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Final LCA case study report

Results of LCA studies of Asian Aquaculture Systems for Tilapia, Catfish, Shrimp, and Freshwater prawn

01 March 2014

Henriksson P.J.G., Zhang W., Nahid S.A.A., Newton R., Phan L.T., Dao H.M., Zhang Z., Jaithiang J., Andong R.,

Chaimanuskul K., Vo N.S., Hua H.V., Haque M.M., Das R., Kruijssen F., Satapornvanit K., Nguyen P.T., Liu Q., Liu L.,

Wahab M.A., Murray F.J., Little D.C. and Guinée J.B.

SEAT Deliverable Ref: D 3.5

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Table of Contents Table of Contents .................................................................................................................. iii List of Tables .......................................................................................................................... v List of Figures ........................................................................................................................ xi List of acronyms and abbreviations ..................................................................................... xiv Preface ................................................................................................................................ xvi Executive summary ............................................................................................................ xvii 1 Introduction ..................................................................................................... 1 2 Goal and scope definition ............................................................................... 2

2.1 Goal definition ......................................................................................................... 2 2.1.1 Determining the goal, application, initiator, performer and target group ........... 2

2.1.1.1 Goal of the study ........................................................................................ 2 2.1.1.2 Intended application of the study results ..................................................... 3 2.1.1.3 Initiator ....................................................................................................... 3 2.1.1.4 Performer ................................................................................................... 3 2.1.1.5 Target group ............................................................................................... 4

2.1.2 Type of analysis: attributional and consequential ............................................. 4 2.2 Scope definition ...................................................................................................... 5

2.2.1 Level of sophistication ..................................................................................... 5 2.2.2 Sampling design and data collection ................................................................ 6

2.2.2.1 Grow-out farm selection criteria for in-depth survey .................................... 6 2.2.2.2 Other actors addressed by the in-depth survey .......................................... 8

2.2.3 Functional unit ................................................................................................. 9 2.2.4 Functionally equivalent alternative systems and their reference flows ........... 11

2.3 Review .................................................................................................................. 12 3 Inventory analysis ..........................................................................................13

3.1 Introduction ........................................................................................................... 13 3.2 System boundaries ............................................................................................... 13

3.2.1 Temporary carbon storage and biogenic carbon emissions ........................... 14 3.3 Drawing up the flow charts .................................................................................... 15 3.4 Process data and cut-off ....................................................................................... 16 3.5 Allocation .............................................................................................................. 17 3.6 Quantification and propagation of overall dispersions ........................................... 20

3.6.1 Monte Carlo simulations ................................................................................ 21 3.7 Scaling and aggregation ....................................................................................... 21 3.8 Inventory results .................................................................................................... 22

4 Impact assessment ........................................................................................23 4.1 Selection and definition of impact categories, models and indicators .................... 23 4.2 Characterisation results ........................................................................................ 25 4.3 Normalisation results............................................................................................. 26

5 Interpretation .................................................................................................45 5.1 Discussion of inventory results .............................................................................. 45 5.2 Discussion of impact assessment results .............................................................. 45

5.2.1 Global warming .............................................................................................. 46 5.2.1.1 L. vannamei ...............................................................................................46 5.2.1.2 P. monodon ...............................................................................................48 5.2.1.3 Macrobrachium rosenbergii .......................................................................49 5.2.1.4 Tilapia........................................................................................................51 5.2.1.5 Pangasius .................................................................................................53

5.2.2 Acidification ................................................................................................... 53 5.2.2.1 L. vannamei ...............................................................................................54 5.2.2.2 P. monodon ...............................................................................................56 5.2.2.3 Macrobrachium rosenbergii .......................................................................58

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5.2.2.4 Tilapia........................................................................................................59 5.2.2.5 Pangasius .................................................................................................60

5.2.3 Eutrophication ................................................................................................ 62 5.2.3.1 L. vannamei ...............................................................................................62 5.2.3.2 P. monodon ...............................................................................................64 5.2.3.3 Macrobrachium rosenbergii .......................................................................66 5.2.3.4 Tilapia........................................................................................................67 5.2.3.5 Pangasius .................................................................................................68

5.3 Summary of hot spots identified by contribution analyses ..................................... 70 5.4 Uncertainties ......................................................................................................... 71 5.5 Sensitivity analyses ............................................................................................... 72 5.6 General discussion ............................................................................................... 72

