 1.0 To be connected with diesel supply

Emissions

CO2 - g/kg 453

CH4 - g/kg 0.021

N2O - g/kg 0.0094

Comments: - The total emission factor including upstream emissions to produce 1 kg of K2O fertilizer is: 590.0 gCO2 eq./kgK2O. - K2O fertilizer production and transport

Source: 1 Kaltschmitt and Reinhardt, 1997. Nitrogen (N) fertilizer supply chain. Table 7: Supply of N-fertilizer

I/O Unit Amount

N-fertilizer Output kg 1.0

Emissions

CO2 - g/kg 3794

CH4 - g/kg 7.93

N2O - g/kg 7.315

Comments: - The total emission factor including upstream emissions to produce 1 kg of N fertilizer is: 6172.12 gCO2 eq./kgN. - This process represents a Mix of imported fertilizer and fertilizer produced inside the EU. Mix of Ammonium nitrate and urea.

Source: 1 Edwards, R., JRC own calculations, 2011.

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Limestone (Aglime - CaCO3) supply chain. The supply chain for the provision of aglime fertilizer includes the processes for the mining, grinding and drying of limestone. The results are quoted per kg of CaO in the CaCO3 even though the product is ground limestone. At one time limestone was converted to CaO by strong heating (calcining), using fuel. But now~90% of aglime is ground limestone (or dolmite), and even the small amount of CaO which is used on soil comes from industrial processes which produce it as a by-product.

Table 8: Limestone mining

I/O Unit Amount

Diesel Input MJ/kg 0.0297


Limestone Output kg 1

Source: 1 Gemis v. 4.7, 2011. “Xtra-quarrying\limestone-DE-2010” Table 9: Limestone grinding and drying for the production of CaCO3

I/O Unit Amount

Limestone Input kg/kg 1


CaCO3 Output kg 1

Source: 1 Gemis v. 4.7, 2011. “Nonmetallic minerals\CaCO3-powder-DE-2000” Since the aglime (CaCO3) inputs to cultivation processes are quoted in terms of the CaO content (“Calcium fertilizer as CaO”) of the limestone, the inputs per kg CaO areincreased by the molecular weight ratio CaCO3/CaO = 1.7848.

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3.2.2 Chemicals Calcium oxide (CaO) as a process chemical (NOT aglime) Table 10: CaO as a process chemical

I/O Unit Amount

Natural gas Input MJ/kg 4.63

Hydro power Input MJ/kg 0.02

Wind power Input MJ/kg 0.00

Crude oil Input MJ/kg 0.26

Hard coal Input MJ/kg 0.10

Nuclear fuel Input MJ/kg 0.20

Lignite Input MJ/kg 0.03

Biomass Input MJ/kg 0.00

Waste Input MJ/kg 0.03

Geothermal Input MJ/kg 0.00

CaO Output kg 1.0

Emissions

CO2 - g/kg 1048

CH4 - g/kg 0.86

N2O - g/kg 0.007

Comment: - This is pure CaO as a process chemical, not agricultural lime

Source: 1 GEMIS, version 4.5.0.0

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Hydrogen chloride (HCI) supply chain. Table 11: Supply of Hydrogen Chloride

I/O Unit Amount

Chlorine Input kg/kg 0.9724


H2 Input kg/kg 0.0276

HCl Output kg 1.0

Comment: - The total emission factor for the supply of 1 kg of HCl is 979.46 gCO2 eq/kg.

Source: 1 Ecoinvent v. 2.2 (2007), Ecoinvent report nr. 8 Table 12: Supply of Hydrogen via steam reforming of natural gas

I/O Unit Amount

NG Input kg/kg 3.3994

Electricity Output MJ/kg 6

H2 Output kg 1

Emissions

CO2 Output g/kg 9325

CH4 Output g/kg 1.9

Source: 1 Scholz, W 1992 2 Pehnt, M, 2002

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Table 13: Supply of Chlorine via membrane technology

I/O Unit Amount

Heat (from NG boiler)

Input MJ/kg 0.27


Na2CO3 Input kg/kg 0.022

NaCl Input kg/kg 0.855

H2 Output kg/kg 0.028

Chlorine Output kg 1

Source: Gemis v. 4.7, 2011. “chem.-inorg\chlorine(membrane)-DE-2010”

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Sodium chloride (NaCI) supply chain. Table 14: Supply of NaCl

I/O Unit Amount

Heat (from NG boiler)

Input MJ/kg 0.00324


Diesel Input kg/kg 0.0127

NaCl Output kg 1

Source: Gemis v. 4.7, 2011. “Xtra-mining\sodium chloride-DE-2000” Sodium carbonate (Na2CO3) supply chain.

Table 15: Supply of Na2CO3

I/O Unit Amount


NG Input MJ/kg 1.094

Coal Input MJ/kg 7.94

Coke Input MJ/kg 2.25

CaCO3 Input kg/kg 1.13

Na2CO3 Output kg 1

Emissions

CO2 Output g/kg 976

Source: Gemis v. 4.7, 2011. “chem.-inorganic\sodium carbonate-DE-2000”

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Table 16: Coke production from hard coal

I/O Unit Amount

Hard Coal Input MJ/MJ 1.43

Electricity Input MJ/MJ 0.005

Heat (from coke-oven gas)

Input MJ/MJ 0.266

Coke Output MJ 1

Heat Output MJ/MJ 0.114

Emissions


Source: Gemis v. 4.7, 2011. “conversion\coke-DE-2000” Table 17: Coke-oven gas combustion

I/O Unit Amount

Coke-oven gas Input MJ/MJ 1.1765

Heat (from coke-oven gas)

Output MJ 1

Emissions




Source: Gemis v. 4.7, 2011. “conversion\coke-DE-2000” Sodium hydroxide (NaOH) supply chain. Table 18: Supply of NaOH


Heat (from NG boiler) Input MJ/kg 0.240

Na2CO3 Input kg/kg 0.02



NaOH Output kg 1.0

Comment: • The total emission factor for the supply of 1 kg of NaOH is 501.04 gCO2 eq/kg.

25

• For the upstream processes for provision of NaCl, Na2CO3 see Table 18 and Table 19 •

Source: Gemis v. 4.7, 2011. “chem.-inorg\NaOH (membrane)-DE-2000” Ammonia (NH3) supply chain. Table 19: Supply of NH3.

I/O Unit Amount

Natural gas Input MJ/kg 36.54

NH3 Output kg 1.0

Emissions

CO2 - g/kg 2010

Comment: • For the supply of natural gas see Table 3. • Pers. Comm. from Fertilizers Europe (former EFMA) - Antoine Hoxha - 23 Dec 2010 -data covers Fertilizers Europe members only

Sulpuric acid (H2SO4) supply chain. Table 20: Supply of H2SO4

I/O Unit Amount


NG (for S mining) Input MJ/kg 1.638

S Input kg/kg 0.327

H2SO4 Output kg 1.0

Emissions

CO2 Output g/kg 92.4

Comment: The total emission factor for the supply of 1 kg of H2SO4 is 216.2 gCO2 eq/kg. Source: Frischknecht, R. et al., 1996

26

Phosphoric acid (H3PO4) supply chain. Table 21 Supply of H3PO4.

I/O Unit Amount

Natural gas Input MJ/kg -2.39

Hydro power Input MJ/kg 0.03

Wind power Input MJ/kg 0.00

Crude oil Input MJ/kg 5.74

Hard coal Input MJ/kg 24.83

Nuclear fuel Input MJ/kg 0.26

Lignite Input MJ/kg 0.19

Biomass Input MJ/kg 0.00

Waste Input MJ/kg 0.01

Geothermal Input MJ/kg 0.00

H3PO4 Output kg 1.0

Emissions

CO2 Output g/kg 2785.47

CH4 Output g/kg 8.98

N2O Output g/kg 0.103

Comment: • The total emission factor for the supply of 1 kg of H3PO4 is 3040.6 gCO2 eq/kg.

Source: GEMIS, version 4.7, 2011. “chem.-inorg\phosphoric acid-DE-2000” Cyclohexane (C6H12) supply chain. Table 22: Supply of cyclohexane

I/O Unit Amount Crude oil Input MJ/kg 9.90 Crude oil (mass)

Input kg/kg 1.00

Cyclohexane Output kg 1.0 Emissions

CO2 - g/kg 723

Source: Macedo et al., 2004.

27

Lubricants supply chain. Table 23: Supply of lubricants

I/O Unit Amount Crude oil Input MJ/kg 53.28 Lubricants Output kg 1.0

Emissions CO2 - g/kg 947

Source: Köhler et al., 1996 Alpha-amylase supply chain. Table 24: Supply of alpha-amylase enzymes

I/O Unit Amount Natural gas Input MJ/kg 15.00 Alpha-amylase Output kg 1.0 Emissions CO2 - g/kg 1000

Comment: Assumption: primary energy mainly consists of NG. No upstream emissions necessary. Source: MacLean and Spatari 2009 Gluco-amylase supply chain. Table 25: Supply of gluco-amylase enzymes

I/O Unit Amount Crude oil Input MJ/kg 97.00 Gluco-amylase Output kg 1.0 Emissions CO2 - g/kg 7500

Comment: Assumption: primary energy mainly consists of crude oil Source: MacLean and Spatari 2009

28

Sodium methoxide (CH3ONa) supply chain. Table 26: Supply of Sodium methoxide (CH3ONa)

I/O Unit Amount

Methanol Input kg/kg 0.5931

Na Input kg/kg 0.4256


Sodium Methoxide Output kg 1.0

Comments: • For the supply of Na and methanol see Table 30 and Table 31. • The total emission factor for the supply of 1 kg of sodium methoxide is: 2207.95 gCO2 eq./kg.

Source: 1 Du Pont, 2008. Table 27: Supply of Sodium via molten-salt electrolysis

I/O Unit Amount



Chlorine Output kg/kg 1.5421

Na Output kg 1.0

Comments: • For the supply of NaCl see Table 18.

Source: Table 28: Supply of methanol

I/O Unit Amount

NG Input kg/kg 0.5838

Air-O2 Input kg/kg 0.8309

Methanol Output kg 1.0

Source: Larsen, 1998.

29

Pesticides supply chain. Table 29: Supply of pesticides

I/O Unit Amount

Hard Coal Input MJ/kg 7.62

Diesel Oil Input MJ/kg 58.1


Heavy Fuel Oil (1.8% S)

Input MJ/kg 32.5

NG Input MJ/kg 71.4

Pesticides Output kg 1.0

Emissions

CO2 - g/kg 4921

CH4 - g/kg 0.18

N2O - g/kg 1.51

Source: Kaltschmitt, 1997. Comment:

• The total emission factor including upstream emissions to produce 1 kg of pesticides is: 11377.3 gCO2 eq./kg.

30

3.2.3 Summary of Emission factors for the supply of main products: For ease of reference, Table 30 summarizes the emission factors for various fossil fuels provision and fertilizers supply. Table 30: Emission factors for fossil fuels and main fertilizers. Net GHG

emitted (g CO2eq/MJ biofuel)

CO2 CH4 N2O

Best est. g/MJ g/MJ g/MJ Natural gas Supply 12.75 7.70 0.20 0.0003 Combustion 55.0 55.0 Total 67.8 62.7 0.20 0.000 EU electricity mix (0.4 kV) Supply 144.10 134.8 0.30 0.006 Use 0.0 0.0 0.00 0.000 Total 144.1 134.8 0.30 0.006 EU electricity mix (MV) Supply 140.61 131.5 0.29 0.006 Use 0.0 0.0 0.00 0.000 Total 140.6 131.5 0.29 0.006 Hard coal Supply 16.1 6.4722 0.3818 0.0003 Combustion 96.1 96.1 Total 112.2 102.6 0.38 0.000 Lignite Supply 1.7 1.7 0.00 0.000 Combustion 115.0 115.0 Total 116.7 116.7 0.00 0.000 Fuel oil Supply 6.6 6.64 0 0 Combustion 80.6 80.56 0 0 Total 87.2 87.2 0.00 0.000 Diesel Supply 15.40 14.30 0.04 0.00003 Combustion 73.33 73.3 0.00 0.00 Total 88.7 87.6 0.04 0.000 N-fertilizer g CO2 eq. / kg Supply 6172.12 3794.00 7.930 7.315 P2O5-fertilizer g CO2 eq. / kg

31

Supply 1029.85 980.07 1.370 0.052 K2O-fertilizer g CO2 eq. / kg Supply 590.0 546.22 1.599 0.013 Aglime (as CaO) g CO2 eq. / kg Supply 66.46 62.88 0.112 0.003 Pesticides g CO2 eq. / kg Supply 11372.2 10169.73 27.906 1.694 HCl g CO2 eq. / kg Supply 979.46 932.9814 1.4621 0.0333 NaOH g CO2 eq. / kg Supply 501.04 469.5623 0.961 0.025 H2S g CO2 eq. / kg Supply 216.19 201.2626 0.5411 0.0047 H3PO4 g CO2 eq. / kg Supply 3040.60 2785.469 8.9788 0.1029 Na(CH3O) g CO2 eq. / kg Supply 2207.95 2064.1049 4.7214 0.0866 Pure CaO as process chemical g CO2 eq. / kg Supply 1048 0.86 0.007

32

3.3. EU electricity mix In order to calculate the GHG emissions from electric energy supplied in the European Union, the general WTW approach has been used (JEC, 2011) and the following specific procedure has been adopted. The electric energy mix considered in this document is referring to the EU27 Member States and to the year 2009. 3.3.1 Emissions from gross production of electric energy For each of the EU27 Member States, the consumptions of primary energy in input of power plants have been considered (see Figure 1). This has been done for every kind of fuel used for producing electric energy (e.g. Hard coal, Natural Gas, etc.), both for normal electric power plants and for CHP plants.

Figure 1 – Relation among primary energy and Gross electric energy production In order to quantify the fuel consumed in input and the gross electric energy produced in output of power plants (in year 2009) statistical data from IEA – have been used (IEA, 2011). Whenever the amount of fuel consumption was available not in terms of energy but in weight, a conversion has been done by using LHV values. The LHV values adopted, described in the WTT report v3c Appendix 1 (JEC 2011), are recalled in the Table 31 below. Table 31 – LHV adopted.

LHV [MJ/kg] Coal 26.5 Lignite 8.6 Coke 29.3 Lignite briquettes (BKB) 19.4 Peat 17.7 Refinery gas 48.7 Diesel 43.1 Naphtha 43.7 Heavy fuel oil 41.5 LPG6 46.0 Plant oil 36.0 For renewable energy sources (wind, solar, hydrological, geothermal) and for nuclear energy, the amount of energy in input of power plants is not available in the IEA data set, and cannot be calculated by using LHV as in the case of combustible fuels. For these energy

6 LPG - Liquefied petroleum gas

Power plant Fuel Gross electric energy

33

sources it is commonly adopted, in LCA studies, to define an equivalent primary energy value (expressed in PJeq). These values have been calculated from the gross electric energy produced in output of the power plants (IEA data) multiplying it by an input-output ratio. The ratio R adopted in this study was R=1 for renewable energies and R=0.33 in the case of nuclear energy. In Table 32 are summarised the energy inputs necessary, in 2009, to the EU27 power plants to produce the total amount of gross electric energy (reported in green, in the last row).

34

Table 32 – Fuel consumption for electricity generation in 2009.

Coal

[PJ]

Lignit

e [PJ

]

Coke

[PJ]

BKB

[PJ]

Gas w

.[PJ]

Coke

gas [

PJ]

Blas

t. gas

[PJ]

Peat

[PJ]

NG {P

J]

Ref. g

as [P

J]

Dies

el [P

J]

Naph

tha [P

J]

Fuel

Oil [P

J]

LPG

[PJ]

Biom

ass [

PJ]

Plan

t oil [

PJ]

Biog

as [P

J]

Was

te [P

J

Nucle

ar [P

J eq]

Hydr

o [PJ

eq]

Wind

[PJ e

q]

Solar

[PJ e

q]

Geoth

. [PJ e

q]

Tota

l Inpu

t [P

J eq]

Gros

s el. E

n.

Prod

. [TW

h]

Austria 32 3 10 102 7 0 6 2 40 1 6 15 157 7 0 0 389 69 Belgium 46 1 9 232 0 2 24 3 4 35 515 6 4 1 881 91 Bulgaria 66 211 1 31 32 1 0 6 0 0 166 15 1 530 43 Cyprus 4 49 0 53 5 Czech Rep. 93 323 0 0 17 5 6 19 0 4 13 4 1 297 11 1 0 793 82 Germany 1031 1352 7 16 13 42 683 5 10 16 52 178 5 160 159 1472 89 139 24 0 5455 552 Demark 176 81 1 3 9 39 3 37 0 24 0 374 36 Estonia 85 5 3 15 0 0 3 0 0 1 113 9 Spain 416 21 2 764 7 48 90 17 7 27 576 105 136 22 2237 294 Finland 116 0 1 4 97 87 0 0 6 0 88 0 7 257 46 1 0 711 72 France 215 2 8 18 297 15 6 47 19 20 17 35 4470 225 28 1 5423 542 UK 1053 15 9 23 1282 14 1 36 42 69 36 754 32 33 0 3400 376 Greece 1 563 85 9 15 56 2 0 20 9 0 760 61 Hungary 9 76 1 3 112 2 5 1 27 1 4 168 1 1 0 409 36 Ireland 37 54 128 0 0 9 1 1 5 11 245 28 Italy 404 10 13 24 1230 31 2 4 159 124 40 12 19 60 192 24 2 19 2368 293 Lithuania 39 6 2 0 118 4 2 172 15 Luxemb. 22 0 1 2 3 0 0 28 4 Latvia 0 27 0 1 0.3 12 0 41 6 Malta 3 22 25 2 Netherl. 223 2 17 574 16 0 1 35 7 54 46 0 16 0 991 114 Poland 1102 484 0 19 7 46 1 1 19 53 0 3 5 11 4 1755 152 Portugal 123 120 1 1 31 13 1 8 32 27 1 1 359 50 Romania 10 275 0 109 1 0 14 1 0 0 128 57 0 595 58 Sweden 9 0 6 17 25 1 8 0 115 4 1 37 569 238 9 0 1040 137 Slovenia 11 38 6 0 0 2 1 0 63 17 0 138 16 Slovakia 20 25 1 2 22 0 11 6 0 1 154 17 0 259 26 Total 5192 3432 57 47 22 76 171 172 6137 112 99 4 612 200 758 25 307 523 9753 1295 479 51 20 29544 3170 Ratio [MJ/MJe] 0.455 0.301 0.005 0.004 0.002 0.007 0.015 0.015 0.538 0.010 0.009 0.000 0.054 0.018 0.066 0.002 0.027 0.046 0.855 0.113 0.042 0.004 0.002 2.589

35

LCA studies use often to express data as energy ratios. In example, in the last line of Table 32 are reported the input/output energy ratios (in MJ/MJelectric) characterizing the power plants of the EU27 electric mix in 2009 (total fuel requirement ratio: 2.589 MJ/MJe). A typical energy ratio often used to characterize the electric energy mix is the total consumption of primary energy, describing how much energy is necessary in input of the (EU27) energy system for producing a unit of gross electric energy in output. However, fuel consumptions reported in Table 32 are not considering the energy used for the supply of fuels to the power plants (“upstream” energy). Upstream energy losses calculations reported in this study rely on the JEC modelling of the fuel supply chains. The JEC modelling of the EU27 energy system has been done by using the E3Database software (www.lbst.de). The main hypotheses and definitions adopted are the followings.

• For the supply of coal, lignite, natural gas, diesel, naphtha, and nuclear fuel we adopt the same assumptions made in: JEC, 2011.

• The energy requirement and GHG emissions from supply of lignite briquettes have been derived from: GEMIS, 2011.

• Refinery gas consists of the light fraction (C1, C2) of the distillation at crude oil refineries. For the calculation it has been assumed that the refinery gas comes from an atmospheric distillation as described by Hedden (1994). The energy requirement and GHG emissions from the atmospheric distillation at the crude oil refinery have been allocated by energy.

• The supply of crude oil is based on JEC (2011) data. • For the supply of biogas it has been assumed that 80% comes from maize whole plant

cultivation and 20% comes from manure. • The supply of nuclear fuel is based on data in: GEMIS, 2011. • Biogas is made from manure and energy crops, solid biomass from wood chips. Manure,

energy crops, and wood inputs are summarized as biomass input. • Coke, coke oven gas, and other coal derived fuels are made from coal. Therefore, these

fuels finally end up as coal input. In this report it is not possible to give full detail on all the variables involved and all the equations adopted to model the EU energy system. However, in the Table 33 below we have summarised the main values (as energy ratios) representing the (upstream) energy requirements for the fuel supply.

http://www.lbst.de/

36

Table 33 – Energy requirement and GHG emissions for the supply of fuels used for electricity generation

Supplied fuel

Coal [MJ/MJf]

Lignite [MJ/M

Jf] Crude

oil [MJ/MJ

f] NG [MJ

/MJf] Nuclea

r[MJ/MJf]

Waste [MJ/MJ

f] Peat[M

J/MJf]

Biomass

[MJ/MJf]

Hydro [MJ/MJ

f] Wind power[

MJ/MJf]

Solar [MJ/MJf]

Total [MJ/MJf

] GHG [g

/MJf]

Coal 1.025 0.002 0.039 0.010 0.011 0.002 0.000 0.000 0.001 0.000 0.000 1.090 16.0 Lignite 0.000 1.014 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.015 1.7 Coke 1.781 0.004 0.059 -0.128 0.019 0.003 0.000 0.000 0.005 0.000 0.000 1.742 28.6 lignite briquettes 0.000 1.170 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.171 19.1 NG 0.000 0.000 0.000 1.138 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.138 12.8 Refinery gas 0.003 0.002 1.131 0.004 0.006 0.000 0.000 0.001 0.001 0.000 0.000 1.147 10.2 Diesel 0.003 0.002 1.182 0.002 0.005 0.000 0.000 0.001 0.001 0.000 0.000 1.196 15.9 Naphtha 0.002 0.002 1.131 0.002 0.005 0.000 0.000 0.001 0.001 0.000 0.000 1.143 11.4 HFO 0.000 0.000 1.088 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.088 6.6 LPG 0.000 0.000 1.162 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.162 13.2 Solid biomass 0.000 0.000 0.009 0.000 0.000 0.000 0.000 1.025 0.000 0.000 0.000 1.034 0.7 Biogas 0.002 0.001 0.041 0.060 0.002 0.000 0.000 1.871 0.000 0.000 0.000 1.977 21.2 Nuclear fuel 0.005 0.003 0.001 0.006 1.180 0.001 0.000 0.001 0.001 0.000 0.000 1.198 1.4 The upstream energy losses (Table 33) and the fuel consumptions of power plants (Table 32), allow computing the total consumption of primary energy in EU27 for the 2009 electric energy mix. This value, represented in form of an energy ratio (MJ/MJe), is reported in Table 34. The total consumption of primary energy (including fuel supply), for a unit of gross electric energy produced in EU 27 in 2009, amounts to 2.744 MJ per MJ of electricity.

37

Table 34 – Primary energy consumption (including upstream) for a unit of gross electric energy production I/O Unit Amount Biomass Input MJ/MJe 0.121 Coal Input MJ/MJe 0.501 Crude oil Input MJ/MJe 0.122 Geothermal Input MJ/MJe 0.002 Hydro power Input MJ/MJe 0.115 Lignite Input MJ/MJe 0.312 NG Input MJ/MJe 0.452 Nuclear Input MJ/MJe 1.009 Peat Input MJ/MJe 0.015 Solar power Input MJ/MJe 0.005 Waste Input MJ/MJe 0.047 Wind power Input MJ/MJe 0.042

Total Input MJ/ MJe 2.744 Electricity Output MJe 1.000 In order to calculate the GHG emissions related to the energy consumptions above it is necessary to multiply the fuel consumptions by specific emission factors characterising the electric power plants and the upstream supply chains. The main references for numerical values adopted in this study are the followings.

• The values of the CO2 emission factors are derived from GEMIS (2011), Hedden (1994), IPCC (2006) and JEC (2011).

• Calculations based on the enthalpies of formation are according to Barrow (1984) and CRC (1988).

• The CO2 emissions from coal combustion are based on the coal mix used by JEC (2011).

• Non-CO2 GHG emission factors from combustion processes are referring to GEMIS (2011) data, except for CFB boilers.

• For the calculation of N2O emissions from CFB boilers assumed for wood chip fuelled power stations the source Vitovec (1999) has been used. The main emission factors used in this study, characterizing electric power plants, have been reported in Table 35 and in Table 36.

38

Table 35 – GHG emission factors – A

GHG Factors Coal Lignite Coke BKB Gas works gas Coke oven gas Blast furnace gas Peat NG gCO2/kWhfuel 346 414 431 354 302 157 379 382 198 gCH4/kWhfuel 0.0054 0.0054 0.0054 0.0054 0.0184 0.0184 0.0088 0.0054 0.0073gN2O/kWhfuel 0.0156 0.0119 0.0156 0.0119 0.0111 0.0111 0.0046 0.0119 0.0039

Table 36 – GHG emission factors – B

GHG Factors Refinery gas Diesel Naphtha Fuel Oil LPG Biomass Plant oil Biogas Waste gCO2/kWhfuel 210 264 256 282 236 0 0 0 72 gCH4/kWhfuel 0.0075 0.0186 0.0108 0.0120 0.0075 0.0014 0.0109 1.2239 0.0063gN2O/kWhfuel 0.0039 0.0070 0.0072 0.0115 0.0039 0.0072 0.0109 0.0059 0.0104 Accordingly to the general approach of the WTW the emissions due to renewable energy sources and to nuclear energy can be considered negligible. Emissions calculated multiplying the fuel consumptions from Table 34 by the emission factors of Table 35 and Table 36 are corrected with upstream emissions (see above) and CHP credits. For the calculation of a credit for heat from CHP plants a natural gas fuelled boiler with an efficiency of 90% and an electricity consumption of 0.02 MJ per MJ of heat have been assumed as reference. The same boiler has been used by JEC (2011). The energy requirements and emissions from the natural gas boiler are based on GEMIS (2011). In GEMIS (2011) the energy requirements and emissions from CHP plants also have been calculated using the substitution method. Electricity is the main output and heat is the by-product which replaces heat from a reference process e.g. a boiler. According to data sets, hypotheses and calculations explained above, the greenhouse gas emissions caused by a kWh of gross electric energy produced in EU27 in 2009, is equal to: 463 g CO2 eq (also 129 gCO2 eq/MJe)

39

3.3.2 Emissions from net production of electric energy The calculations made above in § 3.3.1 are referring to the production of a unit of gross electric energy. However, energy losses in power plants must be taken into account. According to the main statistical sources (IEA, ENTSO7-E, EUROSTAT) we adopt the following definition: Gross and net electricity production: “Gross electricity production is measured at the

terminals of all alternator sets in a station; it therefore includes the energy taken by station auxiliaries and losses in transformers that are considered integral parts of the station. Net electricity production is defined as gross production less own use of power plants. Net electricity production is measured at the station busbars, after deduction of electricity consumed within the station”. (IEA 2011c, p. I.5) Energy flows involving gross and net production of electric energy can be represented as in Figure 2.

Figure 2 – Gross and net production of electric energy According to the statistical sources we are using for this study, the gross electric energy production (for EU27 in 2009) is 3170 TWh (IEA, 2011), while the total net production of electric energy is: 3046 TWh (IEA, 2011 and EUROSTAT, nrg_105a). This could be also represented by the Table 37 below. Table 37 – Electricity lost for own use of Power plants I/O Unit Amount Electricity Input MJe 1.0407 Electricity Output MJe 1.0000

The greenhouse gas emissions caused by a kWh of net electric energy produced in EU27 in 2009, is equal to 482 g CO2 eq (also 134 gCO2eq/MJe)

7 European Network of Transmission System Operators for Electricity

Own use by power plant Gross production of electric energy (3170 TWh) Net production of electric energy (3046 TWh)

40

3.3.3 Emissions for electric energy supplied The net production of electric energy (see § 3.3.2 above) is still not the electric energy that will be supplied to the electric grid or commercialised (imported/exported). Another issue to be considered is the energy consumed by pumping plants in order to store water in the higher elevation reservoir of hydro power plants. The main statistical sources (IEA, ENTSO-E, EUROSTAT) share the following definition: Energy Supplied. For electricity, this is the electrical energy supplied from the plant. In

the case of a national network this is equal to the sum of the net electrical energy production supplied by all power stations within the country, reduced by the amount used simultaneously for pumping and reduced or increased by exports to or imports from abroad.

This concept could also be represented by the Figure 3 below. Figure 3 – Net production and energy supplied In this study we are not going to separate electric energy supplied from the Import-Export balance. This is, mainly, because it is not easy to know the origin of electric energy imported/exported. The fact that the electric energy imported can be produced from coal or from hydropower changes the emissions calculations. Secondly, we are considering not the emissions in a single Member State, but the EU27 market as a whole. In this case, it is possible to demonstrate that the difference in considering or not EU27 net imports (15 TWh according IEA, 2011) on the calculation of energy supplied is less than 0.5%. The loss factor due to the pumping is reported in Table 38 below.

Table 38 – Electricity lost for pumping I/O Unit AmountElectricity Input MJe 1.0140 Electricity Output MJe 1.0000 The GHG emissions caused by a kWh of electric energy supplied (including import-exports) in EU27 in 2009, is equal to 488 g CO2eq (136 gCO2eq/MJe).

Net production of electric energy (3046 TWh) Used for pumped tEnergy supplied + Imports - Exports (3004 TWh)

41

3.3.4 Losses for transmission and distribution of electric energy After having considered the energy losses for power plants management (§ 3.3.2) and pumping (§ 3.3.3), we want now to quantify the energy lost in the grid for the transmission (High Voltage, HV) and distribution (Medium Voltage, MV and Low Voltage, LV) of electric energy. The total amount of the losses reported by IEA (2011a, 2011b) is 188 TWh (6.3% of the energy supplied). The partitioning among HV, MV and LV power losses is not easy to calculate because of lack of specific statistic data. High Voltage (380 kV, 220 kV, 110 kV) electricity transport According to the European Network of Transmission System Operators (ENTSO-E) the energy lost in the transmission network is about 1.5% (ENTSO-E 2011, pag.10). We will consider this percentage reliable also for our EU27-year 2009 network.

Table 39 – Electricity transmission losses in the High Voltage grid I/O Unit Amount Electricity Input MJ/MJe 1.0152 Electricity Output MJ 1.0000 Electricity distribution at Medium Voltage (10-20 kV) and Low Voltage (0.4 kV) Seeing this lack of statistical data, some authors (ECOINVENT, p.197), adopt as standard for the EU mix the Swiss shares of electric losses. In this report we have assumed to be typical for EU 27 the data extracted from a recent study (AEEG, 2012) on the Italian electric network. After some elaborations, the MV and LV losses can be divided as shown in Table 40 and Table 41 below. Losses in electric transformers are included.

Table 40 – Electricity distribution in the Medium Voltage grid I/O Unit Amount Electricity Input MJe/MJe 1.0378 Electricity Output MJ 1.0000 The GHG emissions due to a kWh of electric energy supplied (in EU27, in 2009) at Medium Voltage is equal to 514 g CO2 eq (143 gCO2eq/MJe).

Table 41 – Electricity distribution losses to Low Voltage (LV) I/O Unit Amount Electricity Input MJ/MJe 1.0636 Electricity Output MJ 1.0000 The GHG emissions due to a kWh of electric energy supplied (in EU27 in 2009), at Low Voltage is equal to 547 g CO2 eq (also 152 gCO2eq/MJe)

42

3.4. N fertilizer manufacturing emissions calculation Nitrogen fertilizer production emissions: • average for all N fertilizer consumed in EU, including imports. • The data is principally from Fertilizers Europe8 submission to ETS (Emission Trading Scheme). That includes data from an IFA world survey of fertilizer plant emissions. • 2011 data by interpolation between 2007 emissions and 2020 benchmark • There is only one N fertilizer value: mix for urea and AN; mix of EU production and imports – little data on which N fertilizer used where for which crop – (urea and Ammonium Nitrate (AN) emissions are approaching each other in future). • JRC 2005 value [Kaltschmitt 2001] was about right in 2005-7. • other sources for EU fertilizer ignore upstream emissions for NG and are for selected factories. • There is a big scope for producers to reduce emissions by choosing a good fertilizer factory! • Issue 1: imported urea is assumed to come from middle east (no breakdown of exporters to EU is available). – and emissions in Middle East plant are assumed the same as an EU urea plant – but China is largest world exporter (with very high emissions) • Issue 2: same default N fertilizer emissions used for foreign crops

8 Fertilizers Europe: http://www.fertilizerseurope.com

43

44

Input data for fertilizer manufacturing emissions calculation based on ETS AMMONIA

AMMONIA PRODUCTION IN EU per tonne product

2007 average fertilizers-Europe emissions for making ammonia [1] 2.01 tCO2/t NH3

includes upstream electricity emissions but not upstream NG emissions

2020 benchmark emissions for making ammonia [1][2] 1.619 tCO2/t NH3

includes upstream electricity emissions but not upstream NG emissions

NG CO2 content 55.00 kgCO2e/GJ WTW v3

EU 4000km NG upstream emissions 14.5 kgCO2e/GJ WTWv3 standard value for marginal EU gas

AMMONIA PRODUCTION IN RUSSIA "Russian ammonia uses 20% more NG than EU ammonia"[3]

Dr. Werner [3] was referring to 2005 data. However, FE emissions data for 2007 are practically the same as the data he quoted for 2005.

Assumption: emissions from future Russian ammonia production are reduced in proportion to EU ones, remaining at 20% higher than EU ones as EU emissions reduce under ETS upstream emissions russian NG IN RUSSIA (1000km) 8.7 kgCO2e/GJ

NG CO2 content 55.00 kgCO2e/GJWTW v3

OTHER DATA TO CALCULATE AVERAGE EMISSIONS FROM EU-CONSUMED AMMONIA Import fraction of ammonia to EU, also used to make solid fertilizer in EU (see trade statistics sheet) 16% Assumption: fraction of imports (ammonia and solid fertilizers) remains constant at last-reported values: 2008-9

45

NITRIC ACID N2O EMISSIONS FROM Nitric acid plants in EU per tonne product 2007 EU average 5.4 5.40 kg N2O/t HNO3 2020 EU average (ETS bencmark) 1.01 kg N2O/t HNO3 SOLID FERTILIZER PLANTS Minor inputs for EU fertilizer plants (assumed constant 2005…2020)(EU data, but assumed the same for outside EU) electricity for Ammonium nitrate plant "is less than.."[3] 1 GJ/t AN

electricity for Urea plant [3] 5GJ/t Urea

emissions for EU-electricity-2007 (JEC-WTW v3) 0.128 tCO2/GJ emissions for EU-electricity-2020 0.11 tCO2/GJ Calcium Ammonium nitrate is assumed to have same emissions per tonne of N as Ammonium Nitrate (emissions from CaO are relatively small). Note: urea (=ammonium carbonate) manufacture reacts ammonia with otherwise-emitted CO2. The CO2 is however lost when urea decomposes on the field. We count neither the sequestration nor the emission..

46

IMPORTED AMMONIUM NITRATE ASSUMPTIONS Imports are mostly from Russia nd Ukraine [6]: we represent them with data for Russia. Emissions from Russian AN without upstream emissions [3] (Dr. Werner's data were for 2005, but FE data for 2005 very nearly equals that for 2007) 7.7 tCO2e/tNFraction of EU-consumed AN -type fertilizer imported [5] 8% TRADE DATA

EU trade 2009 in kilotonnes of NITROGEN ammonia

ammonium nitrate

Calcium ammonium nitrate urea

ammonium sulphate total

NH3 [4] AN [5] CAN [4] AN+CAN U [5] AS[4] U+AS IMPORTS 3173 165 1 646 1811 1524 443 2 467 4277.5EXPORTS 914 1824 1 824 737 737 2561EU CONSUMPTION 13975 2 097 2811 4907.5 2 024 745 2 769 7676% imported per type 16% 8% 8% 75% % imports=imports/(use+exports) per type, the % of EU N consumption 64% 36%

Sources: [1] E-mail from Fertilizers Europe (former EFMA) - Antoine Hoxha - 23 Dec 2010 – Comment: data covers Fertilizers Europe members only. [2] Commission proposal Dec 2010 for ETS benchmarking of EU fertilizer industry, via Heiko Kunst, DG-CLIMA [3] A. Werner, BASF SE, Chairman of TESC in EFMA:presentation at EFMA conference "Agriculture, fertilizers and Climate change", 12 Feb. 2009, download from EFMA website. Number are based in IFA world benchmarking report on fertilizer emissions. [4] IFA STATISTICS FOR 2009 http://www.fertilizer.org/ifa/Home-Page/STATISTICS/Production-and-trade-statistics accessed Feb 2011 [5] e-mail from FERTILIZERS EUROPE: A. Hoxha 20/02/2100 FOR AGRICULTURAL USE ONLY (important for urea and AN) AVERAGE OF 2008/9 AND 2009/10 DATA [6] C. Palliere, Fertilizers Europe, personal communication to JRC, December 2010

47

Nitrogen fertilizer 2011 N fertilizer production and transport: EU supply mix

I/O Unit AmountNatural gas Input MJ/kg 54.79 Hydro power Input MJ/kg 0.12 Crude oil Input MJ/kg 0.59 Hard coal Input MJ/kg 0.40 Nuclear fuel Input MJ/kg 0.35 N-fetilizer Output kg 1.0 Emissions CO2 - g/kg 3082 CH4 - g/kg 7.9 N2O - g/kg 7.84 CO2 emissions from neutralization of acidity from N fertilizer oxidation after N application

- g/kg 590

48

49

3.5 Diesel, handling and storage, drying and plant protection use in cultivation. Bonn University supplied new input data on diesel use, crop drying and pesticide application from the CAPRI database9. Several pathways have been updated with this new data: 1. “Diesel use in cultivation” The CAPRI data used to calculate the diesel use in cultivation are shown in Table 43. The diesel use has been weighted using percentage of tillage system per ha i.e. conservation tillage (No-tillage) or plough tillage (conventional agriculture). CAPRI data on Diesel use in Soya cultivation in EU has not been used because the soya pathways are derived from a mix of non-EU sources. Table 42 Diesel use in cultivation derived from CAPRI data:

Total Diesel input (a) weighted using

percentage of tillage system per

ha

CAPRI "fresh weight" yields MJ diesel /tonne of moist crop

Crop

MJ/ha Kg/ha MJ/tonne

Barley 3240 4406 0.7355

EU maize 3311 6601 0.5016

Rapeseed 2987 3120 0.9575

Rye and meslin 3014 3222 0.9353

Sugar beet 3457 57131 1.2379

Sunflower 3288 1723 0.0605

Soft wheat 3276 5718 1.9085

Maize fodder 3889 39000 0.5729

Comment: a. Total diesel input using diesel LHV of 35.9MJ/litre) Sources: 1. Capri, 2012 (converted to JEC format using information in refs 2 and 3). 2. Kraenzlein, 2011 3. Kempen and Kraenzlein, 2008. 9 http://www.ilr.uni-bonn.de/agpo/rsrch/capri/capri_e.htm

50

2. “Handling and Storage” This data was extrapolated from the wheat to ethanol pathway assuming the handling and storage depends on the weight of the harvest. 3. “Crop drying” This data was calculated from CAPRI average primary-energy-MJ/ha-results per crop. CAPRI reports that most of these primary emissions from drying are electricity. We converted the primary energy they report (which includes minor contributions from heating oil and NG) back to effective kWh electricity and then to MJ electricity. Table 43 CAPRI Handling & Storage and Drying data

Crop Process Harvest Input By Process

drying cereals (mainly electricity)

MJ/ha Mjelectricity / tonne of moist crop

Barley 695.75 0.048592 Maize 16122.73 0.751550 Rapeseed unchanged Rye and meslin 310.95 0.029693 Sugar beet Not dried Sunflower unchanged Soft wheat 1351.03 0.072704

Comments: - CAPRI's calculated total MJ of primary energy for drying, which is mostly electricity. This was converted to MJ of electricity using the MJ of primary energy assumed by CAPRI to make 1 KWh electricity. - Drying of rapeseed and sunflower (not reported by CAPRI) has been corrected by

LBST10. The reason was a misunderstanding of the text in the original literature. The diesel input for the drying process derived from [UBA 1999] is indicated per kg of removed water and not per tonne of rapeseed. The text in [UBA 1999] states: “Storage and drying (per t of corn): 12.6 kWh electricity; 0.12 l of heating oil and 0.1 kWh of electricity per kg of water removed”. Initially it was assumed that the amount of heating oil is related to 1 t of rapeseed grain. According to LBST the light heating oil is often used as heat source for drying (not for diesel engines for mechanical drives for handling) and as a result the consumption of light heating oil (= diesel) depends on the water content. In contrast to the 0.1 kWh of electricity plus 0.12 l of heating oil (which are per t of removed water) the 12.6 kWh probably are the electricity requirement for handling and therefore a per t of rape seed grain.]

Source: 1. Capri, 2012 (converted to JEC format using information in refs 2 and 3. 10 Pers. Comm. LBST W.Weindorf 22rd March 2012

51

2. Kraenzlein, 2011 3. Kempen and Kraenzlein, 2008 4. UBA, 1999 Table 44 CAPRI data on Primary Energy for inputs, used to convert CAPRI output to our input data

Direct energy component Cumulative energy demand

Unit

Diesel 45.7 MJ/l Electricity (at grid) 11.7 MJ/kWh Heating Gas (in industrial furnace) 47.9 MJ/m3 Heating Oil (in industrial furnace) 49.7 MJ/l

Source: Ecoinvent (2003) shown in Kranzlein, T. Capri manual p167, (Chapter 7.5 Energy use in Agriculture). 4. "Pesticides" = "plant protection ingredients". This is back-calculated from CAPRI's reported data (MJprimary energy for pesticides)/ha per-crop data. Table 45 Calculation of Pesticide use

crop (kg/ha) kg/tonne of

moist crop Barley 0.00391464 0.00089Maize 0.00702575 0.00106Rapeseed 0.00660998 0.00212Rye and meslin 0.00169601 0.00053Soybean (EU) 0.00753972 0.00293Sugar beet 0.01802965 0.00032Sunflower 0.00260309 0.00151Soft wheat 0.005853 0.00102Sources: 1. Capri, 2012 (converted to JEC format using information in refs 2 and 3. 2. Kraenzlein, 2011 3. Kempen and Kraenzlein, 2008. 4. LBST, 2012

52

4. Soil emissions from biofuel crop

cultivation

4.1 Background Typical soil N2O emission values for wheat, rapeseed, sugar beet and sunflower cultivation in the RED are based on results from DNDC (DeNitrification DeComposition) biogeochemistry model runs for Europe. For oil palm, maize, soybean and sugarcane typical soil N2O emissions were calculated following the IPCC (2006) TIER 1 approach (with modifications for soybean and oil palm). The RED (Article 19.2) asks EU member states to provide typical soil N2O emissions from potential biofuel crops on a NUTS2 level “taking into account different environmental conditions”. However, no guidance on the calculation method is given. Soil N2O field measurements are usually not available for all crops and environmental conditions in a country as they are expensive. Complex biogeochemistry models (as e.g. DNDC) fulfil the RED specification with regard to considering environmental aspects but would require extensive data input and specific expertise. The IPCC (2006) TIER 1 method to calculate N2O emissions from managed soils can easily be applied but does not take into account varying environmental aspects. Therefore we present an approach applicable for the major crops in most regions of the world, easily replicable and taking into account the influence of soil conditions and climate on the emission of N2O from soils due to potential biofuel crop cultivation. 4.2 Pathways of N2O emission from managed soils According to IPCC (2006) the emissions of N2O that result from fertilizer N inputs to agricultural soils occur through both:

- direct pathway (i.e., directly from the soils to which the N is added/released), and: - two indirect pathways: (i) following volatilisation of NH3 and NOx from managed soils and the subsequent re-deposition of these gases and their products NH4+ and NO3- to soils and waters; and (ii) after leaching and runoff of N, mainly as NO3- (IPCC, 2006).

53

4.3 General approach to estimate soil N2O emissions from cultivation of potential biofuel crops In the IPCC TIER1 method (IPCC, 2006, Vol. 4 Ch. 11) to calculate N2O emissions from managed soils the single global emission factor (EF1) for direct emissions from mineral fertilizer and manure input is based on Fertilizer-Induced Emissions (FIE). FIE is defined as the direct emission from a fertilized plot, minus the emission from an unfertilized control plot (all other conditions being equal to those of the fertilized plot), expressed as a percentage of the N input from fertilization (Stehfest and Bouwman, 2006). In our approach we substitute the IPCC TIER 1 emission factor EF1 with TIER 2 disaggregated crop specific emission factors for different environmental conditions (EF1i) by applying the statistical model developed by Stehfest and Bouwman (2006) to calculate a crop and site specific FIE (i.e. EF1i). For all other N sources (crop residues, organic soils) and pathways (indirect emissions from mineral soils and emissions from organic soils) the IPCC (2006) TIER 1 method is applied. IPCC (2006) does not provide default values for crop residues from some of the potential biofuel crops. In these cases (e.g. oilpalm and coconut) the lacking parameters were taken from the literature. For soybean the nitrogen content in belowground biomass was updated based on recent findings (Singh, 2010 and Edwards, 2012) as described in Chapter 4.7 “Note on soybean crop residues”. Compost, sewage sludge, rendering waste and N input from grazing animals are not considered as likely N sources in biofuel crop cultivation. Following the naming conventions in the IPCC (2006) guidelines (Vol. 4 Ch. 11) the calculation for a potential biofuel crop at a specific location and under a specific management system (e.g. fertilizer input) can be expressed as:

NONNONNON indirectdirecttotal −+−=− 222 with ][][][)( ,2,,,2,,112 TropCGTropCGOSTempCGTempCGOSCRiiON

iSNdirect EFFEFFEFFEFFFNON •+•+•+•+=− ∑

])[(]))()[(( 5)(42 EFFracFFFEFFracFFracFNON HLeachCRONSNGASMONGASFSNindirect ••+++••+•=− − Where NON total −2 = direct and indirect annual N2O–N emissions produced from managed soils; kg

N2O–N ha-1 a-1 NON direct −2 = annual direct N2O–N emissions produced from managed soils; kg N2O–N ha-1 a-1 NON indirect −2 = annual indirect N2O–N emissions (i.e. annual amount of N2O–N produced from

atmospheric deposition of N volatilised from managed soils and annual amount of N2O–N produced from leaching and runoff of N additions to managed soils in regions where leaching/runoff occurs); kg N2O–N ha-1 a-1

SNF = annual synthetic N fertilizers input; kg N ha-1 a-1

54

ONF = annual animal manure N applied as fertilizer; kg N ha-1 a-1 CRF = annual amount of N in crop residues (above-ground and below-ground); kg N ha-1

a-1

TempCGOSF ,, = annual area of managed/drained organic soils under cropland in temperate climate; ha a-1

TropCGOSF ,, = annual area of managed/drained organic soils under cropland in tropical climate; ha a-1

GASFFrac = 0.10 (kg N NH3–N + NOx–N) (kg N applied)-1 . Volatilisation from synthetic fertilizer

GASMFrac = 0.20 (kg N NH3–N + NOx–N) (kg N applied)-1 . Volatilisation from all organic N fertilizers applied

)(HLeachFrac − = 0.30 kg N (kg N additions) -1. N losses by leaching/runoff for regions where leaching/runoff occurs

iEF1 = crop specific emission factors developed for N2O emissions from synthetic fertiliser and organic N application under conditions i (kg N2O–N (kg N input) -1); i = 1, …n.

1EF = 0.01 [kg N2O–N (kg N input) -1] TempCGEF ,,2 = 8 kg N ha-1 a-1 for temperate organic crop and grassland soils

TropCGEF ,2 = 16 kg N ha-1 a-1 for tropical organic crop and grassland soils 4EF = 0.01 [kg N2O–N (kg N NH3–N + NOx–N volatilised) -1] 5EF = 0.0075 [kg N2O–N (kg N leaching/runoff) -1]

4.4 Determining crop and site specific Fertilizer Induced Emissions (EF1i) The Stehfest and Bouwman (2006) statistical model, in the following referred to as S&B, describes on-field N2O emissions from soils under agricultural use based on the analysis of 1008 N2O emission measurements in agricultural fields under different environmental conditions and for 6 agricultural land use classes as: ∑+= )exp( evcE where E = N2O emission (in kg N2O-N ha-1 a-1) c = constant (s. Table 47) ev = effect value for different drivers (s. Table 47)

55

Table 46: Constant and effect values for calculating N2O emissions from agricultural fields after S&B Constant value -1.516 Parameter Parameter class or unit Effect value (ev) Fertilizer Input 0.0038 * N application rate in kg N ha-1 a-1 Soil organic C content <1 % 0 1-3 % 0.0526 >3 % 0.6334 pH <5.5 0 5.5-7.3 -0.0693 >7.3 -0.4836 Soil texture Coarse 0 Medium -0.1583 Fine 0.4312 Climate Subtropical climate 0.6117 Temperate continental climate 0 Temperate oceanic climate 0.0226 Tropical climate -0.3022 Vegetation Cereals 0 Grass -0.3502 Legume 0.3783 None 0.5870 Other 0.4420 Wetland rice -0.8850 Length of Experiment 1 yr 1.9910 For the calculations the potential biofuel crops are assigned to the different vegetation classes as given in Table 48. Table 47 Potential biofuel crops assignment to S&B vegetation classes

Potential Biofuel Crop S&B Vegetation class Barley Cereals Cassava Other Coconut Other Maize Other1 Oilpalm Other Rapeseed Cereals Rye Cereals Safflower Other Sorghum Cereals

56

Soybean Legumes Sugarbeet Other Sugarcane Other Sunflower Other Triticale Cereals Wheat Cereals 1 following the classification of crop types in Stehfest and Bouwman (2006), row crops are summerized in the vegetation class “other” Applying the S&B, the EF1i of the biofuel crop is calculated as: EF1i = (Efert – Eunfert)/ Nappl where Efert = N2O emission (in kg N2O-N ha-1 a-1) based on S&B where the fertilizer input is actual N application rate (mineral fertilizer and manure) to the biofuel crop Eunfert = N2O emission of the unfertilized plot (in kg N2O-N ha-1 a-1) based on S&B. The N application rate is set to 0, all the other parameters are kept the same. Nappl = N input from mineral fertilizer and manure (in kg N ha-1 a-1) Figure 4 shows the potential variation of the of EF1i based on the S&B model as described above for cereals cultivated in temperate oceanic climate on different soils (low-medium-high soil organic carbon content, low-medium-high pH, fine - coarse soil texture) and for different levels of fertilizer N input. The red line gives the IPCC (2006) factor (EF1) for direct N2O emissions from fertilizer input based on a global mean of the EF1i. EF1 is replaced in our approach by the crop and site specific EF1i for direct emissions from mineral fertilizer and manure N input based on the crop and site specific EF1i applying the S&B model.