5.6.1 Competitiveness of Chinese systems ............................................................ 73 5.6.2 Motivate institutions and feed producers to reduce their inclusions of fishmeal73 5.6.3 Technology investments in fishmeal factories ................................................ 74 5.6.4 Improving aeration of ponds .......................................................................... 74 5.6.5 Livestock co-products .................................................................................... 75 5.6.6 Practices related to agricultural straw ............................................................ 75 5.6.7 Shifting from farm-made to commercial feeds ................................................ 75 5.6.8 Aim for intensification rather than expansion of aquaculture .......................... 75 5.6.9 Improved freezers .......................................................................................... 76 5.6.10 Allocation ....................................................................................................... 76 5.6.11 Limitations ..................................................................................................... 77

6 Overall conclusions and recommendations ....................................................79 6.1 Industry recommendations .................................................................................... 79 6.2 LCA relevant methodological recommendations ................................................... 80 6.3 Research recommendations ................................................................................. 81

Acknowledgement ................................................................................................................82 Appendix 1: Characterisation results including uncertainty information .................................89 Appendix 2: Contribution analyses ..................................................................................... 121

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List of Tables Table 1: Major and minor species analysed in LCA studies for each of the four Asian

countries. .............................................................................................................. 3 Table 2: CML baseline impact categories ...........................................................................23 Table 3: Best available characterization methods at midpoint. Methods that are classified as

level I, II or III are recommended under the ILCD and these are only included here. ............................................................................................................................24

Table 4: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation ..........................................................28

Table 5: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] ................29

Table 6: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation ...............................................................................30

Table 7: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] .....................................31

Table 8: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation .................................................................32

Table 9: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1] .......................33

Table 10: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation ......................................................................................34

Table 11: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1] ............................................35

Table 12: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation .............................................36

Table 13: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] ...37

Table 14: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation ..........................................................38

Table 15: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] ................39

Table 16: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation ....................................................40

Table 17: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1] ..........41

Table 18: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation .................................................................42

Table 19: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1] .......................43

Table 20: Global warming, mass allocation, Shrimp and prawns. Emissions as kg of CO2-eq. per tonne product at European importer. ..............................................................89

Table 21: Global warming, mass allocation, Tilapia and Pangasius. Emissions as kg of CO2-eq. per tonne product at European importer. ........................................................90

Table 22: Global warming, economic allocation, Shrimp and prawns. Emissions as kg of CO2-eq. per tonne product at European importer ................................................91

Table 23: Global warming, economic allocation, Tilapia and Pangasius. Emissions as kg of CO2-eq. per tonne product at European importer. ...............................................92

Table 24: Eutrophication, mass allocation. Shrimp and prawns. Emissions as kg of PO4-eq. per tonne product at European importer. ..............................................................93

Table 25: Eutrophication, mass allocation. Tilapia and Pangasius. Emissions as kg of PO4-eq. per tonne product at European importer. ........................................................94

Table 26: Eutrophication, economic allocation. Shrimp and prawns. Emissions as kg of PO4-eq. per tonne product at European importer. ........................................................95

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Table 27: Eutrophication, economic allocation. Tilapia and Pangasius. Emissions as kg of PO4-eq. per tonne product at European importer. ................................................96

Table 28: Acidification, mass allocation. Shrimp and prawns. Emissions as kg of SO2-eq. per tonne product at European importer. ....................................................................97

Table 29: Acidification, mass allocation. Tilapia and Pangasius. Emissions as kg of SO2-eq. per tonne product at European importer. ..............................................................98

Table 30: Acidification, economic allocation. Shrimp and prawns. Emissions as kg of SO2-eq. per tonne product at European importer. ..............................................................99

Table 31: Acidification, economic allocation. Tilapia and Pangasius. Emissions as kg of SO2-eq. per tonne product at European importer. ...................................................... 100

Table 32: Abiotic depletion (elements, ultimate reserves), mass allocation. Shrimp and prawns. Resource use as kg of antimony-eq. per tonne product at European importer .............................................................................................................. 101

Table 33: Abiotic depletion (elements, ultimate reserves), mass allocation. Tilapia and Pangasius. Resource use as kg of antimony-eq. per tonne product at European importer .............................................................................................................. 102

Table 34: Abiotic depletion (elements, ultimate reserves), economic allocation. Shrimp and prawns. Resource use as kg of antimony-eq. per tonne product at European importer .............................................................................................................. 102

Table 35: Abiotic depletion (elements, ultimate reserves), mass allocation. Tilapia and Pangasius. Resource use as kg of antimony-eq. per tonne product at European importer .............................................................................................................. 103