57

0.00

0.01

0.02

0.03

0.04

0.05

0.06

1 51 101 151 201 251 301 351 401 451 501

N input kg ha-1

Ferti

lizer

indu

ced

emis

sion

s (k

g N

2O-N

Em

issi

ons

/ kg

Ferti

lizer

N

inpu

t)

Agricultural Fields: Minimum case for Cereals in Temperate Oceanic Climate (SOC<1%; pH >7.3; medium soil texture)Agricultural Fields: Mean case for Cereals in Temperate Oceanic Climate (SOC 1-3%; pH 5.5-7.3; coarse soil texture)Agricultural Fields: Maximum case for Cereals in Temperate Oceanic Climate (SOC>3%; pH <5.5; fine soil texture)IPCC (2006) factor for direct N2O emissions from fertilizer input

Figure 4 Variation of fertilizer induced emissions from agricultural soils under different environmental conditions and fertilizer input rates applying the S&B 4.5 The Global crop and site specific Nitrous Oxide emission Calculator (GNOC) To calculate soil N2O emissions from potential biofuel feedstocks for the varying environmental conditions and management systems we built the Global Nitrous Oxide Calculator (GNOC). Following the combined S&B / IPCC(2006) approach described before, GNOC enables to calculate crop and site specific soil N2O emissions for a 5min by 5min (~10km by 10km) grid globally for the year 2000. The choice of the reference year was driven by the availability of the required data sets at high resolution. To minimize inconsistencies in the results due to different detail or accuracy levels for different parts of the world, only spatial data sets with a global coverage were taken into consideration. Main input data sets for the GNOC: Crop area and yield: Maps giving area and yield for individual crops (grid cell size of 5 min by 5 min) based on remote sensing information and FAO crop statistics for the year 2000 have been produced by Monfreda et al. (2008).

58

Mineral fertilizer and manure application: Crop and country specific mineral fertilizer N rates are available from the Food and Agriculture Organization of the United Nations (FAO) (2010) for the years ~2000. The International Fertilizer Organisation (IFA) provides country level total consumption of mineral fertilizer N (IFA, 2010). In case of lacking crop specific N fertilizer input for some countries, the fertilizer rates were estimated based on crop specific N fertilization rates for other countries as well as crop area and yield data from Monfreda et al. (2008). Crop specific N input from FAO or estimated values are calibrated to meet the IFA country totals. N input has been disaggregated to the 5 min by 5 min grid cell using crop area and yield data from Monfreda et al. (2008) and information on soil organic carbon content from the Harmonized World Soil database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009) Country level manure N application for the year 2000 is available from the European Commission’s Emission Database for Global Atmospheric Research data base (EDGAR v4.1, 2010). Manure N in EDGAR is calculated according the IPCC (2006) Vol. 4 Ch. 10 and distributed homogeneously over all arable land in a country. N input from crop residues: N input from crop residues was calculated based on crop area and yield data from Monfreda et al. (2008) and applying the default method described in IPCC (2006) Vol. 4 Ch. 11 with modifications for certain crops based on EDGAR v4.1 (2010) and Edwards (2012). Soil properties: Soil properties required were calculated based on the Harmonized World Soil Database Version 1.1, March 2009 (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009) by Hiederer (2009). Ecological zones: A ecological zones map as defined in IPCC (2006) Vol. 4 Ch. 3 / 4 for the calculation of carbon stock changes was prepared and made available online by Hiederer et al. (2010). Areas where leaching/runoff occurs: IPCC (2006) Vol. 4 Ch. 11 defines the area where leaching/runoff occurs as areas where Σ(rain in rainy season) - Σ (PE - potential evaporation - in same period) > soil water holding capacity, or where irrigation (except drip irrigation) is employed. The rainy season(s) can be taken as the period(s) when rainfall > 0.5 * Pan Evaporation. A global map delineating areas where leaching/runoff occurs was compiled based on climate and soil information as described in Hiederer et al. (2010).

59

4.6 GNOC results and the JEC-WTW

The soil N2O emissions from the GNOC for the year 2000 are presented in Figure 5, which gives: 1. the weighted global average N2O emissions from biofuel crop cultivation in the year 2000 based on GNOC (green bars). The results are weighted by feedstock quantities supplied to the EU market (including EU domestic production); 2. the weighted global results from running GNOC with the default IPCC TIER 1 emission factors for direct and indirect N2O emissions from crop cultivation in the year 2000 (blue bars) 3. the weighted global average GNOC results corrected for more recent yield and fertilizer input (year 2006/2007). These are the basis for the update of the RED default values. For all crops, except oilpalm, we assumed that they are not grown on peatlands if there are enough mineral soil area ressources available. On the contrary, high shares of oilpalm cultivations in the main producing countries are found in peatland areas due to scarcity of available fertile mineral soil resources and advancements in land conversion technology (Miettinen et al., 2012). Due to their “coarse” resolution, the GNOC spatial data sets do not reflect the real share of oilpalm grown on peatland in South East Asia. Based on GNOC input data, 9% of oilpalm is grown on peatland in the main coutries supplying palm oil to the EU market i.e. Indonesia and Malaysia. According to (Robert can you add a reference?) 16% is a more realistic share for oilpalm beeing grown on peatland. Therefore the soil N2O emissions from oilpalm cultivation was calculated based on standard GNOC data and 3 different peatland scenarios - the 16% peatland share, cultivation on mineral soils only or cultivation on organic soils only (Figure 5). On the global level, the main influencing parameters causing the differences between N2O emissions based on the IPCC (2006) approach and GNOC are the crop type and the climatic zone. Emissions from cereal feedstock, having a low “effect value” (s. Table 47, S&B) leading to low EF1i for the crop type and beeing grown dominantly in the temperate climatic region (e.g. wheat, barley, rye) are lower than applying the IPCC (2006) TIER 1 approach. Oilseeds and row crops (vegetation type “other”) tend to have higher average emissions if they are grown in temperate and especially subtropical regions (e.g. sugarbeet and sugarcane) based on GNOC compared to applying IPCC (2006) default. While emissions from oil crops grown in tropical regions (e.g. oilpalm and coconut) are similar for both calulation methods applied. This picture changes on a higher spatial level. Here, soil parameters as pH, texture and soil carbon content may cause a higher variation in N2O emissions (based on GNOC) from one crop grown on different soils than we can see between the crops on the global level. Country average N2O emission e.g. from wheat (Figure 6) expressed as gCO2eq per MJ of fresh feedstock produced varies largely between the countries. The differences result as

60

well from environmental factors as from the fertilizer use efficiency (kg fertilizer N input per kg of wheat harvested) as emissions are related to yield rather than to cropping area. Figure 5 Weighted global average N2O soil emissions from biofuel feedstock cultivation. The results are weighted by feedstock quantities supplied to the EU market (including EU domestic production). The graph shows emissions based on GNOC calculations for the year 2000 and the results corrected for 2006/2007 yields and fertilizer input.

oilp

alm

GNOC resutlts (combined S&Band IPCC)

GNOC results (IPCC based)

corrected GNOC results

oilpalm fruit average (16% organicsoil) - corrected GNOC results

oilpalm fruit on mineral soil -corrected GNOC results

oilpalm fruit on organic soil -corrected GNOC results

0

2

4

68

10

12

14

16

18

20

2224

26

28

30

32

barle

y

rye

tritic

al

whe

at

mai

ze

sorg

hum

suga

rbee

t

suga

rcan

e

cass

ava

rape

seed

saffl

owe

sunf

low

er

cotto

n

soyb

ean

coco

nut

g C

O2e

q / M

J of

fres

h cr

op

GNOC results for oilpalm refer to a share of ~9% organic soil

Figure 6 Country average N2O emissions (expressed as gCO2eq MJ-1 of fresh feedstock) from wheat cultivation based on GNOC.

61

The update of the JEC-WTW data with GNOC results on soil N2O emissions from biofuel crop cultivation require some adjustments and corrections: - mean emissions of the potential biofuel crop will not equal the global mean emissions but the weighted average emissions from suppliers of each crop to the EU market (including EU domestic production). - correction for changes in fertilizer input since the year 2000 - For the spatial distribution of country level mineral fertilizer and manure N input the regional difference in availability of manure N or crop specific application of manure N could not be taken into account due to lack of information. Thus, each crop in a country received the same amount of N from manure. Although manure application in biofuel crop cultivation is minor, we consider half of the manure N input due to possible underestimation of the real mineral fertilizer N input to biofuel crops from our fertilizer N distribution method.

4.7 Outlook for GNOC The GNOC allows the calculation of N2O emission from a wide range of potential biofuel crops taking into account the influence of varying environmental conditions as requested by the RED. It is foreseen to provide GNOC as online calculator.

62

Table 48 GNOC results

crop

in M

3 da

taba

se

Min

eral

nitr

ogen

per

tonn

e cr

op in

GN

OC

(bas

ed o

n yr

. 200

0 da

ta)

(min

N_so

c_co

rr_k

gNpe

rton

moi

styi

eld)

Min

eral

N fr

om F

ertil

izer

s E

urop

e pe

r-cro

p da

ta 2

005

(per

m

oist

tonn

e)

Min

eral

N fr

om IF

A fo

r 20

06/7

div

ided

by

FAO

yie

ld 2

007

best

est

imat

e kg

N/to

nne

moi

st c

rop

From

GNO

C: N

in A

nim

al m

anur

e ap

plie

d pe

r moi

st to

nne

of c

rop

(anw

N_kg

Npe

rtonm

oist

yiel

d)

From

GNO

C: N

in c

rop

resi

dues

, per

moi

st to

nne

of c

rop

(der

ived

from

IPCC

coe

ffici

ents

) (c

ropr

es_k

gNpe

rtonm

oist

yiel

d)

From

GNO

C: t

otal

N in

put i

n N

2O m

odel

with

hal

f of m

anur

e in

200

0 (g

/kg

wet

yie

ld)

Best

est

imat

eof p

rese

nt to

tal N

inpu

t with

hal

f of m

anur

e N

(g/k

g w

et

yiel

d)

corr

ectio

n fa

ctor

for

upda

ting

N2O

em

issi

ons

calc

ulat

ed b

y G

NO

C fo

r ye

ar 2

000,

to a

lign

with

our

bes

t-est

imat

e us

ed o

f pre

sent

N fe

rtiliz

er

inpu

t.

GNO

C es

timat

e of

N2O

em

iions

in C

O2

equi

vale

nt p

er M

J of

cro

p in

ye

ar 2

000

COR

RECT

ED

N2O

mod

el r

esul

t for

min

org

anic

soi

l and

hal

f man

ure

(gC

O2e

/MJc

rop)

COR

RECT

ED

N2O

mod

el r

esul

t for

min

org

anic

soi

l (ex

cpt p

alm

) and

ha

lf m

anur

e, in

g N

2O/M

J cr

op

COR

RECT

ED

N2O

mod

el r

esul

t for

min

org

anic

soi

l (ex

cpt p

alm

) and

ha

lf m

anur

e, in

g N

2O/k

Wh

crop

barley 25.2 20.8 20.8 10.0 9.9 40.1 35.64 0.89 14.39 12.80 0.043 0.1546cassava 0.6 0.6 0.6 1.2 2.2 2.15 1.00 1.05 1.05 0.004 0.0127coconut 1.5 1.5 1.8 10.0 12.4 11.80 0.95 8.07 7.69 0.026 0.0929cotton seed 57.9 46.2 46.2 4.8 0.0 60.3 48.61 0.81 30.74 24.78 0.083 0.2994maize EU-consumed 18.8 17.8 22.2 20.0 5.1 7.0 28.4 29.53 1.04 13.29 13.83 0.046 0.1671oil palm fruit, average (16% peat) 4.5 4.2 4.2 0.5 9.2 13.9 13.61 0.98 9.31 9.11 0.031 0.1101oil palm fruit, on mineral soil >4.5 4.2 >0.5 9.2 0.98 5.13 5.02 0.017 0.0606oil palm fruit, on peat <4.5 4.2 <0.5 9.2 0.98 31.27 30.61 0.103 0.3698rapeseed 61.7 44.1 44.1 16.3 17.4 87.2 69.63 0.80 20.51 16.38 0.055 0.1978rye 25.7 25.7 13.2 8.8 41.1 41.11 1.00 14.76 14.76 0.050 0.1783safflower 50.3 50.3 6.9 0.0 53.8 53.76 1.00 11.07 11.07 0.037 0.1337grain-sorghum 22.2 22.2 3.0 8.7 32.4 32.40 1.00 9.02 9.02 0.030 0.1090sugarbeet 2.7 1.7 1.3 1.3 1.1 0.6 3.9 2.48 0.64 7.46 4.76 0.016 0.0575sugarcane 0.9 0.97 0.97 0.3 0.3 1.3 1.36 1.02 1.93 1.97 0.007 0.0239sunflower 31.2 22.30 22.30 15.3 13.9 52.7 43.85 0.83 13.71 11.40 0.038 0.1377triticale 27.1 20.9 20.9 10.6 9.0 41.3 35.17 0.85 14.65 12.47 0.042 0.1506feed-wheat 22.3 21.0 19.6 19.6 8.1 11.4 37.7 35.05 0.93 13.57 12.61 0.042 0.1523maize US 18.5 17.5 17.5 1.0 7.1 26.1 25.13 0.96 10.64 10.23 0.034 0.1236

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soybean weighted av. IPCC 4.6 1.4 1.40 5.9 9.9 17.4 14.21 0.82 4.99 4.08 0.014 0.0493SYB Argentina av. =A 0.2 1.05 1.05 1.6 9.0 10.0 10.91 1.09 2.52 2.74 0.009 0.0331SYB Brazil av 3.8 1.50 1.50 7.4 8.5 16.0 13.72 0.85 4.85 4.15 0.014 0.0501SYB US av. 8.6 1.32 1.32 3.4 12.8 23.2 15.84 0.68 6.17 4.22 0.014 0.0510soybean weighted av. JRC/E4tech 4.6 1.4 1.4 5.9 24.6 32.1 28.90 0.90 8.92 8.03 0.027 0.0971

NO-TILL SOY IPCC residues

share of EU- soy imports

share of NT in national soy crop=k

ratio of N input CA/NT =B

total N in 2000 (GNOC)

total N latest estimate: NT or CA

SYB-NT average 0.7 2.27 0.916 3.26 0.0109 0.0393SYB Argentina NT 0.59 0.8 2.27 0.85 1.6 9.0 10.0 10.70 1.07 2.52 2.69 0.009 0.0324SYB Brazil NT 0.16 0.9 2.27 1.33 7.4 8.5 16.0 13.55 0.84 4.85 4.10 0.014 0.0495SYB US NT 0.24 0.5 2.27 0.81 3.4 12.82 23.2 15.33 0.66 6.17 4.08 0.014 0.0493

NO-TILL SOY JRC-E4tech residues

share of EU- soy imports

share of NT in national soy crop=k

ratio of N input CA/NT =B

SYB-NT average 0.7 2.27 0.916 6.93 0.0233 0.0837SYB Argentina NT 0.59 0.8 2.27 0.85 1.6 23.4 24.4 25.02 1.03 5.87 6.03 0.020 0.0729SYB Brazil NT 0.16 0.9 2.27 1.33 7.4 22.0 29.5 27.04 0.92 8.61 7.88 0.026 0.0952SYB US NT 0.24 0.5 2.27 0.81 3.4 29.7 40.0 32.21 0.80 10.54 8.48 0.028 0.1024

CONVENTIONAL AGRI SOY IPCC res

share of EU imports 0.014619

SYB-CA average 2.1 3.57 0.0120 0.0431SYB Argentina-CA 0.59 1.93 1.6 9.04 10.0 11.78 1.17 2.52 2.96 0.010 0.0357SYB Brazil- CA 0.16 3.03 7.4 8.5 16.0 15.24 0.95 4.85 4.61 0.015 0.0557SYB US-CA 0.24 1.83 3.4 12.82 23.2 16.35 0.71 6.17 4.36 0.015 0.0526

CONVENTIONAL SOY JRC-e4tech re

share of EU imports

SYB-CA average 2.1 7.23 0.0243 0.0873SYB Argentina-CA 0.59 1.93 1.6 23.4 24.4 26.10 1.07 5.87 6.29 0.021 0.0760SYB Brazil- CA 0.16 3.03 7.4 22.0 29.5 28.73 0.97 8.61 8.37 0.028 0.1012SYB US-CA 0.24 1.83 3.4 29.7 40.0 33.23 0.83 10.54 8.75 0.029 0.1057

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4.8. Manure calculation

Why do we consider 50% of manure in calculation of N2O emissions? 1. Argument for no manure: Manure is mostly used in areas around livestock production (increasingly concentrated in intensive areas), because of high manure transport costs. In these areas probably more manure is used than is needed for the crop N demand, as a means of waste disposal. If there is a greater density of cropping in these areas, extra production is achieved without extra synthetic N or extra manure use. However, in non-manure areas, extra production needs extra synthetic N. Therefore the extra N associated with extra production is just the average synthetic N per tonne of crop in that region. 2. Argument for including all manure N: Growing wheat needs a certain amount of N either from manure or synthetic sources. However, the amount of manure available is fixed (to a first approximation). Therefore if more crop is needed, all the extra N must be synthetic. The amount required per tonne of extra production is taken to be the average total synthetic+manure N per tonne of crop in that region. 3. "the truth is somewhere in the middle". So the marginal emissions per tonne of crop can be best approximated by including half the N in manure for both N2O calculations and in calculating the amount of fertilizer production emissions per tonne of crop. Note we do not take into account extra emissions from farming marginal land, although this effect appears to be important (CAPRI). 4. “irrational world of attributional LCA” In the irrational world of attributional LCA, the N2O emissions should include ALL the manure but the fertilizer production emissions attributed to the crop should only be applied to the average synthetic N used. 4.9. Disaggregation between no-tillage and conventional agriculture data 1. DISAGGREGATING DATA ON FARMING INPUTS Our most authoritative data on soybean farming inputs inputs refer to national averages; a mix of NT and CA; in the three main suppliers to EU. The problem is how to disaggregate this data to NT and CA areas. The best data we have for making this disaggregation comes from data in [Hilbert 2010] showing average data for regions of Argentina practicing NT and CA. This indicates that no-till areas have much lower diesel and nitrogen fertilizer use, but slightly higher use of

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"pesticides etc" (which includes herbicide) and seeding materials per tonne of crop. We use the Argentine data to provide "CA/NT" ratios of diesel, N, "pesticides" and seed use for all regions. These ratios are then used to estimate the CA and NT averages separately for all regions, whilst maintaining the given national averages. This requires some algebra, as detailed below. There is one exception: disaggregation of diesel use in US uses US-specific data on the amount of diesel saved by NT. (see SYFA-USA (NT) and SYFA-USA (CA ) sheets), We believe that NT areas of soy generally have higher aglime inputs, but we have no quantitative data to make a disaggregation. 2. NT and N2O The formula (by Stehfest and Bouwman) which GNOC uses to estimate N2O from crops at specific GIS locations, does not have tillage practice (NT or CA) as a variable. The effect of no-till on N2O emissions is complex. Modern reviews show that NT can either increase or decrease N2O emissions, depending on the aeration of the soil [Rochette 2008]. JRC has not yet developed GNOC far enough to account for this; therefore the only effect of no-till on N2O emissions in these results come as a result of the savings in N fertilizer, as explained above. However, as little N fertilizer is used on soybean, (most of the N considered in GNOC coming from plant residues), this only slightly reduces our estimate of average N2O emissions from NT soybeans. 3. SOIL CARBON AND NO-TILL In its GHG emission calculations for biofuels, JRC does not take into account changes in soil carbon stock. The subject is complex, and credits for increasing carbon stock through changes in farming practice should be complemented with debits for the general deterioration in soil C stock in many regions due to existing practices. Although NT may increase soil C stocks in some cases, the increase is not as great as previously believed (because early measurments did not measure C through the whole soil profile), and is often more than compensated by an increase in N2O emissions [Powlson 2011][Rochette 2008]. 4. ALGEBRA Definitions: x = input (e.g. diesel/tonne soy) for NT soy in a country y = input for CA soy in a country A = average input for soy in a country (whether or not NT), from national data k = fraction of NT in total production B = ratio of input from CA/NT in a country, from comparative data in the literature Then we have 2 simulaneous equations: y = Bx and to maintain the correct weighted average: kx + (1-k)y = A Solving these equations gives: x = A/(B+k(1-B)) y = A/(1+k(1/B-1)) These formulae are also used in CA and NT data sheets for individual countries, where

66

diasagregation is made for diesel use and pesticides). 5. REFERENCES 1 Hilbert et al., 2010 2 Powlson et al., 2011 3 Rochette 2008 4.10. Correction of IPCC method for estimating N2O emissions from leguminous crops We do not have detailed enough information to calculate the N2O emissions from crops grown outside EU using a soils model. The best we can do is to use the “tier 1” 2006 IPCC guidelines. This leads to a larger uncertainty in emissions. Fortunately, however, the crops we consider do not have a high ratio of nitrogen input to yield (and therefore generate moderate emissions per tonne, under the IPCC assumption that emissions are proportional to N rate), so the effect on the uncertainty in the overall GHG balance is still acceptable. Between their 1996 and 2006 guidelines, IPCC changed their default emission guidelines for soybeans: this had the effect of drastically reducing the N2O emissions calculated for soybean. We think the true emissions actually lie between the two, as described below. This discussion originated from the staff of E4tech, UK in 2008, working on behalf of the UK Renewable Fuels Agency. The resulting correction to the N2O emissions for leguminous plants was incorporated in UK RFA default values for soybeans. Subsequently E4tech discussed their analysis with JRC. JRC agreed, but added a small correction, described below. The text below is our interpretation of the survey in the original E4tech document, and appears with their permission. JRC agrees with E4tech that the 2006 IPCC (tier 1) approach significantly underestimates the N2O emissions from soybeans and other leguminous plants. The old 1996 IPCC methodology for calculating N2O emissions from soil (used in v2 of JEC-WTW) did not consider the Below-Ground Nitrogen (BGN) in plants at all, but did assume that the nitrogen naturally fixed by leguminous plants (such as soybean) contributed to the release of N2O. This would mean that the nitrogen-fixing bacteria in the roots were emitting N2O at the same time as they were fixing nitrogen from the air. The distribution of biologically-fixed nitrogen in leguminous plants is shown in Figure 8. However, a paper in 2005 by [Rochette and Janzen, 2005] argued that there was little evidence for significant N2O emissions from legumes during the nitrogen fixation process. Therefore in the revised 2006 methodology (published in 2007), IPCC no longer include emissions directly from the natural nitrogen-fixing process. On the other hand, the 2006

67

guidelines do take into account the contribution of below-ground N content of the plants themselves to the nitrogen pool in the soil which contributes to N2O emissions. IPCC attribute these extra emissions to the current crop. However [Rochette 2004] shows that most of these will actually take place during the following season. He found that although the soil mineral N content under legumes were up to 10 times greater than under a grass, this was not closely related to the N2O emissions measured during the growth phase of the plant. However, he found greater emissions of N2O after the plant had been harvested, strongly dependent on the soil type. So for the current season, what should be taken into account is the contribution of below-ground nitrogen from the residues of the previous crop. From the point of view of a national average, it does not matter much to which crop a certain amount of soil nitrogen is attributed. But it does make a difference if you are calculating N2O emissions per crop in a rotation. Of course the distinction is not important if the same crop is grown in successive years, which is generally the case in Brazil (the assumed source of soybeans in our study).

Part of the nitrogen biologically fixed by soy plants ends up in the above- and below-ground crop residues and in principle IPCC 2006 takes emissions from this into account. However, we think IPCC have seriously underestimated the amount of below-ground nitrogen. This comes both from underestimating the below-ground biomass and from ignoring nitrogen from rhizodeposition. Rhizodeposition (Jensen, 1995) is the process whereby N enters the soil from the plant roots in the form of NH4, NO3, amino acids, cell lysates, as well as decay of sloughed-off and senescent roots. It can now be quantified through techniques such as 15N shoot labelling (Khan et al., 2002). The literature shows that leguminous plants such as soy exude significant volumes of N from their roots (Martens, 2006).

above-ground nitrogen nitrogen in roots nitrogen from rhizodeposition

below- ground nitrogen Figure 7 Distribution of biologically-fixed nitrogen in leguminous plants

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Table 11.2 of the 2006 IPCC Guidelines gives default factors for estimation of N added to soil from crop residues. According to this, only 16% (= .19/1.19) of the soybean plant residues are in the underground biomass, and that they all have the same nitrogen concentration. These default factors are based on an extensive literature review, the references for which they provide in Annex 11A.1. The default value for BGN content of soybean comes from a 1925 paper. Whilst E4tech could not obtain a copy of this reference, their review of more recent literature suggests such an old work will have missed not only the N released by rhizodeposition, but also that in fine root hairs that are very difficult to collect using the old techniques of physical root recovery. [Aruja et al. 2006] confirms that the roots recoverable by traditional methods only contain 5-10% of the total N accumulated by the plant. For comparison, [Alves 2003] report results using modern techniques of between 30-35% of total plant N11. This implies IPCC has underestimated nitrogen in the roots by at least a factor 3. If we include also nitrogen from rhizodeposition, the IPCC defaults might seem even further out. [Khan 2002b] concluded that the traditional methods only recovered 20-30% of the total below-ground nitrogen (including that from rhizodeposition) obtained using N-labelling methods. [Mayer 2003] found that N rhizodeposition represented about 80% of the below ground plant N. These studies suggest that the N from rhizodeposition is roughly four times the below-ground nitrogen in the roots, so at least an order of magnitude greater than the below-ground nitrogen calculated from IPCC defaults. We think that in reality only the part of the biologically fixed nitrogen released by rhizodeposition counts towards N2O emissions from the soil during a particular growing season. The rhizodeposition gradually builds up during the season, but after the harvest the all plant residues gradually decay and release their nitrogen into the soil. There is not enough data to estimate the amount of rhizodeposited nitrogen from soy by direct measurements of soil nitrogen. A more pragmatic and accurate approach is to back-calculate the total effective below-ground nitrogen, coming from the combination of below-ground biomass itself plus rhizodeposition, from the measured nitrous oxide emissions from soybeans grown without synthetic nitrogen. That figure would reflect the actual nitrogen content in the soil which is giving rise to N2O emissions. Referring to IPCC 2006 table 11.2, it does not matter for the N2O emissions calculation whether we do this by changing the default value “RBG-BIO“, (ratio of below-ground to above-ground biomass) or “NBG”, the effective nitrogen concentration in the below-ground biomass (kgN/ kg dry matter). We have chosen to change only the second value; this is equivalent to assuming that the contribution of dead roots to the mass of below-ground biomass is small. 11 Similarly [Rochester 1998] found that ~40% of the N in legumes either resided in, or was released from nodulated roots, and this is confirmed by [Russel and Fillery 1996]. [Rochester 2001] states “in the past, belowground N has either been ignored or grossly underestimated when N balances have been calculated for legume crops”.

69

We start off by averaging the measurements of N2O emissions from soy without N fertilizer quoted in [Rochette 2005]. The result is 1.075 kg N-N2O/ha. We assume this all originated from rhizodeposition and decay of crop residues from the previous season. Using the , IPCC default direct emissions factor of 0.01 kg N-N2O / kg N(CR)12, JRC estimates the total nitrogen which gave rise to those emissions to be 107.5 kg N/ha. From this we need to subtract the nitrogen in the above-ground residues to find the effective below-ground nitrogen. First we need to estimate the yield. The N2O data are mostly from USA, where the average yield from FAO (the source suggested by IPCC) dry-matter yield was 2.26 tonnes /ha. Using the formula in IPCC 2006 table 11.2, this yield corresponds to above ground residue of 3454 kg DM/ha. Combining this with the default concentration of nitrogen in above-ground biomass (0.008 kgN/kgDM, confirmed by [NREL 2005]) gives 27.6 kg N/ha in above-ground residue. Subtracting this from the total N in plant-residue leaves us with an effective 79.9 kgN/ha in below-ground biomass. To find our new value for the default nitrogen concentration in below-ground-biomass, we need to divide the last number by the amount of below-ground biomass. Following the IPCC 2006 method we calculated this was 1086 kg/ha13 Now our new value for the effective nitrogen associated with below-ground biomass is NBG = 79.9/1086 = 0.074 kgN/kgDM below-ground biomass. JRC recommends using this in place of the default value of 0.008 in IPCC 2006 - table 11.2 -, in order to calculate N2O emissions from soy which are comparable with measurements14. We can check whether this value is reasonable by looking at which value it implies for the fraction of the total nitrogen associated with the plant. This can be checked against measurements in the literature, which mostly range from 30 to 35%, according to [Alvez 2003] and a wider literature survey by E4tech. The nitrogen concentration in the beans is 6.5% DM according to [NREL 2005], corresponding to 147kgN/ha in beans. The total plant N is this plus below-ground nitrogen and above-ground nitrogen in residues. Adding this all up using the figures above gives a total of 255 kgN/ha associated with the plant. Then the fraction of below ground nitrogen implied by our method is 31%. This is indeed within the range of measured values, giving us confidence that we are at least approximately correct. It is likely that non-leguminous crops also exude some nitrogen from the roots, but in this case it is only returned to the soil after having been absorbed from the soil, so correcting for it could lead to double-counting. 12 Only the DIRECT N2O emissions are measured in the field. E4tech inadvertently used total direct + indirect emissions 13 Following the formula underneath equation 11.6 in [IPCC 2007], this is calculated by applying the IPCC default ratio of below-ground to above-ground biomass (“RBG-BIO“ = 0.19 for soy) to the total above-ground-biomass found by summing the above-ground residue and crop yield shown above. 14 By considering also indirect emissions, E4tec came to a slightly lower value for NBG

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4.11 Comparison of the corrected IPCC method for soybean with site measurements As suggested in chapter 4.10 we substituted the default value of 0.008 kg N kg-1 below ground biomass dry matter in belowground residues given by IPCC (2006) with 0.074 kg N kg-1 below ground biomass dry matter. We ran the GNOC with both factors and compared measurement data collected by Stehfest and Bouwman (2006)15 and the GNOC results on a country level16. The measurement data results from 5 measurement campaigns in 5 locations in the US (Idaho and Ohio), Canada and China. Thus, the global representativity is limited. N2O emissions at the measurement sites range from 0.2 - 2.3 kg N2O-N ha-1 (Figure 7), with the majority of the sites having emissions between 0.5 and 2 kg N2O-N ha-1. The measurements cover a period of ~120 days at the sites in Sheyang (China), Elora (Canada) and Ohio (US) while for Iowa (US) and Guelph (Canada) measurements were carried out for an entire year (365 days). None of the soybean plots received N input from mineral fertilizer or manure except one of the Chinese sites (50 kg N ha-1). It has to be noted, that the measurements most probably do not cover all the emissions pathways e.g. via the indirect paths of leaching, volatilization and re-deposition. Figure 8 shows the country level N2O-N emissions based on the GNOC for the two different NBG values. The majority of the country mean emission values applying the default IPCC factor (blue points and line) lay below 1 kg N2O-N ha-1 a-1. Applying the updated NBG (orange points and line), the emissions from soybean cultivation in most countries range between 0.5 and 1.5 kg N2O-N ha-1 a-1. This comes much closer to what we can see from the experimental sites but the values are still lower, especially if we consider that the higher emissions (>1 kg N2O-N ha-1 a-1) occur in countries with an extra fertilizer N input of around 30kg N ha-1 a-1 or more, while most of the experimental sites are unfertilized. This comparison supports the assumption that nitrous oxide emissions from soils cultivated with soybean is underestimated when applying the default IPCC (2006) Tier 1 factors. For the final GNOC results presented previously, the default IPCC (2006) value for NBG has been substituted with the corrected value. 15 Measurement sites with N fertilization >100kg ha-1 were excluded as they are considered to be not representative 16 The GNOC results are displayed only for countries where soybean is cultivated on more than 1000 ha

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Figure 7: Measurements of soil N2O emissions from soybean cultivation (Stehfest and Bouwman, 2006) N2O measurements in soybean cultivations

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except one Chinese site all plots are unfertilized Figure 8: Country average soil N2O emissions from soybean cultivation from the GNOC. 1. applying IPCC default value for nitrogen content in belowground biomass (NBG) and 2. applying the updated NBG based on Edwards (2012)

Country level N20 emissions from soybean cultivation

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4.12. Emissions from acidification and liming methodology

Emissions from Neutralization of Fertiliser Acidification and Application of Aglime This section has been kindly reviewed and corrected by Anne and Jean-Luc Probst of the Laboratoire d’Ecologie Functionelle (ECOLAB) of ENSAT, France. (A) Adding neutralization contribution to fertilizer emissions Acidification from N fertilizer causes emissions whether or not aglime is applied Most N fertilizers generate acid as they are oxidized by bacteria in the soil. Some farmers apply aglime to neutralize the acid. However, we shall attribute CO2 emissions for the neutralization of this acidity to the fertilizer rather than the aglime, because most of the neutralization emissions occur whether or not the farmer applies aglime; through reactions with carbonates naturally present in the soil or lower down in the watershed (Semhi, 2000; West, 2005; Perrin, 2010; Brunet, 2011). Calculating the neutralization emissions if all N acidifies The main fertilizers used in the world and EU are urea and Ammonium Nitrate (AN)17. These generate the same amount of acid per kg of N when they oxidize to nitrate in the soil: CH4N2O + 4 O2 = 2 HNO3 + CO2 + H2O (see footnote 18)...................................................(1) (NH4)NO3 + 2O2 = 2HNO3 + H2O.............................................................................................(2) The neutralization reaction of this acid, by carbonates already in the soil, or added as lime: 2HNO3 + CaCO3 = Ca++ + 2(NO3-) + CO2 + H2O.....................................................................(3) emits 1.57 kg CO2/kg N according to stoichiometry. But not all the applied N causes acidification However, not all the N ends up causing acidification. Some of it is absorbed by plants as ammonium ions, before the acid-generating oxidation can occur; some of the acidification 17 Sodium nitrate causes no acidification, but CO2 emissions arise when it is manufactured from alkali. On the other hand, ammonium sulphate and aqueous ammonia give double the acid per kg N. Calcium ammonium nitrate is AN pre-mixed with a variable quantity of lime. 18 (the CO2 emitted in this reaction balances the CO2 sequestration JRC did not count in urea manufacture)

73

is reversed when nitrogen is released to the air by denitrifying bacteria19, and some of the acidity is neutralized when plants take up nitrate. Three approaches to estimating the fraction of the stoichiometric CO2 emissions from N fertilizer. 1. The Association of Official Analytical Chemists (AOAC) Method 936.01 recommended farmers to apply 1.8 kg CaCO3 per kg N (as urea or ammonium nitrate), to neutralize acidification caused by the fertilizer20,21. Sinistore, (2010) uses the AOAC recommendation to estimate CO2 emissions from neutralization of fertilizer-acidity, and also the use of aglime in LCA of Wisconsin corn farms. However, Barak, (2000) reported that US farmers actually apply substantially less aglime than is required to neutralize the acidity according to this guideline. The recommended lime dose is 50% of the stoichiometric quantity which would be required if all the nitrogen ended up acidifying the soil. Thus the recommendation must take into account the neutralization coming from plant-uptake of nitrogen and the action of denitrifying bacteria. However, Barak (2000) reported that using less lime than recommended by AOAC was causing a degradation of the buffer capacity of US farm soils, (as indicated by its cation exchange capacity, a component of soil fertility). Thus we suppose that the AOAC estimated the amount of lime required to neutralize the acidity from N fertilizer without drawing on the natural neutralizing capacity of the soil. Since the AOAC recommendation was made in 1935, improved techniques may have increased the fraction of N taken up by crops, so that now perhaps less than 50% of the nitrogen causes acidification. On the other hand, total use of nitrogen fertilizer has greatly increased in the same time, and this tends to reduce the nitrogen uptake efficiency. 2. Another indication of the fraction of applied N causing acidification is the amount of aglime mixed with CAN fertilizer: a mixture of ammonium nitrate and ground limestone, which is supposed to neutralize acidification. The lime content varies, but for example in Australia it is said to be typically 8% Ca and 27% N, which implies that 21% of the stoichiometric acidification is neutralized. However, the Australian fertilizer guidelines warn this quantity of lime is insufficient where there is leaching. 3. A third way to estimate the fraction of applied N causing acidification is to look at the fate of N in agricultural watersheds. According to Oh (2005), starting from urea or 19 The N2O from denitrification is accounted separately in RED calculations. 20 [Barak 2000] explains that method 936.01 is listed as “obsolete” by AOAC not because it was inaccurate, but because few farmers now use it: modern aglime recommendations tend to be based on measured soil pH and soil type, which has the advantage of combating all forms of acidification and of saving lime in cases where the soil already contains sufficient carbonates. 21 The recommendation is reflected in various handbooks and recommendations on fertilizer and soil health around the world. Some of these reduce the recommendation for urea to compensate for urea loss by volatilisation, but that just makes acidification somewhere else, so globally volatization should not be accounted.

74

ammonium nitrate, the only part of nitrogen which contributes to acidification is the part which after oxidation is leached into ground-water or run-off (carrying away alkali ions like Ca2+). However, this may underestimate acidification, because Barak (1997) says that only some of the N uptake by plants results in neutralization. Van Bremen (2002) reports the fate of N for watersheds coving most of North-East USA. His results table shows that for agricultural land, a weighted average of 1921 kg N were leached per km2 of watershed, compared to 243+1954 kgN/km2 taken off in biomass and 5562 kgN/km2 denitrified. Then a minimum of 24.8% of the total N input would need neutralization22. But N fertilizer contributed less than a quarter of the total N supply in this region: less than that from manure (3979 kgN/km2) or agricultural fixation by plants (3727 kgN/km2). One would expect N fixed by plants to be less prone to leaching. If we exclude that, the loss by leaching could be up to 51% of the “mobile” N. We recall that not all the N taken up by the plant should be excluded from causing acidification, according to Barak (1997), so these are estimates of the minimum value. CONCLUSION: emissions from neutralization of fertilizer acidity The emissions from neutralization of fertilizer acidity lie between about 25 and 50% of the stoichiometric value of 1.57 tonnes CO2/tonne N. We shall use a mean value of 37.5%, which gives 0.59 tonnes CO2 per tonne N. These emissions should be added to the emissions from N fertilizer provision, and not to liming, as they occur whether or not lime needs top be added to the soil. One may argue this value is too high, because it does not account for some neutralization reactions (of minor importance in cultivated watersheds) which do not generate CO2: notably weathering of clays23 and feldspar. On the other hand, one can say it is too low because by only considering urea and ammonium nitrate fertilizers, we miss the fact that most of the other N fertilizers used, such as ammonium sulphate and aqueous ammonia, give at least double the acidification per tonne of N. (B) ADDING OTHER CO2 RELEASES TO AGLIME APPLICATION Why farmers use aglime Aglime is used to stop acidification due to: (1) CO2 dissolved in rain, (2) Decay of organic matter in the soil, (3) Oxidation of N from fertilizer, bio-fixation, and rain, (4) To replace Ca taken off in the crop 22 We do not count changes in carbon stored in the soil, as the final fate of this N has yet to be determined: we suppose some is leached and some taken off as biomass or denitrified, in the same proportions as the other N. For the same reason we ignore N lost by volatilization to outside the region. 23 Clay in soil mostly has a pH-buffering effect, which only delays the need to neutralize acidification. However, if liming is insufficient, there is also a long-term weathering reaction which degrades the soil by reducing its Cation Exchange Capacity (Barak 2000).

75

Mechanism (2) is important in organic/peaty soils, releasing CO2 immediately (Biasi 2008), but not in mineral soils with near-neutral acidity. (4) only consumes a small fraction of the applied lime (Oh, 2005), and can be neglected in the following discussion. This is a correction to existing calculations for Renewable Energies Directive In the calculations of GHG emissions from biofuel cultivation for Annex V of the Renewable Energies Directive, (and also in JEC WTWv3), we did not account for CO2 release from aglime reactions in the soil. We shall do so now, but we must avoid double-counting of emissions from the part of aglime which is used neutralizing acidity for N fertilizer, as we have just recommended attributing these to the fertilizer. We now consider only crushed limestone Nowadays, the great majority of aglime is ground limestone (CaCO3) or sometimes dolomite (CaCO3.MgCO3). We now consider exclusively ground limestone in calculating emissions from aglime supply and application. In our previous calculations, for RED Annex V and JEC-WTWv1-v3, we did not account for any emissions from applying aglime to soils, and included 15% calcined limestone in aglime production emissions. Calcined limestone; CaO or Ca(OH)2; is more costly and is only used where a quick effect is needed. Calcined limestone does not emit CO2 during neutralization of acid in the soil, but the CO2 and fossil fuel emissions released during production probably make it more GHG intensive than ground limestone. IPCC guidelines on emissions from aglime application are too pessimistic According to IPCC guidelines on national GHG inventories (IPCC 1997), all the CO2 in aglime is emitted in the end. But according to more recent work (West, 2005; Perrin, 2008) , some of it is sequestered. But it depends on the pH of the soil. On acid soils ( pH is less than ~6.4)24 aglime is dissolved by soil-acids to form predominantly CO2 rather than bicarbonate. Then most of the CO2 in the aglime is released (Biasi 2008; West 2005). By stoichiometry, that is 0.44 kgCO2/kg aglime.

24 JRC calculation based on equilibrium constants of bicarbonate reactions. At this pH, dissolved CO2 and bicarbonate are in equal concentrations [Schulte 2011] modified from [Drever 1982] . These are for 25C, so there may be a small error depending on soil temperature.

76

On more neutral soil. Above ~pH 6.4 aglime is dissolved mainly as bicarbonate, and part of its CO2 content is sequestered in the end. The bicarbonate is either decomposed by acidity deeper in the soil (releasing the aglime’s CO2 ) or is exported to the ocean, where some is sequestered (West 2005).25. The flows in fig. 2 of [West 2005] indicate that from 15.17 Mtonnes of bicarbonate ions produced by dissolution of lime (consuming 12.44 Mtonnes of CaCO3 by stoichiometry), a net 0.98 Mtonnes of CO2 are emitted if the whole system from soil to ocean is considered26. So if soil pH > 6.4 we will assume that 0.98/12.44 = 0.079 kgCO2 are emitted per kg of aglime applied, apart from the emissions due to neutralization of the acidification from fertilizer. Avoiding double-counting The carbonate which is dissolved by acidity resulting from N fertilizer is not sequestered at sea or anywhere else (West 2005; Gandois, 2011). But we are already adding this to our emissions from fertilizer. To avoid double-counting, we should first subtract this CO2 emission (estimated to be 0.59 kgCO2/kgN) from our estimate of emissions from aglime dissolved by (1) and (2). SUMMARY: AGLIME RULES If soil pH > 6.4 (that applies to most crops on temperate mineral soils) Emissions attributed to aglime = (kg aglime applied)*0.079 - (kg of N applied)*0.59 (in kg of CO2/kg lime) If the result is negative, the CO2 emissions attributed to lime are zero (it means they are already covered by the neutralization emissions attributed to the N fertilizer: insufficient lime in this range of soil pH usually means the N-acidity is also being neutralized by carbonates in the soil). If soil pH < 6.4 Emissions attributed to aglime = (kg aglime applied)*0.44 - (kg of N applied)*0.59 (in kg of CO2/kg lime) If the amount of aglime applied is less than the amount needed to neutralize acidity from the fertilizer, then no emissions are allocated to soil reactions of lime: they are already 25 All the papers reviewed assume that, as a soluble species, the bicarbonate content of soils or river basins must be roughly steady in the long term, so in the end effectively all bicarbonate produced from aglime dissolution is either decomposed by acidity in the soil (releasing all the carbon content as CO2) or is exported to the ocean. In the ocean a part of the bicarbonate is converted back to carbonate, releasing some of the CO2 (see discussion and references in (West, 2005), whilst some CO2 in the bicarbonate is sequestered as dissolved bicarbonate in the ocean, as well as in deposited carbonate. 26 Oh (2006) shows that in the frame of a river basin, aglime may actually lead to slight sequestration of CO2, but that ignores what happens to the bicarbonate after it is exported to the ocean.

77

covered by the emissions attributed to N fertilizer application (the residual emissions from fertilizer acidification are taking place downstream from the soil). Liming results table: Crop groups: We do not have data to distinguish lime use on peat and mineral soils for oil palm. Soybean weighted average: source-country weighted by oil plus oil-equivalent of beans imports. Calculated total emissions from lime: Emissions from application of lime to counter soil acidity. Calculated in model according to soil pH of crop areas: (kg lime applied * 0.079 if pH > 6.4 and kg lime applied * 0.44 if pH < 6.4) Emissions from neutralizing acid from synthetic: The part of the total lime emissions Emissions attributed to mineral fertilizer N input. But some of these emissions come from natural carbonates in soil, nit only from applied lime. (kg mineral fertilizer N applied * 0.59) Emissions from neutralizing acid N in Manure: Emissions attributed to 50% of the manure N input (kg manure N applied * 0.5 * 0.59) How to read the table: The RED COLUMNS in Table 49 provide input for the further GHG calculations. Figures in column 7 are the updated weighted average figures for calculating aglime supply emissions. Transport distance for Brazil aglime is ~500km. Column 9 is the extra CO2 emissions which should be added to fertilizer provision to account for emissions from neutralizing the acidity generated by the synthetic nitrogen. (kg lime applied * 0.079 if pH > 6.4 and kg lime applied * 0.44 if pH < 6.4) On some fields this is more than the emissions from aglime, because the acidity is neutralized by natural carbonates on or off the field. Column 13 is the remaining emissions from application of aglime (after subtracting field-by-field the emissions already attributed to synthetic fertilizer reduction), This is caused by neutralization of pre-existing soil acidity and a bit from neutralizing acid from N in manure. The figures in column 9 and 13 add up to more than the total emissions from aglime in column 8 because part of the CO2 from N acidification come from neutralization by natural carbonates and not applied aglime.

78

Table 49 Liming results

CRO

P G

ROUP

n2oc

ropg

r

wei

ghte

d av

. YIE

LD k

g/ha

n2oc

ropg

r_yl

d_kg

ha

AV Y

IELD

MJc

rop/

han2

ocro

pgr_

yld_

MJh

a

AV M

INER

AL N

kgN

/ha

(cor

rect

ed fo

r SO

C)m

inN_

soc_

corr

_kgh

a

N fr

om a

nim

al w

aste

kgN

/ha

anw

N_kg

ha

ESTI

MAT

ED A

VERA

GE

AGLI

ME

INPU

T kg

/ha

aglim

_inp

ut_r

eal_

kgha

ESTI

MAT

ED A

VERA

GE

AGLI

ME

/MJ

CRO

P ag

lime

inpu

t g C

aCO

3/M

Jcro

p

CALC

ULAT

ED to

tal e

mis

sion

s fro

m li

me

lime_

emis

s_gC

O2p

erM

Jcro

p

EMIS

SIO

NS F

ROM

NEU

TRAL

IZIN

G A

CID

FRO

M S

YNTH

ETIC

Nfe

rtNm

in_a

cid_

emis

s_gC

O2p

erM

Jcro

p

EM

ISS

/NE

UTR

LZG

AC

ID F

RO

M N

in M

anur

efe

rtN05

man

_aci

d_em

iss_

gCO

2per

MJc

rop

RE

MA

ININ

G E

MIS

SIO

NS

FR

OM

LIM

E U

SE

lime_

min

us_f

ertN

min

_aci

d_em

iss_

posi

tive_

gCO

2per

MJc

rop

lime_

min

us_f

ertN

min

plus

fertN

05m

an_a

cid_

emis

s_po

sitiv

e_gC

O2p

erM

Jcro

p

REM

AING

CO

2 FR

OM

LIM

E AN

D M

ANUR

Elim

eplu

sfer

tN05

man

_min

us_f

ertN

min

_aci

d_em

iss_

posi

tive_

gCO

2per

MJc

rop

fact

_wet

_to_

dry_

biom

ass

barley 4021 59814 101.54 40.16 216.93 3.63 1.37 1.00 0.20 0.95 0.87 1.15 0.870cassava 11851 133977 8.49 7.16 44.02 0.33 0.12 0.04 0.02 0.10 0.09 0.12 0.700coconut 4587 41695 6.71 7.92 81.54 1.96 0.89 0.10 0.06 0.81 0.76 0.87 0.300cotton 3042 61727 178.02 14.60 177.99 2.88 0.99 1.68 0.07 0.70 0.68 0.76 0.910maize 5839 85859 111.80 30.04 107.68 1.25 0.47 0.76 0.10 0.24 0.21 0.34 0.850oilpalm 17675 279967 80.35 8.85 161.41 0.58 0.24 0.17 0.01 0.12 0.12 0.13 0.660

rapeseed 2549 60562 164.89 42.83 245.15 4.05 1.49 1.53 0.20 0.84 0.77 1.04 0.900rye 2947 43592 75.86 38.86 273.26 6.27 2.42 1.03 0.26 1.82 1.68 2.09 0.870safflowe 1067 25158 59.46 7.94 44.54 1.77 0.53 1.26 0.09 0.17 0.17 0.26 0.910sorghum (GRAIN) 4059 60035 89.71 12.50 86.92 1.45 0.50 0.89 0.06 0.25 0.23 0.31 0.870soybean weighted av 2474 49491 11.39 14.45 139.52 2.82 1.18 0.13 0.09 1.07 1.01 1.16 0.850SYB Brazil 2500 50009 9.59 18.48 156.70 3.13 1.37 0.11 0.11 1.26 1.16 1.37 0.850SYB US 2532 50638 21.86 8.62 229.35 4.53 1.81 0.25 0.05 1.60 1.56 1.65 0.850SYB Argentina 2442 48833 0.46 3.96 19.49 0.40 0.17 0.01 0.02 0.16 0.15 0.19 0.850

sugarbeet 52446 200992 143.26 57.07 217.99 1.08 0.38 0.42 0.08 0.22 0.20 0.31 0.235sugarcane 69986 376915 65.90 18.48 127.21 0.34 0.14 0.10 0.01 0.07 0.06 0.08 0.275sunflower 1398 33205 44.85 21.36 73.00 2.20 0.71 0.77 0.19 0.48 0.44 0.67 0.900tritical 3988 58989 107.89 42.33 243.27 4.12 1.53 1.08 0.21 1.02 0.93 1.23 0.870wheat 4705 69190 106.26 38.50 177.55 2.57 0.91 0.90 0.16 0.56 0.51 0.72 0.865

US maize 8439 124094 156.11 8.62 194.59 1.57 0.60 0.74 0.02 0.18 0.17 0.20 0.850

79

Sources for Emissions from acidification and liming; Barley JEC-WTW input database Cassava JRC calculation from composition in International Starch Institute

http://www.starch.dk/isi/starch/cassava.asp acessed Dec 2010 Coconut JRC-RE calculation from components file

\"Pathways_for_DG_TREN_May2010_MB-4.xls\" Cotton JRC calculation from composition in Nutrient requirements of dairy cattle, 7th

rev. ed. National Academy of Sciences 2001 Maize JEC-WTW input database UPDATED US maize JEC-WTW input database UPDATED Oilpalm JEC-WTW input database Rapeseed JEC-WTW input database Rye JEC-WTW input database Safflower JRC calc based oil and water content in

http://safflower.wsu.edu/SafflowerProduction_Canada.pdf …and meal composition in Nutrient requirements of dairy cattle, 7th rev. ed. National Academy of Sciences 2001

Sorghum (GRAIN) JEC-WTW input database Soybean weighted av JEC-WTW input database SYB Brazil JEC-WTW input database SYB US JEC-WTW input database SYB Argentina JEC-WTW input database Sugarbeet JEC-WTW input database Sugarcane JEC-WTW input database Sunflower JEC-WTW input database Tritical JEC-WTW input database Wheat JEC-WTW input database

80

4.13. Global crop specific calculation of CO2 emissions from agricultural lime application and fertilizer acidification. Global CO2 emissions caused by agricultural lime application are calculated for a 5min by 5min (~10km by 10km) grid, similar to the approach for soil N2O emissions. For soil parameters, crop species distribution and fertilizer N input we resorted to the same data set used to calculate soil N2O emissions. As a prerequisite for the emission calculations the site specific lime application to a certain crop within a 5min by 5min grid cell had to be estimated. Country level limestone (CaCO3) and dolomite (CaMg(CO3)2) consumption to counteract acidification of soil (and water bodies) is available from the EDGAR v4.1 database (EC-JRC/PBL). In the following we refer to lime but intend the sum of limestone and dolomite. The data of the EDGAR v4.1 database originates from the reporting of Annex I27 countries to the UNFCCC. Data on lime consumption for the year 2000 is taken from the (mainly year 2008) submissions of the common reporting format (CRF) tables. In EDGAR v4.1 all the lime use reported in CRF table 5 (IV) is taken into account, no matter whether lime is applied to agricultural soils, forests or lakes. In the case of Non-Annex I countries1 which are not obliged to report emissions to UNFCCC, the estimated amount of lime applied is based on the calculated need to balance the use of ammonium fertilizers. It is assumed that all calcium is applied as lime. It has to be noted that in reality several factors affect soil acidity and, subsequently, liming need, and therefore the estimates are highly uncertain (EC-JRC/PBL). As the EDGAR v4.1 data set does not distinguish lime input to different land uses/covers the UNFCCC CRF submissions (2008) for the year 2000 (UNFCCC, 2012) were re-screened and the shares of input into other land uses than cropland were subtracted in our calculations for those countries providing the information. Country level data as extracted from the EDGAR v4.1 data base and the shares of lime input to other land uses/cover are listed in Table 50. There are various shortcomings using the EDGAR v4.1 data set for the calculations of CO2 emissions from liming of potential biofuel crops - but to our knowledge this is the only global data set on lime and dolomite application based on values reported officially by the individual Annex I countries. The two main shortcomings are: 27 Annex 1 is an Annex in the United Nations Framework Convention on Climate Change listing those countries which are signatories to the Convention and committed to emission reductions. The Non-Annex 1 countries are developing countries, and they have no emission reduction targets.