Table 36: Abiotic depletion (fossil fuels), mass allocation. Shrimp and prawns. Resource use as MJ per tonne product at European importer .................................................. 103

Table 37: Abiotic depletion (fossil fuels), mass allocation. Tilapia and Pangasius. Resource use as MJ per tonne product at European importer ............................................ 104

Table 38: Abiotic depletion (fossil fuels), economic allocation. Shrimp and prawns. Resource use as MJ per tonne product at European importer ............................................ 104

Table 39: Abiotic depletion (fossil fuels), mass allocation. Tilapia and Pangasius. Resource use as MJ per tonne product at European importer ............................................ 105

Table 40: Ozone layer depletion, mass allocation. Shrimp and prawns. Emissions as kg CFC-11 eq. per tonne product at European importer.......................................... 105

Table 41: Ozone layer depletion, mass allocation. Tilapia and Pangasius. Emissions as kg CFC-11 eq. per tonne product at European importer.......................................... 106

Table 42: Ozone layer depletion, economic allocation. Shrimp and prawns. Emissions as kg CFC-11 eq. per tonne product at European importer.......................................... 107

Table 43: Ozone layer depletion, economic allocation. Tilapia and Pangasius. Emissions as kg CFC-11 eq. per tonne product at European importer ..................................... 108

Table 44 Photochemical ozone formation (high NOx), mass allocation. Shrimp and prawns. Emissions as kg ethylene eq. per tonne product at European importer .............. 109

Table 45: Photochemical ozone formation (high NOx), mass allocation. Tilapia and Pangasius. Emissions as kg ethylene eq. per tonne product at European importer .......................................................................................................................... 110

Table 46: Photochemical ozone formation (high NOx), economic allocation. Shrimp and prawns. Emissions as kg ethylene eq. per tonne product at European importer . 111

Table 47: Photochemical ozone formation (high NOx), economic allocation. Tilapia and Pangasius. Emissions as kg ethylene eq. per tonne product at European importer .......................................................................................................................... 112

Table 48: Human toxicity HTP inf, mass allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ........................... 113

Table 49: Human toxicity HTP inf, mass allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ..................... 114

Table 50: Human toxicity HTP inf, economic allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ..................... 115

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Table 51: Human toxicity HTP inf, economic allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ................ 116

Table 52: Freshwater aquatic ecotoxicity, mass allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ................ 117

Table 53: Freshw. aquatic ecotoxicity, mass allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ................. 118

Table 54: Freshwater aquatic ecotoxicity, eco. allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ................ 119

Table 55: Freshw. aquatic ecotoxicity, eco. allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer ................. 120

Table 56: Contribution analysis for CMLCML and ILCD global warming results, economic allocation, for [A1] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Khulna, Bangladesh for consumption in the EU (reference period 2010-2011) ............................................................. 121

Table 57: Contribution analysis for CMLCML and ILCD global warming results, economic allocation, for [A2] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Bagerhat, Bangladesh for consumption in the EU (reference period 2010-2011) ........................................ 122

Table 58: Contribution analysis for CMLCML and ILCD global warming results, economic allocation, for [A3] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) ............................................................................. 123

Table 59: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A4] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) ............................................................................. 124

Table 60: Contribution analysis for CML and ILCD global warming results, economic allocation, for, economic allocation, for [A5] (1 tonne of frozen, edible yield of Pangasius produced in small systems in the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ........................................ 125

Table 61: Contribution analysis for CML and ILCD global warming results, economic allocation, for, economic allocation, for [A6] (1 tonne of frozen, edible yield of Pangasius produced in medium systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ........................................ 126

Table 62: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A7] (1 tonne of frozen, edible yield of Pangasius produced in large systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ........................................................................................................ 127

Table 63: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A8] (1 tonne of frozen, edible yield of Shrimp produced in low-level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011) ........................................................................................................ 128

Table 64: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A9] (1 tonne of frozen, edible yield of Shrimp produced in high level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011) ........................................................................................................ 129

Table 65: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A10] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ....................................................................... 130

Table 66: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A11] (1 tonne of frozen, edible yield of Shrimp (L. Vannamei) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ....................................................................... 131

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Table 67: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A12] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in semi-intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ................................................................. 132

Table 68: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A13] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Guangdong, China for consumption in the EU (reference period 2010-2011) .............................................................................................. 133

Table 69: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A14] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Hainan, China for consumption in the EU (reference period 2010-2011) ........................................................................................................ 134

Table 70: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A15] (1 tonne of frozen, edible yield of Tilapia produced in polyculture reservoirs in Guangdong/Hainan, China for consumption in the EU (reference period 2010-2011) ............................................................................. 135

Table 71: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A16] (1 tonne of frozen, edible yield of Tilapia produced in ponds integrated with pigs in Guangdong, China for consumption in the EU (reference period 2010-2011) .............................................................................................. 136

Table 72: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A17] (1 tonne of frozen, edible yield of Tilapia produced in pond systems in Chachoengsao/Nakhon Patom/Petchburi, Thailand for consumption in the EU (reference period 2010-2011) ................................................................. 137

Table 73: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A18] (1 tonne of frozen, edible yield of Tilapia produced in intensive cages systems in Suphanburi, Thailand for consumption in the EU (reference period 2010-2011) .............................................................................................. 138

Table 74: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A19] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in small-medium improved extensive systems in Bagerhat/Khulna/Satkhira, Bangladesh for consumption in the EU (reference period 2010-2011) .............................................................................................. 139

Table 75: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A20] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in large improved extensive systems in Cox’s Bazar, Bangladesh for consumption in the EU (reference period 2010-2011) ........................................ 140

Table 76: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A21] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) ............................................................. 141

Table 77: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A22] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) ............................................................. 142

Table 78: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A1] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Khulna, Bangladesh for consumption in the EU (reference period 2010-2011) ............................................................................. 143

Table 79: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A2] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Bagerhat, Bangladesh for consumption in the EU (reference period 2010-2011) ............................................................................. 144

Table 80: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A3] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in

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intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) .............................................................................................. 145

Table 81: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A4] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) .............................................................................................. 146

Table 82: Contribution analysis for CML and ILCD global warming results, mass allocation, for, mass allocation, for [A5] (1 tonne of frozen, edible yield of Pangasius produced in small systems in the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ............................................................................. 147

Table 83: Contribution analysis for CML and ILCD global warming results, mass allocation, for, mass allocation, for [A6] (1 tonne of frozen, edible yield of Pangasius produced in medium systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) ............................................................................. 148

Table 84: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A7] (1 tonne of frozen, edible yield of Pangasius produced in large systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) .......................................................................................................................... 149

Table 85: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A8] (1 tonne of frozen, edible yield of Shrimp produced in low-level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011) ................................................................................................................. 150

Table 86: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A9] (1 tonne of frozen, edible yield of Shrimp produced in high level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011) ................................................................................................................. 151

Table 87: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A10] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) .............................................................................................. 152

Table 88: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A11] (1 tonne of frozen, edible yield of Shrimp (L. Vannamei) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) .............................................................................................. 153

Table 89: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A12] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in semi-intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) .............................................................................................. 154

Table 90: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A13] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Guangdong, China for consumption in the EU (reference period 2010-2011) .... 155

Table 91: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A14] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Hainan, China for consumption in the EU (reference period 2010-2011) ............ 156

Table 92: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A15] (1 tonne of frozen, edible yield of Tilapia produced in polyculture reservoirs in Guangdong/Hainan, China for consumption in the EU (reference period 2010-2011) .............................................................................................. 157

Table 93: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A16] (1 tonne of frozen, edible yield of Tilapia produced in ponds integrated with pigs in Guangdong, China for consumption in the EU (reference period 2010-2011) ................................................................................................................. 158

Table 94: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A17] (1 tonne of frozen, edible yield of Tilapia produced in pond systems in

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Chachoengsao/Nakhon Patom/Petchburi, Thailand for consumption in the EU (reference period 2010-2011) ............................................................................. 159

Table 95: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A18] (1 tonne of frozen, edible yield of Tilapia produced in intensive cages systems in Suphanburi, Thailand for consumption in the EU (reference period 2010-2011) ........................................................................................................ 160

Table 96: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A19] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in small-medium improved extensive systems in Bagerhat/Khulna/Satkhira, Bangladesh for consumption in the EU (reference period 2010-2011) ............... 161

Table 97: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A20] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in large improved extensive systems in Cox’s Bazar, Bangladesh for consumption in the EU (reference period 2010-2011) ....................................................................... 162

Table 98: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A21] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) ............................................................................. 163

Table 99: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A22] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) ............................................................................. 164

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List of Figures Figure 1: Data collection and sourcing for WP3 throughout the project. ............................... 6 Figure 2: Generalized flow chart for aquaculture systems ..................................................15 Figure 3 : Data categories distinguished when collecting unit process data (Guinée et al.,