81

only a few countries report the share of lime applied to other land use/cover than cropland. Especially in developed countries with high shares of managed grasslands / pastoral systems (e.g. New Zealand) this may lead to an overestimation of liming in croplands as the share of input to e.g. grassland soils is unknown. For Non-Annex I countries the estimated amount of lime is estimated on the country level to balance acidity from use of ammonium fertilizer input. The soil status in these countries is not taken into account. In Brazil, having a high share of soils susceptible to acidification, the government approved a program in 1998 to improve the Brazilian agriculture productivity by intensification of liming. According to national statistics (Bernoux et al., 2003), 19,812 ktonnes of lime were consumed in the year 2000. An amount three times higher than given in the EDGAR v4.1 data set (6,980 ktonnes). However, it is unknown if the whole amount given by the statistics was applied to arable land or if liming of e.g. permanent pastures is happening as well. Contrary to the Brazilian case, in poorer countries, the values of the EDGAR v4.1 data set might as well be overestimated. To break down country level lime consumption to site specific application rates we follow the recommendations by the Agricultural Lime Association -ALA-(2012) developed in partnership with the University of Hertfordshire; Agriculture and the Environment Research Unit (AERU). The application rates recommended are aiming at a target soil pH of 7.0 for arable and a soil pH of 6.5 for permanent grassland. This holds for mineral soils. For organic soils the target pH is 0.3 and 0.7 pH units lower for arable land and grassland respectively. The ALA recommendations depend on soil pH, texture as well as organic matter content and whether the soil is cultivated as arable land or grassland (see Table 51). Based on globally available information on soil pH, organic matter and texture from the Harmonized World Soil Data Base (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009)28 and the harvested area of the single crops in the year 2000 (Monfreda et al., 2008) the theoretically required lime input according the recommendations of the Agricultural Lime Association (2012) can be calculated for the harvested area of each crop in each 5min by 5min grid cell. Figure 10 illustrates the underlying data sets for global pH and harvested crop area. The lime was distributed to harvested area (accounts for multiple cropping) rather than to cropland area (physical land area) assuming that areas with double or triple cropping receive higher fertilizer input and need higher rates of lime application to counteract acidity caused by fertilizer N. The final lime input to the grid cell was calibrated in order to fit with the country level lime input from the EDGAR4.1 database. We compared the results of the disaggregation with field level data given in the literature for Germany and UK. (See section 4.14) - in both cases the estimated lime input per ha of this work is in the range of what is mentioned in the literature. The total emissions from lime application to the crop on a grid cell bases were calculated as: 28 the calculations are based on the dominant soil type in a soil mapping unit of the Harmonize World Soil Data Base

82

CO2 Emissionslimetot in kg = kg lime applied * 0.079 if pH ≥ 6.4 and CO2 Emissionslimetot in kg = kg lime applied * 0.044 if pH < 6.4 However, emissions from lime input required to neutralize fertilizer acidity are attributed to the emissions caused by the fertilizer. These emissions need to be subtracted from the total emissions caused by lime application to avoid double counting. According the anlysis described before, the neutralization of one tonne of synthetic fertilizer applied releases 0.59 tonnes CO2. From the global data set on crop specific synthetic fertilizer input data29 the emissions caused by neutraliziation of fertilizer input were calculated on the grid cell basis for each crop as: CO2 Emissionsynthfert in kg =kg synthetic N applied * 0.59 If for a specific crop in a grid cell the CO2 emissions from lime input exceed the CO2 emissions needed to neutralize synthetic fertilizer N input we attribute the difference in emissions to lime application. Due to the method of accounting for N input from mineral fertilizer and manure to a specific crop, we also take into account 50% of the manure input given in the global fertilizer data set, assuming that in the case of biofuel crops we underestimate the total amount of mineral fertilizer, to which in most cases no manure is applied30 . The emissions resulting from neutralizing 50% of N applied as manure (CO2 Emissions50%man) to the biofuel crops in our database are calculated the same way as for synthetic fertilizer, as we consider that in reality this manure will be applied as synthetic fertilizer to biofuel crops. However, the emissions are not added on the synthetic fertilizer side but we add it to the final lime emissions (CO2 Emissionslime_net) to ensure that globally emissions attributed to synthetic fertilizer are not overestimated. Hence, if CO2 Emissionslimetot ≥ CO2 Emissionsynthfert we calculate CO2 Emissionslime_net= CO2 Emissionslimetot + CO2 Emissions50%man - CO2 Emissionsynthfer otherwise if CO2 Emissionslimetot < CO2 Emissionsynthfert we set CO2 Emissionslime_net = CO2 Emissions50%man Country and crop specific emissions (in kg CO2 MJ-1 of fresh crop) attributed to lime application are calculated then as sum of the CO2 Emissionslime_net from each grid cell for each crop in the country divided by the country’s total yield of the crop (in MJ fresh crop). 29 description of the crop specific mineral fertilizer input s. Chapter 4.5 30 discussion of the 50% manure input s. Chapter 4.6

83

Country and crop specific emissions (in kg CO2 MJ-1 of fresh crop) attributed to synthetic fertilizer input are calculated then as sum of the CO2 Emissionssynthfer from each grid cell for each crop in the country divided by the country’s total yield of the crop (in MJ fresh crop). Final emissions (See Table 49) attributed to a specific biofuel crop equal the global weighted average emissions from suppliers of each crop to the EU market (including EU domestic production). Table 50: Limestone and dolomite consumption for the years 2000 as reported in the EDGARv4.1 database (EC-JRC/PBL) and share of limestone and dolomite applied to other land use/cover than cropland.

Country Limestone and dolomite consumption in the year 2000 (1000t)

Percentage of limestone and dolomite input to other land use than cropland

Country Limestone and dolomite consumption in the year 2000 (1000t)

Percentage of limestone and dolomite input to other land use than cropland

ALBANIA 16 KOREA, REP. 64 ALGERIA 92 KYRGYZSTAN 99 ARGENTINA 424 LATVIA 5 ARMENIA 25 LEBANON 103 AUSTRALIA 2404 LIBYAN ARAB 9 AUSTRIA 205 LITHUANIA 29 AZERBAIJAN 8 MACEDONIA,

FYRO24

BANGLADESH 77 MALAYSIA 1695 BELARUS 3375 MEXICO 4768 BELGIUM 91 MOLDOVA, REP. 6 BRAZIL 6983 MOROCCO 561 BULGARIA 2 NEPAL 18 CAMEROON 17 NETHERLANDS 255 CANADA 558 NEW ZEALAND 1279 CHILE 54 NICARAGUA 25 CHINA 5033 NIGERIA 14 COLOMBIA 311 NORWAY 340 19.6 (Lakes) COSTA RICA 107 PAKISTAN 259 COTE D'IVOIRE 29 PERU 185 CROATIA 13 PHILIPPINES 1122 CUBA 240 POLAND 2542 CYPRUS 16 PORTUGAL 67 CZECH REPUBLIC 1087 3.9 (Grassland) ROMANIA 382 DENMARK 737 RUSSIAN FED. 9267 DOMINICAN REP. 176 SAUDI ARABIA 122 ECUADOR 37 SENEGAL 50 EGYPT 2223 SERBIA AND

MONT50

EL SALVADOR 325 SLOVAKIA 2 ESTONIA 46 SLOVENIA 2 ETHIOPIA 76 SOUTH AFRICA 230 FINLAND 918 SPAIN 1680 FRANCE 2273 SRI LANKA 259 GEORGIA 47 SUDAN 122 GERMANY 4402 6.9 (Forest) SWEDEN 272 GREECE 598 SWITZERLAND 45 GUATEMALA 281 SYRIAN ARAB

REP286

HUNGARY 147 TAIWAN, PR. CHINA

1044 ICELAND 5 TAJIKISTAN 16 INDIA 4336 TANZANIA, UN.

REP35

INDONESIA 1553 THAILAND 1717

84

IRAN, ISLAMIC REP. 656 TUNISIA 208 IRAQ 65 TURKEY 1698 IRELAND 665 89.8 (Grassland) TURKMENISTAN 265 ISRAEL 71 UKRAINE 2776 ITALY 1009 UNITED

KINGDOM2242 37.9 (Grassland)

JAPAN 1989 UNITED STATES 20556 JORDAN 26 URUGUAY 41 KAZAKSTAN 100 UZBEKISTAN 719 KENYA 83 VENEZUELA 188 KOREA, DEM. REP. 186 VIET NAM 1365

85

Table 51 Lime application recommendations (Agricultural Lime Association, 2012). The values given are the amount of ground limestone (with a neutralizing value of 54 and 40% passing through a 150 micron mesh) required to achieve the target soil pH (explanation s

Arable land Grassland

Mea

sure

d pH

San

d an

d lo

amy

sand

s

San

dy lo

ams

and

silt

loam

s

Cla

y lo

ams

and

clay

s

Org

anic

soi

ls (1

0-25

% o

rgan

ic m

atte

r)

Pea

ty s

oils

abo

ve 2

5% o

rgan

ic m

atte

r

San

d an

d lo

amy

sand

s

San

dy lo

ams

and

silt

loam

s

Cla

y lo

ams

and

clay

s

Org

anic

soi

ls (1

0-25

% o

rgan

ic m

atte

r)

Pea

ty s

oils

abo

ve 2

5% o

rgan

ic m

atte

r

Recommended lime application (tonnes/ha) 7 0 0 0 0 0 0 0 0 0 0

6.9 2 2 2 0 0 0 0 0 0 06.8 2 2 2 0 0 0 0 0 0 06.7 2 2 2 0 0 0 0 0 0 06.6 2 3 3 2 0 0 0 0 0 06.5 3 4 4 2 0 0 0 0 0 06.4 4 4 5 3 0 2 2 2 0 06.3 4 5 6 4 0 2 2 2 0 06.2 5 6 6 5 2 2 2 2 0 06.1 5 6 7 6 3 2 2 2 2 0

6 6 7 8 7 5 2 3 3 2 05.9 7 8 9 8 6 3 3 4 2 05.8 7 8 10 9 9 3 4 4 3 05.7 8 9 10 10 10 4 4 5 4 25.6 8 10 11 11 11 4 5 5 5 25.5 9 11 12 12 13 5 5 6 5 45.4 10 11 12 13 14 5 6 7 6 55.3 10 12 13 14 16 5 6 7 7 65.2 11 13 14 15 18 6 7 7 7 75.1 11 13 15 16 19 6 7 7 7 7

5 12 14 16 17 21 7 7 7 7 74.9 13 15 16 18 22 7 7 7 7 74.8 13 15 17 19 24 7 7 7 7 74.7 14 16 18 20 26 7 7 7 7 74.6 14 17 19 21 27 7 7 7 7 7

4.531 15 17 20 22 29 7 7 7 7 7 31 for this work we assume the lime application to soils with pH < 4.5 the same as for pH 4.5 soils

86

Figure 10: Global distribution of soil pH (FAO/IIASA/ISRIC/ISS-CAS/JRC) and harvested area (Monfreda et al., 2008)

87

4.14 Lime application in the UK and Germany: Survey data vs. disaggregated country total lime consumption Farm and field level information on lime application is scarce. For most countries lime application in agriculture is given as country total derived from lime consumption/production in the country. In many cases, even the shares of lime applied to either arable land or grassland are unknown. To check the results of the disaggregation of country level lime consumption to crop level application as described in the previous chapter we compare the results with field data described in the literature. UK In the UK, the Department for Environment, Food and Rural Affairs (DEFRA) is sponsoring an annual survey about the fertiliser use on farm crops in Great Britain32 since 1983. In 2000 approximately 1,400 farms were surveyed (DEFRA, 2001). Lime application is assessed at farm and field level for 4 different liming products (ground limestone, ground chalk, magnesian limestone and sugar beet lime). Input of all other types of liming products are summarized under the group “others” (Table 1). DEFRA lime application data33 is compared with the results of the disaggregation of country level lime consumption to the crop level for the year 2000. In the UK 2.242 million tonnes of CaCO3 (limestone and dolomite) were applied to agricultural fields (EC-JRC/PBL) thereof 37.9% were applied to grassland, leaving 1.39 million tonnes for arable land34. From the disaggregation described in chapter x we can calculate an average input of 0.28 tonnes of CaCO3 ha-1 yr-1 to arable land for which lime application is recommended. This is the case for around 4.9 million ha or 91.2% of the arable land. Lime application recommendations usually give application rates to reach the optimum pH level for the crop cultivation. Thus, the application has to be repeated only if the desired pH level decreases again below a critical threshold. A repetition rate frequently mentioned in lime application recommendations is 5 - 10 years, but may vary strongly depending on the soil properties, climatic conditions and farming practices. The DEFRA (2001) survey (Table 53) gives the %age of crop area receiving dressing and the amount of liming product applied in 2000. Depending on the crop, lime is applied in quantities of 1.1 - 2.7 tonnes CaO per ha on ~5 - 35% of the crops area. On the average for all tilled crops 2.5 tonnes CaO from all liming products are applied to 8.4% of the tilled crops area. Ground limestone, ground chalk and magnesian limestone contribute with 2.2 tonnes of CaO ha-1 on 7.7% of the tilled crops area. From our analysis of the spatial data on soil properties in the UK35 we calculated that around 91.2% of the arable crops area needs lime application to reach optimum pH for cultivation. If we assume that over time all the area will be limed, we can calculate the number of years until 32 Wales, England, Scotland 33 Excluding “sugar beet lime” and “other” 34 this excludes permanent grassland 35 this includes Northern Ireland, it is assumed that conditions in Northern Ireland (share of crop area requiring liming and liming frequency) are not significantly different from the mean conditions in Great Britain

88

all the whole area is limed once by dividing 91.2% by 7.7% (the annual share of area limed with ground limestone, ground chalk and magnesian limestone given by DEFRA). Hence, in 11.8 years all arable crops growing on soils with non-optimal pH will be limed once. Assuming liming practices constant over time, the single field gets one application every 11.8 years on the average. The annual application rate of lime3 thus can be calculated by dividing the application of 4 tonnes of CaCO3 ha-1 (or 2.2 tonnes of CaO ha-1) by 11.8 years. This results in an average application rate of 0.34 tonnes of CaCO3 ha-1 yr-1 on arable land based on DEFRA survey data. This compares to 0.28 tonnes of CaCO3 ha-1 yr-1 on arable land with non-optimal pH conditions from the spatial disaggregation of country level lime application (see. Table 54). Table 52: Lime application at field level (DEFRA, 2001) and estimation of mean annual application rates on tillage crops in the year 2000 (explanation s. text)

Crop area receiving dressing (%)

Average field rate of CaO equivalent (tonnes/ha)

Calulation of CaCO3 input per ha#

Crop type

Ground limest

one Ground

chalk Magne

sian limestone

Sugar b

eet lime

Other All Ground limest

one Ground

chalk Magne

sian limestone

Sugar b

eet lime

Other All Fields limed

Total nr. of fie

lds Liming

frequency (ye

ars) Weight

ed average app

lication

rate of Ground

limestone, gr

ound chalc a

nd Magnesian

limestone

(tonnes of CaC

O3 ha-1 ) Annual

application r

ate (tonnes

of CaCO3 ha-1 yr-1 )

Spring wheat 3 62Winter wheat 4.0 0.8 0.9 0.2 5.9 2.3 2.0 2.4 2.8 2.3 149 2796 16.0 4.1 0.25Spring barley 4.9 0.8 5.0 0.3 11.0 2.0 1.8 2.4 3.3 2.2 119 881 8.5 3.9 0.45Winter barley 6.5 0.2 1.5 0.4 8.6 2.4 2.1 1.8 3.4 2.4 78 841 11.1 4.1 0.37Oats 2.5 2.3 4.8 2.2 1.5 2.0 12 199 19.0 3.3 0.18Rye/Triticale/Durum wheat 3 51Seed potatoes 1 21Early potatoes 0 142nd Early/Maincrop potatoes 0 227Sugar beet 10.6 9.3 5.3 10. 35.9 2.5 2.1 1.9 2.9 2.7 89 273 3.6 4.0 1.10Spring oilseed rape 1.2 4.7 2.0 4.4 12.3 1.2 1.5 2.0 0.4 1.1 7 73 11.5 2.8 0.24Winter oilseed rape 7.5 1.0 1.8 0.9 11.2 2.3 2.0 2.2 1.6 2.3 47 525 8.9 4.0 0.45Linseed 0 60Forage maize 15.2 1.8 3.5 20.5 2.7 2.5 1.6 2.5 27 149 4.4 4.5 1.00Rootcrops for stockfeed 11.9 2.9 0.7 15.5 1.9 2.2 1.1 1.9 11 78 6.2 3.5 0.57Leafy forage crops 5.0 0.4 13.3 18.7 1.7 1.5 2.6 2.0 14 56 4.9 4.2 0.85Arable silage/Other fodder 0.4 12.4 12.8 2.5 2.7 2.5 5 40 7.1 4.8 0.67Peas - human consumption 2 96Peas - animal consumption 1.9 3.8 1.9 7.6 2.5 0.8 2.6 1.6 7 112 12.0 3.0 0.25Beans - animal consumption 0.3 1.7 0.3 2.3 1.2 0.6 1.2 1.3 5 170 45.6 1.2 0.03Vegetables (brassicae) 4 56Vegetables (other) 5.2 0.5 0.7 2.5 8.9 2.5 4.0 2.1 1.3 2.7 14 126 14.3 4.6 0.32Soft fruit 1 47Top fruit 4.9 1.9 6.8 0.3 0.2 0.1 8 78 13.4 0.5 0.04Other tillage 0.4 0.3 0.1 0.1 5 117 130.3 0.2 0.00All tillage 4.8 1.1 1.8 0.7 8.4 2.3 2.0 2.2 2.7 2.5 611 7148 11.8 4.0 0.34Grass under 5 years 2.7 0.2 1.9 4.8 2.4 1.3 2.4 2.2 102 1280 19.0 4.2 0.22Grass 5 years and over 1.6 0.1 1.3 0.3 3.3 2.2 2.8 2.4 2.1 2.1 130 2744 30.4 4.1 0.14All grass 1.8 0.1 1.4 0.3 3.6 2.2 2.3 2.4 2.1 2.1 232 4024 27.6 4.1 0.15All crops and grass 3.3 0.6 1.6 0.5 6.0 2.3 2.0 2.3 2.5 2.3 843 11172 16.6 4.0 0.24

89

# 1kg of CaCO3 corresponds to 1.7857 kg of CaO Table 53: Lime application in the UK in the year 2000 based on this study

Crop Crop area whe

re lime

applications is

recommende

d (ha) Annual

lime (limesto

ne and

dolomite) app

lication rate

(tonnes of CaC

O 3 ha-1 yr-1 ) Wheat 1648298 0.29 Barley 1061485 0.34 Other cereals 95219 0.32 Rye 4787 0.27 Tritical 9592 0.30 Potato 146029 0.32 Sugarbeet 159844 0.26 Rapeseed 412317 0.32 Oilseeds 68062 0.32 Forage 968124 0.17 Fibres 15077 0.30 Pulses 199277 0.32 Vegetables 105234 0.30 Fruits 8785 0.29 All crops 4902130 0.28

Germany Study 1 De Vries (2006) suggests for wheat and rye 0.3 to 0.4 tonnes of CaO (0.54 - 0.71 tonnes of CaCO3) per ha and year on soils in Mecklenburg-Vorpommern (north-eastern Germany). Study 2 Ahlgrimm et al. (2000) cited in Hirschfeld et al. (2008) assume 0.35 t CaO (~0.63 t CaCO3 per ha) as a kind of default value for all crops under conventional farming. Study 3 On the basis of statistical data in Germany Knappe et al. (2008) classified different farm types and assessed the fertilizer/lime application requirement based on nutrient balances.

90

For conventional farming based on manure and mineral fertilizer input (Knappe et al., 2008. Tab. C14 and C17) they calculated an annual deficit of ~0.16 - 0.25 tonnes CaO (0.29 - 0.45 tonnes CaCO3) to neutralize acidification from fertilizer N input on arable land and 0.07 tonnes CaO (~ 0.12 tonnes of CaCO3) on permanent grassland In conventional farming systems applying mineral fertilizer in combination with sewage sludge or compost (Knappe et al., 2008. Tab. C15 and C16) the additional liming requirements decrease depending on the amount of sewage sludge or compost applied. There might be even a CaO surplus, especially in case of compost application. In their approach they do not consider lime application for optimizing soil pH. Results of our work: From our study we get 0.48 t CaCO3 per ha in Germany as average input for all arable crop species on soils where lime is applied.

91

5. Utilities & Auxiliary processes This section contains the processes for utilities such as boilers and power plants that are used throughout the various pathways. The following list describes the plant processes that can be used in different ethanol pathways: 1 Ethanol from sugar beet, (no biogas from slop), NG boiler 2 Ethanol from sugar beet, (with biogas from slop), NG boiler 3 Ethanol from sugar beet, (no biogas from slop), NG CHP 4 Ethanol from sugar beet, (with biogas from slop), NG CHP 5 Ethanol from sugar beet, (no biogas from slop), lignite CHP 6 Ethanol from sugar beet, (with biogas from slop), lignite CHP 7 Ethanol from Wheat, NG boiler 8 Ethanol from Wheat, NG GT+CHP 9 Ethanol from Wheat, lignite CHP 10 Ethanol from Wheat, wood CHP 11 Ethanol from corn, NG boiler, 12 Ethanol from corn, NG GT+CHP 13 Ethanol from corn, hard coal CHP 14 Ethanol from barley, NG boiler 15 Ethanol from barley, NG CHP 16 Ethanol from barley, lignite CHP 17 Ethanol from barley, wood CHP 18 Ethanol from sorghum grain, NG boiler 19 Ethanol from sorghum grain, NG CHP 20 Ethanol from triticale, NG boiler 21 Ethanol from triticale, NG CHP 22 Ethanol from triticale, lignite CHP 23 Ethanol from triticale, wood CHP 24 Ethanol from rye, NG boiler 25 Ethanol from rye, NG CHP 26 Ethanol from rye, lignite CHP 27 Ethanol from rye, wood CHP

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Natural Gas CHP Table 54: Process for a natural gas CHP to supply power and heat

Power and Heat from a NG CHP

I/O Unit Amount

NG Input MJ/MJsteam 2.3867

Steam Output MJ 1.000

Electricity Output MJ/MJsteam 0.662

Comments: • Steam: 160-180°C • replaces electricity from a NG fueled CCGT • CO2 emissions from Natural gas combustion are considered to be: 198 gCO2/kWh.

Source: Nitsch, et al. 1999 Natural Gas boiler Table 55: Process for a natural gas boiler

Steam from NG boiler (10 MW)

I/O Unit Amount NG Input MJ/MJheat 1.111

Electricity Input MJ/MJheat 0.020

Steam Output MJ 1.000 Emissions

CH4 Output g/MJheat 0.0056 N2O Output g/MJheat 0.00112

Comments: • Electricity taken from the grid at 0.4kV • Thermal efficiency = 90% (based on LHV) • This process is common to all pathways involving pellet production, case 1. • CO2 emissions from Natural gas combustion are considered to be: 198 gCO2/kWh. Source: 1. GEMIS v. 4.7, 2011. gas-heat plant-medium-DE 2010.

93

H2 generation via steam reforming of NG Table 56 H2 generation

H2 generation via steam reforming of NG I/O Unit Amount

NG Input MJ/MJH2 1.315

H2 Output MJ 1.000 Natural Gas fuelled CCGT (used for credit calculation) Table 57: Process for electricity production from a natural gas fuelled combined cycle (CCGT)

Electricity from NG CCGT

I/O Unit Amount

NG Input MJ/MJe 1.721

Electricity Output MJ 1.00

Emissions

CH4 Output g/MJsteam 0.0054

N2O Output g/MJsteam 0.0043

Comments: • Represents a plant with a capacity of 450 MWe. • CO2 emissions from Natural gas combustion are considered to be: 198 gCO2/kWh.

Source: GEMIS v4.7; gas-CC-DE-2010 Lignite CHP Table 58 Process for a lignite fuelled CHP

Power and Heat from a lignite fuelled CHP

I/O Unit Amount Sources

Lignite Input MJ/MJsteam 1.405 1

Steam Output MJ 1.0

Electricity Output MJ/MJsteam 0.222 1

Emissions

CH4 Output g/MJsteam 0.0023

N2O Output g/MJsteam 0.0126

Comment:

94

• Represents a plant with a capacity of 34.2 MWth. • Replaces electricity from a lignite fueled ST • CO2 emissions from lignite combustion are considered to be: 414 gCO2/kWh.

Sources:

• Larivé, J-F., CONCAWE, Pers. Comm. February 2008 • Punter et al., 2004

Lignite fuelled steam turbine power plant (used for credit calculation) Table 59: Process for a lignite fuelled power plant based on steam turbine technology

Power and Heat from a lignite fuelled steam turbine power plant

I/O Unit Amount

Lignite Input MJ/MJe 2.326


Emissions



Comment: • Represents a plant with a capacity of 800 MWel. • CO2 emissions from lignite combustion are considered to be: 414 gCO2/kWh.

Source: GEMIS v4.7; lignite-ST-DE-2010.

95

Wood chips fuelled CHP (NEW PROCESS) Table 60: Process for a wood chips fuelled CHP

Power and heat from a wood chip fuelled CHP

I/O Unit Amount

Wood chips Input MJ/MJsteam 2.132

Steam Output MJ 1.000

Electricity Output MJ/MJsteam 0.361

Emissions



Comment: • This replaces electricity from a straw fuelled ST process (as requested at the workshop). • Represents a plant with a capacity of 34.2 MWth.

Source: Punter et al., 2004

96

Wood chips fuelled steam turbine power plant (used for credit calculation - NEW PROCESS) Table 61: Process for a power plant fuelled with wood chips based on steam turbine technology

Power plant fuelled with wood chips based on ST technology

I/O Unit Amount

Straw Input MJ/MJe 3.125


Emissions



Source: Jopp, 1999 Industrial wood pellet boiler Table 62: Process for an industrial wood pellet boiler

Heat from industrial wood pellet boiler (0.5 MW) I/O Unit Amount

Wood pellets Input MJ/MJheat 1.1235

Electricity Input MJ/MJheat 0.015

Steam Output MJ 1.0 Emissions

CH4 Output g/MJheat 0.003336 N2O Output g/MJheat 0.000667

Comments: • Electricity taken from the grid at 0.4kV • Thermal efficiency = 89% (based on LHV) • This process is common to all pathways involving pellet production, case 2. Source: 1. GEMIS v. 4.7, 2011. wood-pellet-wood-industry-heat plant-0.5 MW-2005

97

Wood pellet CHP based on ORC technology

Table 63: Process for an industrial CHP based on ORC technology

Heat and electricity from CHP based on ORC engine I/O Unit Amount Source

Wood pellets Input MJ/MJheat 1.436 1

Electricity Output MJ/MJheat 0.234 1

Heat Output MJ 1 1 Emissions

CH4 Output g/MJheat 0.00427 2 N2O Output g/MJheat 0.000854 2

Comments: • Electrical efficiency = 16.3% (based on LHV) • Thermal efficiency = 69.6% (based on LHV) • This process is common to all pathways involving pellet production, case 3. Source: 1. Seeger Engineering AG; 2009;

2. Globales Emissions-Modell Integrierter Systeme (GEMIS), version 4.7; 2011; wood-pellet-wood-industry-heat plant-0.5 MW-2005

Wood pellet power plant based on steam turbine technology

Table 64: Process for an industrial wood pellet power plant based on steam turbine technology.

Electricity from wood pellet ST I/O Unit Amount Source

Wood pellets Input MJ/MJel 3.125 1

Electricity Output MJ 1 1

Emissions CH4 Output g/MJel 0.00928 2 N2O Output g/MJel 0.00186 2

Comments: • Electrical efficiency = 32% (based on LHV) • This process is common to all pathways involving pellet production, case 3. It is used to complement the CHP process that cannot supply all the electricity needed. Source: 1. Estimate derived from wood chip fueled ST, derived from [Jopp, K.: Grüner Strom aus Biomasse; Strom und Wärme aus Biomasse liefert ein neues Heizkraftwerk im bayerischen Altenstadt – Abfallholz und Gräser kommen aus einem Umkreis von nur 50 km; VDI, 2.7.1999]

98

2. Globales Emissions-Modell Integrierter Systeme (GEMIS), version 4.7; 2011; wood-pellet-wood-industry-heat plant-0.5 MW-2005

Light heating oil boiler: Table 65: Process for a light heating oil fuelled boiler

Light heating oil fueled boiler I/O Unit Amount

Diesel Input MJ/MJheat 1.087

Heat Input MJ 1.0 Emissions

CH4 Output g/ MJheat 0.0033 N2O Output g/ MJheat 0.0022

Comment: • CO2 emissions from diesel combustion are considered to be: 264 gCO2/kWh

Source: Macedo, 2004. Ethanol / FAME depot Table 66: Process for the energy consumption in an Ethanol or FAME depot

Ethanol / FAME depot I/O Unit Amount

FAME / Ethanol Input MJ/MJ 1.00


FAME / Ethanol Input MJ 1.000

Source: 1. Dautrebande, O., TotalFinaElf, January 2002 Ethanol / FAME filling station Table 67: Process for the energy consumption in a Ethanol / FAME filling station

Ethanol / FAME filling station I/O Unit Amount

FAME / Ethanol Input MJ/MJ 1.00


FAME / Ethanol Input MJ 1.000

Source: 1. Dautrebande, O., TotalFinaElf, January 2002

99

6. Transport processes This section contains all the processes describing the fuel consumption for all the vehicles and means of transportation used in all the pathways. The section is divided between Road, Maritime, Inland and Rail transportation. The processes are recalled in each pathway. 6.1 Road transportation

40 t truck (27 t payload) The common means of transport considered for road transport is a 40 t truck with a payload of 27 t. For the transport of solid materials a flatbed truck transporting a container is considered. The weight of such tank is considered, for sake of simplicity, to be equal to 1 t. For the transport of liquids and pellets, special tank trucks are used. It is assumed that such trucks have the same general fuel efficiency and general payload of the truck for solids but with a higher, 2 t, weight for the tank to account for the pneumatic system. The truck fuel consumption is linearized on the weight transported and on the distance. The amount of tons per kilometer is calculated from the formula (in this case for solid fuels transport):

( )[ ] [ ]

( )[ ] ⎥⎦

⎤⎢⎣

⎡⋅

⎥⎥⎦

⎤

⎢⎢⎣

⎡⋅−

⋅=

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ⋅

tot

dry

dry

goodsdry kg

kgSolids

kgMJ

LHVt

kmxt

tank27

27MJ

kmtDistancegoods

This value is calculated and reported for each pathway in the following chapters of this report and the specific LHV and moisture content of the analyzed materials will also be underlined. In order to obtain the final fuel consumption of the transportation process, the ”distance” process needs to be multiplied by the fuel consumption of the vehicle considered. For the case of a 40 t truck, this value and the associated emissions are reported in Table 63 Fuel consumption for a 40 t truck.

100

Table 68 Fuel consumption for a 40 t truck

I/O Unit Amount Source Diesel Input MJ/tkm 0.936 1,2

Distance Output tkm 1.00 CH4 Output g/tkm 0.00041 3 N2O Output g/tkm 0.00267 3

Comments: - The return voyage (empty) is taken into account in this value. - This process is commonly used for the transportation of solids and liquids.

Sources: 1 Gruber, Chr., MAN, pers. comm. 21 August 2003. 2 Ökoinventar, 3. Auflage, Teil 3, Anhang B, Transporte und Bauprozesse, Juli 1996; 3 Ecoinvent report No 14, page 49: lorry > 32 t. 40 t truck (27 t payload) for sugarcane

Table 69 Fuel consumption for a 40 t truck, weighted average for sugar cane transport

I/O Unit Amount Diesel Input MJ/tkm 1.37 Distance Output tkm 1.0000

Source: 1 Macedo et al., 2004. MB2213 Dumpster truck Table 70 Fuel consumption for a MB2213 dumpster truck used for filter mud cake


Source: 1 Macedo et al., 2004. MB2318 Tanker truck for seed cane Table 71 Fuel consumption for a MB2318 truck used for seed cane transport


101

MB2318 Tanker truck for vinasse

Table 72 Fuel consumption for a MB2318 Tanker truck used for vinasse transport

I/O Unit Amount Diesel Input MJ/tkm 2.16

Source: 1 Macedo et al., 2004. 12 t truck (6.35 t payload) This process represents a smaller truck used for the transportation of specific materials such as Fresh fruit bunches and coconut copra. Table 73 Fuel consumption for a 12 t truck



Comments: - Process used for transport of Fresh Fruit Bunch (FFB) in Palm Oil-to-FAME pathway - Process used for transport of Coconut Copra in Coconut oil-to-FAME pathway.

Sources: 1 Lastauto Omnibus Katalog 2010. 2 Coo et al., 2011. 3 GEMIS v.4.7, 2011. “truck-Diesel-EU-2010”.

102

20 t truck (10 t payload) This process represents a truck used for the transportation of specific materials such as Jatropha seeds. Table 74: Fuel consumption for a 20 t truck used for Jatropha seeds transport

20 t truck (payload: 5 t) I/O Unit Amount Source

Diesel Input MJ/tkm 1.8 1,2 Distance Output tkm 1.00

CH4 Output g/tkm 0.0016 4 N2O Output g/tkm 0.0012 4

Sources: 1 Lastauto Omnibus Katalog 2010. 2 GEMIS v.4.7, 2011. “truck-Diesel-EU-2010” Note on transportation distance: Distance is multiplied x 2 because of return voyage (empty). Vehicle assumption: “Mercedes Axor 1833 L Pritsche”: 24.5 to 26.0 l of diesel per 100 km [lastauto omnibus Katalog 2010]. The “Mercedes Sprinter Axor 1833 L Pritsche” has a gross weight of 18.0 t (slightly lighter than your 20 t truck) and a pay load of 10.75 t (using a higher platform gate may lead to a lower net payload of about 10 t). A fuel consumption of 25 l per 100 km would lead to a 0.90 MJ/tkm without taking into account the return voyage and 1.80 MJ/tkm including the return voyage (Pers. comm. March 2012, Werner Weindorf, LBST.)

6.2 Maritime transportation

Handysize Bulk Carrier (26,000 t payload) Wood chips and pellets are assumed to be transported to Europe via Handysize bulk carriers with 26000 t of payload. The fuel consumption of these carriers is calculated by JRC via data provided by the International Maritime Organization [IMO, 2009] and it is dependent on several parameters, the most important being the bulk density of the transported goods. In fact, from the calculations performed, it resulted that for goods with bulk density lower than 600 kg/m3, the load is volume-limited. The return trip (empty, with ballast) is considered in this value of fuel consumption. The “distance” parameter (tkm/MJgoods) is calculated with a simple operation since the tank weight is already included in the calculations of the fuel consumption.

103

[ ]

⎥⎦

⎤⎢⎣

⎡⋅

⎥⎥⎦

⎤

⎢⎢⎣

⎡=

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ⋅

tot

dry

drydry kg

kgSolids

kgMJLHV

kmx

goodsMJkmtDistance

The distance values for each material are reported in the specific pathways. Due to the relation of the fuel consumption value with the physical properties of the goods, specific values for each product are reported here. Table 75 Fuel consumption for a bulk carrier for goods with bulk density > 0.6 t / m3

I/O Unit Amount Heavy fuel oil Input MJ/tkm 0.1200

Distance Output tkm 1.0000

Comments: - Valid for payloads with bulk density >0.6 t/m3 - LHV heavy fuel oil = 40.5 MJ/kg - Oil consumption = 2.9636 gHFO/tkm - This fuel consumption is valid for: wood pellets, charcoal, straw pellets and bagasse pellets / briquettes.

Handy class Bulk Carrier (26000 t payload for wood chips) Table 76 Fuel consumption for a bulk carrier for wood chips

I/O Unit Amount

Heavy fuel oil Input MJ/tkm 0.2916


Comments: - Oil consumption = 7.20 gHFO/tkm - The wood chips are considered to have a moisture content of 30%, and the bulk density is calculated roughly as proportional to the bulk density dry (0.155 t/m3), therefore: 0.155/0.7 = 0.221 t/m3.

Handy class Bulk Carrier (26,000 t payload for agri residues) Table 77 Fuel consumption for a bulk carrier for agri-residues with bulk density of 0.125 t/m3

I/O Unit Amount Heavy fuel oil Input MJ/tkm 0.4961


Comments: - Valid for Agricultural residues <0.2 t/m3) with typical bulk density = 0.125 t/m3) - Oil consumption = 12.25 gHFO/tkm

104

Table 78 Fuel consumption for a bulk carrier for agricultural-residues with bulk density of 0.3 t/m3

I/O Unit Amount Heavy fuel oil Input MJ/tkm 0.2211 Distance Output tkm 1.0000

Comments: - Valid for Agricultural residues >0.2 t/m3 with typical bulk density = 0.3 t/m3) - Oil consumption = 5.46 gHFO/tkm

Sources: 1 IMO, 2009. 2 Edwards, R. et al., JRC, own calculations, 2011. Panamax Bulk Carrier (37,000 t payload) For the case of grains and soybeans, larger vessels (Panamax) are used for the transoceanic transport. The calculation of the “distance” value is the same as reported for the Handysize bulk carrier. Generally, grains (and soybeans) have high bulk density so that the load is weight limited and the fuel consumption is common for all materials. Table 79 Fuel consumption for a Panamax bulk carrier for goods with bulk density > 0.6 t / m3 (weight-limited load)

I/O Unit Amount Heavy fuel oil Input MJ/tkm 0.1009 Distance Output tkm 1.00 Comments:

- valid for payloads with bulk density >0.6 t/m3) - The return voyage is considered empty and it is included in the value; - LHV heavy fuel oil = 40.5 MJ/kg; - Oil consumption = 2.492 gHFO/tkm;

Sources: 1 Buhaug et al., (IMO) 2009. 2 Edwards, R. et al., JRC, own calculations, 2011.

105

I DON’T KNOW IF THIS FITS IN HERE, TAKEN FROM SLIDES PRESENTED AT WORKSHOP

106

Product Tanker (12,617 t payload) This process is used to account for the direct import of ethanol produced from sugar cane. Table 80 Fuel consumption for a product tanker for ethanol transport

I/O Unit Amount



Comments: - New data considering av. 90% loading on outward trip [3] and 85% of that on the return trip [2]; - The process is used in Sugar Cane-to-Ethanol pathway to account for ethanol import; - Heavy fuel oil consumption = 2.8432 gHFO/tkm - LHV heavy fuel oil = 40.5 MJ/kg.

Source: 1 Buhaug et al., (IMO) 2009. 2 Statement by Odfell Tankers AS, Bergen, Norway, 26 Jan. 2012, to JRC 3 JRC estimate based on sea distances between intermedia te ports, following discussion in [1] 4 JRC interpolation of emissions data from [1] Product Tanker (15,000 t payload). This process is used to account for the direct import of FAME from rapeseed and sunflower seed. Table 81 Fuel consumption for a product tanker for FAME transport

Product Tanker (payload: 15000 t)

I/O Unit Amount



Comment: - Return voyage is considered empty and it is included in the data; - Heavy fuel oil consumption = 4.2278 gHFO/tkm - LHV heavy fuel oil = 40.5 MJ/kg; - This process is used for the transportation of FAME.

Source: 1 Buhaug et al., (IMO) 2009.

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Product Tanker (22,560 t payload). This process is used to account for the direct import of vegetable oil from coconut, jatropha, palm and soya. Table 82 Fuel consumption for a product tanker for pure vegetable oil transport

Product Tanker (payload: 22560 t)

I/O Unit Amount



Comment: - 83% utilization (return trip not always empty); - Heavy fuel oil consumption = 2.3573 gHFO/tkm - LHV heavy fuel oil = 40.5 MJ/kg. - Process used to transport crude Palm Oil; - Process used to transport Coconut Oil; - Process used to transport Jatropha Oil; - Process used to transport Soya Oil;

Source: 1 Buhaug et al., (IMO) 2009. Additional notes: TELEPHONE CALL TO Arild Viste of Odfjell Tankers 18/05/2012 Average size of ship for:- ethanol transport from Brazil: 14-16 ktonnes deadweight soy-oil transport from S America: 20-30 ktonnes dwt Stowage ratio (design density of cargo) for chemical tankers 0.8 to 0.85, So ethanol loading is (just) volume-limited. Because of fast growth in Brazil, at present there is actually more liquid chemicals going to S Amaerica from Europe/Africa than the other way, but this varies with time. The largest component of liquid chemicals returning to S America is phosphoric acid from Marooco to Brazil, used to make fertilizers. AV agrees that on a world scale, the IMO "68%" is a good guess for average of full load carried, but it is higher on the S America route for chemicals. Palm oil from Asia is more complicated but the situation is similar.

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Larger ships have lower average % filling of cargo-carrying capacity. In both directions, the ships typically make several calls at several ports to fill up for the atlantic crossing. JRC says: SOYOIL AND BIODIESEL We know from Odjell that the return voyage accross the Atlantic contained 15% less cargo than the outward voyage. But we cannot assume that even the ships on the outward voyage were fully loaded during the entire voyage. The ships typically call at several ports to fill up for the atlantic crossing and to discharge at the other end, during which time they are running at part-load, and in particular the part of the return journey from EU to Marocco is probably under ballast. We shall assume that the ship is 90% full on average during the outward trip, and 90%*(1-0.15) = 77% loaded on the return trip. Then on average the ship is 83% loaded for the round trip, which is considerably higher than the IMO estimate of 64% average for chemical tankers. We have not added any distance to account for the intermediate stops. Product Tanker (50,000 t payload). This process is used to account for the direct import of ethanol from the US.

Table 83 Fuel consumption for a product tanker for ethanol transport from US

I/O Unit Amount



Comments: - Loading factor considered to be 55%. - Heavy fuel oil consumption = 1.7 gHFO/tkm. - LHV heavy fuel oil = 40.5 MJ/kg.

Sources: 1 Frischknecht et al., 1996. 2 Specht, ZSW; pers. comm. 28 May 1999.

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6.3 Inland water transportation:

Bulk carrier Barge (8800 t payload). This process represents a barge used to carry bulk materials on inland waters. It is used for the transport of rapeseed and soy beans feedstocks. Table 84: Fuel consumption for a bulk carrier for inland navigation



Comment: - Empty return trip included; - Used for rapeseed supply; - Used for soy beans supply.

Source: 1 Ökoinventar, 3. Auflage, Teil 3, Anhang B, Transporte und Bauprozesse, Juli 1996 2 Ilgmann, G.: Gewinner und Verlierer einer CO2-Steuer im Güter- und Personenverkehr; 1998 3 GEMIS v. 4.7, 2011. ship-freight-DE-domestic-2010. Oil carrier Barge (1200 t payload). Used for transportation of FAME on inland waters. This process is used in the pathways of FAME from rapeseed, sunflower seeds and soy beans. Table 85: Fuel consumption for an oil carrier barge for inland navigation

I/O Unit Amount Source Diesel Input MJ/tkm 0.504 1

Distance Output tkm 1.00 CH4 Output g/tkm 0.03 1

Comment: - Empty return trip included; - Used in the rapeseed-to-FAME pathway for FAME distribution; - Used in the sunflower-to-FAME pathway for FAME distribution; - Used in the soy beans-to-FAME pathway for FAME distribution.

Source: 1. Ökoinventar, 3. Auflage, Teil 3, Anhang B, Transporte und Bauprozesse, Juli 1996

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6.4 Rail transportation

Freight train (diesel) At present and in the foreseeable future, most of the rail transport of solid biomass is assumed to happen in the American continent (US and Canada). Therefore, the fuel consumption is consequently assumed to be the one for US conditions. The distance parameter is calculated as described above for the road and maritime transport and the specific values are reported for each pathway in the following chapters. Table 86: Fuel consumption for a freight train run on diesel fuel (US situation)

I/O Unit Amount Diesel Input MJ/tkm 0.252

Distance Output tkm 1.00 CH4 Output g/tkm 0.005 N2O Output g/tkm 0.001

Comments: - This process is used for the transportation of pellets, wood chips and agricultural residues from the mill to the harbour in US or Canada prior to shipping to Europe.

Source: 1. GEMIS v. 4.7, 2011. Train-diesel-freight-US-2010. Freight train (electric). This process represents the fuel consumption for rail transportation with electric carriages. Table 87: Fuel consumption for a freight train run on grid electricity

I/O Unit Amount Electricity Input MJ/tkm 0.21 Distance Output tkm 1.00

Source: 1 GEMIS v. 4.7, 2011. Train-el-freight-DE-2010.

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6.5 Pipeline transportation

Table 88: Fuel consumption for the pipeline distribution of FAME (5 km)

I/O Unit Amount FAME Input MJ/MJFAME 1.00

Electricity Input MJ/MJFAME 0.0002 FAME Output MJ 1.00

Source: 1 Dautrebande, O., TotalFinaElf, January 2002.