2002). ..................................................................................................................16 Figure 4: Example of applying different allocation principles. ..............................................20 Figure 5: Example of the relationship between number of iterations and reproducability of

results produced from the present model. ............................................................21 Figure 6: Global warming, mass allocation, per tonne peeled tail-on L. vannamei shrimp

from Eastern Thailand (alternative 3) , Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11). ...............................................................................47

Figure 7: Global warming, economic allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11). .........................................................47

Figure 8: Global warming, mass allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20????) and shrimp & prawn systems in Bangladesh (alternative 21). ............................................................................................................................48

Figure 9: Global warming, economic allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21). ....................................................................................................49

Figure 10: Global warming, mass allocation, per tonne shell-on head-on M rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22). ..................................50

Figure 11: Global warming, economic allocation, per tonne shell-on head-on M rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2), and shrimp & prawn ponds in Bangladesh (alternative 22). .............................50

Figure 12: Global warming, mass allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18)...........................52

Figure 13: Global warming, economic allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18). ....52

Figure 15: Global warming, economic allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam. ...............................................................................................................53

Figure 15: Global warming, mass allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam. ...............................................................................................................53

Figure 16: Acidification, mass allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11). ......................................................................................54

Figure 17: Acidification, economic allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11). ...............................................................................55

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Figure 18: Acidification, mass allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21). ......57

Figure 19: Acidification, economic allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21). ......57

Figure 21: Acidification, economic allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22). ..................................58

Figure 21: Acidification, mass allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22). .........................................58

Figure 22: Acidification, mass allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18)...........................59

Figure 23: Acidification, economic allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18). ....60

Figure 25: Acidification, economic allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam. ...............................................................................................................61

Figure 25: Acidification, mass allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam. ........61

Figure 26: Eutrophication, mass allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11). ..............................................................................63

Figure 27: Eutrophication, economic allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11). ...............................................................................63

Figure 28: Eutrophication, mass allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21). ......65

Figure 29: Eutrophication, economic allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21). ......65

Figure 31: Eutrophication, mass allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22). ..................................66

Figure 31: Eutrophication, economic allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22). ..................................66

Figure 32: Eutrophication, mass allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18)...........................67

Figure 33: Eutrophication, economic allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14),

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reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18). ....68

Figure 35: Eutrophication, mass allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam. ........69

Figure 35: Eutrophication, economic allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam. ...............................................................................................................69

Figure 36: Simplified flow-chart of the life cycle of capture fish used in feeds and the related allocation choices. ................................................................................................76

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List of acronyms and abbreviations ALCA Attributional LCA Avg Average BD Bangladesh BP British Petroleum Br Burr distribution C Cauchy distribution C2 Chi-squared distribution CALCAS Co-ordination Action for innovation in Life-Cycle Analysis for

Sustainability CFC Chlorofluorocarbon CLCA Consequential LCA CML Institute of Environmental Sciences (Leiden University) CMLCA Scientific software for LCA, IOA, EIOA CMM Coal Mine Methane CN China CNG Compressed Natural Gas CV Coefficient of Variation D Dagum distribution DCB Dichlorobenzene DG Directorate-General E Error distribution EAFI Ethical Aquatic Food Index eFCR economic Feed Conversion Ratio EIOA Environmentally extended Input-Output Analysis EU European Union Ex Exponential distribution FAO Food and Agriculture Organization (of the UN) FGD Flue-Gas Desulphurization unit FL Fatigue Life distribution FP Framework Programme Fr Frechet distribution Gce Grams of Coal Equivalent GEV Generalized Extreme Value distribution GG Generalized Gamma distribution GHG GreenHouse Gases GSD Goal and Scope Definition GVC Global Value Chain GWP Global Warming Potential IEA International Energy Agency IG Inverse Gaussian distribution ILCD International Reference Life Cycle Data System IO Input-Output IOA Input-Output Analysis IPCC Intergovernmental Panel on Climate Change ISO International Organization for Standardization J Johnson distribution JRC Joint Research Centre JRC-IES Joint Research Centre - Institute for Environment and Sustainability JSB Johnson SB distribution JSU JohnsonSU distribution LCA Life Cycle Assessment LCC Life Cycle Costing