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Horowitz, J, Ebel, R, Ueda, K, (2010). "No-till farming is a growing practice" USDA Economic Information Bulletin no. 70, Nov. 2010 IEA, International Energy Agency (2011a), Energy Statistics of OECD Countries, International Energy Agency, Paris, 2011. IEA, (2011b), Energy Statistics of Non-OECD Countries, International Energy Agency, Paris, 2011. IEA, (2011c), Electricity Information, International Energy Agency, Paris, 2011 (IFA) International Fertilizer Industry Association (2010): IFA fertilizer N consumption (kg) per country for the year 2000 (downloaded on 30.09.2010 from http://www.fertilizer.org/ifa/ifadata/search) Ilgmann, G., (1998). Gewinner und Verlierer einer CO2-Steuer im Güter- und Personenverk. (INTA) Instituto Nacional de Tecnología Agropecuaria (2011), Actualización Técnica Nº 58 - Febrero 2011. http://inta.gob.ar/documentos/siembra-directa/at_multi_download/file?name=Siembra+Directa+2011.pdf (IPCC) Intergovernmental Panel on Climate Change, 1997. Guidelines for National GHG Inventories. J.T. Houghton, L.G. Meira Filho, B. Lim, K. Treanton, I. Mamaty, Y. Bonduki, D.J. Griggs and B.A. Callender (eds)] IPCC, Meteorological Office, Bracknell, Great Britain. IPCC: Paustian, K., et al, (2006), IPCC Guidelines for National Greenhouse Gas Inventories; IPCC National Greenhouse Inventories Programme; published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the Intergovernmental Panel on Climate Change (IPCC), 2006; http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf JEC - Joint Research Centre-EUCAR-CONCAWE collaboration, (2011), Well-to-Wheels Analysis of Future Automotive Fuels and Powertrains in the European Context; Version 3c; Report EUR 24952 EN - 2011; http://iet.jrc.ec.europa.eu/about-jec/ Kaltschmitt, M. and Reinhardt, G., (1997). Nachwachsende Energieträger: Grundlagen, Verfahren, ökologische Bilanzierung; Vieweg 1997; ISBN 3-528-06778-0. Knappe, F.; Moehler, S.; Ostermayer, A.; Lazar, S. und Kaufmann, C. (2008): Vergleichende Auswertung von Stoffeintraegen in Boeden ueber verschiedene Eintragspfade. Umweltbundesamt, Text 36/08. ISSN 1862-4804 Köhler, D.; Rosenbauer, G.; Schwaiger, K.; Wabro, R.: Ganzheitliche energetische Bilanzierung der Energiebereitstellung (GaBiE) - Teil VI Energetische Untersuchung von Blockheizkraftwerken; im Auftrag Bayerische Forschungsstiftung, IAW; Forschungstelle für Energiewirtschaft (FfE); München Oktober 1996

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uptake at regional and global scales Geochimica et Cosmochimica Acta, 72 (13), pp. 3105-3123. Powlson, D.S., Whitmore, A.P, and Goulding, K.W.T, (2011). "Soil carbon sequestration to mitigate climate change: a critical re-examination to identify the true and the false" European J. of Soil Science 62 42-55 Rochette, P. (2008). "No-till only increases N2O emissions in poorly-areated soils" Soil and tillage research 101 97-100. Scholz, W. (1992). Verfahren zur großtechnischen Erzeugung von Wasserstoff und ihre Umweltproblematik, Berichte aus Technik und Wissenschaft 67/1992, Linde; S. 13 – 21. Schulte P., Van Geldern R., Freitag H., Karim A., Negrel P., Petelet−Giraud E., Probst A., Probst J.L., Telmer K., Veizer J. and Barth J.A.C. (2011). Applications of stable water and carbon isotopes in watershed research: weathering, carbon cycling and water balances. Earth−Science Review, 109, 20−31. Singh, G. editor (2010): The Soybean: botany, production and uses. Copyright CAB Inernational 2010, Oxfordshire, UK. ISBN 978-1-84593-644-0. J. C. Sinistore, W. L. Bland, (2010). “Life-Cycle Analysis of Corn Ethanol Production in the Wisconsin Context”, Biological Engineering 2(3): 147-163 Stehfest E. and Bouwman A. F. (2006). N2O and NO emissions from agricultural fields and soils under natural vegetation: summarizing available measurement data and modeling of global annual emissions. Nutr. Cycl. Agroecosyst. V74 (3): 207-228. http://dx.doi.org/10.1007/s10705-006-9000-7. N2O and NO emission data set available under http://www.pbl.nl/en/publications/2006/N2OAndNOEmissionFromAgriculturalFieldsAndSoilsUnderNaturalVegetation.html UNFCCC (2012): CRF country submissions 2008. Downloaded 17.04.2012 at http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/4303.php Vitovec, W., (1999). EVN AG, Umweltcontrolling und Sicherheit: Pyrogene N2O-Emissionen; ACCC-Workshop „N2O und das Kyoto-Ziel. West, T. O. and McBride, A. C. (2005). “The contribution of agricultural lime to carbon dioxide emissions in the United States: dissolution, transport, and net emissions”, Agriculture, Ecosystems and Environment, 108, 2, pp. 145-154 PART THREE – LIQUID BIOFUELS PROCESSES AND INPUT DATA

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http://www.pbl.nl/en/publications/2006/N2OAndNOEmissionFromAgriculturalFieldsAndSoilsUnderNaturalVegetation.html

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7. Biofuels processes and input data

List of liquid biofuels pathways Ethanol pathways: 1 Wheat to Ethanol 2 Maize (EU) to Ethanol 3 Maize (US) to Ethanol 4 Sugar Beet to Ethanol 5 Grain Sorghum to Ethanol 6 Sweet Sorghum to Ethanol 7 Barley to Ethanol 8 Sugarcane to Ethanol 9 Rye to Ethanol 10 Triticale to Ethanol Biodiesel pathways: 11 Rapeseed to biodiesel 12 Sunflower to biodiesel 13 Soybeans to biodiesel a. No tillage and soybean import mix b. No- tillage and soya oil import mix c. Conventional agriculture and soybean import mix d. Conventional agriculture and soya oil import mix 14 Palm oil to biodiesel 15 Jatropha oil to biodiesel 16 Coconut to biodiesel 17 Waste oil to biodiesel Pure vegetable oils pathways: 18. Cottonseed oil 19. Tallow oil 20. HVO 21. Tall oil 22. Black liquor 23. Wood to FT diesel 24. Wood to methanol 25. Wood to DME 26. Organic waste to biogas

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Pathways not included in this report: Camelina Camelina is a flowering plant of the family of the Brassicaceae (like broccoli, cauliflower, rapeseed). It is traditionally grown on marginal land and can be planted as a rotation crop for wheat, in the “fallow” period, so it is a promising sustainable alternative energy crop. Historically, camelina has been used as a crop for animal feed and vegetable oil in northern Europe and in the Russian-Ukrainian area from the Neolithic to years 1930-1940. Unfortunately, nowadays camelina is no longer cultivated in relevant quantities and there is not an established market for camelina seeds nor for camelina oil. Consequently, reliable technical and market data are missing and we do not have a data base large enough to propose a default pathway for camelina. We have studied an experimental camelina pathway based on the (scarce) bibliography available, involving test cultivations performed in the Northern states of the USA, seed crushing in central USA, transport of camelina oil from USA to EU, and (by HVO process) production of jet fuel in Europe. However, these values refer only to pilot stage projects, not to large scale productions, and the hypotheses of delivering in Europe camelina produced in Montana, USA is quite unrealistic, in a market perspective. In fact, USA stakeholders are strongly interested on camelina jet fuel and it is unlikely that the rising USA camelina market could open enough to supply European needs, because of the very high internal demand and very low production. In a European market perspective, it should be much more interesting to build a camelina pathway on cultivation data referring to the following (suitable) production areas: Spain, Romania, Northern Europe, Russia and the former Soviet Union areas. Unfortunately, there are no data on camelina cultivation from these countries.

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Update of Lower Heating Values LHV of crude and refined vegetable oils Refined vegetable oils have a measured LHV of about 37.0 MJ/kg (Pramod and Anand, 2009). According to the ECN Phyllis36 database of biomaterials the LHV of refined vegateble oil is 37.2 and crude vegetable oils are given as 36.0. JRC thinks that crude vegetable oils used for biodiesel cannot be so different from refined vegetable oil LHV (as discussed below) so for simplicity we assume that the LHV of crude vegetable oils are also 37 MJ/kg. DIESTER37/EBB state that refining vegetable oil removes about 2.5% of the mass (so the raw oil is on average above the minimum specification below), but all the compounds removed except moisture have an LHV fairly similar to (maybe up to ~20% lower than) oil. Moisture content is < 0.5% in the FEDIOL raw rapeseed specification, so raw oil LHV cannot possibly be more than (0.5%+2%*0.2)*37 = 0.3 MJ/kg lower than the refined oil: it must be > 36.7 MJ/kg. FEDIOL specifications for crude rapeseed oil:

- Free fatty acids (as oleic): Max 2.00% - Moisture content, Volatile Matter and Impurities : Max 0.50% - Lecithin gum (expressed as Phosphorus) : Max 750ppm = ~2% by weight of

C43H88NO9P - Erucic Acid (a fatty acid): Max 2.00% Chemicals removed in refining DIESTER informed the JRC that the refining for biodiesel consists of: - neutralizing (and removing) fatty-acids and lecithins (similar or slightly lower LHV than oil) - removing any water associated with these - removing gums (slightly lower LHV than oil) - for sunflower: removing wax (winterisation = cooling and centrifuging). Wax has similar LHV to oil (just longer CH2 chains) Density of vegetable oils The density of refined vegetable oils Noureddini et al. (1992) and Dorfman, (2000) at 20C is around 0.92 kg/litre. discussion: according to Noureddini et al. (1992) , the density of rapeseed is particularly low at ~.910, palm highest at ~0.924, whilst soybean, corn and sunflower are ~0.922).

36 Energy research Centre of the Netherlands (ECN) found at http://www.ecn.nl/phyllis/ 37 Diester Industrie: http://www.sofiproteol.com

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The density of crude vegetable oils at 20C [CODEX STANDARD 210-1999] is not significantly different from this: - crude Rapeseed 0.9145 +/- 0.0045 - crude soy 0.920 +/- 0.005 - crude Palm 0.925 +/- 0.003, (40C data corrected to 20C using expansion coefficient in Noureddini et al. (1992) - crude Sunflower 0.9205 +/- 0.0025

Sources: 1 Pramod et al. 2009 2 Noureddini, et al., 1992 3 Dorfman, 2000 4 CODEX standard for named vegetable oils. CODEX STAN 210-1999. available at: www.codexalimentarius.org.

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7.1 Wheat grain to Ethanol Description of pathway The following processes are included in the wheat grain to ethanol pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Wheat cultivation The new data for wheat cultivation are shown in Table 80. The updated data include:

• Diesel and pesticide use in wheat cultivation updated using data from CAPRI (see Section 3.5). • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12). • Straw allocation has been removed.

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In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ per MJ of crop. Table 89 Cultivation of wheat

I/O Unit Amount Source Comment

Diesel Input MJ/MJwheat 0.03896 3, 5, 6, 7 See CAPRI data

N fertilizer Input kg/MJwheat 0.001336 2, 3 See GNOC data

CaCO3 fertiliser Input kg/MJwheat 0.002566 8 See liming data

K2O fertilizer Input kg/MJwheat 0.00021 3, 4 16.4 kg K2O/(ha*yr)

P2O5 fertilizer Input kg/MJwheat 0.00028 3, 4 21.6 kg P2O5/(ha*yr)

Pesticides Input kg/MJwheat 0.0000696 3, 5, 6, 7 See CAPRI data

Seeding material Input kg/MJwheat 0.00157 1, 3, 4 120 kg/(ha*yr)

Wheat Output kWh 1.0000 4, 5 5200 kg @ 13.5% H2O/(ha*yr)

Field N2O emissions gN2O/MJwheat 0.042 2 See GNOC data CO2 from neutralization & other soil acidity g/MJwheat 0.719 8 See liming data

Comment: - LHV = 17.0 MJ/ kg dry wheat grain (ref 3). - 13.5% water content.

Sources: 1 Gover et al., 1996. 2 Edwards and Koeble, 2012 (see Chapter 4). 3 Kaltschmitt and Hartmann, 2001. 4 EFMA, 2008. 5 CAPRI database, 2012 converted to JEC format using information in refs 6 and 7. 6 Kraenzlein, 2011. 7 Kempen and Kraenzlein, 2008. 8 JRC: Acidification and liming data (see Section 4.12). Step 2. Drying of wheat grain A new process for drying wheat grain, derived from CAPRI data (see Section 3.5), has been added. The data are shown in Table 81.

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Table 90 Process for drying of wheat grain

I/O Unit Amount Wheat Input MJ/MJwheat 1.0000

Electricity Input MJ/MJwheat 0.004944

Wheat Output MJ 1.0000

Sources: 1 CAPRI database, 2012 converted to JEC format using information in refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. Step 3. Handling and storage of wheat grain Table 91 Handling and storage of wheat grain

I/O Unit Amount

Wheat Input MJ/MJwheat 1.0000 Electricity Input MJ/MJwheat 0.000392 Wheat Output MJ 1.0000 Source: 1 Kaltschmitt and Reinhardt, 1997. Step 4. Transportation of wheat grain Table 92 Transport of wheat grain via 40 t truck (payload 27t) over a distance of 100 km (one way)

I/O Unit Amount

Distance Input tkm/MJwheat 0.0068

Wheat Input MJ/MJwheat 1.0100 Wheat Output MJ 1.0000

Comments: - Fuel consumption for a 40t truck is reported in Table 63.

Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop.

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Step 5. Conversion of wheat grain to ethanol Table 93 Conversion of wheat grain to ethanol


Wheat Input MJ/MJethanol 1.8283 3, 4, 5 3.333 twheat grain @ 13.5% H2O/tethanol

Electricity Input MJ/MJethanol 0.0541 2, 4, 5 1.45 GJ/tethanol

Steam Input MJ/MJethanol 0.3637 2, 4, 5 9.75 GJ/tethanol

NH3 Input kg/MJethanol 0.000226 4, 5, 6 2.1 kg/dry t of wheat grain

NaOH Input kg/MJethanol 0.000559 4, 5, 6 5.2 kg/dry t of wheat grain

CaO Input kg/MJethanol 0.000129 4, 5, 6 1.2 kg/dry t of wheat grain

Alpha-amylase Input kg/MJethanol 0.000086 4, 5, 6 0.8 kg/dry t of wheat grain

Glyco-amylase Input kg/MJethanol 0.000118 4, 5, 6 1.1 kg/dry t of wheat grain

Ethanol Output MJ 1.0000

Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Lywood, W. of ENSUS plc. pers.comm. with Edwards R., JRC, Ispra, 03/12/2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009. Step 5.1. Ethanol Plant generation processes Woodchip fuelled plant generation has been added. The data for the individual plant generation processes are shown in Chapter 5. The processes linked to wheat ethanol are:

• Steam generation (Table 51) • NG CHP (Table 50) • NG fueled CCGT (Table 52) • Lignite CHP (Table 53) • Lignite fueled steam turbine power plant (Table 54) • Wood chip fueled CHP (Table 55) • Wood chip fueled steam turbine power plant (Table 56)

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Step 6. Transportation of Ethanol to the blending depot Table 94 Transport of ethanol to depot via 40 t truck over a distance of 150 km

I/O Unit Amount

Distance Input tkm/MJethanol 0.0060

Ethanol Input MJ/MJethanol 1.0000 Ethanol Output MJ 1.0000

Comment: - For the fuel consumption of a 40 t truck, see Table 63. - 26 t transported ethanol; 2 t tank. - LHV (ethanol) = 26.8 MJ/kg.

Step 7. Ethanol depot distribution inputs Table 95 Ethanol depot

I/O Unit Amount

Ethanol Input MJ/MJethanol 1.00000

Electricity Input MJ/MJethanol 0.00084 Ethanol Output MJ 1.00000

Table 96 Ethanol filling station

I/O Unit Amount Ethanol Input MJ/MJethanol 1.0000

Electricity Input MJ/MJethanol 0.0034

Ethanol Output MJ 1.0000 Comment:

- Distribution is assumed to be same as for fossil diesel and gasoline. Source: 1 Dautrebande, 2002.

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7.2 Maize to Ethanol (made in EU)

Description of pathway The following processes are included in the maize to ethanol pathway:

Two separate pathways for maize to ethanol are shown: maize (corn) to ethanol produced in the EU, and maize (corn) to ethanol produced in the US (see Section 7.3). The data for each process of the EU maize to ethanol pathway are shown below and significant updates are described in more detail with the relevant references. Step 1. Maize cultivation in the EU The new data for maize cultivation are shown in Table 88. The updated data include:

• Diesel and pesticide use in EU maize cultivation updated using data from CAPRI (see Section 3.5). • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity have been calculated by the JRC (see Section 4.12). • Straw has been removed.

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In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop. Table 97 Cultivation of maize in the EU


Diesel Input MJ/MJcorn 0.03372 6, 8, 9, 10 See CAPRI data

N fertilizer Input kg/MJcorn 0.001344 3, 5, 6, 7 See GNOC data

CaCO3 fertiliser Input Kg/MJcorn 0.001254 6, 1 See liming data

K2O fertilizer Input kg/MJcorn 0.000488 2, 3, 5, 6

P2O5 fertilizer Input kg/MJcorn 0.000513 2, 3, 5, 6

Pesticides Input kg/MJcorn 0.000072 6, 8, 9, 10 7.0 kg/(ha*yr) @ 14% crop moisture

Seeding material Input kg/MJcorn 0.000078 4 0.0012 tonnes seed/tonne corn production

Corn Output MJ 1.0000 1

Field N2O emissions g/MJcorn 0.046 7 See GNOC data CO2 from neutralization & other soil acidity

g/MJcorn 0.343 1 See liming data

Comments: - Dry crop LHV = 17.3 MJ/(kg dry corn). - LHV of dry part of moist crop = 14.9 MJ/(kg moist corn). - Crop moisture content: 14% (ref 6). - Diesel and pesticide data from CAPRI is for EU only.

Sources: 1 JRC: Acidification and liming data in this report (see Section 4.12). 2 IFA, 2010. 3 FAPRI database, 2012. 4 FAO Prodstat, 2009. 5 JRC Calculation of the LHV of corn, 22 March 2012. 6 KTBL, 2006. 7 Edwards and Koeble, 2012 (see Chapter 4). 8 CAPRI database, 2012 converted to JEC format using information in refs 9 and 10. 9 Kranzlein, 2011. 10 Kempen and Kraenzlein, 2008. Step 2. Drying of maize A new process for drying the maize derived from CAPRI data (see Section 3.5) has been added. The data are shown in Table 89.

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Table 98 Drying of maize

I/O Unit Amount Corn Input MJ/MJcorn 1.0000

Electricity Input MJ/MJcorn 0.05051

Corn Output MJ 1.0000

Sources: 1 CAPRI database, 2012 converted to JEC format using information in refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. Step 3. Handling and storage of maize A new process for handling and storage of maize has been added. The data are shown in Table 90.

Table 99 Handling and storage of maize

I/O Unit Amount

Corn Input MJ/MJcorn 1.0000


Corn Output MJ 1.0000 Source: 1 Kaltschmitt and Reinhardt, 1997. Step 4. Transportation of maize Table 100 Transport of maize via 40 t truck over a distance of 100 km (one way)

I/O Unit Amount

Distance Input tkm/MJcorn 0.0067

Corn Input MJ/MJcorn 1.0100 Corn Output MJ 1.0000 Comment:

- For the fuel consumption of a 40 t truck, see Table 63. Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop. 3 Dreier, 2000.

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Step 5a. Conversion of maize to ethanol Table 101 Conversion of maize to ethanol

I/O Unit Amount Source Comment Corn Input MJ/MJethanol 1.6577 1, 2, 5 2.8 gal ethanol/bu

Electricity Input MJ/MJethanol 0.0485 1, 2, 5 1.08 kWhe/(gal ethanol) 0.14 kg H2O/kg corn

Steam Input MJ/MJethanol 0.4043 1, 2, 5 30714 Btu/(gal ethanol)

NH3 Input MJ/MJethanol 0.000201 1, 2, 3, 4, 5 2.1 kg/dry t of corn

NaOH Input kg/MJethanol 0.000498 1, 2, 3, 4, 5 5.2 kg/dry t of corn

CaO Input kg/MJethanol 0.000115 1, 2, 3, 4, 5 1.2 kg/dry t of corn

Alpha-amylase Input kg/MJethanol 0.000077 1, 2, 3, 4, 5 0.8 kg/dry t of corn

Glyco-amylase Input kg/MJethanol 0.000105 1, 2, 3, 4, 5 1.1 kg/dry t of corn

Ethanol Output MJ 1.000 0.711 kg moist DDGS/(l ethanol) 0.0334 kg dry DDGS/MJ ethanol

Comments: - LHV = 17.3 MJ/kg dry maize (refs 2 and 5). - Steam: 15.884 MJ/(kg moist DDGS), subtracting heat of evaporation of water content (refs 2 and 3).

Sources: 1 CARB, 2009. 2 JRC calculation from composition in NRC, 2001 and LHV values in ECN Phyllis database; 22 March 2011. 3 Hartmann, 1995. 4 MacLean and Spatari, 2009. 5 KTBL, 2006.

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Step 5b. Conversion of maize to ethanol (DDGS to biogas for process heat) Table 102 Conversion of maize to ethanol (DDGS for biogas)


Corn Input MJ/MJethanol 1.766 1 2.78 gal ethanol/bu

Electricity Input MJ/MJethanol 0.0119 1 0.264 kWhe/(gal ethanol)


N fertilizer Output kg/MJethanol 0.0055 1 0.124 kg DDGS/(l ethanol)

Capacity - MWethanol 88.2 2

Comment: - The heat and a part of the electricity demand is met by biogas from the DDGS

Sources: 1 Hirl, 2006. 2 Dreier, 2000. Step 5.1. Ethanol Plant generation processes The data for the individual plant generation processes are shown in Chapter 5. The processes linked to maize ethanol are:

• Steam generation (Table 51) • NG CHP (Table 50) • Lignite CHP (Table 53)

Step 6. Transportation of Ethanol to the blending depot The same data as for wheat ethanol are used. Step 7. Ethanol depot distribution inputs The same data as for wheat ethanol are used.

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7.3 Maize to Ethanol made in the US

Description of pathway This pathway reports maize (corn) cultivated in the US. The following processes are included in this pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Corn cultivation in the US The new data for corn cultivation are shown in Table 94. The updated data include:

• The corn-farming fuels inputs and pesticides updated using data from GREET1, 2011. • CaCO3 fertilizer use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 from neutralization & other soil acidity calculated by the JRC (see Section 4.12). In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.

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Table 103 Corn cultivation in the US

I/O Unit Amount Source Comment Diesel Input MJ/MJcorn 0.02519 1

N fertilizer Input kg/MJcorn 0.00118 3, 5 17.5 kgN/tcorn

CaCO3 fertilizer Input kg/MJcorn 0.0015681 3, 5 See liming data

K2O fertilizer Input kg/MJcorn 0.000422 2

P2O5 fertilizer Input kg/MJcorn 0.000363 2

Pesticides Input kg/MJcorn 0.00001363 1

Seeding material Output kWh 0.0000112 4 0.0002 tonnes seed/tonne corn production

Corn Input MJ/MJcorn 1.0000

Field N2O emissions gN2O/MJcorn 0.0343 3 See GNOC data CO2 from neutralization & other soil acidity g/MJcorn 0.196 6 See liming data

Comments: - LHV = 17.3 MJ/kg dry corn (refs 3 and 5). - Crop moisture content 14%. - LHV of dry part of moist crop: 14.9 MJ/kg moist crop.

Sources: 1 GREET1, 2011. 2 IFA, 2010. 3 Edwards and Koeble, 2012 (see Chapter 4). 4 FAO Prodstat, 2009. 5 KTBL, 2006. 6 JRC: Acidification and liming data in this report (Section 4.12). Step 2. Transportation of corn The corn transport distance has been updated according to Mueller, 2010. Table 104 Transport of corn via 40 t truck over a distance of 56 km (one way distance)

I/O Unit Amount

Distance Input tkm/MJcorn 0.0038

Corn Input MJ/MJcorn 1.0100 Corn Output MJ 1.0000 Comments:

- For the fuel consumption of a 40 t truck, see Table 63.

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Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop. 3 Mueller, 2010. Step 3. Conversion of corn to ethanol (average US ethanol plant) The new data for the conversion to ethanol are shown in Table 96. The updated data include:

• Ethanol yield, electricity, thermal energy, coal and co-products yields updated using GREET1, 2011 and Mueller, 2010 data.

Table 105 Conversion of corn to ethanol (average US ethanol plant)


Corn Input MJ/MJethanol 1.6706 2 2.8 gal ethanol/bu

Electricity Input MJ/MJethanol 0.0295 2 0.66 kWhe/(gal ethanol)

NG Input MJ/MJethanol 0.3126 2 23745 Btu/(gal ethanol)

Coal Input MJ/MJethanol 0.0423 2 3210 Btu/(gal ethanol)

NH3 Input MJ/MJethanol 0.00020279 1 2.1 kg/dry t of corn

NaOH Input kg/MJethanol 0.00050214 1 5.2 kg/dry t of corn

CaO Input kg/MJethanol 0.00011588 1 1.2 kg/dry t of corn

Alpha-amylase Input kg/MJethanol 0.00007725 4 0.8 kg/dry t of corn

Glyco-amylase Input kg/MJethanol 0.00010622 4 1.1 kg/dry t of corn


0.80 kg moist DDGS/kg ethanol 0.32 kg moist WDG/kg ethanol 0.0018 kg moist corn oil/kg ethanol

Comments: - Steam: 17.376 MJ/(kg moist DDGS), subtracting heat of evaporation of water content. - % Allocation to by-products: 37.8%.

Sources: 1 CARB, 2009. 2 GREET1, 2011. 3 JRC calculation from composition in NRC, 2001 and LHV values in ECN Phyllis database; 22 March 2011. 4 MacLean and Spatari, 2009.

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5 KTBL, 2006. 6 Mueller, 2010. 7 JEC-WTW v3: calculation from standard enthalpy of formation by LBST, and agreeing with FfE, 1998. Step 4. Transportation of ethanol to the blending depot For the corn ethanol transport in the US, an article in the Ethanol Producer Magazine (By train, by truck or by boat, how ethanol moves and where it is going) of November 2011 reports that: “70 percent of all U.S. ethanol is hauled by rail, 20 percent by truck and 10 percent by barge. But, while rail is the undisputed ruler of U.S. ethanol transport, bottlenecks and tank car shortages have led to many headaches over the past year that will likely continue for some time.” A presentation by Eco-Energy (an ethanol marketing firm) shows the percentages of US ethanol by port in 2011 (42%, Houston-Galvestone, TX and 27% New York, NY). Table 106 Transportation of ethanol summary table

Transporter Distance (km) Description Train 1000 Indiana to Baltimore

Ocean bulk carrier (50,000t) 6800 Baltimore to Rotterdam

Truck to depot (40 t) 150 Same as all ethanol pathways Detailed transportation data: Table 107 Transport of ethanol via train over a distance of 1000 km (Indiana to Baltimore)

I/O Unit Amount



Comments: - For the fuel consumption for a freight train run on diesel fuel (US situation), see Table 77.

Table 108 Transport of ethanol via ship (payload 50,000 t) over a distance of 6800 km (Baltimore to Rotterdam)

I/O Unit Amount



Comments:

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- For the fuel consumption for a product tanker (50000 t payload), see Table 74. Table 109 Transport of ethanol via 40 t truck over a distance of 150 km (one way)

I/O Unit Amount Distance Input tkm/MJethanol 0.0060



Comments: - 26 t transported ethanol; 2 t tank. - LHV (ethanol) = 26.8 MJ/kg. - For the fuel consumption of the 40 t truck, see Table 63.

Source: 1 Dautrebande, 2002. Step 5. Ethanol depot distribution inputs The same data as for wheat ethanol are used.

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7.4 Sugar beet to Ethanol Description of pathway The following processes are included in the sugar beet to ethanol pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Sugar beet cultivation The new data for sugar beet cultivation are shown in Table 101. The updated data include:

• Diesel and pesticide use in sugar beet cultivation updated using data from CAPRI (see Section 3.5). • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 from neutralization & other soil acidity calculated by the JRC (see Section 4.12).

In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.

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Table 110 Sugar beet cultivation

I/O Unit Amount Source Source data

Diesel Input MJ/MJsugar beet 0.01485 1, 4, 5, 6 See CAPRI DATA

N fertilizer Input kg/MJsugar beet 0.000324 2 See GNOC data

CaCO3 fertilizer Input kg/MJsugar beet 0.001085 7 See Liming data

K2O fertilizer Input kg/MJsugar beet 0.00048 3 134.9 kg K2O/(ha*yr)

P2O5 fertilizer Input kg/MJsugar beet 0.00021 3 59.7 kg P2O5/(ha*yr)

Pesticides Input kg/MJsugar beet 0.0000774 1, 4, 5, 6 See CAPRI data

Seeding material Input kg/MJsugar beet 0.0000214 1 6.0 kg/(ha*yr) [1]

Sugar beet Output MJ 1.0000 68,860 kg beet @ 75% H2O [4]

Field N2O emissions - g/MJsugar beet 0.0160 2 See GNOC data CO2 from neutralization & other soil acidity

g/MJsugar beet 0.3084 7 See Liming data

Comments: - LHV = 16.3 MJ/(kg dry sugar beet). - Water content = 75% and a sugar content of 16%.

Sources: 1 Dreier and Geiger, 1998. 2 Edwards and Koeble, 2012 (see Chapter 4). 3 EFMA, 2008. 4 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in refs 5 and 6. 5 Kraenzlein, 2011. 6 Kempen and Kraenzlein, 2008. 7 JRC: Acidification and liming data in this report (Section 4.12). Step 2. Handling and storage of sugar beet A new process for handling and storage of sugar beet has been added. The data are shown in Table 102.

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Table 111 Handling and storage of sugar beet

I/O Unit Amount Sugar beet Input MJ/MJcorn 1.0000 Electricity Input MJ/MJcorn 0.00141 Sugar beet Output MJ 1.0000 Source: 1 Kaltschmitt and Reinhardt, 1997. Step 3. Transportation of sugar beet Table 112 Transport of sugar beet via 40 t truck over a distance of 30 km (one way)

I/O Unit Amount Distance Input tkm/MJsugar beet 0.0074

Sugar beet Input MJ/MJsugar beet 1.0000

Sugar beet Output MJ 1.0000

Comment: - For the fuel consumption of the 40 t truck, see Table 63.

Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop. 3 Dreier and Geiger, 1998. Step 4. Conversion to ethanol The data for the conversion of sugar beet to ethanol with no biogas from slops (SBET1a) are shown in Table 104. Table 113 Conversion to ethanol with no biogas from slops (SBET1a)


Sugar beet Input MJ/MJethanol 1.8395 1, 2, 3 0.0777 t ethanol /(t sugar beet @ 76.5% H2O)

Electricity Input MJ/MJethanol 0.0345 1, 3

Steam Input MJ/MJethanol 0.2806 1, 3

Ethanol Output MJ 1.000 0.058 t beet pulp/(t sugar beet @ 76.5% H2O) 14.4313 MJ/(kg pulp)

Capacity - MWethanol 58.8 2 240,000 l ethanol/d 90 d/yr

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The data for the conversion of sugar beet to ethanol using biogas from slops (SBET1b) are shown in Table 105. Table 114 Conversion to ethanol with biogas from slops (SBET1b)

I/O Unit Amount Source Comment Sugar beet

Input MJ/MJethanol 1.8395 1 0.0777 t ethanol /(t sugar beet @ 76.5% H2O)

Electricity Input MJ/MJethanol 0.0398 1

Steam Input MJ/MJethanol 0.1043 1

Ethanol Output MJ 1.000 0.058 t beet pulp/(t sugar beet @ 76.5% H2O) 14.4313 MJ/(kg pulp)

Capacity - MWethanol 58.8 2 240.000 l ethanol/d 90 d/yr

Comment: - LHV (sugar beet pulp) = 16.1 MJ/kg of dry substance (LHV including water vaporization, ref 1).

Sources: 1 Kaltschmitt and Reinhardt, 1997. 2 Dreier and Geiger, 1998. 3 Hartmann, 1995. Step 4.1. Ethanol Plant generation processes The data for the individual plant generation processes are shown in Chapter 5. The process linked to sugar beet ethanol is:

• Steam generation (Table 51) Step 5. Transportation of Ethanol to the blending depot The same data as for wheat ethanol are used. Step 6. Ethanol depot distribution inputs The same data as for wheat ethanol are used.

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7.5 Grain sorghum to Ethanol

Description of pathway The following processes are included in the grain sorghum to ethanol pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Grain sorghum cultivation The new data for grain sorghum cultivation are shown in Table 106. The updated data include:

• CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 from neutralization & other soil acidity calculated by the JRC (see Section 4.12). In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.

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Table 115 Grain sorghum cultivation


Diesel Input MJ/MJsorghum 0.08392 1 30864 BTU/bu

N fertilizer Input kg/MJsorghum 0.00144 2 17.7 kgN/tonne moist crop

CaCO3 fertilizer Input kg/MJsorghum 0.00145

K2O fertilizer Input kg/MJsorghum 0.00026 1 102.3 g/bu

P2O5 fertilizer Input kg/MJsorghum 0.00004 1 16.95 g/bu

Pesticides Input kg/MJsorghum 0.00003 1 13.1 g/bu

Seeding material Input kg/MJsorghum 0.00157 3 56lbs/bu

Grain-sorghum Output MJ 1.0000 1

Field N2O emissions g/MJsorghum 0.03 4 See GNOC data CO2 from neutralization & other soil acidity

g/MJsorghum 0.308 See Liming data

Comments: - Bu = bushel. - Diesel includes some gasoline and fuel for drying. - N fertilizer: Sorghum often follows soybean in rotations and so gets more N than this number suggests. - LHV of moist sorghum = 15.4 MJ/kg wet, Moisture content = 0.11 kg/kg. - Sorghum LHV dry matter = 17.3 MJ/kg dry matter (estimated from composition, same as for corn).

Sources: 1. GREET, 2010. 2. Assumed equal to JEC wheat number. 3. Edwards and Koeble, 2012 (see Chapter 4). 4. JRC: Acidification and liming data in this report (Section 4.12). Step 2: Transportation of grain sorghum Table 116 Transport of grain sorghum via 40 t truck over a distance of 100 km (one way)

I/O Unit Amount

Distance Input tkm/MJsorghum 0.0065

Corn Input MJ/MJsorghum 1.0100 Corn Output MJ 1.0000

Comments: - For the fuel consumption of the 40 t truck, see Table 63.

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Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop. 3 Dreier and Geiger, 1998. Step 3. Conversion to ethanol Table 117 Conversion of grain sorghum to ethanol


Sorghum Input MJ/MJethanol 1.7660 1, 2, 5 2.72 gal ethanol/bu 17.3 MJ/(kg dry sorghum)

Electricity Input MJ/MJethanol 0.0485 1, 2, 5 1.08 kWhe/(gal ethanol) 0.11 kg H2O/(kg sorghum)

Steam Input MJ/MJethanol 0.4043 1, 2, 3, 5

30714 Btu/(gal ethanol) 15.884 MJ/(kg moist DDGS), subtracting heat of evaporation of water content

NH3 Input kg/MJethanol 0.000214 1, 2, 3, 5 2.1 kg/dry t of sorghum

NaOH Input kg/MJethanol 0.000531 1, 2, 3, 4, 5 5.2 kg/dry t of sorghum

CaO Input kg/MJethanol 0.000122 1, 2, 3, 4, 5 1.2 kg/dry t of sorghum

Alpha-amylase Input kg/MJethanol 0.000082 1, 2, 3, 4, 5 0.8 kg/dry t of sorghum

Gluco-amylase Input kg/MJethanol 0.000112 1, 2, 3, 4, 5 1.1 kg/dry t of sorghum

Ethanol Output MJ 1.000 0.711 kg moist DDGS/(l ethanol)

Comment: - 30714 Btu/(gal ethanol) from ref 1 (NG 32330 btu/gallon ethanol). We assume GREET use 95% efficient boiler.

Sources: 1 GREET, 2010. 2 JRC calculation from composition in NRC, 2001 and LHV values in ECN Phyllis database; 22 March 2011. 3 Hartmann, 1995. 4 MacLean and Spatari, 2009. 5 KTBL, 2006. Step 4. Transportation of Ethanol to the blending depot The same data as for wheat ethanol are used. Step 5. Ethanol depot distribution inputs The same data as for wheat ethanol are used.

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7.6 Sweet sorghum to Ethanol Step 1: Sweet sorghum cultivation Table 118 Sweet sorghum cultivation


Diesel Input MJ/MJsorghum 0.0207 1 5645 MJ/(ha*yr)

N fertilizer Input kg/MJsorghum 0.000733 1 200 kg/(ha*yr 15 dry t/(ha*yr))

K2O fertilizer Input kg/MJsorghum 0.000824 1 15 kg/t of dry substance

P2O5 fertilizer Input kg/MJsorghum 0.000275 1 5 kg/t of dry substance

Pesticides Input kg/MJsorghum 0.0000064 4, 5, 6

Seeding material Input kg/MJsorghum 0.000082 1, 3 Sorghum Output MJ 1.0000 Field N2O emissions - g/MJsorghum 0.0150 2

Comment: - LHV = 18.2 MJ/kg of dry substance

Sources: 1 KTBL, 2006. 2 Paustian et al., 2006. 3 LAMNET, 2005. 4 AGRAVIS Raiffeisen AG, Münster; Germany: Energiepflanzenproduktion für die Biogasnutzung - Andere Kulturen und Fruchtfolgesysteme: 09. 5 Syngenta Agro AG, 2009. 6 Bayer Crop Science, 2009. Step 2. Transport of sweet sorghum Table 119 Transport of sweet sorghum via a 40 t (payload 27t) truck over a distance of 20 km (one way)

I/O Unit Amount Distance Input tkm/MJsorghum 0.0050

Sugar cane Input MJ/MJsorghum 1.0000

Sugar cane Output MJ 1.0000

Comments: - For the fuel consumption of the 40 t truck, see Table 63.

Sources: 1 Gruber, Chr., MAN, pers. comm. 21 August, 2003. 2 Frischknecht, R. et al., 1996.

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Step 3. Conversion of sweet sorghum to ethanol Table 120 Conversion of sweet sorghum to ethanol


Sorghum Input MJ/MJethanol 3.593 1, 2 19 kg sorghum/(l ethanol)] 22% dry substance

Ethanol Output MJ 1.000 Electricity Output MJ/MJethanol 0.298 1 Not taken into account

N fertilizer Input kg/MJethanol 0.0003 1, 3

Sources: 1 Praj Industries Limited, 2008. 2 KTBL, 2006. 3 Macedo et al., 2008. Step 4. Transportation of Ethanol to the blending depot The same data as for wheat ethanol are used. Step 5. Ethanol depot distribution inputs The same data as for wheat ethanol are used.

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7.7 Barley to Ethanol

Description of pathway The following processes are included in the barley to ethanol pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Barley cultivation The new data for barley cultivation are shown in Table 112. The updated data include:

• Diesel and pesticide use in barley cultivation updated using data from CAPRI (see Section 3.5). • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity have been calculated by the JRC (see Section 4.12). • Straw has been removed. In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.

148

Table 121 Barley cultivation


Diesel Input MJ/MJbarley 0.0500 3, 5, 6, 7 See CAPRI data

N fertilizer Input kg/MJbarley 0.00141 2, 3 See GNOC data

CaCO3 fertiliser Input kg/MJbarley 0.00363 8 See liming data

K2O fertilizer Input kg/MJbarley 0.00032 1, 3, 4 19.9 kg K2O/(ha*yr)

P2O5 fertilizer Input kg/MJbarley 0.000389 1, 3, 4 23.9 kg P2O5/(ha*yr)

Pesticides Input kg/MJbarley 0.0000604 3, 5, 6, 7 See CAPRI data

Seeding material Input kg/MJbarley 0.00277 1, 3, 4 170 kg/(ha*yr)

Barley Output MJ 1.0000 4178 kg/(ha*yr) 13.5% H2O

Field N2O emissions - g/MJbarley 0.0429 2 See GNOC data

CO2 from neutralization & other soil acidity

g/MJbarley 1.146 8 See liming data

Comments: - Assumption: LHV (barley grain) = LHV (wheat grain). - LHV (wheat grain) = 17 MJ/kg of dry substance (ref 3).

Sources: 1 Lechon et al., 2005. 2 Edwards and Koeble, 2012 (see Chapter 4). 3 Kaltschmitt and Hartmann, 2001. 4 EFMA, 2008. 5 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in refs 6 and 7. 6 Kraenzlein, 2011. 7 Kempen and Kraenzlein, 2008. 8 JRC: Acidification and liming data in this report (Section 4.12). Step 2. Drying of barley A new process for drying the barley derived from CAPRI data (see Section 3.5) has been added. The data are shown in Table 113.

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Table 122 Drying of barley

I/O Unit Amount Barley Input MJ/MJcorn 1.0000


Barley Output MJ 1.0000

Sources: 1 CAPRI database, 2012 converted to JEC format using information in refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. Step 3. Handling and storage of barley A new process for handling and storage of barley has been added. The data are shown in Table 114. Table 123 Handling and storage of barley

I/O Unit Amount Barley Input MJ/MJcorn 1.0000Electricity Input MJ/MJcorn 0.000392Barley Output MJ 1.0000

Source: 1 Kaltschmitt and Reinhardt, 1997. Step 4. Transportation of barley grain Table 124 Transport of barley grain via 40 t truck over a distance of 100 km (one way)

I/O Unit Amount

Distance Input tkm/MJbarley 0.0068

Barley Input MJ/MJbarley 1.0100 Barley Output MJ 1.0000


Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop.

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Step 5. Conversion of barley to ethanol Table 125 Conversion of barley to ethanol


Barley Input MJ/MJethanol 2.0895 1, 4 3.8095 t barley grain @ 13.5 % H2O/(t ethanol)

Electricity Input MJ/MJethanol 0.0541 2, 4, 5 1.45 GJ/(t ethanol)

Steam Input MJ/MJethanol 0.3637 2, 4, 5 9.75 GJ/(t ethanol)

NH3 Input kg/MJethanol 0.000258 4, 5, 6 2.1 kg/dry t of barley grain]

NaOH Input kg/MJethanol 0.000639 4, 5, 6 5.2 kg/dry t of barley grain

CaO Input kg/MJethanol 0.000147 4, 5, 6 1.2 kg/dry t of barley grain

alpha-amylase Input kg/MJethanol 0.000098 4, 5, 6 0.8 kg/dry t of barley grain

gluco-amylase Input kg/MJethanol 0.000135 4, 5, 6 1.1 kg/dry t of barley grain

Ethanol Output MJ 1.000 1.4875 t/(t ethanol 16.5859 MJ/(kg DDGS) [4] [5]

Comments: - 3.8095 t barley grain (ref 1).

Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Lywood, W., ENSUS plc., pers. comm. to Robert Edwards, JRC, Ispra, 03/12/2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009. Step 5.1. Ethanol Plant generation processes The data for the individual plant generation processes are shown in Chapter 5. The processes linked to barley ethanol are:


Step 6. Transportation of Ethanol to the blending depot The same data as for wheat ethanol are used.

151

Step 7. Ethanol depot distribution inputs The same data as for wheat ethanol are used.

152

7.8 Sugarcane to ethanol

Description of pathway The following processes are included in the sugar cane to ethanol pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Sugar cane cultivation The new data for sugar cane cultivation are shown in Table 121. The updated data include:

• CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity have been calculated by the JRC (see Section 4.12). In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.

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Table 126 Sugar cane cultivation

I/O Unit Amount Source Comments

Diesel Input MJ/MJsugar

cane 0.0086 1, 2, 3, 4 0.797 l diesel/(t sugar cane @

72.5% H2O ) N fertilizer Input kg/MJsugar cane 0.000181 1, 2, 5, 7 See GNOC data

CaCO3 fertilzer Input kg/MJsugar cane 0.000338 1, 2, 3 See liming data

Filter mud cake Input kg/MJsugar cane 0.001534 1, 2, 3, 4 600 kg/(ha*yr)

K2O fertilizer Input kg/MJsugar cane 0.000241 1, 2, 5, 6 1.30 kg/tcane

P2O5 fertilizer Input kg/MJsugar cane 0.000092 1, 2, 5, 6 0.50 kg/tcane

Pesticides Input kg/MJsugar cane 0.000005 1, 2, 3 2.36 kg/(ha*yr)

Seeding material Input kg/MJsugar cane 0.005378 1, 2, 3 2104 kg/(ha*yr)

Vinasse Input kg/MJsugar cane 0.183697 1, 2, 3 71867 kg/(ha*yr)

Sugar cane Output MJ 1.0000 Field N2O emissions - g/MJsugar cane 0.0079 3, 7 Including trash burning


g/MJsugar cane 0.0818 See liming data

Comments: - LHV sugar cane 19.6 MJ/kg of dry substance:

o LHV (sugar cane) derived from ref 1; o Water content (sugar cane) derived from ref 2.

- Yield 72.58 t sugar cane/(ha*yr) (ref 3), average of 6 years. Sources: 1 Dreier, 2000. 2 Kaltschmitt and Hartmann, 2001. 3 Macedo et al., 2008. 4 Macedo et al., 2004. 5 Heffer, 2009. 6 FAO Prodstat, 2010. 7 Edwards and Koeble, 2012 (see Chapter 4) 8 JRC: Acidification and liming data in this report (Section 4.12).

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Step 2. Transportation Table 127 Transportation of sugarcane summary table

Commodity Transporter Transport of mud cake Truck MB2213 Transport of seeding material Truck MB2318 Transport of sugarcane Truck (40t) average Table 128 Transport of mud cake via dumpster truck MB2213 over a distance of 8 km (one way)

I/O Unit Amount Distance Input tkm/kg 0.008 Filter mud cake Input kg/kg 1.00 Filter mud cake Output kg 1.00

Comment: - For the fuel consumption of MB2213 truck, see Table 65.

Table 129 Transport of seeding material via MB2318 truck over a distance of 20 km (one way)

I/O Unit Amount Distance Input tkm/kg 0.020 Seeding material Input kg/kg 1.00 Seeding material Output kg 1.00

Comment: - For the fuel consumption of MB2318 truck, see Table 66.

Table 130 Transport of sugar cane via 40 t truck over a distance of 20 km (one way)

I/O Unit Amount Distance Input tkm/MJsugar cane 0.0037

Sugar cane Input MJ/MJsugar cane 1.0000

Sugar cane Output MJ 1.0000

Comment: - For the fuel consumption of 40 t truck weighted average for sugarcane, see Table 64.

Table 131 Transport of vinasse summary table

Share of the vinasse Transporter 4.6% truck MB2318

23.6% tanker truck with water cannons 71.8% water channels

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Table 132 Transport of vinasse via a tanker truck MB2318 over a distance of 7 km (one way)

I/O Unit Amount Distance Input tkm/kg 0.007 Vinasse Input kg/kg 1.00 Vinasse Output kg 1.00

Comment: - For the fuel consumption of the tanker truck MB2318, see Table 67.

Source: 1 Macedo et al., 2004. Table 133 Transport of vinasse via a tanker truck with water cannons over a distance of 14 km (one way)

I/O Unit Amount Distance Input tkm/kg 0.014 Vinasse Input kg/kg 1.00 Vinasse Output kg 1.00

Comment: - For the fuel consumption, see Table 63.

Table 134 Transport of vinasse via water channels

I/O Unit Amount

Diesel Input MJ/kg 0.005 Vinasse Input kg/kg 1.00 Vinasse Output kg 1.00

156

Step 3. Conversion of sugar cane to ethanol Table 135 Conversion of sugar cane to ethanol (no heat export) SCETc


Sugar cane Input MJ/MJethanol

2.949 1, 2, 3 86.3 l ethanol/(t sugar cane, 72.5% H2O)

CaO Input kg/MJethanol

0.000509 3, 4 0.93 kg/(t sugar cane, 72.5% H2O)

Cyclohexane Input kg/MJethanol

0.000028 3 0.6 kg/(m3 ethanol)

H2SO

4 Input kg/MJ

ethanol 0.000427 3 0.00905 kg/(l ethanol)

Lubricants Input kg/MJethanol

0.000007 3, 4 0.01337 kg/(t sugar cane, 72.5% H2O)


Electricity Output MJ/MJethanol

0.0181 3 9.2 kWh/t of cane, replaces electricity from bagasse ST (eff = 25%)

Comments: - Sugarcane:

- LHV (sugar cane) = 19.6 MJ/kg (ref 1); - 86.3 l ethanol/(t sugar cane) for 2005/2006 (ref 3); - H2O content = 72.5% (ref 2); - = > 19600*0.275/(86.3*26.81*0.79) MJ/MJ.

- CaO: - 0.930 kg/ (t sugar cane) (ref 4).

- Cyclehexane: - 0.60 kg cyclohexane/m3 of anhydrous ethanol (ref 4); = > 0.6 kg/(1000*0.79*26.81) MJ ethanol.

- H2SO4 - 0.00905 kg /(l ethanol) (ref 4).

- Lubricants: - 0.01337 kg / (t sugar cane) (ref 4).

- Electricity: - 586: 21 bar / 300°C; - Steam turbine: excess electricity: 9.2 kWh/(t sugar cane).

Sources: 1 Dreier, 2000. 2 Kaltschmitt and Hartmann, 2001. 3 Macedo et al., 2008. 4 Macedo et al., 2004. Step 3.1. Ethanol Plant generation processes The data for the individual plant generation processes are shown in Chapter 5. The process linked to barley ethanol is:

157

• Light heating oil fueled boiler (Table 60) Step 4. Transport of ethanol to blending depot Shipping distance has been updated. Table 136 Summary transport table of sugarcane ethanol

Transporter Distance (km one-way)

Truck (40t, payload 27t) 700

Ocean bulk carrier 10,186

Truck 150 (Same as all ethanol pathways)

Table 137 Transport of ethanol via a 40 t truck a distance of 700 km (one way)

I/O Unit Amount





Table 138 Maritime transport of ethanol via ship over a distance of 10,186 km (one way)

I/O Unit Amount




Comment: - For the fuel consumption of the product tanker, see Table 75.

Step 5. Ethanol depot distribution inputs The same data as for wheat ethanol are used.

158

7.9 Rye to Ethanol

Description of pathway The following processes are included in the rye to ethanol pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Rye cultivation The new data for rye cultivation are shown in Table 134. The updated data include:

• Diesel and pesticide use in rye cultivation updated using data from CAPRI (see Section 3.5). • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12). • Straw has been removed.

159

In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.

Table 139 Rye cultivation


Diesel Input MJ/MJrye 0.06360 2, 5, 6, 7 See CAPRI data

N fertilizer Input kg/MJrye 0.00175 1, 2, 4 See GNOC data

CaCO3 fertilizer Input kg/MJrye 0.00627 8 See liming data

K2O fertilizer Input kg/MJrye 0.000413 1, 2, 3 31.0 kg K2O/(ha*yr)

P2O5 fertilizer Input kg/MJrye 0.000547 1, 2, 3 41.0 kg P2O5/(ha*yr)

Pesticides Input kg/MJrye 0.0000358 2, 5, 6, 7 See CAPRI data

Seeding material Input kg/MJrye 0.00120 1, 2, 3 90 kg/(ha*yr)

Rye grain Output MJ 1.0000 5100 kg/(ha*yr)

Field N2O emissions - g/MJrye 0.050 4 See GNOC data CO2 from neutralization & other soil acidity

2.085 8 See liming data

Comment: - LHV of rye: 17.1 MJ / (dry kg) (ref 2).

Sources: 1 KTBL, 2006. 2 Kaltschmitt and Hartmann, 2001. 3 Kaltschmitt and Reinhardt, 1997. 4 Edwards and Koeble, 2012 (see Chapter 4). 5 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in refs 6 and 7. 6 Kraenzlein, 2011. 7 Kempen and Kraenzlein, 2008. 8 JRC: Acidification and liming data in this report (Section 4.12).

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Step 2. Drying of rye grain A new process for drying the rye derived from CAPRI data (see Section 3.5) has been added. The data are shown in Table 126. Table 140 Drying of rye grain

I/O Unit Amount

Rye Input MJ/MJrye 1.0000

Electricity Input MJ/MJrye 0.002019 Rye Output MJ 1.0000

Sources: 1 CAPRI database, 2012 converted to JEC format using information in refs 2 and 3. 2 Kraenzlein, 2011. 3 Kempen and Kraenzlein, 2008. Step 3. Handling and storage of rye grain A new process for handling and storage of rye has been added. The data are shown in Table 127. Table 141 Handling and storage of rye grain

I/O Unit Amount Rye Input MJ/MJrye 1.0000

Electricity Input MJ/MJrye 0.000392

Rye Output MJ 1.0000

Source: 1 Kaltschmitt and Reinhardt, 1997. Step 4. Transportation of rye grain Table 142 Transport of rye grain via 40 t (payload 27t) truck over a distance of 100 km (one way)

I/O Unit Amount

Distance Input tkm/MJrye 0.0068

Rye grain Input MJ/MJrye 1.0100 Rye grain Output MJ 1.0000


Sources: 1 Kaltschmitt and Hartmann, 2001.

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2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop. Step 5. Conversion of rye grain to ethanol Table 143 Conversion of rye grain to ethanol (same as wheat)


Rye grain Input MJ/MJethanol 1.8283 3, 4, 5 3.333 t rye grain @ 13.5% H2O/(t ethanol)



NH3 Input kg/MJethanol 0.000226 4, 5, 6 2.1 kg/dry t of rye grain

NaOH Input kg/MJethanol 0.000559 4, 5, 6 5.2 kg/dry t of rye grain

CaO Input kg/MJethanol 0.000129 4, 5, 6 1.2 kg/dry t of rye grain

alpha-amylase Input kg/MJethanol 0.000086 4, 5, 6 0.8 kg/dry t of rye grain

gluco-amylase Input kg/MJethanol 0.000118 4, 5, 6 1.1 kg/dry t of rye grain

Ethanol Output MJ 1.0000 1.247 t/(t ethanol) 16.586 MJ/(kg DDGS)

Comment: - Same as for wheat due to same starch content.

Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Lywood, W., ENSUS pers.comm.to JRC, Ispra, 03/12/2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009. Step 5.1. Ethanol Plant generation processes: The data for the individual plant generation processes are shown in Chapter 5. The processes linked to rye ethanol are:


162


163

7.10 Triticale to Ethanol

Description of pathway The following processes are included in the triticale to ethanol pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Triticale cultivation The new data for triticale cultivation are shown in Table 139. The updated data include:

• CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12). • Straw has been removed. In the following table, source numbers in bold represent the main data source, additional references are used to convert data to MJ/MJ crop.

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Table 144 Triticale cultivation


Diesel Input MJ/MJrye 0.0625 1, 2, 3 5090 MJ/(ha*yr)

N fertilizer Input kg/MJrye 0.00144 1, 2, 4 See GNOC data

CaCO3 fertilizer Input kg/MJrye 0.00412 5 See liming data

K2O fertilizer Input kg/MJrye 0.00042 1, 2, 3 34.0 kg K2O/(ha*yr)

P2O5 fertilizer Input kg/MJrye 0.00055 1, 2, 3 45.0 kg P2O5/(ha*yr)

Pesticides Input kg/MJrye 0.0000262 1, 2, 3 See CAPRI data

Seeding material Input kg/MJrye 0.00172 1, 2, 3 90 kg/(ha*yr)

Triticale grain Output MJ 1.0000 5100 kg/(ha*yr)

Field N2O emissions - g/MJrye 0.042 4 See GNOC data CO2 from neutralization & other soil acidity


Comments: - LHV of triticale: 16.9 MJ/(dry kg) (ref 2). - Diesel use includes handling and storage (ref 1).

Sources: 1 KTBL, 2006. 2 Kaltschmitt and Hartmann, 2001. 3 Kaltschmitt and Reinhardt, 1997. 4 Edwards and Koeble, 2012 (see Chapter 4). 5 JRC: Acidification and liming data in this report (Section 4.12). Step 2. Drying of triticale grain Same as for wheat to ethanol pathway. Step 3. Transportation of triticale grain Table 145 Transport of triticale grain via 40 t truck over a distance of 100 km (one way)

I/O Unit Amount

Distance Input tkm/MJtriticale 0.0069

Triticale grain Input MJ/MJtriticale 1.0100 Triticale grain Output MJ 1.0000


165

Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Fahrzeugbau Langendorf GmbH & Co. KG; Bahnhofstraße 115, D-45731 Waltop. Step 4. Conversion of triticale grain to ethanol Table 146 Conversion of triticale grain to ethanol (same as wheat)


Triticale Input MJ/MJethanol 1.8283 3, 4, 5 3.333 t triticale grain @ 13.5% H2/(t ethanol)



NH3 Input kg/MJethanol 0.000226 4, 5, 6 2.1 kg/dry t of triticale grain

NaOH Input kg/MJethanol 0.000559 4, 5, 6 5.2 kg/dry t of triticale grain

CaO Input kg/MJethanol 0.000129 4, 5, 6 1.2 kg/dry t of triticale grain alpha-amylase Input kg/MJethanol 0.000086 4, 5, 6 0.8 kg/dry t of triticale grain

gluco-amylase Input kg/MJethanol 0.000118 4, 5, 6 1.1 kg/dry t of triticale grain

Ethanol Output MJ 1.0000 3, 4, 5 1.247 t/(t ethanol) 16.586 MJ/(kg DDGS)

Capacity - MWethanol 94.0 100,000 t ethanol/yr

Comment: - Same as for wheat due to same starch content

Sources: 1 Kaltschmitt and Hartmann, 2001. 2 Punter et al., 2004. 3 Kaltschmitt and Reinhardt, 1997. 4 Lywood, W., ENSUS pers.comm.to JRC, Ispra, 03/12/2010. 5 Hartmann, 1995. 6 MacLean and Spatari, 2009. Step 4.1. Ethanol Plant generation processes The data for the individual plant generation processes are shown in Chapter 5. The processes linked to triticale ethanol are:

• Steam generation (Table 51) • NG CHP (Table 50) • NG fueled CCGT (Table 52) • Lignite CHP (Table 53)

166

• Lignite fueled steam turbine power plant (Table 54) • Wood chip fueled CHP (Table 55) • Wood chip fueled steam turbine power plant (Table 56)


167

7.11 Rapeseed to biodiesel Description of pathway The following processes are included in the rapeseed to biodiesel pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Rapeseed cultivation The new data for rapeseed cultivation are shown in Table 133. The updated data include:

• Diesel and pesticide use in rapeseed cultivation calculated using data from CAPRI (see Section 3.5). • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12). • Lower heating values of rapeseed, rapeseed cake and vegetable oil (This is valid for all processes that use these LHV values).

168

Table 147 Rapeseed cultivation

Unit Amount Source Comment

Diesel MJ/MJrapeseed 0.039007 5, 6, 7 See CAPRI DATA

N fertilizer MJ/MJrapeseed 0.001796 3, 5 See GNOC data

CaCO3 fertilizer MJ/MJrapeseed 0.004048 8 See liming data

K2O fertilizer MJ/MJrapeseed 0.00065 4 49.5 kg K2O/(ha*yr)

P2O5 fertilizer MJ/MJrapeseed 0.00044 4 33.7 kg P2O5/(ha*yr)

Pesticides MJ/MJrapeseed 0.0000863 5, 6, 7 See CAPRI data

Seeding material MJ/MJrapeseed 0.00008 1, 4 6 kg/(ha*yr)

Rapeseed kWh 1.0000 3113 kg/(ha*yr) [5] 9% H2O/(ha*yr) [5]

Field N2O emissions g/MJrapeseed 0.055 3


1.045 8

Comments: - Yield of 3113 kg/(ha*yr) at 9% H2O/(ha*yr) used to calculate: K2O fertilizer, P2O5 fertilizer and seeding material. - LHV = 26.98 MJ/kg dry rapeseed (JRC calculation: see below. This was calculated using the oil content reported by Diester 2008). - Diesel use calculated using the corrected LHV of rapeseed above - Other data is already worked out per tonne using various yields.

Sources: 1 Dreier and Geiger 1998. 2 J-F Rous, PROLEA, pers. comm. to R. Edwards JRC, 27/07/2009. 3 Edwards and Koeble, 2012 (see Chapter 4). 4 EFMA, 2008. 5 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in refs 6 and 7. 6 Kraenzlein, 2011. 7 Kempen and Kraenzlein, 2008. 8 JRC: Acidification and liming data in this report (see Section 4.12). 9 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers.comm, 2008.

169

Step 2. Rapeseed drying Table 148 Rapeseed drying

I/O Unit Amount Diesel Input MJ/MJrapeseed 0.01242 Electricity Input MJ/MJrapeseed 0.00301 Rapeseed Input MJ/MJrapeseed 1.000 Rapeseed Output MJ 1.000

Comments: - The initial water content is 15%, the final water content is 9%. - Our drying emissions in general are much lower than those of IFEU: we checked our dryer against primary US data. UK figures (for wheat) are also higher, but that can be explained by higher moisture levels. - CAPRI does not report drying emissions for oil seeds; therefore we kept the original values.

Sources: 1 UBA, 1999. 2 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers. comm. 2008. Step 3. Transportation of rapeseed Table 149 Transportation of rapeseed summary table

Share Transporter type Distance km

73.70% 40 tonne Truck payload 27 t 163

4.40% Panamax Ocean bulk carrier Payload 37,000 t 5000

6.10% Inland barge payload 8800 t 376

15.80% Train 309

Table 150 Transport of rapeseed over a distance of 163 km via 40 tonne truck (one way)

I/O Unit Amount Distance Input tkm/MJrapeseed 0.0066

Biomass Input MJ/MJrapeseed 1.0100

Biomass Output MJ 1.0000

170

Comment: - For the fuel consumption for a 40 t truck, see Table 63.

Sources: 1 EBB, 2009. 2 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers. comm. 2008. Table 151 Maritime transport of rapeseed via Panamax ocean bulk carrier over a distance of 5000 km (one way)




Comment: - Fuel consumption for Panamax bulk carrier for maritime transport of grain and oilseed see Table 74.

Sources: 1 EBB, 2009. 2 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers. comm. 2008. Table 152 Transport of rapeseed over a distance of 376 km via inland ship (one way)




Comment: - For the fuel consumption of a bulk carrier for inland navigation barge, see Table 79.

Sources: 1 EBB, 2009. 2 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers.comm, 2008.

171

Table 153 Transport of rapeseed over a distance of 309 km via train (one way)




Comment: - For the fuel consumption of the freight train run on grid electricity, see Table 82.

Sources: 1 EBB, 2009. 2 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers. comm. 2008. Step 4. Oil mill: extraction of vegetable oil from rapeseed Table 154 Oil mill: extraction of vegetable oil from rapeseed

INPUTS Unit Amount Source Comments Electricity MJ/MJoil 0.00972 1, 2 359.60 MJ/(t plant oil) n-hexane MJ/MJoil 0.002280 1, 2 1.87 kg/(t plant oil)

Rapeseed MJ/MJoil 1.57965 1, 2 0.420 kg oil/(kg rapeseed @ 9% H2O) [5]

Steam MJ/MJoil 0.04326 1, 2 1600.6 MJ/(t plant oil) OUTPUTS

Crude vegetable oil MJ 1.00000 1.338 kg cake/(kg plant oil) [1]

Comments: - LHV = 37 MJ/kg of oil (ref 2) (see LHV of rapeseed below). - Water content (cake): 12.8%. - 27.0 MJ/(kg dry seed). - 18.38 MJ (kg dry cake). - 15.715 MJ/kg (ref 4).

LHV of rapeseed This varies with composition of rapeseed. From EBB, we have received the oil content and from PROLEA the water content of rapeseed used by them. We fill out the rest of the composition in proportion to the rest of the composition given in “Nutrient requirements of dairy cattle”, 7th rev. ed. National Academy of Sciences, 2001, and then calculate the LHV from the LHV of the components. As expected, this gives a slightly higher LHV than JEC-WTW previously used, which was measured on rapeseed with lower oil content.

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Table 155 LHV of US rapeseed

US Rapeseed [6] Wet basis

Dry matter basis

LHV components

MJ/kg [3] Contributions

MJ/kg dry matter Component

Rapeseed (Canola seed)

36%

0.899 40.5% 20.5% 21.2% 13.2%

4.6% 100%

37

24.5 15.88 18.27

0

14.99

5.02 3.37 2.41 0.00 25.8

Dry matter [5] Oil Protein Carbohydrate Fibre Ash SUM

23.18 MJ of dry matter/kg moist material

Table 156 LHV of European rapeseed

EBB/DIESTER rapeseed

Wet basis

Dry matter basis

LHV components



Rapeseed Diester spec.

42.6%

0.9146.8%18.3%19.0%11.8%

4.1%100%

3724.5

15.8818.27

0

17.32

4.49 3.01 2.16 0.00

26.976



Comments: - Dry-matter composition from ref 6, except that oil content is raised to that reported in ref 7. - Other dry-mass components are reduced in proportion.

Calculation of consistent LHV of dry rapeseed cake

Table 157 LHV of dry rapeseed cake 0.420 kg extracted/kgrapeseed, moist

0.462 kg/kgrapeseed, dry

0.538 kgcake, dry/kgrapeseed, dry

17.077 MJ bound in the extracted oil9.90 MJ bound in the cake

18.38 MJ/kgcake, dry Sources: 1 EBB, 2009. 2 Edwards, R. 5 September 2012 based on ECN Phyllis database of biomaterials properties. 3 ADM, 2000.

173

4 Hartmann, 1995. 5 ECN Phyllis database of LHV values for biomass. 6 NRC, 2001. 7 M. Rous (Diester), pers. comm to JRC, 18/09/2008. Step 5. Refining of vegetable oil Table 158 Refining of vegetable oil

Unit Amount Source Comment Electricity MJ/MJoil 0.0009 1, 2 34.38 MJ/(t oil) [1] H3PO4 kg/MJoil 0.000032 1, 2 1.19 kg/(t oil) [1] NaOH kg/MJoil 0.000088 1, 2 3.26 kg/(t oil) [1] Crude vegetable oil

MJ/MJoil 1.0246 1

Steam MJ/MJoil 0.0040 1, 2 149.19 MJ/(t oil) [1] Plant oil MJ 1.0000 37 MJ/kg of oil [2]

Sources: 1 EBB, 2009. 2 Edwards, R. 5 September 2012 based on ECN Phyllis database of biomaterials properties. Step 6. Esterification Table 159 Esterification


Electricity Input MJ/MJFAME 0.00405 1, 2, 4 150.5 MJ/(t RME) [1]

Sodium methylate (Na(CH3O))

Input kg/MJFAME 0.00047 1, 2, 4

17.55 kg/(t RME) [1]

HCl Input kg/MJFAME 0.000097 1, 2, 4 3.61 kg/(t RME) [1]

Methanol Input MJ/MJFAME 0.05110 1, 2, 4 95.29 kg/(t RME) [1]

Plant oil Input MJ/MJFAME 1.00063 1, 2, 4 101.87 kgglycerol/(t RME)

Steam Input MJ/MJFAME 0.0330 1, 2, 3, 4 1229 MJ/(t RME)

FAME I/O MJ 1.0000 37.2 MJ/(kg RME) [2]

Comment: - 16 MJ/(kg glyerol) (ref 4).

Sources: 1 EBB, 2009. 2 Edwards, R., 5 September 2012 based on ECN Phyllis database of biomaterials properties. 3 Rous pers. comm. 23 September 2008.

174

4 Edwards, JRC, 22 July 2003: calculation with HSC for windows. Step 6.1. Plant generation processes The data for the individual plant generation processes are shown in Chapter 5. The process linked to refining of rapeseed is:

• Steam generation (Table 51) Step 7. Transportation of FAME to the blending depot Table 160 Transportation of FAME summary table to the blending depot

Share Transporter notes Distance

11.4% truck payload 40 t 305

27.2% product tanker payload: 15,000 t 1118

43.8% inland ship/barge payload 1200t 153

3.8% train 381

13.8% Pipeline

Comment: - Transport of FAME via pipeline assumed to be the same as for gasoline. (The number has been supplied by TotalFinaElf without indicating the distance). See Table 83.

Table 161 Transport of FAME via 40 t truck over a distance of 305 km (one way)

I/O Unit Amount Distance Input tkm/MJFAME 0.0043

FAME Input MJ/MJFAME 1.0000

FAME Output MJ 1.0000

Comment: - For the fuel consumption of the 40 t trunk, see Table 63.

Table 162 Maritime transport of FAME over a distance of 1118 km (one way)


Biomass Input MJ/MJFAME 1.0100


Comment:

175

- For the fuel consumption of the Product Tanker (payload: 15,000 t) see, Table 76. Table 163 Transport of FAME over a distance of 153 km via inland ship (one way)




Comment: - For the fuel consumption for an oil carrier for inland, see Table 80.

Table 164 Transport of FAME over a distance of 381 km via train (one way)




Comments: - For the fuel consumption of the freight train, see Table 82.

Source: 1 Frischknecht et al., 1996. Step 8. FAME depot distribution inputs Table 165 FAME depot

I/O Unit Amount


Electricity Input MJ/MJFAME 0.00084 FAME Output MJ 1.00000

Table 166 FAME filling station

I/O Unit Amount FAME Input MJ/MJFAME 1.0000

Electricity Input MJ/MJFAME 0.0034

FAME Output MJ 1.0000 Comment:

- Distribution is assumed to be same as for fossil diesel and gasoline. Source: 1 Dautrebande, 2002.

176

7.12 Sunflower to biodiesel Description of pathway The following processes are included in the sunflower to biodiesel pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Sunflower cultivation The new data for sunflower cultivation are shown in Table 153. The updated data include:

• Diesel and pesticide use in sunflower cultivation calculated using data from CAPRI (see Section 3.5). • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12).

177

Table 167 Sunflower cultivation


Diesel Input MJ/MJsunfllower seed 0.076976 5, 6, 7 See CAPRI data

N fertilizer Input kg/MJsunflower seed 0.000899 3, 4 See GNOC data

CaCO3 Input kg/MJsunflower seed 0.002199 7

K2O fertilizer Input kg/MJsunflower seed 0.00036 4 See liming data

P2O5 fertilizer Input kg/MJsunflower seed 0.00050 4 30 kg P2O5/(ha*yr)

[1] Pesticides Input kg/MJsunflower seed 0.0000609 4, 5, 6 See CAPRI data Seeding material Input kg/MJsunflower seed

0.000064 1, 2, 8 3.90 kg/(ha*yr) [8]

Sunflower seed Input kWh

1.0000

2440 kg sunflower seed @ 9% H2O/(ha*yr) [3]

Field N2O emissions Output g/MJsunflower seed

0.038 3 See GNOC data


-

0.673

9 See liming data

Comments: - LHV 27.245 MJ/kg dry sunflower seed (ref 9). - Seeding material: calculation of seeding material requirement based on ref 8: hybrid sunflower with high oil content; medium soil quality Number of plants: 50000 plants/ha + 20% Seeding material mass: 0.065 g/seed => 1.2*50000*0.065/1000 kg/(ha*yr) = 3.9 kg/(ha*yr) - 9.0% H2O (ref 1, 10). Approximately the traded water content. The input data refer to a tonne at this water content, even if the fresh harvest has higher water content.

Sources: 1 ADEME, 2002. 2 Kaltschmitt and Reinhard, 1997. 3 Edwards and Koeble, 2012 (see Chapter 4). 4 CAPRI database, Energy use data extracted by Markus Kempen of Bonn University, March 2012, converted to JEC format using information in refs 6 and 7. 5 Kraenzlein, 2011. 6 Kempen and Kraenzlein, 2008. 7 JRC: Acidification and liming data in this report (see Section 4.12). 8 Jósef, 2000. 9 JRC calculation based on Diester 2008. 10 J-F Rous, PROLEA, pers. comm. to R. Edwards JRC, 27/07/2009.

178

Step 2. Sunflower drying Table 168 Sunflower drying

I/O Unit Amount Diesel Input MJ/MJrapeseed 0.01230 Electricity Input MJ/MJrapeseed 0.00298 Rape seed Input MJ/MJrapeseed 1.000 Rape seed Output MJ 1.000

Sources: 1 UBA, 1999. 2 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers. comm. 2008. Step 3. Transportation of sunflower seed Table 169 Transportation of sunflower seed summary table

Share Transporter notes Distance

68.60% 40 t Truck payload 27t 292

31.40% Electric Train 450 Table 170 Transport of sunflower seed over a distance of 292 km via truck (one way)

I/O Unit Amount Distance Input tkm/MJsunflower seed 0.0118

Biomass Input MJ/MJsunflower seed 1.0100



Table 171 Transport of sunflower seed over a distance of 450 km via train (one way)

I/O Unit Amount Distance Input tkm/MJsunflower seed 0.0182

Biomass Input MJ/MJsunflower seed 1.0100


Comment: - For the fuel consumption of the electric train, see Table 82.

Sources:

179

1 EBB, 2009. 2 JRC calculation derived from composition supplied by J-F. Rous, Diester/PROLEA 'bilan vapeur' pers. comm. 2008. Step 4. Oil mill - Extraction of vegetable oil from sunflower seed Table 172 Oil mill - Extraction of vegetable oil from sunflower seed

I/O Unit Amount Source Original data form Electricity Input MJ/MJoil 0.00735 1, 2 415.50 MJ/(t plant oil) [1] n-hexane Input MJ/MJoil 0.001891 1, 2 2.37 kg/(t plant oil) [1] Sunflower seed Input MJ/MJoil 0.9992 1, 2 0.439 kg oil/(kg rapeseed Steam Input MJ/MJoil 0.02217 1, 2 1253.4 MJ/(t plant oil) [1]

Crude vegetable oil Output 1.0000

Comments: - LHV= 37 MJ/(kg of oil) (ref 2). - 1.237 kg cake/kg plant oil (ref 1) - Water content (cake): 11.5% - LHV cake: 15.784 (ref 4)

LHV of sunflower seed

Table 173 LHV of US sunflower

US sunflower seeds without hulls [1]

Wet basis

Dry matter basis

LHV components



38%

0.918 41.9% 19.2% 14.9% 18.9%

5.1% 100.00%

37

24.5 15.88 18.27

0

15.50

4.70 2.37 3.45 0.00 26.0



180

Table 174 LHV of European sunflower

EBB/DIESTER sunflower seed

Wet basis

Dry matter basis

LHV components



Sunflower seed Diester

44.0%

0.9148.4%17.1%13.2%16.8%

4.5%100%

3724.5

15.8818.27

0

17.89

4.18 2.10 3.07 0.00

27.24



Comments: - Dry-matter composition from ref 6, except that oil content is raised to that reported in ref 7. - Other dry-mass components are reduced in proportion.

Calculation of consistent LHV of dry sunflower cake

Table 175 LHV of dry sunflower cake 0.439 kg extracted/kgseed, moist

0.482 kg/kgseed, dry

0.518 kgcake, dry/kgseed, dry

17.85 MJ bound in the extracted oil9.40 MJ bound in the cake

18.15 MJ/kgcake, dry Sources: 1 EBB, 2009. 2 Edwards, R., 5 September 2012 based on ECN Phyllis database of biomaterials properties. 3 ADM, 2000. 4 Hartmann, 1995. 5 Kaltschmitt and Reinhard, 1997. 6 NRC, 2001. 7 M. Rous (Diester), pers. comm to JRC, 18/09/2008.

181

Step 5. Refining of vegetable oil Table 176 Refining of vegetable oil

I/O Unit Amount Source Comment Electricity Input MJ/MJoil 0.00231 1, 2 85.3 MJ/(t oil) [1]

H3PO4 Input kg/MJoil 0.000012 1, 2 0.45 kg/(t oil) [1] NaOH Input kg/MJoil 0.000069 1, 2 2.55 kg/(t oil) [1] Crude vegetable oil

Input MJ/MJoil 1.0256 1

Steam Input MJ/MJoil 0.0058 1, 2 215.4 MJ/(t oil) [1] Plant oil Output MJ 1.0000 37 MJ/kg of oil [2] Sources: 1 EBB, 2009. 2 Edwards, R., 5 September 2012 based on ECN Phyllis database of biomaterials properties. Step 6. Winterization of sunflower Table 177 Winterization of sunflower

I/O Unit Amount

Crude vegetable oil Input MJ/MJoil 1.0101 Plant oil Output MJ 1.0000

Sources: 1 EBB, 2009. Step 7: Esterification Same input data as rapeseed used. Step 8: Transport of FAME to the blending depot Same input data as rapeseed used. Step 9. FAME depot distribution inputs Same input data as rapeseed used. .

182

7.13 Soya to biodiesel We estimate the emissions from the mix of soya biodiesel (and soya-pure-vegetable-oil) used in EU. The emissions for soy biodiesel depend on two factors: • whether the soya is imported as (solid) soybeans or in liquid form, as oil or biodiesel. That is because the shipping is different (although we use the same processing data). • whether they come from no-tillage or conventional agriculture. The emissions are slightly higher if the soya is imported in the form of beans, or if the soya comes from conventional agriculture. Therefore this is the default emission if the provenence of the biodiesel is completely unknown. If the soya is known to come into EU as oil or finished biodiesel, the “No-tillage and soya oil import mix” pathway result should be used. If in addition, it is known that cultivation uses no-tillage soybeans, the “No tillage

and soybean import mix” result may be used. Soya to biodiesel pathways shown in this section include: - A. “No tillage and soybean import mix”:

o Import weighted mix of soybeans under No-tillage cultivation from soybeans US, Brazil and Argentina. - B. “No-tillage and soya oil import mix”:

o Import weighted mix of soy oil/biodiesel under No-tillage cultivation US, Brazil and Argentina - C. “Conventional agriculture and soybean import mix”:

o import weighted mix of soybeans under conventional cultivation from US, Brazil and Argentina - D. “Conventional agriculture and soya oil import mix”:

o import weighted mix of soy oil under conventional cultivation from US, Brazil and Argentina The above pathways are derived from national data for: - Brazil:

o National data for soybeans o No tillage cultivation data o Conventional agriculture cultivation data

- Argentina: o National data for soybeans o No-tillage cultivation data o Conventional agriculture cultivation data

- USA: o National data for soybeans o No-tillage cultivation data o Conventional agriculture cultivation data

183

A. No tillage and soybean import mix The following processes are included in the no tillage and soybean import mix to biodiesel pathway:

This pathway describes an import weighted mix of soybeans under no-tillage cultivation of soybeans from US, Brazil and Argentina.

184

Step 1. Soybean cultivation Table 178 Soybean cultivation under no tillage


Diesel Input MJ/MJsoybeans 0.02342 1

N fertilizer Input kg/MJsoybeans 0.000046 1 See GNOC data Ca fertilizer as CaCO3 Input kg/MJsoybeans 0.002819 2 See liming data

K2O fertilizer Input kgMJsoybeans 0.000176 2 0.0035224 kg K2O/(kg soya)

P2O5 fertilizer Input kg/MJsoybeans 0.000302 1 0.0060500 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.0000673 1 Seeding material Input kg/MJsoybeans 0.0013466 1 Soybeans Output MJ 1.0000 1 Field N2O emissions - IPCC

- g/MJsoybeans 0.011 2 See GNOC data

Field N2O emissions JRC/E4tech residues

g/MJsoybeans 0.023 2

N from below-ground-biomass corrected according to JRC/e4tech/JEC-WTWv3, which accounts for rhizodeposition and corrects the value for kg of BGB



Comments: - LHV = 20.0 MJ/kg moist soybeans as reported in yield data.

o Approximately the dry matter LHV indicated for soybean @ 13% H2O according to ref 5 (23 MJ/kg of dry substance) (ref 5 agrees with JRC estimate based on feed composition). - N fertilizer = 0.0009161 kg N/kg moist soya.

o This is the average use from Argentina, Brazil and US no tillage. - 13% traded water content (ref 6), ideal for transport and storage (ref 4).

Sources: 1 Derived from regional data from Brazil, Argentina and USA. 2 Edwards and Koeble, 2012 (see Chapter 4). 3 JRC: Acidification and liming data in this report (see Section 4.12). 4 EMBRAPA, 2004. 5 Jungbluth, 2007. 6 Beuerlein, 2012.

185

Data on EU soy imports Table 179 Soy Biodiesel made in EU

Soy BioD made in EU Data on EU soy imports 2010 (Mtonnes)

EU soy beans imports

...as soy-oil equivalent

soy-oil imports

total imports as soy-oil equivalent

EU soy biodiesel production

allocated to soy source

Argentina 0.23 0.043 0.32 0.36 0.03 Brazil 6.05 6.05 0.06 6.11 0.58 USA 2.84 0.11 0.00 0.11 0.01 TOTAL 9.12 6.20 0.38 6.58 0.623 0.623 Table 180 Total soy biodiesel used in EU

Total soy bioD used in EU Data on EU soy imports 2010 (Mtonnes)

soy bioD imports total soy biodiesel

use in EU % sourced from...

Argentina 1.20 1.23 56.03 Brazil 0.00 0.58 26.27 USA 0.38 0.39 17.70 TOTAL 1.58 2.203 100 Source: 1 MVO, 2011. Exporter weighting data The weighted average of the 3 major exporters to EU is shown in Table 167. We do not have farming data (except N fertilizer) for other producers. Table 181 National fractions of no-tillage soy production

% NT in soy production per country

Source

Argentina 81% 1 Brasil 90% 2 USA 50% 3

Sources: 1 INTA, 2011. 2 Gazzoni, 2012.

186

3 Horowitz et al., 2010.

Table 182 Average diesel use weighted for imported soybean

% of imports MJ/MJsoybean

MJ/MJsoybean based on import

Argentina NT 56.03 0.0219 0.0123 Brasil NT 26.27 0.0260 0.0068 USA-NT 17.70 0.0245 0.0043 Total 0.0234 Table 183 Pesticide use

kg/MJ soybean

% imports to EU

Contribution to kg/MJ based on import

Overall pesticide use: kg/MJsoybean

Argentina NT 0.000098 56.03 0.000055 Brasil NT 0.000029 26.27 0.000008 USA-NT 0.000028 17.70 0.000005 Total 0.000067

187

From here down, the data shown applies to all soybean import pathways for both No tillage and Conventional agriculture Step 2. Drying

Table 184 Drying to 13% water content

LPG MJ/MJ soybean

NG MJ/MJ soybean

Heating oil and diesel MJ/MJ soybean

ElectricityMJ/MJ soybean

% imports to EU

LPG MJ/MJ soybean weighted

NG MJ/MJ soybean weighted

Heating oil and diesel MJ/MJ soybean weighted

ElectricityMJ/MJ soybean weighted

Argentina 0.0007 0.0011 0.0002 56.03 0.000416 0.000618 0.000090 0.000000

Brasil 0.0021 0.0002 26.27 0.000000 0.000000 0.000549 0.000042

USA 0.0008 0.0026 0.0011 17.70 0.000148 0.000454 0.000000 0.000191

TOTAL 0.000564 0.001072 0.000639 0.000233

Step 3. Transportation of soybeans Transport of soybeans via truck (see Table 180) is derived from national average data (see Table 181). Table 185 Transport of soybeans via 40 t truck over a distance of 447 km (one way)

I/O Unit Amount Distance Input tkm/MJsoybeans 0.0223

Soybeans Input MJ/MJsoybeans 1.0100

Soybeans Output MJ 1.0000


Table 186 Regional truck transport distances

km % contribution to weighted average km

Argentina 350 56.03 196.11 Brasil 900 26.27 236.34 USA 80 17.70 14.16 Total 446.62

188

Transport of soybeans via train (see Table 182) is derived from national average data (seeTable 183).

Table 187 Transport of soybeans via diesel train over a distance of 99 km (one way)




Comment: - For the fuel consumption for a freight train run on diesel fuel, see Table 81.

Table 188 Regional train transport distances

km % contribution to kg/ton based on import

contribution to weighted av km

Argentina 0 56.03 0.00 Brasil 377 26.27 99.13 USA 0 17.70 0.00 Total 99.13 Transport of soybeans via ship and barge (see Table 175) is derived from national average data (see Table 176). Table 189 Transport of soybeans via inland ship over a distance of 382 km (one way)




Comment: - For the fuel consumption of a bulk carrier for inland navigation, see Table 79.

Sources: 1 Frischknecht, R. et al., 1996 2 Ilgmann, 1998.

189

Table 190 Maritime transport of soybeans via ship over a distance of 13171 km (one way)

I/O Unit Amount Distance Input tkm/MJsoybean 0.6582

Soybeans Input MJ/MJsoybean 1.0000


Comment: - For the fuel consumption of a bulk carrier for maritime transport, see Table 74.

Table 191 Regional shipping and barge distances to Rotterdam

nautical miles sea km sea km barge %

contribution to weighted av km (sea)

Contribution to weighted av km (barge)

Argentina (Rosario) 7938 14701 0 56.03 8237 0 Brasil (Mix) 6868 12719 0 26.27 3341 0 USA (New Orleans) 4860 9000 2161 17.70 1593 382 Total 13171 382.50 Source: 1 Edwards, R., JRC, 07 July 2011. Step 4. Pre-drying soybeans at oil mill This is a new process. This is Argentine data, but is used for all oil mills because they are all fed with soybeans at 11% moisture instead of the traded moisture content of 13%. Although drying to 13% is optimal for transport and storage of beans, an extra drying step is often needed to reach 11% moisture before crushing (as reported in the mill data), because otherwise the meal ends up with too much moisture for storage and transport. Table 192 Pre-drying at oil mill

Unit Amount Source NG MJ/MJsoybeans 0.00293 1

Soybeans MJ/MJsoybeans 1.0000

Soybeans MJ 1.0000

Comments: - Ref 1 says 758 Gkcal to dry 75% of 40.5 Mtonnes beans at mills. - Bioheat from sunflower husks is not taken into account.

Source: 1 de Tower and Bartosik, 2012.

190

Step 5. Extraction of vegetable oil from soybeans Table 193 Oil mill


Electricity Input MJ/MJoil 0.0311 1, 2, 5 60 kWh/(t soybeans) [1]

n-hexane Input MJ/MJoil 0.004539 1, 2, 5 0.7 kg/(t soybeans) [1]

Soybeans Input MJ/MJoil 2.9428 1, 2, 5 188 kg oil/(t soybeans) [1], [7]

Steam Input MJ/MJoil 0.1438 1, 2, 5 1000 MJ/(t soybeans) [1] Vegetable oil Output MJ 1.0000 1, 2, 5 37 MJ/(kg vegetable oil) [2] Comments:

- 20.5: LHV of the dry matter at 11%-moist soybeans (refs 1 and 5). - 794 kg cake/(t soybeans) (ref 5). - 11%: water content of beans input.

Calculation of consistent LHV of dry soybean cake - 0.188 kg oil extracted/kgseed, moist [1],[5] - 0.216 kg oil/kgseed, dry - 0.784 kgcake, dry/kgseed, dry (by dry mass balance) - 7.995 MJ bound in the extracted oil - 15.00 MJ bound in the cake - 19.14 MJ/kg dry cake - Ref.1 agrees with the data in ref 5, which is an Ecoinvent survey of several mills. We also have INTA data from Hilbert et al., 2010 for Argentina, but it is unclear which data is per tonne of oil and which per tonne of beans. Pradhan et al., 2011 gives data for one modern US soy-oil-mill but states this does NOT represent the national average.

Consistent water content of cake by mass-balance

- 110 kg water entering per tonne beans - 982 total kg out for 1 tonne beans - lost mass = kg evaporated water - 92 kg water in cake - 11.6% water content of cake - 16.64 MJ/kg wet LHV used for allocation under RED, using formula in [4] - 34% allocation to vegoil

Comments on water content of cake:

- We back-calculate by mass-balance the moisture content of the oilseed cake from: o the traded water content of beans o the reported process yields of oil and cake process yields.

Sources: 1 UBA, 1999.

191

2 Mehta and Anand, 2009. 3 Bunge, 2012. 4 Hartmann, 1995. 5 Jungbluth, 2009. 6 NRC, 2001. Step 6. Refining of vegetable oil from soybean

Table 194 Refining of vegetable oil


Electricity Input MJ/MJoil 0.0023 1, 2 85.3 MJ/(t oil) [1]

H3PO4 Input kg/MJoil 0.000012 1, 2 0.45 kg/(t oil) [1]

NaOH Input kg/MJoil 0.000069 1, 2 2.55 kg/(t oil) [1]

Crude vegetable oil Input MJ/MJoil 1.0256 1

Steam Input MJ/MJoil 0.0058 1, 2 215.4 MJ/(t oil) [1] Vegetable oil Output MJ 1.0000 37 MJ/kg of oil [2] Sources: 1 EBB, 2009. 2 Mehta and Anand, 2009. No winterization is required for soy oil Step 7. Esterification Same input data as rapeseed used. Step 8. Transportation of FAME to the blending depot Table 195 Transport of FAME to depot via 40 t truck over a distance of 150 km

I/O Unit Amount

Distance Input tkm/MJFAME 0.0044

FAME Input MJ/MJFAME 1.0000 FAME Output MJ 1.0000

Comment: - For the fuel consumption of a 40 t truck, see Table 63. - 26 t transported FAME, 2 t tank - LHV (FAMEl) = 37 MJ/kg

Step 9. FAME depot distribution inputs

192

Same input data as rapeseed used.

193

B. No-tillage and soya oil import mix The following processes are included in the no tillage soybean to biodiesel pathway:

This pathway describes the import weighted mix of soy oil/biodiesel under no-tillage cultivation from US, Brazil and Argentina.

194






P2O5 fertilizer Input kg/MJsoybeans 0.000141 1 0.0028274 kg P2O5/(kg

soya) Pesticides Input kg/MJsoybeans 0.0000695 1

Seeding material Input kg/MJsoybeans 0.0008482 1

Soybeans Output MJ 1.0000 1 Field N2O emissions - IPCC - g/MJsoybeans 0.011 2 See GNOC data






Comments: - LHV = 20.0 MJ/(kg moist soybeans as reported in yield data).

o Approximately the dry matter LHV indicated for soybean @ 13% H2O according to ref 5 (23 MJ/kg of dry substance) (ref 5 agrees with JRC estimate based on feed composition). - N fertilizer = 0.0009161 kg N/(kg moist soya). This is the average use from Argentina, Brazil and US no tillage. - 13% traded water content (ref 6), ideal for transport and storage (ref 4).

Sources: 1 Derived from regional data from Brazil, Argentina and USA. 2 Edwards and Koeble, 2012 (see Chapter 4). 3 JRC: Acidification and liming data in this report (Section 4.12). 4 EMBRAPA, 2004. 5 Jungbluth, 2007. 6 Beuerlein, 2012.

195

Data on EU soy imports Table 197 Soy Biodiesel made in EU

soy BioD made in EU Data on EU soy imports 2010 (Mtonnes)

EU soy beans imports

...as soy-oil equivalent

soy-oil imports

total imports as soy-oil equivalent

EU soy bioD production

allocated to soy source

Argentina 0.23 0.043 0.32 0.36 0.11Brazil 6.05 1.14 0.06 1.20 0.36USA 2.84 0.53 0.00 0.53 0.16TOTAL 9.12 1.71 0.38 2.09 0.623 0.623 Table 198 Total soy biodiesel used in EU

total soy bioD used in EU Data on EU soy imports 2010 (Mtonnes)

soy bioD imports total soy biodiesel use in

EU % sourced from...

Argentina 1.20 1.31 59.38Brazil 0.00 0.36 16.17USA 0.38 0.54 24.46TOTAL 1.58 2.203 100Source: 1 MVO, 2011. Exporter weighting data The weighted average of the 3 major exporters to EU is shown in Table 185. We do not have farming data (except N fertilizer) for other producers. Table 199 National fractions of no-tillage soy production

% NT in soy production per country

Source

Argentina 81% 1

196

Brasil 90% 2 USA 50% 3

Sources: 1 INTA, 2011. 2 Gazzoni, 2012. 3 Horowitz et al., 2010. Table 200 Average diesel use weighted for imported soybean

% of imports MJ/MJsoybean


Argentina NT 59.38 0.0219 0.0130 Brasil NT 16.17 0.0260 0.0042 USA-NT 24.46 0.0245 0.0060 Total 0.0232 Table 201 Pesticide use

kg/MJ soybean

% imports to EU


Overall pesticide use: kg/MJsoybean

Argentina NT 0.000098 59.38 0.000058 Brasil NT 0.000029 16.17 0.000005 USA-NT 0.000028 24.46 0.000007 Total 0.000070

From here down, the data shown applies to all liquid soy oil import pathways for both No tillage and Conventional agriculture Step 2. Drying

Table 202 Drying at 13% water content

LPG MJ/MJ soybean

NG MJ/MJ soybean

Heating oil and diesel MJ/MJ soybean

Electricity MJ/MJ soybean

% imports to EU

LPG MJ/MJ soybean weighted

NG MJ/MJ soybean weighted

Heating oil and diesel MJ/MJ soybean weighted

ElectricityMJ/MJ soybean weighted

Argentina 0.0007 0.0011 0.0002 59.38 0.000441 0.000655 0.000095 0.000000 Brasil 0.0021 0.0002 16.17 0.000000 0.000000 0.000338 0.000026 USA 0.0008 0.0026 0.0011 24.46 0.000205 0.000627 0.000000 0.000264

197

TOTAL 0.000646 0.001282 0.000433 0.000290

Step 3. Transportation of soybeans Transport of soybeans via truck (see Table 189) is derived from national average data (Table 190). Table 203 Transport of soybeans via 40 t truck over a distance of 373 km (one way)





Table 204 Regional truck transport distances

km % Contribution to weighted av km

Argentina 350 59.38 207.81 Brasil 900 16.17 145.47 USA 80 24.46 19.57 Total 372.85 Transport of soybeans via train (seeTable 200) is derived from national average data (see Table 201).

Table 205 Transport of soybeans via diesel train over a distance of 61 km (one way)





198

Table 206 Regional train transport distances

km % Contribution to weighted av km

Argentina 0 59.38 0.00 Brasil 377 16.17 61.01 USA 0 24.46 0.00 Total 61.01 Step 4. Pre-drying at oil mill soybeans (new Process) Same input data as “No tillage and soybean import” pathway. Step 5. Extraction of vegetable oil from soybeans Same input data as “No tillage and soybean import” pathway. Step 6. Transport of soy oil Transport of soy oil via ship and barge (see Table 202 and Table 203) are derived from national average data (see Table 195). Table 207 Transport of soy oil via inland ship over a distance of 529 km (one way)

I/O Unit Amount Distance Input tkm/MJsoyoil 0.0143

Soy oil Input MJ/MJsoyoil 1.0100

Soy oil Output MJ 1.0000

Comment: - For the fuel consumption for an oil carrier for inland navigation, see Table 76.

Sources: Frischknecht, R. et al., 1996

199

Table 208 Maritime transport of soy oil via ship over a distance of 12986 km (one way) I/O Unit Amount

Distance Input tkm/MJsoyoil 0.3510

Soy oil Input MJ/MJsoyoil 1.0000

Soy oil Output MJ 1.0000

Comment: - For the fuel consumption for a product tanker for pure vegetable oil transport, see Table 73.

Table 209 Regional shipping and barge distances for soy oil to Rotterdam

nautical sea miles

km sea km barge % contribution to weighted av km (sea)

contribution to weighted av km (barge)

Argentina (Rosario) 7938 14701 0 59.38 8729 0 Brasil (Mix) 6868 12719 0 16.17 2056 0 USA (New Orleans) 4860 9000 2161 24.46 2201 529 Total 12986 529 Source: 1 Edwards, R., JRC, 07 July 2011. Step 6. Refining of vegetable oil from soybean Same input data as “No tillage and soybean import” pathway. Step 7. Esterification Same input data as rapeseed used.

200

Step 8. Transportation of FAME to the blending depot Same input data as “No tillage and soybean import” pathway. Step 9. FAME depot distribution inputs Same input data as rapeseed used.

201

C. Conventional agriculture and soybean import mix The following processes are included in the conventional agriculture and soybean to biodiesel pathway:

This pathway describes the import weighted mix of soybeans under conventional cultivation from US, Brazil and Argentina.

202







Pesticides Input kg/MJsoybeans 0.000067 1


Soybeans Output MJ 1.0000 Field N2O emissions - IPCC - g/MJsoybeans 0.012 2 See GNOC data









203

Percentage of conventional agriculture of soybeans This pathway is the weighted average of the 3 major sources of soy in EU. We do not have farming data (except N fertilizer) for other producers. Table 211 National fractions of conventional agriculture of soybeans

national % of EU soy bioD

% CA in country

Argentina-CA 59.38 19% Brasil-CA 16.17 10% USA-CA 24.46 50% TOTAL 100.00

Sources: 1 INTA, 2011. 2 Gazzoni, 2012. 3 Horowitz et al., 2010. Table 212 Average diesel use weighted for imported soybean

%of CA imports

MJ/MJsoybean MJ/MJsoybean based on

import

Argentina-CA 59.38 0.0444 0.0264 Brasil-CA 16.17 0.0527 0.0085 USA-CA 24.46 0.0319 0.0078 TOTAL 0.0427

Table 213 Pesticide use

kg/MJ soybean

% CA imports to

EU

Contribution to kg/MJ based on

import

overall kg/MJsoybean


Subsequent steps use the same data as for “No tillage and soybean import” pathway

204

D. Conventional agriculture and soya oil import mix The following processes are included in the conventional agriculture and soy oil to biodiesel pathway:

This pathway describes the import weighted mix of soy oil under conventional cultivation from US, Brazil and Argentina.

205

Step 1: Soybean cultivation Table 214 Soybean cultivation under no tillage






Pesticides Input kg/MJsoybeans 0.000058 1


Soybeans Output MJ 1.0000 Field N2O emissions - IPCC - g/MJsoybeans 0.012 2 See GNOC data









206

Percentage of conventional agriculture of soybeans This pathway is the weighted average of the 3 major sources of soy in EU. We do not have farming data (except N fertilizer) for other producers. Table 215 National fractions of conventional agriculture of soybeans

national % of EU soy bioD

% CA in country

% of EU CA-soy imports

Argentina-CA 59.38 19% 44.90 Brasil-CA 16.17 10% 6.43 USA-CA 24.46 50% 48.67 TOTAL 100.00 100.00

Sources: 1 INTA, 2011. 2 Gazzoni, 2012. 3 Horowitz et al., 2010. Table 216 Average Diesel use weighted for imported soy oil

%of CA imports MJ/MJsoybean



Table 217 Pesticide use

kg/MJ soybean

% CA imports to EU


overall kg/MJsoybean


Subsequent steps use the same data as for “No tillage and soy oil import” pathway.

207

The following pages contain the country specific input data used to derive the four soy pathways described above.

208

E. National soy data Brazil soya Soybean cultivation Table 218 Soybean cultivation in Brazil


Diesel Input MJ/MJsoybeans 0.02867 1,2,5,6

N fertilizer Input kg/MJsoybeans 0.0000751 5, 11 See GNOC data Ca fertilizer as CaCO3 Input kg/MJsoybeans 0.003133 12 See liming data

K2O fertilizer Input kgMJsoybeans 0.0004354 4, 5, 9, 10

0.008711821 kg K2O/(kg soya)

P2O5 fertilizer Input kg/MJsoybeans 0.0005015 4, 5, 9, 10

0.010035281 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.0000286 4, 5, 6 Seeding material Input kg/MJsoybeans 0.00125 4, 6, 7 Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans 11 See GNOC data



Comments: - 23.0 MJ/(kg dry soybean) (ref 4)

o 20.5 MJ LHV indicated for soybean @ 11% H2O according to Jungbluth, 2009. - Diesel use: 1606 MJ/(ha*yr). No data specific to Brazil was found. This is the value for US, derived from ref 6. - N fertilizer: 0.00150 kg N/(kg moist soya) calculated in ref 11 from data in ref 9. - Pesticides (etc) use: 1.6 kg/(ha*yr).

o Pradhan et al., 2011: USA 2006 = 1,6 kg/ha o Jungbluth, 2007: BR 2001 = 0,0579 kg/t (=0,2 kg/ha) o Cederberg, 2001: USA 2004= 0,0476 kg/t (=0,14 kg/ha) (USDA, 2004).

- Seeding material: 70 kg/(ha*yr). o USA data: 68.9 kg/ha (ref 6) o AG data: 70-80 kg/ha (ref 7) o USA and BR: 70 kg/ha (ref 4)

- Soybean yield: 2800 kg/(ha*yr) (ref 1, 2, 8) o (ref 1, p. 22) linking to (ref 2, 8), yield used in calculating diesel, pesticides and seed per tonne

209

o 13% ideal water content for transport and storage (ref 5) EMBRAPA, 2004: “soybeans are harvested at 18% humidity” but yields are usually reported at traded water content which is 13%. Sources: 1 CENBIO, 2009. 2 MINISTÉRIO DA AGRICULTURA, PECUÁRIA E ABASTECIMENTO, 2007. 3 Da Silva et al., 2010. 4 Jungbluth, 2009. 5 EMBRAPA, 2004. 6 Pradhan et al., 2011. 7 Panichelli et al., 2009. 8 FAO, 2012. 9 IFA, 2011. 10 FAPRI, 2012. 11 Edwards and Koeble, 2012 (see Chapter 4). 12 JRC: Acidification and liming (see Section 4.12). Drying of soybeans Table 219 Soybean drying

Unit Amount Sources Diesel MJ/MJsoybean 0.002091 1, 2

Electricity MJ/MJsoybean 0.000161 1, 2

soybeans MJ/MJsoybean 1.000

soybeans MJ 1.000 Comment:

- 13% final humidity rate after drying (ref 3). Sources: 1 Da Silva et al., 2010. 2 Marques, 2006. 3 EMBRAPA, 2004.

210

Transport of soybeans Table 220 Weighted average of transport of soybeans from Central West and South to Brazilian seaport

34* km Truck to drying places 865.8** km Truck from drying place to Brazilian port 899.8** km Total truck distance

377.4*** km Railway 208.9*** km Water way

Comments: * 20 km in South Brazil states (weight 0.3) and 40 km in CW states (weight 0.7). ** Da Silva, 2010: CentralWest (weighting 0,7): 1101 km South (weighting 0.3): 317 km. *** Da Silva, 2010: CW (weight 0,7): 393 km, S (weight 0.3): 341 km. Table 221 Truck

Unit Amount

Distance tkm/MJsoybean 0.0450

Soybean MJ/MJsoybean 1.0100

Soybean MJ 1.0000


Table 222 Train

Unit Amount


Soybean MJ/MJsoybean 1.0100 Soybean MJ 1.0000


Table 223 Waterway

Unit Amount



Soybean MJ 1.0000

Comment: - For the fuel consumption of a bulk carrier for inland navigation, see Table 75.

Source: 1 Da Silva et al., 2010.

211

Table 224 Shipping (+barge) distances to Rotterdam nautical

miles seakm sea km barge

Brasil (Santos) 6749 12499 0.00

Brasil (Outerio port) 5900 planned [1] - not considered here

Brasil (Paranagua) 6986 12938 0.00 Sources: 1 Reuters, 2012. 2 Salin, 2009. 3 Flaskerud, 2003.






Subsequent steps are the same as in “no tillage and soybean import mix” or no-tillage and soya oil import mix“ (as appropriate).

212

Brazil soya No-Tillage Soybean cultivation Table 226 Soybean cultivation under no tillage in Brazil



N fertilizer Input kg/MJsoybeans 0.000067 5, 11 Ca fertilizer as CaCO3 Input kg/MJsoybeans


K2O fertilizer Input kgMJsoybeans 0.000435

4, 5, 9, 100.008711821 kg K2O/(kg soya)

P2O5 fertilizer Input kg/MJsoybeans 0.000502

4, 5, 9, 100.010035281 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.000029 4, 5, 6 Seeding material Input kg/MJsoybeans 0.001265 4, 6, 7 Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

11 See GNOC data



Comments: - 23.0 MJ/(kg dry soybean) (ref 4)

o 20.5 MJ LHV indicated for soybean @ 11% H2O according to Jungbluth, 2009. - 90% assumed fraction of No-Tillage in Brazilian soybean cultivation. - N fertilizer: 0.00133 kg N/(kg moist soya) calculated in ref 11 from data in ref 9 and with NT/CA ratio from Argentina applied. - Diesel use: 2.03 ratio CA/NT per tonne soy (ref 3).

o CA area in (ref 3) uses 1575 li/ha for a yield of 2.8 t/ha, whereas the reported NT areas use 998 li/ha for a yield of 3.6 t/ha. - Pesticides (etc) use: 0.96 ratio CA/NT per tonne soy (ref 3)

o CA area in [3] uses 4.33 kg/ha for a yield of 2.8 t/ha, whereas the reported NT areas use 5.77kg/ha for a yield of 3.6 t/ha. - Seeding material: 0.875 ratio CA/NT per tonne (assumed same ratio as Argentina (no specific data for Brazil)) - 13% ideal water content for transport and storage (ref 5)

o EMBRAPA, 2004: “soybeans are harvested at 18% humidity” but yields are usually reported at traded water content which is 13%. Sources: 1 CENBIO, 2009. 2 MINISTÉRIO DA AGRICULTURA, PECUÁRIA E ABASTECIMENTO, 2007. 3 Da Silva et al., 2010. 4 Jungbluth, 2009.

213

5 EMBRAPA, 2004. 6 Pradhan et al., 2011. 7 Panichelli et al., 2009. 8 FAOSTAT, 2012. 9 IFA, 2011. 10 FAPRI, 2012. 11 Edwards and Koeble, 2012 (see Chapter 4). 12 JRC: Acidification and liming (see Section 4.12). Subsequent steps are the same as in “Brazil soya”

Brazil soya Conventional agriculture Soybean cultivation Table 227 Soybean cultivation under conventional agriculture (tilled) in Brazil



N fertilizer Input kg/MJsoybeans 0.000151 5, 11 Ca fertilizer as CaCO3 Input kg/MJsoybeans



4, 5, 9, 100.008711821 kg K2O/(kg soya)


4, 5, 9, 100.010035281 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.000028 4, 5, 6 Seeding material Input kg/MJsoybeans 0.001107 4, 6, 7 Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

11 See GNOC data



Comments: - N fertilizer: 0.00303 kg N/(kg moist soya). Calculated in ref 11 from data in ref 9 and with NT/CA ratio from Argentina applied. - Diesel use: 2.03 ratio CA/NT per tonne soy (ref 3).

o CA area in (ref 3) uses 1575 li/ha for a yield of 2.8 t/ha, whereas the reported NT areas use 998 li/ha for a yield of 3.6 t/ha Sources: 1 CENBIO, 2009. 2 MINISTÉRIO DA AGRICULTURA, PECUÁRIA E ABASTECIMENTO, 2007.

214

3 Da Silva et al., 2010. 4 Jungbluth, 2009. 5 EMBRAPA, 2004. 6 Pradhan et al., 2011. 7 Panichelli et al., 2009. 8 FAOSTAT, 2012. 9 IFA, 2011. 10 FAPRI, 2012. 11 Edwards and Koeble, 2012 (see Chapter 4). 12 JRC: Acidification and liming (see Section 4.12). Subsequent steps are the same as in “Brazil soya”

215

Argentina soya Soybean cultivation Table 228 Soybean cultivation in Argentina


Diesel Input MJ/MJsoybeans 0.02594 1, 2, 5

N fertilizer Input kg/MJsoybeans 0.000053 6 See GNOC data Ca fertilizer as CaCO3 Input kg/MJsoybeans



3, 4 2.1645E-05 kg K2O/(kg soya)


3, 4 0.004761905 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.000097 1 5.77 kg/ha soybeans Seeding material Input kg/MJsoybeans 0.001396 8 Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

6 See GNOC data



Comments: - 20. MJ/(kg soybeans at 13% water) (ref 5)

o Approximately the dry matter LHV indicated for soybean @ 13% H2O according to Jungbluth, 2007 (23 MJ/kg of dry substance). - Diesel use: 1541 MJ/(ha*yr) [ref 1,2]

o JRC: 1660 MJ/ha includes 30km transport to store, and drying, according to ref 2 quoted in ref 1. In the context, we suppose that assumes all drying inputs are diesel. Therefore we subtract the drying energy per ha derived from our drying data. - N fertilizer: 0.00105 kg N/(kg moist soya) . - Soybean yield: 2970 kg/(ha*yr) yield 2,970 kg per ha soybeans @13%H2O (Official information from SAGPyA, in ref 1.

Sources: 1 Muzio et al., 2009. 2 SAGPyA, 2008. 3 IFA, 2011. 4 FAO. 5 FAPRI, 2012. 6 Jungbluth, 2007. 7 Edwards and Koeble, 2012 (see Chapter 4). 8 JRC: Acidification and liming (see Section 4.12). 9 Hilbert et al., 2010.

216

Drying Table 229 Soybean drying

Unit Amount Sources LPG MJ/MJsoybeans 0.00074 1 NG MJ/MJsoybeans 0.00110 1, Diesel MJ/MJsoybeans 0.000160

Soybeans MJ/MJsoybeans 1.000

Soybeans MJ 1.000 Sources: 1 De Tower and Bartosik, 2012. Transport of soybeans

Truck transport DISTANCE km

Argentina 350 Table 230 Truck transport of soybeans

Unit Amount Distance tkm/MJsoybean 0.0175


Soybean MJ 1.0000


Table 231 Shipping and barge distances to rotterdam

nautical miles sea

km sea

Argentina (Rosario) 7938 14701

217






Subsequent steps are the same as in “no tillage and soybean import mix” or no-tillage and soya oil import mix” (as appropriate). Argentina soya No tillage Table 233 Soybean cultivation in Argentina under no tillage






4, 5 2.1645E-05 kg K2O/(kg soya)


4, 5 0.004761905 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.000098 1, 2 5.77 kg/ha soybeans Seeding material Input kg/MJsoybeans 0.00143 8 Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

7 See GNOC data


0.189 9

See liming data

Comments: - 82% fraction of NT in Argentine soy production. - 20.0 MJ/(kg soybeans at 13% water) (ref 5)

o Approximately the dry matter LHV indicated for soybean @ 13% H2O according to Jungbluth, 2007 (23 MJ/kg of dry substance). - Diesel use: 2.03 ratio CA/NT per tonne soy

o CA area in (ref 3) uses 1575 li/ha for a yield of 2.8 t/ha, whereas the reported NT areas use 998 li/ha for a yield of 3.6 t/ha . - N fertilizer: 0.00085 kg N/(kg moist soya) - Seeding material: 80 kg seed-soybean/ha for a yield of 2.8 t/ha (ref 8).

218

Sources: 1 Muzio et al., 2009. 2 SAGPyA, 2008. 3 Hilbert et al., 2010. 4 IFA, 2011. 5 FAPRI, 2012. 6 Jungbluth, 2007. 7 Edwards and Koeble, 2012 (see Chapter 4) 8 Donato et al., 2008. 9 JRC: Acidification and liming (see Section 4.12). Subsequent steps are the same as in “Argentina soya” Argentina Soya Conventonal agriculture Table 234 Soybean cultivation in Argentina under no tillage






4, 5 2.1645E-05 kg K2O/(kg soya)


4, 5 0.004761905 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.000094 1, 2 5.77 kg/ha soybeans Seeding material Input kg/MJsoybeans 0.00125 8 Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

7 See GNOC data


0.189 9

See liming data

Comments: - 82% fraction of NT in Argentine soy production - 20.0 MJ/(kg soybeans at 13% water) (ref 5)

o Approximately the dry matter LHV indicated for soybean @ 13% H2O according to Jungbluth, 2007 (23 MJ/kg of dry substance). - 2.8 tonne/ha yield which pertains to the diesel and pesticides data (ref 3) - Diesel use: 1541 MJ/(ha*yr) (ref 1 and 2).

o JRC: 1660 MJ/ha includes 30km transport to store, and drying, according to ref 2 quoted in ref. 1. In the context, we suppose that assumes all drying

219

inputs are diesel. Therefore we subtract the drying energy per ha derived from our drying data. - N fertilizer: 0.00085 kg N/(kg moist soya) - Seeding material: 80 kg seed-soybean/ha for a yield of 2.8 t/ha (ref 8)

Sources: 1 Muzio et al., 2009. 2 SAGPyA, 2008. 3 Hilbert et al., 2010. 4 IFA, 2011. 5 FAPRI, 2012. 6 Jungbluth, 2007. 7 Edwards and Koeble, 2012 (see Chapter 4) 8 Donato et al., 2008. 9 JRC: Acidification and liming (see Section 4.12). Subsequent steps are the same as in “Argentina soya”

220

US soya Soybean cultivation Table 235 Soybean cultivation in USA






2 0.006903611 kg K2O/(kg soya)


3 0.004213536 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.00003 6 1.60 kg/(ha *yr) Seeding material Input kg/MJsoybeans 0.00121 6 68.9 kg/(ha *yr) Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

5 See GNOC data



Comments: - 20.0 MJ/(kg moist soybeans) (ref 4)

o Jungbluth, 2007: LHV = 23 MJ/kg of dry substance, for Water content: 11%; => LHV (moist) = 20.47 MJ/kg = 5.69 kWh/kg, for Water content: 13% => LHV (moist) = 20.01 MJ/kg = 5.56 kWh/kg

- 13.0% traded moisture content of SB in USA (ref 7) - Diesel use: 1606 MJ/(ha*yr)

o JRC: 33.3 li diesel+12.8 gasoline per ha. Possibly some of this is used for drying rather than cultivation. - N fertilizer: 0.00150 kg N/(kg moist soya) - Pesticides= Pesticides and herbicides. - Soybean yield: 2850 kg/(ha*yr) (FAOstat, average yields 2008-2010) applying to the data from Pradhan et al., 2011.

Sources: 1 Omnitech International, 2010. 2 IFA, 2011. 3 FAO. 4 Jungbluth, 2007. 5 Edwards and Koeble, 2012 (see Chapter 4) 6 Pradhan, et al., 2011. 7 Beuerlein, 2012.

221

8 JRC: Acidification and liming (see Section 4.12). Drying Table 236 Soybean drying

Unit Amount Sources LPG MJ/MJsoybeans 0.00084 7,9NG MJ/MJsoybeans 0.00256 7Diesel MJ/MJsoybeans 0.00108

7Soybeans MJ/MJsoybeans 1.000

7,9Soybeans MJ 1.000

Comments: - Hypothesis: drying consumes the part of the reported US-soy fuel-for-cultivation which is not diesel or gasoline. - 2 litres of LPG. Possibly some of the diesel or gasoline from cultivation is also used for drying, but if so it would only come off the cultivation emissions. - NG: 4.1 m3/ha. - Electricity: 17.1 kWh/ha.

Sources: 7 Beuerlein, 2012. 9 Metrology Centre, 2012. Transport of soybeans

Table 237 Transport of soybeans via 40 t truck over a distance of 80 km (one way)

Unit Amount Distance tkm/MJsoybean 0.0040


Soybean MJ 1.0000

Comments: - US soybeans board says 300 miles! (480km) for transport of fertilizers to farm - For the fuel consumption of the 40 t trunk see Table 63.

Source:

- Pradhan et al., 2011. Shipping and barge transport distances

222

Omnitech Intl (2012) assumes transport of SB from Arkansas via rail to Eastern Seaboard Ports. However, US Soybean Export Council (2011) says: "The U.S. Atlantic Coast was once quite important to U.S. soybean exports. But the role of the Atlantic diminished when rail freight rates were deregulated. Under deregulation, railroads serving the Gulf faced severe competition from barges and water movement, but railroads serving the U.S. East Coast had no competition. That has kept rail rates high going east, so that the geographic freight advantage of a shorter voyage to European destinations is generally eaten up by the higher internal transportation costs. Except for local soybean production, all supplies must be railed in from the central United States, and eastern processors usually absorb the local soybean production to supply the region‘s huge poultry industry with soy meal and the populous East Coast with soybean oil. Like PNW ports, the Atlantic Coast export volume tends to grow when ocean freight rates are relatively high, and the freight advantage to Europe from the Atlantic compared to the Gulf grows large enough to compensate for the cost of railing in Midwestern soybeans." USDA Agricultural Marketing Service, in its "Brazil Soybean Transportation 2008/9" and several other reports of soybean export transport costs, use Davenport Iowa as its typical source for US soybean exports, exporting via the Mississipi. 60% of US soybean exports are said to tranship in the New Orleans region. Therefore we chose the Mississippi export route via New Orleans area, which takes 60% of US soybean exports, according to the USSEC report. Table 238 Transport of soybeans seed via inland ship over a distance of 2161 km (one way)



Soybeans Output MJ 1.0000 Comments:

- Davenport Iowa to New Orleans (ref 1) - For the fuel consumption of a bulk carrier for inland navigation, see Table 75.

Source: 1 USDA Agricultural Marketing Service, Brazil Soybean Transportation, October 28, 2010, example for US soybeans export.

223





Comment: - Shipping distance to rotterdam: 14701 km is considered too much, Google Earth: approx. 9000 km. - For the fuel consumption of a bulk carrier for maritime transport, see Table 70.

Subsequent steps are the same as in “no tillage and soybean import mix” or no-tillage and soya oil import mix” (as appropriate). US soya No tillage Table 240 Soybean cultivation in US under no tillage






2,3 0.006903611 kg K2O/(kg soya)


2, 3 0.004213536 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.00003 7 1.60 kg/(ha*yr) Seeding material Input kg/MJsoybeans 0.00121 7 68.9 kg/(ha*yr) Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

6 See GNOC data


0.189 9

See liming data

Comments: - 50% fraction of NT in US soy production (ref 4) - 20.0 MJ/(kg moist soybeans) (ref 5)

o Jungbluth, 2007: LHV = 23 MJ/kg of dry substance, for water content: 11% =>LHV (moist) = 20.47 MJ/kg = 5.69 kWh/kg for water content: 13% => LHV (moist) = 20.01 MJ/kg = 5.56 kWh/kg

- 13.0% traded moisture content of SB in USA (ref 8) - Diesel use: 1395 MJ/(ha*yr)

224

o National average for soy is 33.3 li diesel+12.8 gasoline per ha. Possibly some of this is used for drying rather than cultivation, but in that case we have made a compensating underestimate in drying emissions. No tillage saves 11.78 li of diesel per ha over conventional agriculture due to less tillage passes (USDA/NASS in Fawcett and Towery, 2002). As half of soy in US is NT, we need to subtract 5.89 li from the national average diesel use figure for NT (and add 5.89 li/ha for the diesel use in CA). - N fertilizer: 0.00081 kg N/(kg moist soya) - Yield: 2850 kg/(ha*yr), applying to the data from Pradhan et al., 2011 (FAOstat, average yields 2008-2010).

Sources: 1 Omnitech International, 2010. 2 IFA, 2011. 3 FAO. 4 Horowitz et al., 2010. 5 Jungbluth, 2007. 6 Edwards and Koeble, 2012 (see Chapter 4) 7 Pradhan et al., 2011. 8 Beuerlein, 2012. 9 JRC: Acidification and liming (see Section 4.12). 10 Fawcett and Towery, 2002.

Subsequent steps are the same as in “US soya”

225

US Soya Conventional agriculture Table 241 Soybean cultivation in US under conventional agriculture






2,3 0.006903611 kg K2O/(kg soya)


2,3 0.004213536 kg P2O5/(kg soya)

Pesticides Input kg/MJsoybeans 0.00003 7 1.60 kg/ha soybeans Seeding material Input kg/MJsoybeans 0.00121 7 68.9 kg/(ha*yr) Soybeans Output MJ 1.0000 Field N2O emissions - g/MJsoybeans

6 See GNOC data


1.647 9

See liming data

Comments: - 50% fraction of NT in US soy production (ref 4). - 20.0 MJ/(kg moist soybeans) (ref 5). - 2850 kg/(ha*yr), yield applying to the data from ref 7. - Diesel use: 1817 MJ/(ha*yr). - N fertilizer: 0.00183 kg N/(kg moist soya).

Sources: 1 Omnitech International, 2010. 2 IFA, 2011. 3 FAO, 2007. 4 Horowitz et al., 2010. 5 Jungbluth, 2007. 6 Edwards and Koeble, 2012 (see Chapter 4) 7 Pradhan, et al., 2011. 8 Beuerlein, 2012. 9 JRC: Acidification and liming (see Section 4.12). 10 Fawcett and Towery, 2002.

Subsequent steps are the same as in “US soya”

226

7.14 Palm oil to biodiesel Description of pathway The following processes are included in the palm oil to biodiesel pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1a. Cultivation of oil palm tree (16% peat) The new data for palm oil tree cultivation are shown in Table 237 and Table 238. The updated data include:

• Diesel and pesticide use in palm oil tree cultivation. • CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12).

227

Table 242 Cultivation of oil palm tree (16% peat)


Diesel Input MJ/MJFFB 0.00537 7 2.37 litres/(t moist FFB)

K2O Input kg/MJFFB 0.00050 2, 3 7.92 kg/(t moist FFB)

N fertilizer Input kg/MJFFB 0.00026 5 See GNOC data

CaCO3 fertilizer Input kg/MJFFB 0.0005765 6 See liming data

P2O5 fertilizer Input kg/MJFFB 0.00010 2, 3 1.56 kg/(t moist FFB)

EFB compost Input kg/MJFFB 0.01420 1 225 kg/(t moist FFB)

Pesticides Input kg/MJFFB 0.000179 7 2.83 kg/(t moist FFB)

Fresh fruit bunches (FFB) Output MJ 1.0000

Field N2O emissions - g/MJFFB 0.031 5 See GNOC data CO2 from neutralization & other soil acidity g/MJFFB 0.134 6 See liming data

Step 1b. Cultivation of oil palm tree (mineral soil) Table 243 Cultivation of oil palm tree (mineral soil)


Diesel Input MJ/MJFFB 0.00537 7 2.37 litres/(t moist FFB)

K2O Input kg/MJFFB 0.00050 2, 3 7.92 kg/(t moist FFB)

N fertilizer Input kg/MJFFB 0.00026 5 See GNOC data

CaCO3 fertilizer Input kg/MJFFB 0.0005765 6 See liming data

P2O5 fertilizer Input kg/MJFFB 0.00010 2, 3 1.56 kg/(t moist FFB)

EFB compost Input kg/MJFFB 0.01420 1 225 kg/(t moist FFB)

Pesticides Input kg/MJFFB 0.000179 7 2.83 kg/(t moist FFB)

Fresh fruit bunches (FFB) Output MJ 1.0000

Field N2O emissions - g/MJFFB 0.017 5 See GNOC data CO2 from neutralization & other soil acidity g/MJFFB 0.134 6 See liming data

Comment: - LHV = 24 MJ/kg dry substance; - 34% moisture (ref 8).

Sources: 1 Schmidt, 2007. 2 IFA, 2010. 3 FAPRI, 2012. 4 Literature survey "palm oil fertilizer.doc"

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5 Edwards and Koeble, 2012 (see Chapter 4). 6 JRC: Acidification and liming data in this report (see Section 4.12). 7 Coo et al., 2011. 8 Cheng Hai T., 2004. Step 2. Transportation of fresh fruit bunches (FFB) Table 244 Transport of fresh fruit bunches via 12 t truck (payload 7t) over a distance of 50 km (one way)

I/O Unit Amount Distance Input tkm/MJFFB 0.0032

FFB Input MJ/MJFFB 1.0000 FFB Output MJ 1.0000


Sources: 1 Lastauto Omnibus Katalog, 2010; ETM EuroTransportMedia Verlags- und Veranstaltungs-GmbH, Stand August 2009. 2 Coo et al., 2011. Step 3. Storage of fresh fruit bunches Table 245 Storage of fresh fruit bunches

I/O Unit Amount FFB Input MJ/MJFFB 1.0000

FFB Output MJ 1.0000

Source: 1 MPOB pers. comm. at data review meeting Ispra Nov. 2011.

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Step 4. Oil mill - Plant oil extraction from fresh fruit bunches Table 246 Plant oil extraction from fresh fruit bunches

I/O Unit Amount Source FFB Input MJ/MJoil 2.1427

grid electricity Input MJ/MJoil 0.000078 5

diesel Input MJ/MJoil 0.00445 5

emission / open POME pond

emission gCH4/MJoil 0.9844 5

emission / closed POME pond

emission gCH4/MJoil 0.1477 5

Crude Palm Oil (CPO)

Output MJ 1.0000

Comments: - Grid electricity:

o 1.76 MJe/tonne CPO, after ref 5 allocated 1/1.64 of the inputs to oil by mass. o this is only grid electricity. In addition 224.08*1.64 MJ are generated from CHP using nutshells.

- Diesel: o 100.33 MJ/tonne CPO, after ref 5 allocated 1/1.64 of the inputs to oil by mass. o most heat and power for mill comes from burning fibre and nutshells.

- Emission/open POME (Palm Oil Mill Effluent) pond: 22.21 kg/tonne CPO, after ref 5 allocated 1/1.64 of the inputs to oil by mass. - Emission/closed POME pond: 85% of methane emissions assumed captured by methane capture technology (ref 5).

Emissions from palm oil mill: We take the emissions from the palm oil mill from the recent publication by MPOB staff [5]. The inputs and emissions reported in that paper are those allocated to 1 tonne of crude palm oil by mass allocation. According to this paper, making 1 tonne palm oil produces 0.64 tonnes of useful by-products, so to find the unallocated emissions per tonne palm oil, we have to multiply the figures by 1.64. Most of the heat and power for the mill (a composite of 12 representative mills) comes from burning all the pressed mesocarp fibre and some nutshells in a CHP generator. However, a little grid electricity and diesel are also in the mix. There is a surplus of nutshells, which is exported, according to ref 5, as a low-cost fuel. In this update, we calculate emissions for palm oil specifically instead of a combination of palm oil and palm kernel oil. Palm kernel oil is as yet rarely used for biofuel as it has higher-value uses for soapmaking (etc), competing with tallow. That means allocating part of the mill emissions to palm kernels. As we do not have an LHV for palm kernels, we have calculated our energy-based allocation calculating separately for the two components of the kernels: palm kernel meal and palm kernel oil.

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The main emission from the mill is methane released from the anearobic effluent pond. Following MPOB (ref 5), without methane capture 11.94 kg methane per tonne of effluent is emitted, whereas with methane capture this is reduced by 85%. Calculation of LHV of palm oil Table 247 LHV of palm oil

Component Wt fraction Source

LHV wet ENER def (MJ/kg)

Source MoistureOutput in

ENER LHV

LHV of dry part per wet

kg Palm oil 0.200 1 37 6 0% 7.393 37.0Palm kernel meal 0.029* 2,3 16.4 2 10% 0.481 16.7Palm kernel oil 0.024 37 6 0% 0.888 37Excess nutshells 0.074 5 0** 4 10% 0.000 17.3Allocation to crude palm oil 84% TOTAL 8.762

Comments: - *All pressed mesocarp fibre and some nutshells are used inside the process for heat. - **The RE directive already defines nutshells as a residue which is accorded zero emissions. Therefore they should not be allocated any emissions as a coproduct.

Sources: 1 Schmidt, 2007. 2 Calculated from composition by JRC “LHV calculator” using composition in ref 4. 3 Chin, 1991. 4 Panapanaan and Helin, 2009. 5 Coo et al., 2011. 6 Mehta and Anand, 2009.

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Step 5. Transport of palm oil Table 248 Transport of palm oil summary table

Transporter Notes Distance (km one-way)

Truck payload 27 120

Product tanker payload 22,560 10,186

Table 249 Transport of palm oil via a 40 t truck over a distance of 120 km (one way)

I/O Unit Amount Distance Input tkm/MJoil 0.0035

Vetegable oil Input MJ/MJoil 1.0000

Vetegable oil Output MJ 1.0000

Comment: - For the fuel consumption of a 40 t truck see Table 63.

Table 250 Maritime transport of palm oil via ship over a distance of 10,186 km (one way)


Vegetable oil Input MJ/MJoil 1.0000

Vegetable oil Output MJ 1.0000

Comment: - For the fuel consumption of the product tanker (payload 22,560 t), see Table 78.

Sources: 1 Coo et al., 2011. 2 Gover et al., 1996. 3 KFZ-Anzeiger 14/2003, Test Mercedes-Benz Actros 1844. 4 Lastauto Omnibus Katalog 2010; ETM EuroTransportMedia Verlags- und Veranstaltungs-GmbH, Stand August 2009. 5 Buhaug et al., 2009. 6 Statement by Odfell Tankers AS, Bergen, Norway, 26 Jan. 2012, to JRC. 7 The JRC estimate based on sea distances between intermendiate ports, following discussion in ref 5. 8 JRC interpolation of emissions data from ref 5.

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Step 6. Refining of vegetable oil from oil palm Table 251 Refining of vegetable oil from oil palm


Electricity Input MJ/MJoil

0.0009 1, 2 34.38 MJ/(t oil) [1]

H3PO4 Input kg/MJoil

0.000032 1, 2 1.19 kg/(t oil) [1]

NaOH Input kg/MJoil

0.000088 1, 2 3.26 kg/(t oil) [1]

Crude vegetable oil Input MJ/MJoil

1.0246 1

Steam Input MJ/MJoil

0.0040 1, 2 149.2 MJ/(t oil) [1]

Plant oil Output MJ 1.0000 37 MJ/kg of oil [2]

Sources: 1 EBB, 2009. 2 Edwards, R., 5 September 2012 based on ECN Phyllis database of biomaterials properties. Step 7. Esterification Table 252 Esterification


Electricity Input MJ/MJFAME 0.0040 1, 2, 4 150.5 MJ/(t RME) [1]

Sodium methylate (Na(CH3O))

Input kg/MJFAME 0.00047 1, 2, 4

17.55 kg/(t RME) [1]

HCl Input kg/MJFAME 0.00010 1, 2, 4 3.61 kg/(t RME) [1]

Methanol Input MJ/MJFAME 0.0511 1, 2, 4 95.29 kg/(t RME) [1]

Plant oil Input MJ/MJFAME 1.0006 1, 2, 4 101.87 kgglycerol/(t RME)

Steam Input MJ/MJFAME 0.0330 1, 2, 3, 4 1229 MJ/(t RME)

FAME I/O MJ 1.0000 37.2 MJ/(kg RME) [2]

Comments: - 16 MJ/(kg glyerol) (ref 4).

Sources: 1 EBB, 2009. 2 Edwards, R., 5 September 2012 based on ECN Phyllis database of biomaterials properties. 3 Rous J.F., Sofiprotéol, Paris, France pers. comm. 23 September 2008. 4 Edwards, JRC, 22 July 2003: calculation with HSC for windows. Step 7.1. Plant generation processes The data for the individual plant generation processes are shown in Chapter 5. The

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process linked to esterification is: • Steam generation (Table 51)

Step 8. Transportation of FAME to the blending depot Table 253 Transport of FAME via 40 t truck over a distance of 150 km





Step 9. FAME depot distribution inputs Same input data as rapeseed used.

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7.15 Jatropha to biodiesel

Description of pathway The following processes are included in the jatropha to biodiesel pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Jatropha seed/plantation – IFEU (mechanised) The new data for jatropha seed plantation are shown in Table 249. All data have been updated.

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Table 254 Cultivation of Jatropha seed / plantation – IFEU (mechanised cultivation)


Diesel Input MJ/MJseed 0.05431 1 3,621 MJ(ha*yr)

K2O fertilizer Input kg/MJseed 0.00116 1 77.312 kg/(ha*yr)

N fertilizer Input kg/MJseed 0.00031 1 20.8 kg/(ha*yr)

P2O5 fertilizer Input kg/MJseed 0.00009 1 5.80 kg/(ha*yr)

Jatropha seed Output MJ 1.000 2 2600 kg/(ha*yr)

Field N2O emissions - g/MJseed 0.024 2, 3

Comments: - Diesel use: 101 l/ha per year (ref 1) - LHV: 27.28 MJ/(kg dry jatropha seed) (ref 2) - Jatropha seed: 6% H2O/ha*yr (ref 1) - Jatropha capsules: 4160 kg/ha*yr (capsules = seeds + husks) (ref 1)

Sources: 1 IFEU, 2011 based on Reinhardt et al., 2008. 2 IFEU/UU 2011 based on van Eijck, 2011 and Wahl et al., 2009. 3 Paustian et al., 2006.

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Calculation of N2O from Jatropha cultivation Table 255 N2O from Jatropha cultivation calculation:

Best Estimate FSN (input of synthetic fertilizer) 21 kg/(ha*yr) [1]

FCR (N-input from crop residues) 61 kg N/(ha*yr) [1]

Jatropha seed yield 2,600 kg/(ha*yr) [1]

H2O-content jatropha seed 6% [1]

Crop0 (production of non-N-fixing crops) 2,444 kg dry biomass/(ha*a)

FracGASF (NH3- and NOx-emissions) 0.1000 kg NH3-N+NOx-N/(kg synthetic fertilizer-N)

FracLEACH (N leaching off) 0.3000 kg N/(kg fertilizer or manure)

EF1 0.01 kg N2O-N/(kg Fertilzer-N)

EF4 0.0100 kg N2O-N/(kg NH3-N+ kg NOx-N-emitted)

EF5 0.0075 kg N2O-N/(kg N leaching off)

Direct N2O-emissions 1.29 kg N2O/ha/a Indirect N2O-emissions from NH3 and NOx-emissions 0.03 kg N2O/ha/a

Indirect emissions form N leaching off 0.29 kg N2O/ha/a

Total 1.61 kg N2O/ha/a

LHV 27.3 MJ/kg of dry substance

Energy jatropha seed yield 66,681 MJ/(ha*a)

18,522 kWh/(ha*a)

Specific N2O-emissions 0.087 g/kWhjatropha seed

Comment: - According to Reinhardt, 2008 cake and most of husk is used as fertilizer.

Sources: 1 IFEU, 2011 based on Reinhardt et al., 2008. 2 Paustian et al., 2006. 3 Jongschaap et al., 2007.

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Step 2. Transportation of Jatropha capsules Table 256 Transport System of "jatropha capsules" via 20 t truck (payload 10 t) over a distance of 190 km (one way)

I/O Unit Amount Distance Input tkm/MJ 0.01185

Jatropha seed Input MJ/MJ 1.00000 Jatropha seed Output MJ 1.00000


Sources: 1 van Eijik et al., 2011. 2 IFEU, 2011 based on van Eijck, 2011. 3 Reinhardt et al., 2008. 4 Lastauto omnibus Katalog, 2010. Step 3. Extraction of vegetable oil from Jatropha Table 257 Extraction of vegetable oil from Jatropha

I/O Unit Amount Source Jatropha seed Input MJ/MJ 1.0502 1, 2 Electricity Input MJ/MJ 0.00501 1, 2 Steam Input MJ/MJ 0.02375 1, 2 Vegetable oil Output MJ 1.0000 Source: 1 IFEU, 2011 based on Reinhardt et al., 2008. 2 IFEU/UU, 2011 based on van Eijck, 2011 and Wahl et al., 2009. Source data used to calculate the values in the table above:

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Conversion of Jatropha seed to plant oil via expeller (allocation by energy) Table 258 Conversion of Jatropha seed to plant oil via expeller (allocation by energy)

Jatropha seed Input 1000 kg [7] Electricity Input 122.4 MJ Steam Input 580 MJ [7] Jatropha oil Output 215 kg per t seeds [7] residues (returned to field, mostly) Husks (capsules = seeds + husks) Residue 600 kg [7] Jatropha cake (cake = meal + shells) Residue 656 kg wet per t wet

seeds [7]

Comments: - LHV (crude Jatropha oil): 37 MJ/kg (ref 2). - Only a small proportion of the husks is needed for expeller process heat; most is returned to field (ref 7).

Sources: 1 Prueksakorn and Gheewala, 2006. 2 Kerkhof, 2008. 3 Openshaw, 2000. 4 Jongschaap et al., 2007. 5 Pelletbase, 2010. 6 Hartmann, 1995. 7 Reinhardt et al., 2008. 8 IFEU/UU 2011 based on van Eijck, 2011 and Wahl et al., 2009. Step 4. Transport of Jatropha oil Table 259 Transport of Jatropha oil summary table

Transporter Type Distance (km one-way)

Truck (40t) payload 27 t 150

Product tanker payload 22,560 t 11,727

Table 260 Transport of Jatropha oil via a 40 t truck over a distance of 150 km (one way)


Vegetable oil Input MJ/MJoil 1.0000 Vegetable oil Output MJ 1.0000

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Comment: - For the fuel consumption for the 40 t truck, see Table 63.

Table 261 Maritime transport of jatropha oil via ship over a distance of 6,332 nautical miles (one way)




Comment: - For the fuel consumption for the product tanker (payload 22560 t), see Table 73.

Source: 1 Gover et al., 1996. 2 KFZ-Anzeiger 14/2003: Test Mercedes-Benz Actros 1844. 3 Lastauto Omnibus Katalog, 2010. 4 Buhaug et al., 2009. Step 5. Refining of vegetable oil from Jatropha Table 262 Refining of vegetable oil from Jatropha


Electricity Input MJ/MJoil 0.000584 1, 2 21.6 MJ/(t oil) [1]

Fuller's earth Input kg/MJoil 0.000162 1, 2 6 kg/(t oil) [1]

Crude vegetable oil Input MJ/MJoil 1.0417 1

Steam Input MJ/MJoil 0.00800 1, 2 296 MJ/(t oil) [1]

Plant oil Output MJ 1.0000 37 MJ/kg of oil [2]

Sources: 1 IFEU, 2011a (own calculations). 2 Edwards, R., 5 September 2012 based on ECN Phyllis database of biomaterials properties. Step 6. Esterification Same as palm oil.

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Step 7. Transportation of FAME to the blending depot Table 263 Transport of FAME via 40 t truck over a distance of 150 km




Comment: - 150 km from esterification plant to the depot; 150 km from depot to the filling stations. - 6 t transported ethanol; 2 t tank. - LHV (FAMEl) = 37 MJ/kg - For the fuel consumption for the 40 t truck, see Table 64.

Source: 1 Dautrebande, 2002. Step 8. FAME depot distribution inputs Same input data as rapeseed used.

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7.16 Coconut to biodiesel Description of pathway The following processes are included in the coconut oil to biodiesel pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Coconut cultivation (Indonesia) The new data for coconut cultivation are shown in Table 259. The updated data include:

• CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • N fertilizer. • Pesticide. • N2O emissions calculated by JRC data using the JRC GNOC model (see Section 4.6). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12).

Table 264 Coconut cultivation (Indonesia)


Diesel Input MJ/MJcopra 0.01980 1,3,6,7 896 MJ diesel/ha, for 25 li/ha/a

CaCO3 fertilizer Input kg/ MJcopra 0.001956 9 See liming data

K2O Input kg/ MJcopra 0.000552 1,3,6,7 16.65 kg/(t copra)

N fertilizer Input kg/ MJcopra 0.001546 4, 5, 8 46.62 kg/(t copra)

P2O5 fertilizer Input kg/ MJcopra 0.000486 1,3,6,7 14.65 kg/(t copra)

Pesticides Input kg/ MJcopra 0.000066 1,3,6,7 2.00 kg/(t copra)

Copra Output MJ 1.0000 1,2,3,4 1.50 t copra /(ha*yr) at water content of 6%

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Field N2O emissions - g/ MJcopra 0.026 8 See GNOC data CO2 from neutralization & other soil acidity

g/ MJcopra 0.867 9 See liming data

Comments: - LHV= 32.07 MJ/(kg dry copra) (refs 4 and 5). - Copra 6% H2O/ha*yr

Sources: 1 E4Tech, 2010 pers. comm. 2 FAOSTAT, 2010. 3 Renewable Fuels Agency, 2009. 4 ECOFYS, 2008. 5 Biofuels from Philippine Plants 6 Hemstock, 2008. 7 Trewren, 2008 pers. comm. 8 Edwards and Koeble, 2012 (see Chapter 4). 9 JRC: Acidification and liming data in this report (see Section 4.12). Step 2. Transportation of copra Table 265 Transport of copra via 12 t truck (payload 6.35t) over a distance of 200 km (one way)

I/O Unit Amount Distance Input tkm/MJcopra 0.0066

Copra Input MJ/MJcopra 1.0000 Copra Output MJ 1.0000


Step 3. Extraction of vegetable oil from Coconut Table 266 Extraction of vegetable oil from Coconut

I/O Unit Amount Source Comment Copra Input MJ/MJ

oil 1.2271 2 0.630kg oil/(kg copra @ 6% H2O)

Electricity Input MJ/MJoil

0.0164 0.310kg coconut meal/(kg copra @ 6% H2O)

Vegetable oil Output MJ 1.0000 Comments:

- 39 MJ/(kg coconut oil) (ref 1). - 18.0 MJ/(kg dry coconut meal). - 16.77 MJ/(kg wet coconut meal) (ref 3).

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Sources: 1 ECOFYS, 2008. 2 E4Tech, 2010 pers. comm. 3 Hartmann, 1995. Step 4. Transport of coconut oil Table 267 Transport of coconut oil via ship (payload 22,560 t) from port to biodiesel plant over a distance of 15,000 km (one way)


Vegetable oil Input MJ/MJoil 1.0000 Vegetable oil Output MJ 1.0000

Comment: - For the fuel consumption of the product tanker (payload 22560 t), see Table 77.

Sources: 1 E4Tech 2010 pers. comm. 2 Buhaug et al., 2009.

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7.17 Waste cooking oil to biodiesel

Waste cooking oil (used cooking oil) is defined as a waste in the RED and therefore is attributed zero GHG emissions at its point of collection. However, used cooking oil is being brought into EU from considerable distances. The major world exporter is China, and if its use in EU continues to increase as we expect, it could supply a large part of the EU UCO market. Transport of Waste cooking oil Table 268 Transport of waste oil via 40 t truck over a distance of 100 km




Comment: - For the fuel consumption for the 40 t truck, see Table 64.

Sources: 1 Gover et al., 1996. 2 KFZ-Anzeiger 14/2003: Test Mercedes-Benz Actros 1844. 3 Lastauto Omnibus Katalog, 2010. Additional sea transport for pure plant oil from waste cooking oil (WOPP) and Waste cooking oil to FAME (WOFA). Table 269 Maritime transport of waste cookng oil via ship over a distance of 10,000 nautical miles (18 500 km)




Comments

- LHV waste cooking oil = 37 MJ/kg - For the fuel consumption of the product tanker (payload 22,560 t), see Table 78. - Shipping distance derved using Searates Port to Port Distances calculator.

Sources: 1 see "chemical_tankers” data as noted in second comment above 2 Statement by Odfjell Tankers AS, Bergen, Norway, 26 Jan. 2012, to JRC.

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3 Sea-Rates Port to Port Distances calculator available http://www.searates.com/reference/portdistance/ accessed November 15th 2012. 4 JRC interpolation of emissions data from ref 5. 5 Buhaug et al., 2009. Transesterification of animal fat and used cooking oil to FAME Table 270 Transesterification of animal fat & used cooking oil to FAME


Electricity Input MJ/MJFAME

0.0038 1 41 kWh/(t Fat)

H3PO4 Input kg/MJFAME

0.00037 1 14.2 kg/(t Fat)

KOH Input kg/MJFAME

0.00034 1 13.2 kg/(t Fat)

Methanol Input MJ/MJFAME

0.0572 1 110 kg/(t Fat)

Fat Input MJ/MJFAME

0.9660 2 1025 kgFat/tFAME

NG Input MJ/MJFAME

0.0927 1 801 kgSteam/tFat

FAME Output MJ 1.0000 1 975 kgFAME/tFat

K2SO4 Output kg/MJFAME

0.00068 1 26 kg/(t Fat)

Comments: - 19.95 MJ/(kg Methanol). - 37.1 MJ/(kg Fat). - 4.44MJNG/kg Steam. - 37.2 MJ/(kg FAME) (refs 2 and 3).

Table 271 By-products used for allocation


Glycerol Output MJ/MJFAME

0.0432 1 98 kg/(t Fat)

Biooil Output MJ/MJFAME

0.0150 1 25 kg/(t Fat)

Comments: - 16 MJ/(kg Glyerol). - 21.8 MJ/(kg Bio-oil).

Sources: 1 BDI, Input-Output Factsheet, Plant Capacity 50.000 t Biodiesel, Department Research and Development BDI – BioDiesel International AG. 2 Wörgetter et al., 2006. 3 Mittelbach and Remschmidt, 2004. Rest of processing assumed like in rapeseed oil

http://www.searates.com/reference/portdistance/

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7.18 Pure cottonseed oil

Description of pathway Pure cottonseed oil: used within 100km JRC has found that the main existing energy use of cottonseed oil in EU is as pure plant oil in local boilers in Greece. The following processes are included in the cotton oil to biodiesel pathway:

The data for each process are shown below and significant updates are described in more detail with the relevant references. Step 1. Pure cottonseed oil cultivation The new data for cottonseed plantation are shown in Table 267. The updated data include:

• CaCO3 fertiliser use calculated by the JRC (see Section 4.12). • CO2 emissions from neutralization & other soil acidity calculated by the JRC (see Section 4.12).

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Table 272 Pure cottonseed oil cultivation


Diesel Input MJ/MJcottonseed 0.2522 1 194 l/ha.yr

N fertilizer Input kg/MJ cottonseed 0.00168 1 98.8 kg/ha.yr

CaCO3 fertilizer Input kg/MJ cottonseed 0.002883

K2O fertilizer Input kg/MJcottonseed 0.00181 1 49.7 kg/ha.yr

P2O

5 fertilizer Input kg/MJcottonseed 0.00145 1 40 kg/ha.yr

limestone 0.01548 1 425.9 kg/ha.yr

Pesticides etc. 0.00031 1 8.60 kg/ha.yr

Seeding material Input kg/MJcottonseed 0.00049 1 13.5 kg/ha.yr

Cottonseed Output MJ 1.00000

Field N2O emissions - gN2O/MJcottonseed 0.0832 4


- g/MJcottonseed 0.764 7

Comments: - LHV cottonseed oil: 36.6 MJ/kg - LHV dry cottonseed: 21.9 MJ/kg

Sources: 1 University of Texas at Austin, 2011. 2 Demirbas, 2008. 3 JRC calculations based on FAO import matrix, FAO 2007 yields and IFA fertilizer use by crop, 2007. 4 Calculation using JRC global N2O calculator. 5 Sauvant et al., 2004. 6 TIS-GDV. 7 JRC: Acidification and liming data in this report (see Section 4.12). Step 2. Transportation of cotton seed Table 273 Transport of cottonseed by 40 t truck to processing plant km (one way)

I/O Unit Amount Distance Km 50


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LHV of cottonseed Table 274 Cottonseed and products composition (ref 5)

Whole seed

Oilmeal (deoiled and

partially peeled)

Oilcake expeller (partially peeled) Hull

cotton lint [6]

Dry matter (%) 92 90 93 92 0.92

Proteins (%) MS 22 (19-25) 42 (35-53)40 (28-

49) 5 (3-7) 0.006 Rough cellulose (%) MS 28 (23-37) 18 (11-23)

15 (11-23)

53 (49-62) 0.988

Fatty matter (%) MS 20 (10-28) 3 (0,4-6) 7 (4-11) 3 (0,6-5) 0.004 Ashes (%) MS 4 7 7 3 0.002 Calcium (%) MS 0,2 0,3 0,2 0,15 Phosphorus (%) MS 0,6 1,3 1,2 0,19

Table 275 LHV of cottonseed

Component cotton seed whole %

dry matter basis

LHV components MJ/kg [3] contributions component

dry matter 92 0.92 dry matter oil 20 (10-28) 20% 36.6 7.32 oil

protein 22 (19-25) 22% 24.5 5.39 protein cellulose 28 (23-37) 28% 15.88 4.45 cellulose

fibre assumed to be the rest 26% 18.27 4.75 fibre

ash 4% 4% 0 0.00 ash SUM 100.00% 21.91 MJ/kg dry SUM

20.15 MJ/kg moist material

20.0MJ/moist kg BY R.E.D. DEFINITION OF LHV

Table 276 LHV of Cotton seed meal de-oiled and partly peeled

Component range

dry matter basis (centre value)


dry matter 90 0.9 dry matter

249

oil 3 3% 36.6 1.10 oil protein 42 (35-53) 42% 24.5 10.29 prote

cellulose 18 (11-23) 18% 15.88 2.86 cellul

fibre assumed to be the rest 30% 18.27 5.48 fibre

ash 7 7% 0 0.00 ashSUM 100.00% 19.7 MJ/kg dry SUM

17.75 MJ/kg moist material

17.5 MJ/moist kg BY R.E.D. DEFINITIOLHV

Table 277 LHV of Cotton Lint

Component

dry matter basis


rc

dry matter 0.92 dry matter oil 0.4% 36.6 0.15 oil

protein 0.6% 24.5 0.15 protein cellulose 98.8% 15.88 15.69 cellulose

fibre 0.0% 18.27 0.00 fibre ash 0.2% 0 0.00 ash

SUM 100.00% 16.0 MJ/kg dry SUM 14.73 MJ/kg moist material

14.5MJ/moist kg BY R.E.D. DEFINITION OF LHV

Sources: 1 Friedlet al. 2005 2 International Starch Institute: website: http://www.starch.dk/ 3 ECN Phyllis database of LHV values for biomass Table 278 Product ratios for allocation by wet-LHV at cotton oil mill

products lint meal hulls oil linterMOIST weight composition of cottonseed [1] 45.7% 27.0% 16.0% 8.0%MOIST weight composition of harvest[1] 41% 27% 16% 9% 5%LHV by DG-ENER DEFINITION (wetMJ/wet kg) 14.5 20.0 "residue" 36.6 14.0ALLOCATION RATIO by wet LHV 39% 35% 0% 22.3% 4%no allocation to husks or trash(residues)

Comment: Conclusion: the oil yield is 16% in terms of mass of oil per moist mass of cottonseed Conclusion: 36% of the emissions from the oil-mill should be allocated to the oil, according to DG-ENER rules Step 3. Extraction of vegetable oil from pure cottonseed oil

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Cotton oil refining assumed same as soy oil: Cottonseed oil mill = soybean oil mill per kg seed Step 4. Transport of cotton oil to local boiler Table 279 Transport of cotton oil via 40 t truck over a distance of 50 km (one way)





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7.19 Tallow oil

Step 1. Animal fat processing from carcass (biodiesel) Table 280 Animal fat processing from carcass (biodiesel)

Rendering I/O Unit Amount Source

Carcass Input kg/MJtallow 0.0929 4

Electricity Input MJ/MJtallow 0.0176 4

Natural Gas Input MJ/MJtallow 0.1889 4,6

Fuel oil Input MJ/MJtallow 0.0187 6

Tallow Output MJ 1.0000

Comment: - LHV = 37.1 MJ/dry kg. - Ref. 6 gives CO2 emitted (per million pounds of animal wate) from burning NG, and some of the animal fat. These CO2 emissions are converted back into tonnes of fuel (and GJ fuel) per tonne of fat. We think that if any fat is used for biodiesel, the first to be used will be the cheapest fat, which is now burnt in the process. That fat will be replaced by fuel oil (pers. comm. UK renderers association) because these plants are not on the NG grid. So the part (~10%) of the process-heat formerly delivered by fat is replaced by process heat from fuel oil. (n.b. in a marginal calculation, it would all be replaced by fuel oil, with negative GHG consequnces.

Sources: 1 Ecoinvent, LCI of tallow production. 2 Notarnicola et al., 2007. 3 Raggi et al., 2007. 4 De Camillis et al., 2010. 5 LCA report from BIODIEPRO project. http://www.argentenergy.com/articles/article_8.shtml 6 US National Renderers Association website: http://nationalrenderers.org. Step 2. Transport distances Table 281 Transport of carcass via 40 t truck over a distance of 30 km (one way)

I/O Unit Amount Distance Input tkm/MJTallow (part of carcass) 0.0028 Carcass Input MJ/MJTallow (part of carcass) 1.0000 Carcass Output MJ 1.0000

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Comments: - The number is related to the oil content of the carcass, 3.4483 kg carcass have to be transported per kg of tallow, no information about the LHV of the carcass is available until now. - For the fuel consumption of the 40 t truck, see Table 63.

Table 282 Rendering

I/O Unit Amount Source Carcass Input MJ/MJTallow (part of carcass) 1.0000 Electricity Input MJ / MJ tallow 0.0176 4 Natural Gas Input MJ / MJ tallow 0.1889 4,6 Fuel oil Input MJ / MJ tallow 0.0187 6 Tallow Output MJ 1.0000

Comment: - Related to the LHV of the oil content.

Sources: See above

Table 283 Transport of tallow via 40 t truck over a distance of 150 km (one way)

I/O Unit Amount Distance Input tkm/MJtallow 0.0044

Talllow Input MJ/MJtalllow 1.0000 Talllow Output MJ 1.0000

Comment: - LHV (tallow) = 37.1 MJ/kg. - For the fuel consumption of the 40 t truck, see Table 63.

Sources: 1 Gover et al., 1996. 2 KFZ-Anzeiger 14/2003: Test Mercedes-Benz Actros 1844. 3 Lastauto Omnibus Katalog 2010; ETM EuroTransportMedia Verlags- und Veranstaltungs-GmbH, Stand August 2009. We assume the rest of the processing is the same as for rapeseed oil.

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7.20 HVO This process applies to hydrotreating of Rapeseed oil (ROHY), Sunflower oil (SOHY), all Soy oil pathways (SYHY NT, CA), Palm oil (POHY) and Cotton oil (COHY): NExBTL deep hydrogenation process process and distribution Table 284 Hydrotreating of vegetable oil via NExBTL process (generation of a BTL like fuel):

I/O Unit Amount H

2 Input MJ/MJ

BTL 0.0857

Vegetable oil Input MJ/MJBTL

1.0341

H3PO

4 Input kg/MJ

BTL 0.0000170

NaOH Input kg/MJBTL

0.0000273

BTL-like fuel Output MJ 1.0000

Electricity Output MJ/MJBTL

0.0015

Comment: - Replaces electricity from NG CCGT

Source: 1 Reinhardt et al., 2006. Table 285 Hydrotreating of palm oil via NExBTL process (generation of a BTL like fuel)

I/O Unit Amount Source H2 Input MJ/MJBTL 0.0669 1, 2 Vegetable oil Input MJ/MJBTL 1.0341 1 H3PO4 Input kg/MJBTL 0.0000170 1 NaOH Input kg/MJBTL 0.0000273 1 BTL-like fuel Output MJ 1.0000 Electricity Output MJ/MJBTL 0.0015 1

Comments: - H2 - 3.77 GJ / (44 GJ), probably for rape oil, ConocoPhillips, 25 October 2007: -21.9% for palm oil. - Replaces electricity from NG CCGT - Steam from NG boiler (10 MW) See table Table 51 - H2 Generation via steam reformimg. See Table 52

Sources:

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1 Reinhardt et al., 2006. 2 ConocoPhillips, 25 October 2007 Table 286 Transport of BTL like fuel via a 40 t truck over a distance of 150 km (one way)

I/O Unit Amount Distance Input tkm/MJBTL 0.0037

BTL Input MJ/MJBTL 1.0000

BTL Output MJ 1.0000

Comments: - For the fuel consumption of the 40 t trunk, see Table 64.

Source: 2 Dautrebande, 2002. Table 287 BTL depot

I/O Unit Amount BTL Input MJ/MJBTL 1.00000

Electricity Input MJ/MJBTL 0.00084


Source: 3 Dautrebande, 2002. Table 288 BTL filling station




Source: 4 Dautrebande, 2002.

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7.21 Tall Oil Crude Tall oil is a valuable by-product which may be separated from black liquor in a pulp mill. It is not necessary to gasify the black liquor to do this. However, the pulping process considered here is the same as the one used as the base-case for studies of black liquor gasification pathways. Table 289 Cultivation of roundwood (mainly pine)

I/O Unit Amount

Diesel Input MJ/MJroundwood 0.0107

Roundwood Output MJ 1.0000 Table 290 Wood chipping

I/O Unit Amount

Roundwood Input MJ/MJwood chips 1.025

Diesel Input MJ/MJwood chips 0.0040

Wood chips Output MJ 1.0000 Table 291 Transport of wood chips via a 40 t truck over a distance of 50 km (one way)

I/O Unit Amount

Distance Input tkm/MJwood

chips 0.0041

Wood chips Input MJ/MJwood chips 1.0000

Wood chips Output MJ 1.0000

Comments: - For the fuel consumption of the 40 t truck, see Table 64. - 26 t transported wood chips - 1 t "tank" - LHV (wood chips) = 18 MJ/kg of dry substance - H2O content: 30%

Table 292 Crude Tall oil production (Liquid biofuel)

I/O Unit Amount

Wood chips Input MJ/MJbiofuel 2.13

Liquid biofuel Output MJ 1.00

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Market pulp mill Market pulp mill "KAM1" model (capacity: 1000 Air Dried (AD) tonnes pulp per day, with recovery boiler and electrcicty export): We have to use the KAM1 model because it is the only one for which we have both tall oil production and wood input figures.

Table 293 Market pulp mill (Inputs)

INPUT

dry tonnes/ADt pulp

GJ LHV of dry-matter REF

LHV (GJ/dry tonne)

Source for LHV

% moisture

Source for moisture

Roundwood (dry tonnes per ADt pulp) 2.1 37.8 2 18

JRC estimate 50%

JRC estimate

Net input with electricity credit Electricity export (KWh/ADt pulp) 550 1.980 2 Electricity efficiency for credit Electricity export (GJ/ADt pulp) 1.98 1 40% 7 Wood credit for electricity 4.95 NET wood input (GJDRY per ADt pulp) 32.85 Net dry tonnes wood input 1.83

Comment: Most chemicals used in pulp production are recycled, but we could find no data on the amounts which are not recycled, which would be inputs.

Table 294 Market pulp mil (Outputs)

OUTPUTS

dry tonnes/ADt pulp

GJ per ADt pulp, by RED definition REF

LHV (GJ/dry tonne)

LHV ref

% moisture REF

WET LHV (RED definition)

Pulp (air-dried tonnes/day) 0.9 14.05 15.88 3 10% 4 14.05"tall oil" 1.37 1,2 39 1 0% 1 39.00TOTAL OUTPUTS GJ by RED definition/ADt pulp 15.42

Sources: 1 Berglin et al., 2003 2 Berglin et al., 1999

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3 ECN Phyllis database (Value for cellulose) 4 Pulp & Paper Resources & Information site "pulp properties" page http://www.paperonweb.com/pulppro.htm. Accessed November 2012. 5 Ekbom et al. 2003 6 Landälv, I. 2007 7 Ekbom et al. 2005 Tall oil results by RED allocation methodology, the inputs are allocated equally to GJ of tall-oil and wood-pulp

Table 295 Tall oil results

Comment dry tonnes wood per GJ outputs (excluding electricity) 0.12

GJ dry wood per RED-GJ outputs 2.13= GJ Dry roundwood per GJ crude tall oil

dry tonnes roundwood allocated per tonne tall oil 4.62

Comments: Small amounts of BL could be exported if the electricity export is reduced, or if plant efficiency is increased. This is unlikely though, because 45 % (dry matter) of the BL is processing chemicals which need to be recycled to the pulp mill. Under RED rules emissions must be calculated by allocation to all products (not substitution), so the roundwood (and thus emissions) allocated to 1GJ BL will be the same as for 1GJ tall oil. However, if one insists on a result for BL, the RED definition of LHV has to be used, which subtracts the heat needed to evaporate the water content of the BL. LHV for BL at 75% water, according to this definition, is 8.46475 GJ/tonne at 75% water. Therefore dry tonnes of roundwood allocated per tonne black-liquor at 75% water, is 1.00 dry tonnes wood/tonne BL at 75% water (dry matter is 45% smelt solids).

http://www.paperonweb.com/pulppro.htm

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7.22 Black liquor Liquid Biofuels via Black Liquor gasification, including DME, Methanol, FT liquids Table 296 Cultivation of roundwood (mainly pine)

I/O Unit Amount

Diesel Input MJ/MJroundwood 0.0107

Roundwood Output MJ 1.0000 Table 297 Wood chipping

I/O Unit Amount

Roundwood Input MJ/MJwood chips 1.025

Diesel Input MJ/MJwood chips 0.0036

Wood chips Output MJ 1.0000 Table 298 Transport of wood chips via a 40 t truck over a distance of 50 km (one way)

I/O Unit Amount

Distance Input tkm/MJwood

chips 0.00547

Wood chips Input MJ/MJwood chips 1.0000

Wood chips Output MJ 1.0000


259

Table 299 Forestry residues collection including stump harvesting and chipping

I/O Unit Amount Sources Wood Input MJ/MJwood chips 1.025 1 Diesel Input MJ/MJwood chips 0.0154 2 Wood chips Output MJ 1.0000 CH4 Output g/MJwood 2.76E-05 3 N2O Output g/MJwood 2.76E-05 3

Comments - Wood - Dry mass loss during storage and chipping - Diesel - Includes: Forwarding, Bundling/Lifting, Oil use, Machine Transport, Load/Unload, Chipping - Wood chips - includes an average between central electrical chipping and roadside chipping (diesel) - CH4 - Includes the case of loose residues, bundled residues and stump lifting

Sources:

- Sikkema et al. 2010 - Lindholm et al. 2010 - GEMIS, 4.7, 2011, "dieselmotor-EU-agriculture-2010"

Table 300 Liquid biofuel production

I/O Unit Amount

Pulp wood chips Input MJ/MJbiofuel 1.500

Forest residues chips 0.4400 Liquid biofuel Output MJ 1.00

Liquid biofuel transport and distribution This is assumed to be equal to those of ROHY (hydrogenated rapeseed biodiesel) (e.g. 150 km truck etc.)

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Detailed calculations per fuel 1. Black liquor gasification to methanol. Alternatively, some or all of the black liquor can be gasified instead of burnt in the recovery boiler. Various fuels: methanol, DME or fischer-tropsch products mix (naphtha, gasoline, diesel) can be made from the gas. Here we use data on the CHEMREC oxygen-blown BL gasification process. Gasification produces less heat and electrcicty than burning the black liquor in the reference pulp plant. Therefore, extra biomass (in the form of forest residuals) is required to make the plant self-sufficient in heat and electricity. In the modelled plant, the tall oil is gasified along with the black liquor. Table 301 Black liquor gasification to methanol (Inputs)

INPUT

dry tonnes/ADt pulp

GJ (dry definition) REF

LHV (GJ/dry tonne)

Source for LHV

% moisture

Source for moisture

Roundwood (dry tonnes per ADt pulp) 2.05 36.9 2,6 18

JRC estimate 50% JRC

Shortfall in electricity (GJ/AD/t) 2.42 7 Efficiency of electricity generator 7 40% Forest residues for more electricity (GJ/ADt) 6.05 1 Forest residues for mill (GJ/ADt pulp)

5.56

Total forest residues input (GJ/ADt pulp)

11.61

Total forest residues input (tonnes dry wood/ADtonne pulp) 0.645 Total forest residues input (dry GJ/dry GJ pulp) 0.73

Comment: - Roundwood (dry tonnes per ADt pulp) - References 1, 5 and 7 give no figure. Ref 2 gives 2.1 or 2.03 for different pulping processes: it isn't clear which is used in ref. 7. But the BL yield in ref 7 is in between that for the two pulping processes in ref 2, which indicates the pulp yield should be about 2.05, and that

261

is confirmed by ref. 6, for the KAM2 model pulp plant. It anyway varies with tree species. - Wet LHV (RED definition) = 7.78

Table 302 Black liquor gasification to methanol (Outputs)

OUTPUTS

dry tonnes/ADt pulp


LHV (GJ/dry tonne)

LHV ref

% moisture REF


Pulp (air-dried tonnes/day) 0.9 14.05 15.88 3 10% 3 14.05Methanol 0.59 11.78 7,WTW 19.8 7 0% 7 19.80TOTAL OUTPUTS GJ by RED definition/ADt pulp 25.83

Methanol results by RED allocation methodology, the inputs are allocated equally to GJ of methanol and wood-pulp

Table 303 Methanol results

Comments Dry tonnes roundwood per GJ (RED definition) outputs (excluding electricity) 0.079 PLUS dry tonnes forest residues per GJ (RED definition) output 0.025 GJ dry roundwood per GJ methanol 1.43 PLUS GJ dry forest residues allocated per GJ methanol 0.45 Effective efficiency of 53.24% dry tonnes roundwood allocated per tonne methano 1.57 PLUS GJ dry forest residues per GJ(RED definition) output 0.49

Comments: - These figure needs to be combined with the emissions from roundwood provision and forest residue provision to give emissions for methanol - NOTE: JEC-WTW, ALTENER reports, and papers from Chemrec all use a marginal calculation for the effective efficiency and emissions. This gives much better results for this process. (effective efficiency ~66%).

262

2. Black liquor gasification to DME Table 304 Black liquor gasification to DME (Inputs)

INPUT

dry tonnes/ADt pulp


LHV (GJ/dry tonne)

Source for LHV

% moisture

Source for moisture



Shortfall in electricity (GJ/AD/t) 2.39 7 Efficiency of electricity generator 7 40% Forest residues for more electricity (GJ/ADt) 5.975 1 Forest residues for mill (GJ/ADt pulp) 5.38 Total forest residues input (GJ/ADt pulp)

11.355

Total forest residues input (tonnes dry wood/ADtonne pulp) 0.631 Total forest residues input (dry GJ/dry GJ pulp) 0.72

Table 305 Black liquor gasification to DME (Outputs)

OUTPUTS

dry tonnes/ADt pulp


LHV (GJ/dry tonne)

LHV ref

% moisture REF


Pulp (air-dried tonnes/day) 0.9 14.05 15.88 3 10% 3 14.05DME 0.42 11.87 7,WTW 28.4 7 0% 7 19.80TOTAL OUTPUTS GJ by RED definition/ADt pulp 25.92

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DME results by RED allocation methodology, the inputs are allocated equally to GJ of methanol and wood-pulp

Table 306 DME results


Comments: - These figure needs to be combined with the emissions from roundwood provision and forest residue provision to give emissions for methanol - NOTE: JEC-WTW, ALTENER reports, and papers from Chemrec all use a marginal calculation for the effective efficiency and emissions. This gives much better results for this process. (effective efficiency ~66%).

264

2. Black liquor gasification to FT liquids Table 307 Black liquor gasification to FT liquids (Inputs)

INPUT

dry tonnes/ADt pulp


LHV (GJ/dry tonne)

Source for LHV

% moisture

Source for moisture



Shortfall in electricity (GJ/AD/t) 3.59 7 Efficiency of electricity generator 7 40% Forest residues for more electricity (GJ/ADt) 8.98 1 Forest residues for mill (GJ/ADt pulp) 1.11 Total forest residues input (GJ/ADt pulp) 10.09 Total forest residues input (tonnes dry wood/ADtonne pulp) 0.560 Total forest residues input (dry GJ/dry GJ pulp) 0.64

Table 308 Black liquor gasification to FT liquids (Outputs)

OUTPUTS

dry tonnes/ADt pulp


LHV (GJ/dry tonne)

LHV ref

% moisture REF


Pulp (air-dried tonnes/day) 0.9 14.05 15.88 3 10% 3 14.05DME 0.24 10.55 7,WTW 43.2 7 0% 7 43.2TOTAL OUTPUTS GJ by RED definition/ADt pulp 24.60

265

FT liquids results by RED allocation methodology, the inputs are allocated equally to GJ of methanol and wood-pulp


Comments: - We have chosen the plant in ref.7 in which waxes are cracked to FT liquids - These figure needs to be combined with the emissions from roundwood provision and forest residue provision to give emissions for methanol - NOTE: JEC-WTW, ALTENER reports, and papers from Chemrec all use a marginal calculation for the effective efficiency and emissions. This gives much better results for this process. (effective efficiency ~66%).

266

7.23 Wood to FT diesel

Plantation and transportation same as for biomass pathways (see chapter 8) FT diesel plant Table 309 FT diesel plant

I/O Unit Amount Source Biomass Input MJ/MJFT diesel 2.075 1, 2, 3 FT diesel Output MJ 1.000 Capacity - MWFT diesel 96.4 1, 2, 3 Construction time - yr 3 Useful lifetime - yr 25 Equivalent full load period

- h/yr 7000

Sources: 1 Hamelinck, 2004 2 Tijemsen, et al. 2002 3 Paisley, 2001 4 Woods and Bauen, 2003 Table 310 Transport of FT diesel via a 40 t truck over a distance of 150 km (one way)


FT diesel Input MJ/MJBTL 1.0000

FT diesel Output MJ 1.0000


Source: 1 Dautrebande, 2002. Table 311 FT diesel depot




267

Source: 1 Dautrebande, 2002. Table 312 FT diesel filling station





268

7.24 Wood to Methanol

Plantation and transportation same as for biomass pathways (see chapter 8) Methanol plant Table 313 methanol production (gasification, synthesis)

I/O Unit Amount Biomass Input MJ/MJMethanol 1.960

Methanol Output MJ 1.000

Sources: 1 Katofsky, 1993 2 Dreier et al. 1998 3 Paisley, 2001 4 Edwards, R., JRC, 13 October 2003 Table 314 Transport of methanol via a 40 t truck over a distance of 150 km (one way)





Source: 1 Dautrebande, 2002. Table 315 Methanol filling station





269

7.25 Wood to DME

Plantation and transportation same as for biomass pathways (see chapter 8) DME plant Table 316 DME production (gasification, synthesis)

I/O Unit Amount Biomass Input MJ/MJMethanol 1.960

DME Output MJ 1.000

Sources: 1 Katofsky, 1993 2 Dreier et al. 1998 3 Paisley, 2001 4 Edwards, R., JRC, 13 October 2003 Table 317 Transport of DME via a 40 t truck over a distance of 150 km (one way)





Source: 1 Dautrebande, 2002. Table 318 Methanol filling station





270

7.26 Organic waste to biogas

Step 1: Transport to the filling station via local (10 km) NG pipeline Table 319 Transport to the filling station via local (10 km) NG pipeline

I/O Unit Amount CH

4 Input MJ/MJ

CH4 1.0000

CH4 Output MJ 1.0000

Step 2: CNG filling station Table 320 CNG filling station

I/O Unit Amount CH

4 Input MJ/MJ

CH4 1.0000

Electricity Input MJ/MJCH4

0.0220 Compressed methane Output MJ 1.0000

271

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PART FOUR – SOLID AND GASEOUS BIOFUELS PROCESSES AND INPUT DATA

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8. Biogas processes and input data One of the uses of biomass is converting it to gaseous energy sources. Biogas production by anaerobic digestion is generally regarded by many as a very sustainable practice that can guarantee high GHG savings when used for electricity generation or as substitute for natural gas. Based on the current uses in Europe, two main feedstocks were chosen: - An energy crop – Maize - An agricultural waste – Manure There were combined with two ways of digestate management: - Open tank storage - Closed tank storage and with two end-use processes for the biogas produced: - Biogas for electricity - Biogas upgrading to biomethane As a result, the following pathways are studied: A. Maize 1. Biogas for electricity from maize – Open digestate 2. Biogas for electricity from maize – Closed digestate 3. Biomethane from maize – No off-gas combustion – Open digestate 4. Biomethane from maize – No off-gas combustion – Closed digestate 5. Biomethane from maize – Off-gas combustion – Open digestate 6. Biomethane from maize – Off-gas combustion – Closed digestate B. Manure 1. Biogas for electricity from wet manure – Open digestate 2. Biogas for electricity from wet manure – Closed digestate 3. Biomethane from wet manure – No off-gas combustion – Open digestate 4. Biomethane from wet manure – No off-gas combustion – Closed digestate 5. Biomethane from wet manure – Off-gas combustion – Open digestate 6. Biomethane from wet manure – Off-gas combustion – Closed digestate

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8.1 Biogas from Maize A. BIOGAS FOR ELECTRICITY

B. BIOMETHANE

Digestion

Cultivation

transport

CHP

Digestate

heat

Electricity from the grid

fertilizers

Cultivation

Transport

Digestion

Biogas upgrading

Digestate storage

Heat (small biogas boiler)

fertilizers



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Step 1: EU Maize cultivation Process updated according to the workshop outcomes Following the suggestions received during the workshop, the data for maize cultivation where corrected / integrated with the data found in the European database CAPRI for the category of “maize fodder”. As shown in the following table, the values for diesel consumption and for pesticides / herbicides use resulted to be higher than initially reported. The emissions due to neutralization of fertilizer acidification and application of aglime are added. CH4 and N2O emission due to the combustion of diesel from agriculture machinery was taken into account. The new values are reported in the following table: Table 321: Process for cultivation of maize whole plant

Maize whole plant cultivation I/O Unit Amount Source

Diesel Input MJ/MJBiomass 0.016228 1 N fertilizer Input kg/MJBiomass 0.000580

K2O fertilizer Input kg/MJBiomass 0.000850 P2O5 fertilizer Input kg/MJBiomass 0.000310 CaO fertilizer Input kg/MJBiomass 0.000941

Pesticides Input kg/MJBiomass 0.000029 Seeding material Input kg/MJBiomass 0.000093 Maize whole plant Output MJ 1.0000

Field N2O emissions - g/MJBiomass 0.028635 Field CO2 emissions-

acidification - g/MJBiomass 0.000384 2

CH4 Output g/MJBiomass 2.91E-05 3 N2O Output g/MJBiomass 2.91E-05 3 Source 1. CAPRI database, data extracted by Markus Kempen of Bonn University, March 2012. 2. Boulamanti A, Edwards R., Joint Research Centre, (JRC-IET), Petten, The Netherlands, August 2012 3. GEMIS 4.7.1., “dieselmotor-EU-agriculture-2010”.

Comments: The amount of fertilizer considered accounts already for the application of the digestate (the residue of the anaerobic digestion) as fertilizer on the field.

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Step 2: Transport Process updated according to the workshop outcomes The description of the road transport processes is given in chapter 6 and it will not be repeated here. Only the value of the “distance” parameter is given in Table 183. Following the suggestions received during the workshop, the average transport distance for maize from the field to the biogas plant has been raised to 50 km. The new values are reported in the following table: Table 322: UPDATED Transport distance for maize to biogas plant

Transport of wet maize via a 40 t truck over a distance of 50 km (one way) I/O Unit Amount

Distance Input tkm/MJmaize

(60% H2O) 0.0088

Maize Input MJ/MJmaize

(60% H2O) 1.0000

Maize Output MJ 1.0000 Source: Consensus during the workshop Comments: • LHV (maize whole plant) = 16.7 MJ/kg dry; • Moisture (maize whole plant) = 65 %;

Step 3: Digestion Process updated according to the workshop outcomes Following the suggestions received during the workshop, the electricity consumption for the digestion process was differentiated for manure and maize. Below is the new process considered for maize digestion. Table 323: UPDATED process for anaerobic digestion of maize whole plant

Anaerobic digester (maize whole plant) I/O Unit Amount

Electricity Input MJ/MJbiogas 0.0250

Heat Input MJ/MJbiogas 0.1010

Maize whole plant Input MJ/MJbiogas 1.6667 Biogas Output MJ 1.0000 Source: The biogas handbook: science, production and applications; IEA, to be published in 2012

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Comments: The efficiency of the digestion is considered to be equal to 60% (in terms of energy content). The details for this calculation are explained in the following section Step 4: Digestate storage.

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Step 4: Digestate storage Digestate is the name generally assigned to the residue from the anaerobic digestion. It is a liquid product that is generally used as fertilizer back on the fields. Once it is collected from the digester, the digestate must be stored before it is again applied on the fields. However, the digestion process actually continues during the storage period and the gases released can have an important impact on the final GHG balance of the pathway. The digestate can be stored in either an open or closed tank, with the difference that in a closed tank, the additional biogas released during storage is recovered, while in an open tank, the methane is released in the atmosphere. Table 324: Process for open tank storage of digestate from maize

Open tank storage of digestate from Maize I/O Unit Amount Biogas Input MJ/MJbiogas 1.00 Methane Output MJ/MJbiogas 0.035 Biogas Output MJ 1.00 Source: 1. IPCC, IPCC Guidelines for National Greenhouse Gas Inventory, vol. 4, Emissions fron Livestock and Manure Management, 2006. 2. Boulamanti A.,Giuntoli, J., Joint Research Centre (JRC-IET), Petten, The Netherlands, own calculations. Calculations were based on experimental data from various sources [Amon, B. et al., 2006a, Amon, B. et al., 2006b, Amon, Th. et al., 2007a, Amon, Th. et al., 2007b, Khalid et al., 2011, Oechsner et al., 2003, Braun et al. 2009, Bruni et al., 2010] and the IPCC Guidelines. However, several assumptions had to be made in order to have a final value. The following range of numbers was used: • LHV dry (maize): 15 – 18 MJ/kg • Moisture (maize): 65 – 70% • VS (maize): 28.8 – 33.6% • Methane yield: 280 – 380 lCH4/kgVS • Biogas composition: CH4 = 55%vol., CO2 = 45%vol. • VS reduction in digestion: 45 – 80% • Density of digestate: 1000 kg/m3 • Temperature in digestate in summer storage: 19.5°C • Temperature in digestate in winter storage: 9.3°C • Emissions are weighted 50 – 50 for summer and winter conditions. • The resulting range of emissions for summer: 0.03 – 0.1 MJCH4/MJbiogas • The resulting range of emissions for winter: 0.002 – 0.01 MJCH4/MJbiogas • The final result, chosen for the pathways: 0.035 MJCH4/MJbiogas

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Step 5: Biogas use A. Electricity production – Combined Heat and Power (CHP) Table 325: Process for electricity generation via a biogas fuelled gas engine CHP

Electricity generation via biogas fuelled gas engine CHP I/O Unit Amount Biogas Input MJ/MJel. 2.78 Electricity Output MJ 1.00 Methane slip Output MJ/MJbiogas 0.017 N2O Output g/MJel. 0.00397 Sources: 1. Murphy, J. et al., Biogas from Crop Digestion, IEA Bioenergy Task 37, September 2011; 2. Liebetrau, J. et al., Eng. Life Sci. 10 (2010) 595 – 599. 3. GEMIS, 4.7.1, 2011, "biogas-maize-noLUC-ICE-500-DE-2010/gross" Comments: The gross electrical efficiency of the CHP engine is considered to be 36% based on a pool of references gathered by JRC. From this efficiency, 1% is considered to be internal consumption and should be subtracted.

B. Biomethane production There are currently many different technologies used to remove CO2 from the biogas stream in order to obtain a gas with the quality needed to be injected in the natural gas grid. None of such technologies is actually yet prominent in the market, since biogas upgrading is still in its emerging phase. Therefore, for the purposes of this work, several different techniques of upgrading are grouped into two broad categories: • Physical upgrading without combustion of the off-gas: This group includes Pressure Swing Absorption, Pressure Water Scrubbing and Organic Physical Scrubbing and the methane lost in the off-gas is considered to be emitted to the atmosphere. • Physical or Chemical upgrading with combustion of the off-gas: This group includes Pressure Swing Absorption if the water is recycled, Organic Physical Scrubbing, Chemical Scrubbing and Cryogenic. In this case the off-gases are considered to be flared so that no methane is released in the atmosphere. The biogas that is lost in the process is considered to amount to: 3 – 10% PSA; 1 – 2% water scrubbing; 2 – 4% organic physical scrubbing; 0.1% chemical scrubbing; <1% cryogenic.

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Table 326: Process for physical upgrading without combustion of the off-gas

Physical upgrading without combustion of the off-gas I/O Unit Amount Source Comment

Biogas Input MJ/MJCH4 1.03 1, 2, 3, 4, 5 Electricity Input MJ/MJCH4 0.03 6 Biomethane Output MJ 1.00 CH4 Output MJ/MJCH4 0.03 3% of the methane is emitted from upgrading Table 327: Process for chemical upgrading with combustion of the off-gas

Chemical upgrading or physical upgrading with combustion of the off-gas I/O Unit Amount Source Comment

Biogas Input MJ/MJCH4 1.03 1, 2, 3, 4, 5 Electricity Input MJ/MJCH4 0.03 6 Biomethane Output MJ 1.00 no methane emitted from upgrading Sources:

1. Petersson, A., Wellinger, A., Biogas Upgradng technologies - Developments and innovations, IEA Task 37, 2009.

2. Hullu et al., Comparing different biogas upgrading technologies, 2008. 3. Maria Berglund; biogas production from a system analytical perspective, 2006. 4. Patterson, et al., 2011. 5. Lukehurst et al., Utilisation of digestate from biogas plants as biofertilizer, IEA Task 37, 2010. 6. Schulz, W., 2004.

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8.2 Biogas from Manure

A. BIOGAS FOR ELECTRICITY

B. BIOMETHANE

transport

Digestion

CHP

Digestate storage

heat Electricity from the grid

transport Digestate storage

Digestion

Biogas upgrading

Heat (small biogas boiler)



291

Manure is considered to be a residue, so no production step is required. Step 1: Transport The description of the road transport processes is given in chapter 6 and it will not be repeated here. Only the value of the “distance” parameter is given in Table 191. Table 328: Transport distance for manure to biogas plant

Transport of wet manure via a 40 t truck over a distance of 10 km (one way) I/O Unit Amount

Distance Input tkm/MJmanure (85%

H2O) 0.0060

Manure Input MJ/MJmanure (85%

H2O) 1.0000

Manure Output MJ 1.0000 Comments: • LHV (manure) = 12 MJ/kg dry; • Moisture (manure) = 85 %.

Step 2: Digestion Process updated according to the workshop outcomes Following the suggestions received during the workshop, the electricity consumption for the digestion process was differentiated for manure and maize. Below is the new process considered for manure digestion. Table 329: UPDATED Process for anaerobic digestion of manure Anaerobic digester (manure) I/O Unit Amount Electricity Input MJ/MJbiogas 0.0200 Heat Input MJ/MJbiogas 0.1010 Manure Input MJ/MJbiogas 2.5000 Biogas Output MJ 1.0000 Source: The biogas handbook: science, production and applications; IEA, to be published in 2012 Comments: The efficiency of the digestion is considered to be equal to 40% (in terms of energy content). The details for this calculation are explained in the following section Step 3: Digestate storage.

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Step 3: Digestate storage Digestate is the name generally assigned to the residue from the anaerobic digestion. It is a liquid product that is generally used as fertilizer back on the fields. Once it is collected from the digester, the digestate must be stored before it is again applied on the fields. However, the digestion process actually continues during the storage period and the gases released can have an important impact on the final GHG balance of the pathway. The digestate can be stored in either an open or closed tank, with the difference that in a closed tank, the additional biogas released during storage is recovered, while in an open tank, the methane is released in the atmosphere. Table 330: Process for open tank storage of digestate from manure

Open tank storage of digestate from Manure I/O Unit Amount Biogas Input MJ/MJbiogas 1.00 Methane Output MJ/MJbiogas 0.050 Biogas Output MJ 1.00 Source: 1. IPCC, IPCC Guidelines for National Greenhouse Gas Inventory, vol. 4, Emissions fron Livestock and Manure Management, 2006. 2. Boulamanti A.,Giuntoli, J., Joint Research Centre (JRC-IET), Petten, The Netherlands, own calculations. Calculations were based on experimental data from various sources [Amon B. et al., 2006a; Amon B. et al., 2006b; Amon B. et al., 2006c; Amon Th. et al., 2006; Amon Th. et al., 2007a; Braun R., 1982; El-Mashad et al., 2010; Kaparaju et al., 2011; Sami et al., 2001; Wang et al., 2011] However, several assumptions needed to be made in order to have a final value. The following range of numbers was used: • LHV dry (manure): 10 – 15 MJ/kg • Moisture (manure): 80 – 95% • VS (manure): 4 – 16% • Biogas yield: 200 – 300 lCH4/kgVS • Biogas composition: CH4 = 55%vol., CO2 = 45%vol. • VS reduction in digestion: 45 – 80% • Density of digestate: 1000 kg/m3 • Temperature in digestate in summer storage: 19.5°C • Temperature in digestate in winter storage: 9.3°C • Emissions are weighted 50 – 50 for summer and winter conditions. • The resulting range of emissions for summer: 0.045 – 0.125 MJCH4/MJbiogas • The resulting range of emissions for winter: 0.01 – 0.02 MJCH4/MJbiogas • The final result, chosen for the pathways: 0.05 MJCH4/MJbiogas

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Step 4: Biogas use This step is considered to be the same as in the pathway for maize.

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Manure credits (part 1) When manure is stored waiting to be spread on the fields, it releases gases in the atmosphere as result of bacterial activity. Methane is the main gas released by manure decomposition. When the manure is used in an anaerobic digester, the methane produced is collected as biogas and either distributed in the natural gas grid or it is burned in loco in a gas engine to produce power and heat. In any case, the methane is oxidized to CO2. This biogenic CO2 was the same that was originally absorbed by the biomass fed to the cattle and it is therefore considered neutral to the atmosphere. However, the methane released from the manure would not be absorbed again by biomass and it is thus fully accounted as a GHG emission (and with a GWP 25 times higher than CO2). This is the rationale why emissions credits are generally assigned to biogas produced from manure. JRC has considered the three following approaches: 1) Well-to-Wheel approach: The most immediate solution would be to use the same approach as for the WTW calculations; the choice there has been to assign credits to the wet manure pathways for a value of 0.15 MJCH4/MJbiogas. 2) Calculations based on Measured manure storage emissions: This approach is based upon the papers of [Amon, B.et al., 2006] In this work, the authors measured the emissions from storage of untreated manure and, in the same conditions, of digestate. JRC has calculated the emissions from this process in the same way that emissions from digestate storage are calculated. This results in a value of emissions of 0.285 MJCH4/MJbiogas but only for the summer storage (no available data in the same paper for winter conditions). However, from the data available on digestate, it can be assumed that emissions in the winter season are much lower, close to zero (sustained also by the model in the attached paper by Sommer et al., 2004). Therefore, averaging 6 months of summer-like conditions and 6 months of winter-like, the result would also approach the value of 0.15 MJCH4/MJbiogas.

295

Manure credits (part 2) 3) Calculations based on Modelled storage emissions: This approach is based on the paper by [Sommer et al. , 2004] The authors modelled emissions from slurry storage depending on storage temperature and type of slurry (e.g. Pig slurry has higher emissions, Cattle Slurry lower). The average value they obtain (for cattle manure) is around 18 gCH4/kgVS. Applying a simple calculation, the following equation is obtained:

Where the VS content is considered to be equal to about 11%, the LHV of wet manure is 12 MJ/kg dry * 0.14 solids = 1.66 MJ/kg slurry, the digester efficiency is considered to be 0.4 MJ biogas/MJ slurry and the LHV of methane is 50 MJ/kg. These data are the same used by JRC for the calculations for emissions from digestate. Such a calculation is subject to assumptions on several values, but there is an underlying coherence between the calculations for digestate and slurry storage emissions. Conclusions: The analysis of data from two independent sources (with some assumptions) using both experimental and modelling results, gives a very close result to the assumption used by the JEC consortium in the WTT report which is a value of 0.15 MJCH4 avoided emissions per MJ of biogas produced. Following this analysis, JRC suggestion is to use the same value and approach for the calculations of biogas and biomethane pathways from wet manure. References: 1. Edwards et al., Well-to-Tank report, July 2011; 2. Amon, B.et al., Agriculture, Ecosystems and Environment 112 (2006) 153-162. 3. Sommer et al., Nutrient Cycling in Agroecosystems 69 (2004), 143 – 154.

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8.3 Co-Digestion In real life, plants with only one substrate are very rare both due to the limited availability of any single feedstock but also for the convenience of simply disposing of multiple residues from the agricultural activities into the digester. So, the case of co-digestion between maize and manure is taken into consideration, using a typical ratio of 30% manure and 70% maize on the basis of their wet mass. The current decision has been to treat the GHG balance of co-digestion as a simple weighted average of the results obtained for single feedstock. The underlying assumption is that no significant synergies exist among the different substrates in the digester so to change dramatically the productivity of biogas. This assumption is within the accuracy of the results needed for these calculations. The important methodological issue, however, resides in the choice of the basis for the weighted average. In fact, it would not be correct to simply use the LHV of the feedstocks as basis since maize and manure have very different productivities of biogas. Therefore, the methodology chosen has been to base the average upon the share of biogas produced by each feedstock. The following formulas describe the calculations needed: ( )

⎥⎥⎦

⎤

⎢⎢⎣

⎡⋅

⎥⎥⎦

⎤

⎢⎢⎣

⎡⋅

⎥⎥⎦

⎤

⎢⎢⎣

⎡= 3biogas

3

LHVsolidsVolatileyieldBiogasfeedstockFactorbiogas

biogas

feedstockwet

VS

VS

biogas

mMJ

kgkg

kgm Where the following standard values have been used in JRC calculations:

• Biogas yield (maize) = 0.55 [m3 biogas / kg volatile solids] • Biogas yield (manure) = 0.3 [m3 biogas / kg volatile solids] • Volatile solids (maize) = 0.336 [kg volatile solids / kg maize] (or 96% of dry matter content) • Volatile solids (manure) = 0.11 [kg volatile solids / kg manure] (or 80% of dry matter content) • LHV biogas (55% CH4) = 19.7 [MJ / m3 biogas (@0°C, 1 atm)] The final share to be used for the weighted average is then given by:

( ) ( ) ( )[ ]( ) ( )( ) ( ) ( )( )ManureFactorWeightManureFactorMaizeFactorWeightMaizeFactor

MaizetorWeight facMaizeFactor MaizeShare ⋅+⋅

⋅= Where the weight factor is considered to be the initial mass share assigned to each feedstock in co-digestion. In our typical example they would be equal to 0.7 and 0.3 for maize and manure respectively. And the share factors would be equal to 0.93 and 0.07 for maize and manure respectively. The final result for a co-digestion case would then be given by the following:

297

( ) ( ) ( ) ( ) ( )manuresharemanureGHGmaizesharemaizeGHGMJgCO

digestioncoGHG biogas

eq ⋅+⋅=⎥⎥⎦

⎤

⎢⎢⎣

⎡− .2

298

9. Biomass and solid densified biomass pathways The drawbacks of biomass as a fuel alternative to fossil fuels are attributed mainly to its low energy density, high moisture content and heterogeneity. A product with standard and homogeneous characteristics of energy content, moisture and geometrical shape is essential for efficient industrial use of biomass and that is why pellets are gaining importance in the bioenergy market. For this study four types of biomass are considered:

- Chips; - Pellets; - Bales; - Charcoal. and in combination with eight different raw materials: - Forest residues; - Short rotation forestry (SRF); - Wood industry residues; - Roundwood; - Agricultural residues; - Straw ; - Sugar Cane Bagasse; - Miscanthus; - Palm Kernel Meal. As a result the following pathways are studied: 1. Woodchips from forest residues; 2. Woodchips from SRF; 3. Woodchips from wood industry residues; 4. Woodchips from roundwood; 5. Wood pellets from forest residues; 6. Wood pellets from SRF; 7. Wood pellets from wood industry residues; 8. Wood pellets from roundwood; 9. Charcoal from forest residues; 10. Charcoal from SRF; 11. Agricultural residues with bulk density < 0.2 t/m3; 12. Agricultural residues with bulk density > 0.2 t/m3; 13. Straw pellets; 14. Bagasse pellets / briquettes;

299

15. Miscanthus bales; 16. Palm Kernel Meal.

300

Transport scheme for solid biomass Table 331: Transport scheme for all the solid biomass pathways. Distances are to final destination

Typical distances (km)

Pathways Distance Tag

Representative Geographic Origin

Truck (chips/raw)

Truck (pellets/finished product)

Train (chips/pellets)

Bulk Carrier (chips / pellets)

1 - 500 km Intra EU 500 - - - 500 - 2500 km Russia 250 - - 2000 2500 - 10000 km Brazil 200 - - 8000 Woodchips

> 10000 km Western Canada - - 750 16500

1 - 500 km Intra EU 50 500 - - 500 - 10000 km Brazil 50 200 - 8000 Wood Pellets

> 10000 km Western Canada 100 - 750 16500

1 - 500 km Intra EU 500 - - - 500 - 2500 km Russia 250 - - 2000 2500 - 10000 km Brazil 200 - - 8000

Agricultural Residues

> 10000 km Western Canada - - 750 16500

1 – 50 km Intra EU - 50 - - Charcoal

> 10000 km Brazil - 700 - 10186

1 - 500 km Intra EU 50 500 - - 500 - 10000 km Brazil 50 200 - 8000 Straw Pellets

> 10000 km Western Canada 100 - 750 16500

500 - 10000 km Brazil - 200 - 8000 Bagasse

Pellets / Briquettes > 10000 km Brazil - 700 - 10186

Palm kernel meal > 10000 km

Malaysia – Indonesia

50 700 13000

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Moisture schemes for solid biomass

The moisture content of solid biomass fuels is a very important parameter throughout the pathways. It has a significant effect especially on long-distance hauling of wood chips. The following figures aim at defining the moisture content of the woody fuels along their production chain.

Residues Collection + Forward to Roadside (+ bundling) Seasoning at roadside (open-air storage – covered bundles)

Moisture = 50%Moisture = 50% 30% Dry matter loss = 5% Sources: Hamelinck, 2005 + Kofman, 2012

Chipping Moisture = 30%Transport of chips (truck – train – bulk carrier) Moisture = 30%

Wood chips pathway Wood pellets pathway Forest Residues Wood Chips and Pellets Pathways

Moisture = 10%Transport of pellets (truck – train – bulk carrier)

Pellet Mill Moisture = 50% 10%Moisture = 50%Transport to Pellet Mill (50 – 100km)

Chipping Moisture = 50%

Moisture = 50%Residues Collection + Forward to Roadside (+ bundling)

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Roundwood Harvest + forwarding Seasoning at terminal (open-air storage - covered)

Moisture = 50%

Chipping Moisture = 30%Transport of chips (truck – train – bulk carrier) Moisture = 30%

Roundwood Harvest + Forward Chipping

Moisture = 50%

Moisture = 50%Transport to Pellet Mill (50 – 100km) Moisture = 50%

Transport of pellets (truck – train – bulk carrier)

Moisture = 50% 10%Pellet MillMoisture = 10%

Moisture = 50% 30% Dry matter loss = 5% Source: Hamelinck, 2005 + Kofman 2012.

Wood pellets pathway Wood chips pathwayRounwood to Wood Chips and Pellets Pathways

303

SRF (Eucalyptus from Brazil) to Wood Chips and Pellets PathwaysSRF Cultivation + Harvest Moisture = 50%

Chipping Moisture = 50%Transport of chips to terminal (truck 50 km)

Moisture = 50% 30% Dry matter loss = 12% Source: Kofman, COFORD, 2012 Seasoning at terminal of chips (indoor storage of chips – no mechanical ventilation)

Moisture = 50%

Wood pellets pathwayWood chips pathwaySRF Cultivation + Harvest Chipping

Moisture = 50%

Moisture = 50%Transport to Pellet Mill (50 – 100km) Moisture = 50%

Transport of pellets (truck – train – bulk carrier)

Moisture = 50% 10%Pellet MillMoisture = 10%

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9.1 Woodchips The transportation schemes in the case of woodchips are shown in Table 196: Table 332: Transportation scheme for wood chips pathways. Total travel-distance range Truck (chips) Truck (pellets) Train Ship Notes 1 to 500 km 500 Intra - EU 500 to 2500 km 250 2000 E.g. Russia 2500 to 10000 km 200 8000 E.g. Brazil Woodchips Pathways Above 10000 km 750 16500 E.g. Western Canada A. Woodchips from forest residues (Pathway nr. 1)

Step 1: Forest residues collection: In the case of forest residues, a specific process to account for the energy spent for their collection is needed. In Table 197 the process depicted includes stump harvesting. Moreover, various logisticchoices that are being developed especially in Scandinavian

Forest residues collection

Transport (chips – Truck / Ship)

Forest residues seasoning at roadside

Forest residues chipping

305

countries are considered, including the use of bundled and loose residues. In more details, the following steps are included in the process: - Forwarding; - Bundling / Lifting; - Oil use; - Forestry machinery transport; - Load / Unload.

Table 333: Process for forest residues collection.

Forestry residues collection including stump harvesting and chipping I/O Unit Amount Source

Wood Input MJ/MJwood chips 1.00 2

Diesel Input MJ/MJwood chips 0.0120 1 Wood chips Output MJ 1.00 1 CH4 Output g/MJwood chips 2.16E-5 3 N2O Output g/MJwood chips 2.16E-5 3 Source: 1. Lindholm et al., 2010; 2. Sikkema et al., 2010; 3. GEMIS, 4.7, 2011, "dieselmotor-EU-agriculture-2010". Comments: • LHV dry = 19 MJ/kg; • Moisture = 50%; • This step is common for pathways nr. 1, nr. 5 and nr. 9. Step 2: Forest residues seasoning: By storage of bundled residues at roadside over a period of 3 – 12 months, it is possible to reduce the moisture of the wood from 50% down to about 30%. This is essential to reduce costs and energy use in long-distance hauling of low-bulk, high moisture biomass such as wood chips. The moisture loss is, though, accompanied with dry matter losses due to bacterial activity within the stored wood. The storage technique is essential in order to minimize dry matter losses, that is why in this pathway it was decided to consider the storage of bundled residues, open-air but covered with plastic or paper wrap and for a period of 3 – 8 months. Table 334: Process for forest residues bundles seasoning at forest roadside.

Forestry residues seasoning at roadside

I/O Unit Amount Wood Input MJ/MJwood 1.050 1, 2, 3 Wood Output MJ 1.0000

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Source: 1. Hamelinck et al., 2005; 2. Kofman, 2012; 3. Lindholm et al., 2010. Comments: • LHV dry = 19 MJ/kg; • Moisture = from 50% to 30%; • It includes open air seasoning at roadside with the residues covered from rain; • Storage is usually for a period of 3-8 months; • This process is used for the wood chips pathways prior to chipping and prior to long-distance hauling.

Step 3: Forest residues chipping: In the case of fores residues, the output of the collection is loose or bundled residues. As a result, an additional process for chipping is necessary. Table 335: Process for wood chipping

Wood chipping I/O Unit Amount Source

Wood Input MJ/MJwood chips 1.025 1,2

Diesel Input MJ/MJwood chips 0.00336 1

Wood chips Output MJ 1.00 CH4 Output g/MJwood chips 6.02E-06 3 N2O Output g/MJwood chips 6.02E-06 3 Sources: 1. Lindholm et al, 2010; 2. Sikkema et al., 2010; 3. GEMIS, 4.7, 2011, "dieselmotor-EU-agriculture-2010". Comments: • LHV dry = 19 MJ/kg; • Moisture = 30%; • Bulk density (chips) = 0.155 dry tonne/m3; • The process covers a range of scenarios including roadside chipping with small-scale diesel chipper and comminution at power plant using large-scale electrical chipper. • This step is common for pathways nr. 1, nr. 2, nr. 4, nr. 6, nr. 8 and nr. 10.

Step 4: Transport The description of the transport processes is given in chapter 6 and it will not be repeated here.

307

The transport distances, calculated as explained in chapter 6, for all the road cases are reported in Table 198, while the ones for maritime transport are detailed in Table 199. Table 200 instead reports the distance value for the train transport section. Table 336: Transport distances via a 40 t truck of woodchips to final destination

Transport of wood chips via a 40 t truck over the planned distances (one way) I/O Unit 200 km 250 km 500 km

Distance Input tkm/MJwood chips 0.0156 0.0195 0.0390

Wood chips Input MJ/MJwood chips 1.0000 1.0000 1.0000 Wood chips Output MJ 1.0000 1.0000 1.0000

Table 337: Transport distances via bulk carrier of woodchips to final destination

Maritime transport of woodchips over the planned distances (one way) I/O Unit 2000 km 8000 km 16500 km

Distance Input tkm/MJwood chips 0.1504 0.6015 1.2406

Wood pellets Input MJ/MJwood chips 1.0000 1.0000 1.0000 Wood pellets Output MJ 1.0000 1.0000 1.0000

Table 338: Transport distances via freight train of woodchips to port

Transport of wood chips via a train over a distance of 750 km (one way) I/O Unit Amount

Distance Input tkm/MJwood pellets 0.0564

Wood chips Input MJ/MJwood pellets 1.0000 Wood chips Output MJ 1.0000 Comments: LHV (woodchips) = 19 MJ/kg dry; Moisture (woodchips) = 30 %. These values are valid for any pathway which involves the transportation of wood chips to a final destination.

308

B. Woodchips from SRF (Pathway nr. 2)

Step 1: Eucalyptus cultivation Process updated according to the workshop outcomes Short rotation forestry (SFR) refers to the growing of trees in extremely dense stands, harvested at specific intervals and regenerated from the stools, which are expected to survive 5 rotations at least. They differ from common forestry operations (i.e. for logging or for pulp and paper) because the rotation between harvests is shortened to about 3 – 5 years. The most common species used are willow, poplar or eucalyptus, even though their use in SRF for bioenergy is not yet commercially widespread. Currently, the cultivation of eucalyptus in tropical areas is probably the most common practice among plantations for energy and that is why it has been chosen as the representative process for SRF. In the future, when cultivation of poplar and willow for energy will become more common within EU, these processes will also be added by JRC.

Eucalyptus cultivation

Wood chipping


Transport (chips to terminal – 50 km)

Chips seasoning at terminal

309

Following indications received during the workshop, the values for yields and N-fertilizer input were checked against additional literature data. After investigating several publications concerning eucalyptus plantations, it was concluded that the data available in literature are rather scattered. The values for the yield were found to be varying from 5.5 t dry substance/(ha*yr) [Patzek and Pimentel, 2005] up to even 22 t dry substance/(ha*yr) [Franke, B. et al., 2012]. Depending on the soil quality, the GEF study indicates yields as low as 6.8 t dry substance/(ha*yr) for Mozambique and as high as 22 t dry substance/(ha*yr) for suitable land in Brazil. Analyzing the recently published data from the GEF study, JRC has decided to update the process of Eucalyptus cultivation by choosing average values among the ones reported for the cases of Mozambique and Brazil. In addition, the emissions due to neutralization of fertilizer acidification and application of aglime was taken into account. The new process is the following: Table 339: Process for Cultivation of Eucalyptus Plantation of eucalyptus I/O Unit Amount Source Diesel Input MJ/MJwood 0.0071 4 N fertilizer Input kg/MJwood 0.00093 4 P2O5 fertilizer Input kg/MJwood 0.000356 4 K2O fertilizer Input kg/MJwood 0.000743 4 CaO fertilizer Input kg/MJwood 0.001084 4 Pesticides Input kg/MJwood 0.0000081 4 Seeds Input kg/MJwood 0.000084 4 Wood Output MJ 1.0000 Field N2O emissions - g/MJwood 0.0193 4,5 Field CO2 emissions-acidification - g/MJwood 0.3030 4,6 Comments:

• LHV dry = 19 MJ/kg; • Moisture = 50%; • Yield = 12.93 t dry substance/(ha*yr) [4]; • Diesel = 1469 MJ diesel/(ha*yr) [4]; • N- fertilizer = 228.2 kg N/(ha*yr) [4]; • P2O5 fertilizer = 87.5 kg P2O5/(ha*yr) [4]; • K2O fertilizer = 182.6 kg K2O/(ha*yr) [4]; • Pesticides = 1.57 kg/(há*yr) [4]; • Cao fertilizer = 266.3 kg CaO/(ha*yr) [4]; • This step is common for pathways nr. 2, nr. 6 and nr 10.

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Sources: 1. Patzek, T. W. and D. Pimentel, Critical Reviews in Plant Sciences 24(2005) 327-364. 2. van den Broek, R. et al. Biomass and Bioenergy 19(2000) 311-335. 3. van den Broek, R. et al. Biomass and Bioenergy 21(2001) 335-349. 4. Franke, B.; Reinhardt, G.; Malavelle, J.; Faaij, A.; Fritsche, U. Global Assessments and Guidelines for Sustainable Liquid Biofuels. A GEF Targeted Research Project. Heidelberg/Paris/Utrecht/Darmstadt, 29 February 2012. 5. IPCC, 2006, N2O Guidelines. 6. Giuntoli J., Edwards R., Joint Research Centre, (JRC-IET), Petten, The Netherlands, August 2012 Step 2: Wood chipping In the case of SRF and roundwood, the output of the cultivation is the whole stem of the trees. As a result, an additional process for chipping is necessary. Table 340: Process for wood chipping

Wood chipping I/O Unit Amount Source

Wood Input MJ/MJwood chips 1.025 1,2

Diesel Input MJ/MJwood chips 0.00336 1

Wood chips Output MJ 1.00 CH4 Output g/MJwood chips 6.02E-06 3 N2O Output g/MJwood chips 6.02E-06 3 Sources: 1. Lindholm et al, 2010; 2. Sikkema et al., 2010; 3. GEMIS, 4.7, 2011, "dieselmotor-EU-agriculture-2010". The value of diesel consumption is an average between an efficient centralized electrical chipper and a smaller-scale, diesel, roadside chipper. This step is common for pathways nr. 1, nr. 2, nr. 4, nr. 6, nr. 8 and nr. 10.

Step 3: Transport to terminal The chips are transported from plantation roadside to a central terminal where they will be stored to decrease the moisture content before long-distance hauling.

311

Table 341: Transport of wood chips from roadside to terminal

Transport of wood chips via a 40 t truck over 50 km I/O Unit 50 km

Distance Input tkm/MJwood chips 0.0055

Wood chips Input MJ/MJwood chips 1.0000 Wood chips Output MJ 1.0000 Comments: LHV (woodchips) = 19 MJ/kg dry; Moisture (woodchips) = 50 %.

Step 4: Wood chips storage When wood chips are stored, depending on the storage conditions, dry matter losses can be severe. In this pathway it is considered an indoor storage of a pile of chips, covered by plastic or paper wrap and with good natural ventilation in the room. With these conditions, the following losses are considered: Table 342: Storage and seasoning of wood chips at terminal

SRF Seasoning at terminal I/O Unit Amount

Wood Input MJ/MJwood 1.120 1 Wood Output MJ 1.0000 Sources: 1. Kofman, 2012; Comments: • LHV (woodchips) = 19 MJ/kg dry; • Moisture (woodchips) = from 50 % to 30%; • It includes storage at central terminal in a closed environment without artificial ventilation but good natural ventilation; • The most common harvesting technique for SRF at present is a combined harvester+chipper so that chips need to be stored; • Storage is usually for a period of 3-8 months;

Step 5: Transport to end-user See Table 198, Table 199 and Table 200 for the detailed values. C. Woodchips from wood industry residues (Pathway nr. 3)

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Residues from the wood industry such as sawdust and wood shavings are indeed considered as residues thus no emissions are allocated to these products from their upstream processes. Moreover, they are already delivered as small chips and thus they do not require any additional process before they are delivered and transported. Step 1: Transport See Table 198, Table 199 and Table 200 for the detailed values.

D. Woodchips from roundwood (Pathway nr. 4)

Step 1: Cultivation + harvest of roundwood Process updated according to the workshop outcomes

Roundwood cultivation

Wood chipping


Roundwood seasoning at terminal

Residues grinding / chipping


313

Table 343: Process for cultivation and harvesting of roundwood

Cultivation of roundwood (mainly pine) I/O Unit Amount Source

Diesel Input MJ/MJbio 0.0107 1, 2

Biomass Output MJ 1.00 CH4 Output g/MJbio 1.91E-05 3 N2O Output g/MJbio 1.91E-05 3 Sources: 1. Berg and Lindholm, 2005; 2. Aldentun, Y., 2002; 3. GEMIS, 4.7, 2011, "dieselmotor-EU-agriculture-2010". The data collected include diesel, petrol, engine oil and electricity consumption for the following steps:

- Seedling production and cultivation (from [Aldentun, Y., 2002]); - Soil scarification; - Cut-over clearing; - Fertilization (energy for application of N); - Cleaning; - Regeneration; - Logging; - Forward to terminal. Comments:

• LHV dry = 19 MJ/kg; • Moisture = 50%; • The effects of standing carbon stock change are not yet included in the calculations; • Even though fertilization is included in the operations considered (diesel consumption), no N2O emissions are included in the process nor emissions for N-fertilizer production because fertilization of native forests with urea is not a common practice in Europe but it is limited to a few parts of Scandinavia. Following indications received during the workshop, the value for energy consumption in roundwood cultivation and harvesting was checked against additional literature sources. The result of such investigation was that the value chosen is appropriate for several cases in European countries. Other sources indicate values of diesel consumption for forestry harvesting in the range of 0.6 – 0.8% [MJdiesel / MJroundwood], but most of these values are only for the actual mechanical harvesting and primary hauling [Schwaiger and Zimmer, 2001; Michelsen et al., 2008]. The value chosen by JRC includes also energy consumption for seedling establishment and forest regeneration. Values for countries other than Scandinavian

314

might differ slightly regarding the latest processes, but we do not expect large variations on the harvesting/logging operations which are the most energy intensive. Possible future improvements might include the use of urea as nitrogen fertilizer if this practice will become more common in European forests. This would imply additional emissions of N2O from the soil and the emissions due to the production and application of urea balanced by the increased productivity of the forest [Sathre et al., 2010; Adams et al., 2005; Nohrstedt, 2001]. Sources: 1. Schwaiger, H. and Zimmer, B., in Karjalainen et al., European Forest Institute, 2001 2. Michelsen et al., Journal of Industrial Ecology 12 (2008) 69 - 81 3. Nohrstedt, H-Ö., Scandinavian J. of Forest Res. 16 (2001) 555 – 573. 4. Adams et al., Forest Ecology and Management 220 (2005) 313 – 325. 5. Sathre et al., Biomass and Bioenergy 34 (2010) 572 – 581. Step 2: Wood seasoning. By storage of roundwood stems at a central terminal for a period of 3 – 12 months, it is possible to reduce the moisture of the wood from 50% down to about 30%. This is essential to reduce costs and energy use in long-distance hauling of low-bulk, high moisture biomass such as wood chips. The moisture loss is, though, accompanied with dry matter losses due to bacterial activity within the stored wood. The storage technique is essential in order to minimize dry matter losses, that is why in this pathway it was decided to consider the storage of stems, open-air but covered with plastic or paper wrap and for a period of 3 – 8 months. Table 344: Process for seasoning of roundwood at central terminal

Roundwood seasoning at roadside I/O Unit Amount

Wood Input MJ/MJwood 1.050 1, 2 Wood Output MJ 1.0000 Sources: 1. Hamelinck, 2005; 2. Kofman, 2012.

Comments: • LHV dry = 19 MJ/kg; • Moisture = from 50% to 30%; • It includes open air seasoning at terminal with the stems covered from rain; • Storage is usually for a period of 3-8 months; • This process is used for the wood chips pathways prior to chipping and prior to long-distance hauling.

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Step 3: Transport See Table 198, Table 199 and Table 200 for the detailed values.

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9.2 Pellets

Pellets are a solid biofuel with consistent quality – low moisture content, high energy density and homogeneous size and shape. The transportation schemes for the pathways involving the use of pellets are shown in Table 204. Table 345: Transportation scheme for pellets pathways. Total travel-distance range Truck (chips) Truck (pellets) Train Ship Notes 1-500 km 50 500 Intra - EU 500-10000 km 50 200 8000 E.g. Brazil Pellets Pathways Above 10000 km 100 750 16500 E.g. Western Canada Three cases are considered for the pellets pathways depending on the fuel source used for drying the feedstock in the pellet mill:

• Case 1: Process heat from a fossil fueled boiler (usually NG); • Case 2: Process heat from an industrial pellet boiler; • Case 3: Process heat and electricity from a pellet CHP based on ORC technology.

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A. Pellets from forest residues and stumps (Pathway nr. 5)

Step 1: Forest residues collection and chipping The same as in pathway nr. 1, see Table 197. Step 2: Transport The transport processes are described in details in chapter 6 and they will not be repeated here. The transport distances are calculated as explained in chapter 6 and are reported in Table 346, Table 346, Table 347 and Table 348. This processes are common to all the pellet pathways, including the wood chips transport to the pellet mill. Table 346: Transport distance via a 40 t truck for wood chips to pellet mill

Transport of wood pellets via a 40 t truck over the planned distances (one way) I/O Unit 50 km 100 km

Distance Input tkm/MJwood chips 0.0055 0.0109

Wood chips Input MJ/MJwood chips 1.0000 1.0000

Forest residues collection + chipping

Truck transport (chips – 50km)

Pellet Mill

NG / Pellet Boiler

Heat

Electricity

Transport (Pellets /Truck – Ship)

Forest residues collection + chipping


Pellet Mill Electricity


Pellets CHP

Grid

Case 1 / 2

Case 3

Heat

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Wood chips Output MJ 1.0000 1.0000 Comments: • LHV (wood chips) = 19 MJ/kg dry; • Moisture (wood chips) = 50 %;

Table 346: Transport distance via a 40 t truck for wood pellets to final destination

Transport of wood pellets via a 40 t truck over the planned distances (one way) I/O Unit 200 km 500 km

Distance Input tkm/MJwood pellets 0.0126 0.0316

Wood chips Input MJ/MJwood pellets 1.0000 1.0000 Wood pellets Output MJ 1.0000 1.0000

Table 347: Transport distance via a bulk carrier for wood pellets to final destination

Maritime transport of wood pellets over the planned distances (one way) I/O Unit 8000 km 16500 km

Distance Input tkm/MJwood pellets 0.4678 0.9649

Wood pellets Input MJ/MJwood pellets 1.0000 1.0000 Wood pellets Output MJ 1.0000 1.0000

Table 348: Transport distance via a freight train for wood pellets to port

Transport of wood pellets via a train over a distance of 750 km (one way) I/O Unit Amount

Distance Input tkm/MJwood pellets 0.0439

Wood pellets Input MJ/MJwood pellets 1.0000 Wood pellets Output MJ 1.0000 Comments:

• LHV (wood pellets) = 19 MJ/kg dry; • Moisture (wood pellets) = 10 %;

Step 3: Pellet mill Process updated according to the workshop outcomes Following the comments received during the workshop, it was decided to revise the values for energy requirements in a pellet mill. JRC received data from Dr. Sven-Olov Ericson for Swedish sources and from Mr. Yves Ryckmans from Laborelec. These data are representative of more than 50 pellet plants in the world, processing different feedstocks in various combinations (from sawmill residues to 100% roundwood chips) and were based on real figures audited by an accredited independent company. According to this new information, the data for electricity consumption in a pellet mill using fresh chips (considered at 50% moisture) have been revised as following:

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Table 350: UPDATED Process for the production of pellets from fresh woodchips.

Production of wood pellets & briquettes from fresh forest chips: moisture~ 50% and final pellet moisture of 10%.

I/O Unit Amount Source Wood chips Input MJ/MJwood pellets 1.01 4 Electricity Input MJ/MJwood pellets 0.050 5 Heat Input MJ/MJwood pellets 0.185 1,2 Diesel Input MJ/MJwood briquettes 0.0020 1,3 Wood pellets Output MJ 1.00 Sources: 1. Hagberg et al.,2009; 2. Obernberger, I. and Thek, G., The Pellet Handbook, 2010; 3. Mani, S., 2005; 4. Sikkema et al., 2010; 5. Ryckmans, 2012. The values for a pellet mill using a mix of wet and dry sawdust have been left unchanged since the current values were confirmed by the new information received. The values for heat and fuel for internal consumption have remained unchanged and they are in the same range indicated by several independent sources [Hagberg et al., 2009, Obernberger and Thek, 2010, Mani, S., 2005]. The addition of a limited amount of organic additives is permitted under international standards; however, the use of such materials is generally limited to pellets for domestic use since they need better characteristics to work efficiently in the small-scale domestic stoves. The amounts used are also limited and with high variation throughout the market; additives can also be avoided with proper mixing of the feedstocks [Obernberger and Thek, 2010]. JRC decided thus not to include the energy and emissions due to additives. If their use will become more important in the future, JRC will update the pathways. Comments: • Pellets LHV dry = 19 MJ/kg; • Moisture woodchips = 50%; • Moisture pellets = 10%; • Bulk density (chips) = 0.155 dry tonne/m3; • Bulk density (pellets) = 0.650 dry tonne/m3; • Fuel: Diesel internal transport; • This process is similar for all pathways involving pellet production. The electricity needed for the process can be taken either from the grid at 0.4kV (case 1 and 2) or produced internally by CHP (case 3). The heat needed can be produced by a NG boiler (case1), by a pellet boiler (case 2) or by CHP (case 3). The processes for these auxiliary components are summarized in Table 43 and Table 44.

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B. Pellets from SRF (Pathway nr. 6)

The processes involved in this pathway have been all previously described in Table 201 and Table 208. The transport distances are indicated in Table 346, Table 346, Table 347 and Table 348.



Pellet Mill

NG / Pellet Boiler

Heat

Wood chipping

Electricity




Pellet Mill

Wood chipping

Electricity


Pellets CHP

Grid

Case 1 / 2

Case 3

Heat

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C. Pellets from wood industry residues (Pathway nr. 7)

Step 1: Pellet mill For this pathway a different process for the pellet mill is needed because of a lower consumption of electricity (less power is needed for the grinding phase compared to chips) and of heat (since the mix of wet and dry feedstock has a lower moisture content than fresh chips). Table 351: Process for the production of pellets from a mix of wet and dry residues

Production of wood pellets & briquettes from wood industry residues I/O Unit Amount Source

Saw dust Input MJ/MJwood pellets 1.01 5 Electricity Input MJ/MJwood pellets 0.028 1, 3, 4 Heat Input MJ/MJwood pellets 0.111 1, 2 Diesel fuel Input MJ/MJwood briquettes 0.0016 1, 3 Wood pellets Output MJ 1.00 Source: 1. Hagberg et al., IVL, 2009; 2. Obernberger and Thek, 2010; 3. Mani, S., 2005;

Truck transport (sawdust – 50km)

Pellet Mill (sawdust)

NG / Pellet Boiler

Heat

Electricity


Truck transport (sawdust – 50km)

Pellet Mill (sawdust)Electricity


Pellets CHP

Grid

Case 1 / 2 Case 3

Heat

322

4. Christian Rakos, Propellets Austria, personal communication, 27/06/2011; 5. Sikkema et al., 2010. Comments: • Chips / Pellets LHV dry = 19 MJ/kg; • Moisture pellets = 10%; • Moisture wet sawdust = 50%; • Moisture dry sawdust = 10%; • Fuel: Diesel internal transport • Bulk density (chips) = 0.155 dry tonne/m3; • Bulk density (pellets) = 0.650 dry tonne/m3; • The results are a weighted average between the process for dry and wet industry residues. The weight was based on a market research and it amounts to 60% wet and 40% dry sawdust. [4] The electricity needed for the process can be taken either from the grid at 0.4kV (case 1 and 2) or produced internally by CHP (case 3). The heat needed can be produced by a HFO boiler (case1), by a pellet boiler (case 2) or by CHP (case 3). Step 2: Transport The transport distances are indicated in Table 346, Table 346, Table 347 and Table 348. D. Pellets from roundwood (Pathway nr. 8)

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All the processes of this pathway have been already described and can be found in Table 202, Table 203 and Table 208. The transport distances are indicated in Table 346, Table 346, Table 347 and Table 348.

9.3 Charcoal

A. Charcoal from forest residues (Pathway nr. 9)



Pellet Mill

NG / Pellet Boiler

Heat

Wood chipping

Electricity




Pellet Mill Heat

Wood chipping

Electricity


Pellets CHP

Grid

Case 1 / 2

Case 3

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Step 2: Log splitting In order to have the needed size for the production of charcoal, wood stems need to be splitted in smaller logs. Table 352: Process for log splitter

Log splitter I/O Unit Amount

Wood Input MJlogs 1.00

Diesel Input MJ/MJwood logs 0.0012

Wood logs Output MJ 1.00 Source: 1. Jas P Wilson Forest Machines: Posch SplitMaster; 2009; 2. Kaltschmitt and Hartmann, 2001. Comments • LHV dry (wood logs) = 19 MJ/kg; • Moisture = 50%; • This process is common for all charcoal pathways. Step 3: Charcoal production

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Table 353: Process for the production of charcoal from wood logs.

I/O Unit Amount Wood logs Input MJ/MJcharcoal 2.0750

Wood chips Input MJ/MJcharcoal 0.0281

Electricity Input MJ/MJcharcoal 0.0084

Charcoal Output MJ 1.00 Source: 1. Johnson, E., 2009; Comments • LHV dry (charcoal) = 32 MJ/kg; • Moisture = 0%; • For the heat production woodchips are used; • The electricity needed is taken from the grid; • High emissions of biogenic methane (40.33 gCH4/kgcharcoal); • This process is common for all charcoal pathways. Step 2: Transport The transport distances for charcoal are indicated in Table 213 and Table 214. Table 354: Transport distances via a 40 t truck for charcoal to final destination

Transport of charcoal via a 40 t truck over the planned distances (one way) I/O Unit 50 km 700 km

Distance Input tkm/MJcharcoal 0.0016 0.0227

Charcoal Input MJ/MJcharcoal 1.0000 1.0000 Charcoal Output MJ 1.0000 1.0000

Table 355: Transport distance via a bulk carrier for charcoal to final destination

Maritime transport of charcoal over 10186 km (one way) I/O Unit Amount

Distance Input tkm/MJcharcoal 0.3183

Charcoal Input MJ/MJcharcoal 1.0000 Charcoal Output MJ 1.0000 Comments: • LHV (charcoal) = 32 MJ/kg dry; • Moisture (charcoal) = 0 %;

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B. Charcoal from SRF (Pathway nr. 10)

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Process updated according to the workshop outcomes Pathway: Charcoal vs. Torrefied biomass Following indications received during the workshop, JRC decided that the pathway for charcoal production has no place in a regulation addressed to feedstocks for power and heat production. In fact, in agreement with the experts, it was made clear how charcoal produced / imported to Europe, is mostly used either for recreational purposes (e.g. heat source for barbeque), or for the metallurgical industry (as a source of heat and Carbon) but surely it has no use in the industrial production of power and heat. Emissions from the charcoal production pathway should then be accounted for with other policy instruments (e.g. ETS). It was proposed that, rather than a pathway for charcoal production, JRC should focus on the definition of a pathway for torrefied and densified torrefied biomass. Because of the lack of a substantial number of commercial operations and thus lack of operational data, this is not yet possible, but JRC will continue to monitor the technological developments in the field.

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9.4 Other Raw Materials

A. Agricultural residues with bulk density <0.2 tonne/m3 (Pathway nr. 11)

This group of materials includes agricultural residues with a low bulk density and it specifically comprises materials such as straw bales (chosen as model component), oat hulls and rice husks Properties of model compound: • Bulk density: 0.125 tonne/m3; • LHV dry = 18 MJ/kg; • Moisture = 13%.

B. Agricultural residues with bulk density >0.2 tonne/m3 (Pathway nr. 12)

The group of agricultural residues with higher bulk density includes materials such as corn cobs, nut shells, soybean hulls, palm kernel shells and sugar cane bagasse bales. Properties of model compound: • Bulk density: 0.3 tonne/m3; • LHV dry = 18 MJ/kg; • Moisture = 13%;

Step 1: Processing

Baling of residues

Transport (Bales /Truck – Ship)

Residues Processing


329

Since all of these materials require a pre-processing step before they are transported, whether this is baling or additional grinding or clustering, one single process was chosen and it was assimilated to the process for baling straw. Table 356: Process for agri-residues pre processing

Baling / Processing I/O Unit Amount Source

Agri-residue Input MJ/MJbale 1.0 1

Diesel Input MJ/MJbale 0.010 1 Bales Output MJ 1.0 1 CH4 Output g/MJbale 1.72E-05 2 N2O Output g/MJbale 1.72E-05 2 Source: 1. GEMIS v. 4.7, 2011. Xtra-residue\straw bales-DE-2010; 2. GEMIS, 4.7, 2011, "dieselmotor-EU-agriculture-2010".

Comments: • This process is valid for straw baling, but can also be considered valid for other processes, such as nut crushing etc. • This process is used in both agricultural residues pathways (nr. 11 and nr. 12), but also for the straw baling in the straw pellets pathway (nr. 13) Step 2: Transport Table 357: Transport distances via a 40 t truck of agri-residues to final destination

Transport of Agri-residues via a 40 t truck over the planned distances (one way) I/O Unit 200 km 250 km 500 km

Distance Input tkm/MJresidues 0.0133 0.0166 0.0332

Agri-residues Input MJ/MJresidues 1.0000 1.0000 1.0000 Agri-residues Output MJ 1.0000 1.0000 1.0000

Table 358: Transport distances via a bulk carrier of agri-residues to final destination

Maritime transport of Agri-residues over the planned distances (one way) I/O Unit 2000 km 8000 km 16500 km

Distance Input tkm/MJresidues 0.1277 0.5109 1.0536

Agri-residues Input MJ/MJresidues 1.0000 1.0000 1.0000 Agri-residues Output MJ 1.0000 1.0000 1.0000

Table 359: Transport distance via a freight train of agri-residues to port

Transport of agri-residues via a train over a distance of 750 km (one way) I/O Unit Amount

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Distance Input tkm/MJresidues 0.0479

Agri-residues Input MJ/MJresidues 1.0000

Agri-residues Output MJ 1.0000 Comments: • LHV dry (residues) = 18 MJ/kg; • Moisture (residues) = 13%.

C. Straw pellets (Pathway nr. 13)

Step 1: Baling The process for straw baling is assumed to be the same as the process illustrated in Table 215. Step 2: Pellet mill Table 360: Process for the production of pellets from straw bales

Production of straw pellets I/O Unit Amount Source

Straw bales Input MJ/MJpellet 1.01 1,5

Electricity EU mix LV Input MJ/MJpellet 0.020 1,2,3,4 Straw pellets Output MJ 1.00 Source:

1. Sultana et al., 2010;

Straw Baling

Truck Transport (Bales – 50km)

Pellet Mill (straw)

Transport (Pellets – Truck / Ship)

Electricity

Grid

331

2. GEMIS v. 4.7, 2011. straw pellets-CZ-production; 3. GEMIS v. 4.7, 2011. processing/straw-pellets-DE-2030; 4. Pastre, O., Analysis of the technical obstacles related to the production and the utilisation of

fuel pellets made from agricultural residues, EUBIA, Pellets for Europe, 2002; 5. Sikkema et al., 2010.

Comments • LHV dry (straw) = 17.2 MJ/kg • Moisture pellets = 10%; • Moisture bales = 15%; • Bulk density (bales): 0.125 dry tonne/m3; • Bulk density (pellets): 0.600 dry tonne/m3; • The electricity needed is taken from the grid; • No process heat is needed since straw is already sufficiently dry by nature. • The electricity consumption is an average value among the sources 1 – 4. Step 3: Transport Table 361: Transport distances via a 40 t ttruck of straw bales to pellet mill

Transport of Straw Bales via a 40 t truck over the planned distances (one way) I/O Unit 50 km 100 km

Distance Input tkm/MJbales 0.0036 0.0071

Straw Bales Input MJ/MJbales 1.0000 1.0000 Straw Bales Output MJ 1.0000 1.0000

Table 362: Transport distances via a 40 t truck for straw pellets to final destination or port

Transport of straw pellets via a 40 t truck over the planned distances (one way) I/O Unit 200 km 500 km

Distance Input MJ/MJstraw pellet 0.0140 0.0349

Straw Pellets Input MJ/MJstraw pellet 1.0000 1.0000 Straw Pellets Output MJ 1.0000 1.0000

Table 363: Transport distances via a bulk carrier for straw pellets to final destination

Maritime transport of straw pellets over the planned distances (one way) I/O Unit 8000 km 16500 km

Distance Input MJ/MJstraw pellet 0.5168 1.0659

Straw Pellets Input MJ/MJstraw pellet 1.0000 1.0000 Straw Pellets Output MJ 1.0000 1.0000

Table 364: Transport distances via a freight train for straw pellets to port

Transport of straw pellets via a train over a distance of 750 km (one way) I/O Unit Amount

Distance Input MJ/MJstraw pellet 0.0484

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Straw Pellets Input MJ/MJstraw pellet 1.0000 Straw Pellets Output MJ 1.0000 Comments

• LHV dry (straw) = 17.2 MJ/kg; • Moisture (straw bales) = 15 %; • Moisture (straw pellets) = 10 %.

Process updated according to the workshop outcomes Straw bales transportation It was suggested during the workshop that, due to the limited scales of projected straw pellets production facilities, the distance for transport of bales could be reduced from the originally stated 50 km. However, in view of future development with larger-scale plants and with the objective of being conservative in the choice of values, JRC has decided to maintain the value of 50 km for transportation of straw bales from the field to the processing plant. Moreover, Sultana and Kumar, 2011 indicate that for a Canadian situation, the optimum radius of straw collection could be even up to 94 km. In another reference, Monforti et al. have suggested an average transport distance of 70 km to supply a CHP straw-fired power plant of 50 MWth capacity. Source: 1. Sultana, A. and A. Kumar, Bioresource Technology 102(2011) 9947-9956. 2. Monforti, F., Bodis, K. and Scarlat, N., submitted to Renewable & Sustainable Energy Reviews, 2012.

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D. Bagasse pellets / briquettes (Pathway nr. 14)

Step 1: Utilities While bagasse bales are included in the pathway nr. 12 with other similar residues, the production of pellets requires an additional process and thus a different pathway. For the purposes of this work, the process for a pellet mill is considered to be the same as the one for pellets from fresh woodchips and described in Table 208. Moreover, no transport of the bagasse bales is considered because it is assumed that the production of pellets is done in the sugar mill and thus the associated emissions do not need to be allocated to the bagasse. Table 365: Process for bagasse CHP.

Bagasse CHP I/O Unit Amount Source

Bagasse Input MJ/MJheat 2.1676 2

Heat Output MJt 1.00 2 Electricity Output MJ/MJheat 0.3621 2 CH4 emissions - g/MJheat 0.0053 1 N2O emissions - g/MJheat 0.0027 1 Source:

1. GEMIS v. 4.7, 2011. "Bagasse-ST-BR-2010"; 2. Fulmer, M. E., 1991. Comments • LHV wet (bagasse) = 6.8 MJ/kg; • Moisture = 50%; • Thermal efficiency (on LHV) = 46.1%;

Pellet Mill (same as for fresh woodchips)

Transport (Pellets – Truck /

Ship)

Electricity

Bagasse CHP Heat

Bagasse Steam Turbine

334

• Electrical Efficiency (on LHV) = 16.7%; • The electricity produced is used so as to replace part of the electricity from the steam turbine; • The process heat is fully provided by the CHP; • Methane and N2O, even though biogenic, are included in the GHG emissions from the process. Table 366: Process for electricity generation by bagasse steam turbine

Bagasse Steam Turbine I/O Unit Amount

Bagasse Input MJ/MJel 4.00


CH4 emissions - g/MJel 0.0098 N2O emissions - g/MJel 0.0049 Source: 1. GEMIS v. 4.7, 2011. Bagasse-ST-BR-2010.

Comments • LHV wet (bagasse) = 6.8 MJ/kg • Moisture = 50% • Electrical efficiency (on LHV) = 25% • Part of the process electricity is provided by the CHP engine Step 2: Transport Table 367: Transport distances via a 40 t truck for bagasse pellets / briquettes to final destination

Transport of bagasse briquettes via a 40 t truck over the planned distances (one way) I/O Unit 200 km 700 km

Distance Input tkm/MJbagasse

briquettes 0.0134 0.0469

Bagasse pellets Input MJ/MJbagasse briquettes 1.0000 1.0000

Bagasse pellets Output MJ 1.0000 1.0000 Table 368: Transport distances via a bulk carrier for bagasse pellets / briquettes to final destination

Maritime transport of bagasse pellets over the planned distances (one way) I/O Unit 8000 km 10186 km

Distance Input tkm/MJbagasse briquettes 0.4966 0.6323

Bagasse Pellets Input MJ/MJbagasse briquettes 1.0000 1.0000

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Bagasse Pellets Output MJ 1.0000 1.0000 Comments: • LHV dry (Bagasse) = 17.9 MJ/kg; • Moisture (Bagasse pellets) = 10 %. • Bulk Density (Bagasse pellets) = 0.56 t / m3.

E. Miscanthus bales (Pathway nr. 15)

Step 1: Miscanthus cultivation Table 369: Process for the cultivation of Miscanthus

Plantation of miscanthus I/O Unit Amount Source

Diesel Input MJ/MJbio 0.0077 1

CaO fertilizer Input kg/MJbio 0.000147 1

K2O fertilizer Input kg/MJbio 0.000486 1

N fertilizer Input kg/MJbio 0.000200 1

P2O5 fertilizer Input kg/MJbio 0.000060 1

Pesticides Input kg/MJbio 0.0000010 1 Miscanthus Output MJ 1.0000 1

Field N2O emissions - g/MJbio 0.0047 1, 2 Sources: 1. GEMIS v. 4.7, 2011. Farming\miscanthus-DE-2010. 2. IPCC, 2006. N2O guidelines. Comments: • LHV dry (Miscanthus) = 17.6 MJ/kg; • Moisture = 30%; • Yield = 14.4 t dry substance/(ha*yr);

Truck Transport (Bales)

Miscanthus cultivation

Miscanthus baling

336

• Diesel = 1653 MJ diesel/(ha*yr); • N- fertilizer = 34 kg N/(ha*yr); • P2O5 = 10.2 kg P2O5/(ha*yr); • CaO = 25 kg CaO/(ha*yr); • K2O = 82.7 kg K2O/(ha*yr); • Pesticides = 0.165 kg/(ha*yr). Step 2: Miscanthus baling Table 370: Process for the production of Miscanthus bales

Production of miscanthus bales I/O Unit Amount

Miscanthus Input MJ/MJbio 1.00

Electricity Input MJ/MJbio 0.0025

Heat Input MJ/MJbio 0.0085

Mischanthus bales Output MJ 1.00 Source: 1. GEMIS v. 4.5, 2009. Comments • LHV dry (Miscanthus) = 17.6 MJ/kg; • Moisture miscanthus = 15%; • Moisture bales = 13%; • Bulk density (bales): 0.250 dry tonne/m3; • Heat from diesel fueled boiler.

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F. Palm kernel meal (Pathway nr. 16) Palm kernel meal is a co-product from the production of palm oil together with palm kernel oil and nut shells that sometimes is imported to be used for energy production. According to the Directive 2009/28/EC the allocation of emissions to the co-products needs to be done based on the wet LHV of the products.

Process updated according to the workshop outcomes Pathway: Miscanthus Following indications received during the workshop and further investigation of the available literature, JRC has come to the conclusion that there are not enough reliable data on commercial operations with Miscanthus to produce a meaningful pathway. The market for Miscanthus cultivation is not yet developed; trials have been conducted in Europe since the 1990s and they have more recently started also in the US. However, the total cultivated area in Europe is estimated to be only in the range of 2000 – 5000 ha [1]. Also, the results of the trials have produced largely scattered data with yields varying dramatically depending on soil type, water availability and temperature [Heaton et al., 2010]. Moreover, the nutrients cycle is not yet completely defined and recommendations do not exist [Cadoux et al., 2012]. Finally, specialized machinery for harvesting and baling does not yet exist and are used machines originally designed for hay and straw, which are, however, not optimal for the characteristics of Miscanthus [Nixon and Bullard, 2003]. By analysing different values for Miscanthus cultivation found in the literature, it was possible to find a large variation in GHG emissions. Without going into details about such calculations, it suffice to say that using coherent emission factors between different sets of data, values ranging between 0.5 to 8.6 gCO2eq. / MJMiscanthus were found [5 – 8]. It is thus at present, very difficult to assess typical values for Miscanthus cultivation; this pathway will likely become important in the near – medium future, at which point JRC will be ready to present a reliable model. Sources: 1. http://miscanthus.de/flachen.htm

2. Heaton et al., Advances in Botanical Research 56 (2010) 76-137 3. Cadoux et al., Biomass and Bioenergy 38 (2012) 14 - 22 4. Nixon, P. and Bullard, M., Energy Power Resources Ltd, 2003. 5. Elsayed et al., Sheffield Hallam University, 2003. 6. Monti et al., European Journal of Agronomy 31 (2009) 77-84. 7. Blengini et al., Resources, Conservation and Recycling 57 (2011) 36-47. 8. GEMIS v. 4.7, 2011. Farming\miscanthus-DE-2010.

338

This leads to the following allocation factors as indicated in Table 312. Table 371: Allocation to co-products of palm oil extraction from FFB

Component wt. fraction [kg / kgFFB] Moisture Source LHV wet [MJ / kg] Outputs in wet LHV [MJ / kg FFB] palm oil 0.200 0% 1, 6 37 7.393 palm kernel meal 0.029 10% 2,3 16.4 0.481 nutshells (used as fuel) 0.074 10% 4, 5 17.1 0.00 palm kernel oil 0.024 0% 37 0.888 Total for allocation 8.762 Source: 1. Schmidt, 2007; 2. Chin, 1991; 3. JRC own calculation; 4. Panapanaan, 2009; 5. Coo, 2011. 6. Pramod, 2009. This leads to the allocation of upstream processes as: Table 372: FFB cultivation emissions allocated by energy to all co-products.

I/O Unit Amount Sources FFB Input MJ/MJPKM 1.8079 See process POFA Palm Kernel Meal Output MJ 1.00 Emissions CH4 (Open Pond) Output g/MJPKM 0.8306 1 CH4 (Closed Pond) Output g/MJPKM 0.1246 1 Comment: The methane emissions come from the effluent stream. An additional pathway is created where these emissions are avoided. Source: 1. Coo, 2011. For the upstream processes of FFB see pathway ‘Palm Oil to Biodiesel’. PKM is then transported by a 40 t truck (see Table 64 for fuel consumption) for 700 km and by a bulk carrier for 13000 km.

339

Table 373: Transport of PKM via a 40 t truck over 700 km.

Transport of PKM via a 40 t truck over the planned distances (one way) I/O Unit 700 km

Distance Input tkm/MJPKM 0.0157173

PKM Input MJ/MJPKM 1.00

PKM Output MJ 1.00 Table 374: Maritime transport of PKM via a bulk carrier over 13000 km.

Maritime transport of PKM via a bulk carrier over the planned distances (one way) I/O Unit 13000 km

Distance Input tkm/MJPKM 2.8108

PKM Input MJ/MJPKM 1.00

PKM Output MJ 1.00

340

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APPENDIX

347

Appendix 1. Fuel/feedstock properties

Reference

Feedstocks Biogas

Feedstocks Pellets

Solid and Gaseous BioFuels

Fuel Property Value Unit Reference Comment

LHV (mass) 50 MJ / kg Directive 2009/28/EC

Density (STP) 0.717 kg / Nm3 At STP conditions: 0°C, 1 atm

Density (NTP) 0.668 kg / m3 At NTP conditions: 20°C, 1 atm Methane

LHV (vol.) 35.9 MJ / Nm3 Standard conditions (0°C, 1 atm)

Methane content (vol.) 55% m3 CH4 /

m3 biogas JRC poll of internet sources: IEA

LHV (vol.) 19.7 MJ / Nm3 Considering LHV of methane as: 33.4 MJ / Nm3

Density (NTP) 1.28 kg / Nm3 Considering biogas composition as: 55% vol. CH4 and 45% vol. CO2

Biogas

LHV (mass) 15.4 MJ / kg

348

LHV dry 17 MJ / kg dry

Gylling, M. (2001). Energiafgrødeprogrammet. Statens

Jordbrugs- og Fiskeriøkonomiske Institut, København (Main report; report no. 131).

Found in: Kathrine Anker Thyø, Henrik Wenzel, Life Cycle Assessment of Biogas from Maize silage and from Manure for transport and for heat and power production under displacement of natural gas based heat works and marginal electricity in northern Germany. Report for Xergi A/S, Sofiendalsvej 7, 9200 Aalborg SV. June 2007

Moisture 35% kg water / kg total

LHV wet (RED) 10.4 MJ / kg wet

Maize Kernels

Yield 46% kg kernels / kg whole

plant

LHV dry 16.5 MJ / kg dry

Domalski, E. S., Jobe Jr, T.L., Milne, T.A.(1986) Thermodynamic Data for Biomass Conversion and Waste Incineration. National

Bureau of standards/Solar Technical information Program of the Solar Energy

Research Institute, USA.

Found in: Kathrine Anker Thyø, Henrik Wenzel, Life Cycle Assessment of Biogas from Maize silage and from Manure for transport and for heat and power production under displacement of natural gas based heat works and marginal electricity in northern Germany. Report for Xergi A/S, Sofiendalsvej 7, 9200 Aalborg SV. June 2007



Corn Stover

Yield 54% kg stover / kg whole

plant

Maize Whole Crop LHV dry 16.9 MJ / kg

dry Obtained as weighted average of Maize kernels and stover

349

Moisture 65% kg water / kg total Used for transport to biogas plant


Yield 42.5 wet ton/ha Eurostat


Moisture 85% kg water / kg total Manure

LHV wet (RED) -0.3 MJ / kg wet To be considered 0



Used for transport but 50% is used for short transport from roadside chipping

to pellet plant


Woodchips (general)

Bulk Density dry 155 kg / m3 JRC poll of internet sources: dry-mass density is 0.155 +/- 25




Wood Pellets

(general)

Bulk Density dry 650 kg / m3 JRC poll of internet values for DIN standard pellets: 0.650 moist matter at <= 10% water

(typical 9%). Almost unanimous value

Sawdust (wet) LHV dry 19 MJ / kg

dry

350



Yield 60% kg sawdust

wet / sawdust

pool

When calculating emissions or energy inputs for pellets production, the values are a weighted average of

inputs for wet and dry sawdust




Sawdust (dry)

Yield 40% kg sawdust

wet / sawdust

pool

When calculating emissions or energy inputs for pellets production, the values are a weighted average of

inputs for wet and dry sawdust




Eucalyptus (SRF)

Yield 12.9 dry ton / ha year

Franke, B. et al., 2012



Roundwood (pine)


351

LHV dry 17.2 MJ / kg dry For the pathway of Agriresidues this

value was considered to be 18

Moisture 15% kg water / kg total GEMIS 4.7: Straw Pellets ; Pastre, 2002. For Straw pellets the moisture is

assumed to be 10%

LHV wet (RED) 14.3 MJ / kg wet GEMIS 4.7: Straw Pellets ; Pastre, 2002

Bulk Density dry (Chopped) 50 kg / m3 GEMIS 4.7: Straw Pellets ; Pastre, 2002

Bulk Density dry (Bales) 125 kg / m3 GEMIS 4.7: Straw Pellets ; Pastre, 2002

Straw

Bulk Density dry (Pellets) 600 kg / m3 Pastre, 2002

LHV dry 17.8 MJ / kg dry Phyllis Database

Moisture 50% kg water / kg total Elsayed et al., 2003

LHV wet (RED) 7.7 MJ / kg wet Calculated

Yield 36.0 wet ton/(ha*yr) Elsayed et al., 2003 Value at 50% moisture

Miscanthus

Bulk Density dry (Bales)

250 kg / m3 Nolan et al., 2009


EC21: Indonesia bagasse exporters, suppliers, manufacturers, factories and

related to bagasse; 2010;

Pellets are considered dry. Bagasse


Assumption for pellets moisture. (Drying process is included)

352


EC21: Indonesia bagasse exporters, suppliers, manufacturers, factories and

related to bagasse; 2010;

Bulk Density dry (exit mill)

120 kg / m3 http://www.sugartech.co.za/density/index.php

Bulk Density dry (Bales)

165 kg / m3 http://www.sulekhab2b.com/viewoffer/sell/381529/biomass-briquettes-ground-nut-and-

sugar-cane.htm

Bulk Density dry (Pellets)

560 kg / m3 http://pellets-wood.com/wood-pellets-bagasse-of-sugar-cane-o332.html




Agri residues

with density <200 kg/m3

(Husks, Straw bales,

Bagasse Bales, Oat

Hulls)

Bulk Density dry 125 kg / m3




Agri residues

with density >200 kg/m3 (Corn Cobs, Nut Shells, Soybean

Hulls, Coconut Shells)

Bulk Density dry 300 kg / m3

LHV dry 18.5 MJ / kg dry Palm Kernel

Meal


http://www.sulekhab2b.com/viewoffer/sell/381529/biomass-briquettes-ground-nut-and-sugar-cane.htm



353


Bulk Density dry 570 kg / m3 Sundu, B., 2007

354

Reference

Common flows

Feedstocks

By-products

Liquid BioFuels (Et-OH)


LHV (Mass) 43.2 MJ / kg Directive 2009/28/EC - WTW App 1 Ver 3c

LHV (Volume) 32 MJ / l Calculated Gasoline

Density 0.745 kg / l Directive 2009/28/EC - WTW App 1 Ver 3c

LHV (Mass) 43.1 MJ / kg Directive 2009/28/EC - WTW App 1 Ver 3c

LHV (Volume) 36 MJ / l Calculated Diesel

Density 0.832 kg / l Directive 2009/28/EC - WTW App 1 Ver 3c

LHV (Mass) 42 MJ / kg Directive 2009/28/EC - WTW App1 Ver. 3c Crude

LHV (Volume) 34 MJ / l Calculated

355

Density 0.820 kg / l WTW App1 Ver. 3c

LHV (Mass) 44 MJ / kg Directive 2009/28/EC - WTW App1 Ver. 2c

LHV (Volume) 34 MJ / l Calculated FT - Diesel


LHV (Mass) 26.8 MJ / kg Directive 2009/28/EC - WTW App1 Ver. 3c

LHV (Volume) 21 MJ / l Calculated Ethanol



LHV (Volume) 16 MJ / l Calculated Methanol



LHV (Volume) 19 MJ / l Calculated DME


356

LHV dry 16.3 MJ / kg dry From E3 database file


European Fertiliser Manufacturer Association (EFMA), 2008 Sugarbeet

LHV wet (RED) 2 MJ / kg wet


1) Kaltschmitt and Reinhardt, 1997 -- 2) Hartmann, H., 1995


Sugarbeet Pulp


LHV dry 17 MJ / kg dry Kaltschmitt and Hartmann, 2001


Crop Energies, Südzucker, Cristal Union, 5 February 2008 - WTW App1 Ver 2c


Wheat (Grain)

% grain in whole plant 58.75%

dry kg grain / dry kg whole plant

Gover et al., 1996

Wheat (Straw) LHV dry 17.1 MJ / kg

dry JRC E3 Database

357

Moisture 13.5% kg water / kg total


% of straw in whole plant 41.25%

dry kg straw / dry kg whole plant

Gover et al., 1996

LHV dry 18.7 MJ / kg dry Hartmann, H., 1995



DDGS (Wheat)

DDGS / grain 0.374

kg wet DDGS / kg wet wheat grains

Lywood, W., ENSUS plc., e-mail to Robert Edwards, Joint Research Centre (JRC), Ispra,

03/12/2010

DDGS @ 10% moisture and wheat grains @ 13.5% moisture

LHV dry 17 MJ / kg dry Kaltschmitt and Hartmann, 2001


Crop Energies, Südzucker, Cristal Union, 5 February 2008

Barley (Grain)


DDGS (Barley) LHV dry 18.7 MJ / kg

dry Hartmann, H., 1995

358



DDGS / barley 0.391

kg wet DDGS / kg wet barley grains


03/12/2010

DDGS @ 10% moisture and barley grains @ 13.5% moisture

LHV dry 19.6 MJ / kg dry Dreier, Th., 2000


Crop Energies, Südzucker, Cristal Union, 5 February 2008; Kaltschmitt, 2001 (page 592) Sugar Cane


LHV dry 14 MJ / kg dry Calculated on E3 database Molasses


1) Nutrient requirements of beef cattle --- 2) ECOINVENT report no.17: lifecycle inventory

of bioenergy

359


LHV dry 14 MJ / kg dry Calculated on E3 database


1) Nutrient requirements of beef cattle --- 2) ECOINVENT report no.17: lifecycle inventory

of bioenergy Vinasse



1) Edwards, R., JRC, Calculation of the LHV of corn; 22 March 2011 --- 2) KTBL, 2006.

Moisture 14% kg water / kg total KTBL, 2006

Maize (Grain)


DDGS (Maize) LHV dry 18.7 MJ / kg

dry

360



DDGS / maize 0.297

kg wet DDGS / kg wet corn

Detailed California-Modified GREET Pathway for Corn Ethanol v.2.0 Jan 2009, from

California Air Resources Board website

DDGS @ 10% moisture and corn grains @ 15.5% moisture


1) Edwards, R., JRC, Calculation of the LHV of corn; 22 March 2011 --- 2) KTBL, 2006


Sorghum (Grain)


LHV dry 18.2 MJ / kg dry KTBL, 2006

Moisture 78% kg water / kg total KTBL, 2006 Sweet

Sorghum


361

LHV dry 16.9 MJ / kg dry Kaltschmitt and Hartmann, 2001


Triticale (Grain)





Triticale (Straw)

Yield on whole plant 1.1 kg straw / kg grain Kaltschmitt and Reinhardt, 1997

362




DDGS (Triticale)

DDGS / triticale 0.374

kg wet DDGS / kg wet triticale


03/12/2010

DDGS @ 10% moisture and triticale grain @ 13.5% moisture

(Same value as for Wheat DDGS)


Moisture 14% kg water / kg total KTBL, 2006 Rye (Grain)


LHV dry 17.2 MJ / kg dry Kaltschmitt and Hartmann, 2001 Rye (Straw)


363


% in whole plant 1.1 kg straw / kg grain Kaltschmitt and Reinhardt, 1997




DDGS (Rye)

DDGS / wet rye 0.374

kg wet DDGS / kg wet

rye


03/12/2010

DDGS @ 10% moisture and triticale grain @ 13.5% moisture

(Same value as for Wheat DDGS)

LHV dry 15.9 MJ / kg dry Cellulose: ECN Phyllis database Assumed LHV of Cellulose Pulp


364


LHV dry 39 MJ / kg dry Berglin et al., 2003

Moisture 0% kg water / kg total Tall Oil


LHV dry 12.1 MJ / kg dry Berglin et al., 2003


Black Liquor


365

366

Common flows

Feedstocks

By-products

Oil

Liquid BioFuels (Biodiesel)


LHV (Mass) 37 MJ / kg Dreier et al., 1998 Crude and refined

Vegetable Oil

Density

0.920 kg / l Calculated

LHV dry 18.5 MJ / kg dry Dreier et al., 1998 Oil Cake


European Fertiliser Manufacturer Association (EFMA), 2008

367


LHV (Mass) 37.2 MJ / kg Dreier et al., 1998

LHV (Volume) MJ / l

RME

Density 0.000 kg / l

Glycerol LHV (Mass) 16 MJ / kg Edwards, R, JRC, 22 July 2003: calculation with HSC for windows

LHV dry 26.976 MJ / kg dry JRC calculation (See Chapter 7) EU rapeseed only

Moisture 9% kg water / kg total Prolea, 2012 J-F Rous pers. comm to JRC,

Rapeseed


LHV of rapeseed This varies with composition of

rapeseed. From Diester, we have received the oil content

and water content of rapeseed used by them. We fill out the

rest of the composition in proportion to the rest of the

368

composition given in [Nutrient requirements of dairy cattle,

7th rev. ed. National Academy of Sciences 2001], and then calculate the LHV from the LHV of the components. As

expected, this gives a slightly higher LHV than JEC-WTW

previously used, measured on rapeseed with lower oil

content.

LHV (Mass) 18.38 MJ / kg dry

back calculated from rapeseed and EBB data on oil mill

LHV (Volume) 12.8% kg water / kg total

Back calculated from EBB data on oil mill for rapeseed and soybean

0.420 kg oil / kg rapeseed; 0.562 kg wet meal / kg

rapeseed

Rapeseed cake

Density 0.000 kg / l Calculated

LHV dry 26.4 MJ / kg dry Dreier et al., 1998 Assumed equal to the value for

Rapeseed


Sunflower Seed


369


back calculated from sunflower and EBB data on oil mill

Moisture 10% kg water / kg total Nutrient requirements of dairy cattle Sunflower

Cake

LHV for allocation according to RED 16.1 MJ / kg

wet


Jungbluth, N. (ed.): Life Cycle Inventories of Bioenergy, data v2.0 (2007); Econivent-report No. 17; Uster, December 2007 (Agrees with JRC estimate based on feed composition)


Soybeans


370


Dreier, T.; Geiger, B.; Lehrstuhl für Energiewirtschaft und Kraftwerkstechnik, TU

München (IfE); Saller, A., Forschungsstelle für Energiewirtschaft (FfE): Ganzheitliche

Prozeßkettenanalyse für die Erzeugung und Anwendung von biogenen Kraftstoffen; Studie

im Auftrag der Daimler Benz AG, Stuttgart und des Bayerischen Zentrums für

Angewandte Energieforschung e.V. (ZAE); Mai 1998


back calculated from mass balance soybean cake 794 kg cake / 1000 kgs moist

soybean; 188 kg oil / 1000 kgs moist soybean

however Bunge report maximum 12.5%

Soybeans Cake


LHV dry 24.0 MJ / kg dry JRC E3 Database

Moisture 34% kg water / kg total JRC E3 Database

Palm (Fresh Fruit

Bunch)


Palm Kernel Meal LHV dry 18.5 MJ / kg

dry JRC E3 Database

371


Edwards, R., Joint Research Center (JRC), Ispra,11 June 2009


Bulk Density 570.0 kg / m3 Sundu, B. 2007

LHV dry 27.0 MJ / kg dry JRC E3 Database

The value reported in the E3 database is only 25.5 MJ/kg for

wet jatropha seed, no indication is given on the

moisture content. The reference indicates a moisture content of 5% for the seeds.


Jongschaap, R., E., E.; Corré, W., J.; Bindraban, P., S.; Brandenburg, W., A.:

Claims and Facts on Jatropha curcas L.; Plant Research International B.V. Wageningen, The

Netherlands; October 2007

Jatropha Seed


Jongschaap, R., E., E.; Corré, W., J.; Bindraban, P., S.; Brandenburg, W., A.:

Claims and Facts on Jatropha curcas L.; Plant Research International B.V. Wageningen, The

Netherlands; October 2007

Jatropha Oil Cake +

Shells LHV dry 18.84

MJ / kg

dry

Pelletbase, 2010; http://www.pelletbase.com/show_info.php?lid

=1643

372

Moisture 3% kg water / kg total Openshaw, K., 2000

LHV wet (RED) 24.3 MJ / kg wet Calculated


1) ECOFYS (2008) TECHNICAL SPECIFICATION: GREENHOUSE GAS CALCULATOR FOR BIOFUELS --- 2)

Biofuels from Philippine Plants

The value is calculated as a weighted average between a 63% oil @ 39MJ/kg and 31%

coconut meal at 18MJ/kg


Copra (coconut)



ECOFYS (2008) TECHNICAL SPECIFICATION: GREENHOUSE GAS

CALCULATOR FOR BIOFUELS


Coconut Oil


373

Oil / copra 63% ton oil / wet ton copra

Wet ton of copra @ 6% moisture




Coconut Meal

Meal / copra 31% ton meal / wet ton

copra Wet ton of copra @ 6%

moisture

Animal Fat (also Tallow

Oil) LHV dry 37.1 MJ / kg

dry Wörgetter et al., 2006.

Cotton Seed LHV dry 22.64 MJ / kg

dry Calculated from composition: ECN Phyllis database of LHV

values for biomass

374



LHV (Mass) 36.6 MJ / kg Demirbas, 2008.

LHV (Volume) 33 MJ / l CottonSeed Oil

Density 0.914 kg / l


Calculated from composition: ECN Phyllis database of LHV

values for biomass


Cotton seed Meal (De-oiled and partly

peeled)


375

LHV dry 15.9 MJ / kg dry Cellulose: ECN Phyllis database Assumed LHV of Cellulose


Cotton seed Lint


376

377

378

European Commission EUR 25595 – Joint Research Centre – Institute for Energy and Transport Title: Assessing GHG default emissions from biofuels in the EU legislation. Review of input database to calculate “Default GHG emissions”, following expert consultation, 22-23 November 2011, Ispra (Italy) Authors: Robert Edwards, Declan Mulligan, Jacopo Giuntoli, Alessandro Agostini, Boulamanti Aikaterini, Reante Koeble, Luisa Marelli, Alberto Moro, Monica Padella Luxembourg: Publications Office of the European Union 2013 – 390 pp. – 21.0 x 29.7 cm EUR – Scientific and Technical Research series – ISSN 1831-9424 (online), ISSN 1018-5593 (print) ISBN 978-92-79-27363-6 (pdf) ISBN 978-92-79-27364-3 (print) Doi: 10.2788/66442 Abstract The EU legislation contains a set of mandatory targets specific for the EU transport sector which aims at achieving the overall objective of a European sustainably fuelled transport system. In particular, Directives 2009/28/EC (Renewable Energy Directive) and 2009/30/EC (Fuel Quality Directive) fix a threshold of 35% savings of GHG emissions for biofuels and bioliquids and set out the rules for calculating the greenhouse impact of biofuels, bioliquids and their fossil fuels comparators. Further, the Commission is working on a report and possible legislative proposal that could have similar or the same provisions for other bioenergy sources (biogas and biomass). To help economic operators in calculating GHG emission savings, default and typical values are also listed in the Annexes of the two directives, which were calculated on the basis of the JEC (JRC-EUCAR-CONCAWE consortium) database of input data in its version of 31 July 2008, following a consultation process with stakeholders launched by the Commission. According to article 19.7 in the Renewable Energy Directive and 7d.7 in the Fuel Quality Directive, the Commission can also adapt default values to technical and scientific progress by updating the existing values and by adding additional pathways. On request of the Commission’s DG ENER, the JRC worked on the update of the existing input database, and the list of pathways in the Directives will therefore likely be modified accordingly. This report aims at describing the JRC assumptions made to build-up the dataset used for the calculation of default GHG emissions for the different pathways in annexes of the Directives.

z

As the Commission’s in-house science service, the Joint Research Centre’s mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the Member States and international community. Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and consumer protection; information society and digital agenda; safety and security including nuclear; all supported through a cross-cutting and multi-disciplinary approach.

LB-NA- 25595 -EN

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Documents

Assessing GHG default emissions from biofuels in EU ...publications.jrc.ec.europa.eu/repository/bitstream/JRC76057/reqno... · Report EUR 25595 EN 2012 Robert Edwards Declan Mulligan