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LCI Life Cycle Inventory LCIA Life Cycle Impact Assessment LCSA Life Cycle Sustainability Analysis Le Levy distribution LG Log-Gamma distribution LHV Lower Heating Value LL Log-Logistic distribution LN Log-normal LN Log-Normal distribution LP Log-Pearson distribution LPG Liquefied Petroleum Gas MJ Mega Joule N Normal distribution P Pearson distribution Pcs Pieces Ppt parts per thousand Qty Quantity RA Risk Assessment RER Europe (in ecoinvent database) SEAT Sustaining Ethical Aquatic Trade SETAC Society for Environmental Toxicology and Chemistry S-LCA Social LCA SME Small and Medium Enterprise Stdev Standard deviation T Triangular distribution TH Thailand Tk Bangladeshi taka U Uniform distribution UK United Kingdom UNEP-SETAC United Nations Environment Programme - Society for Environmental

Toxicology and Chemistry VN Vietnam We Weibull distribution Wk Wakeby distribution WP Work Package

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Preface Aquaculture has, over the last decades, grown faster than any other animal production sector and today supplies half of the world’s finfish. Simultaneously the growth of the import of Asian aquatic products into the EU has increased steadily. Current EU policy supporting international trade between Asia and Europe concentrates on issues of food safety as measures of quality, whilst market-forces drive development of standards and labels that identify social and environmental parameters. The SEAT (Sustaining Ethical Aquatic Trade) project proposed to establish an evidence-based framework to support current and future stakeholder dialogues organised by third party certifiers. Among other things, the evidence-based framework has been based on detailed Life Cycle Assessment (LCA) studies of four farmed aquatic products, Tilapia (Oreochromis spp.), Shrimp (Penaeid spp.), Catfish (Pangasius spp.) and Freshwater prawn (Macrobrachium spp.) in China, Thailand, Vietnam and Bangladesh, all major producing countries. This document is deliverable D3.5 of work package 3 (WP3) on LCA of the SEAT project. D3.5 concerns the final LCA case study report and presents the results of the LCA studies performed for the aquatic species selected for each of the four countries. This is also the final report of WP3 as part of the SEAT project. After this report, a selection of these results together with their supporting scientific methods will be published in one or two articles in international scientific journals.

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Executive summary In an effort to evaluate environmental sustainability, Life Cycle Assessment (LCA) has been implemented in the EU FP7 SEAT project (www.seatglobal.eu). LCA has its own series of ISO standards (14040-14044). ISO 14040 identifies four phases for an LCA: goal and scope definition, life cycle inventory analysis, life cycle impact assessment (LCIA) and life cycle interpretation. The present deliverable presents the final results of the LCA studies performed for the aquatic species selected for each of the four countries and constitutes the final deliverable of Work Package 3 as part of the SEAT project. Earlier deliverables of SEAT Work Package 3 (WP3) include a protocol for horizontal averaging of unit process data including estimates for uncertainty (D3.3; Henriksson et al. 2013), and an internal report presenting the data collected for each parameter of each unit process as needed to implement the protocol and their underlying models (D3.4). D3.4 reflected the result of an extensive unit process data collection effort that was carried out between 2011 and 2013 in three steps:

1. A project wide random integrated survey of grow-out farmers, from which relevant farming practices was identified (n=1 600)

2. A joint in-depth survey between WP3 and WP5, revisiting grow-out farmers (n≈80) for more detailed LCA and LCC data, as well as data on other actors in the value chain (n≈100+)

3. A literature review collecting secondary data for supporting processes. From the initial sample, the most relevant production practices and value chain actors could be identified. This provided the outline for the in-depth survey. Outside the grow-out farms, industry sensitive data proved most difficult to access, including data on processing plants and feed mills. Other data were simply non-existing in literature and needed to be collected (e.g. rice farming in Vietnam). In D3.5 we report the result of combining these different kinds of data in our LCA studies of the aquatic species selected for each of the four Asian countries. The results of the LCA case studies will be used as input to various standards development processes together with the results of other tools from other WPs. Intermediate LCA results have been used for identifying improvement options - by analysing major contributions, sensitivities and uncertainties, ranking of relative importance of different life cycle steps - to improve product/ production processes through action research. The final LCA results have resulted to further suggestions for possible improvements and these are reported in this deliverable.

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1 Introduction Sustaining Ethical Aquaculture Trade (SEAT) is a large collaborative project within the “Food, Agriculture and Fisheries, and Biotechnology” theme of the EU 7th Framework Programme (FP). The overall aim of the SEAT project is to enhance the sustainability (environmental impact, social justice, economic efficiency, nutritional quality and safety) of four major aquatic food commodities farmed in Asia and exported to Europe by developing an improved framework for sustainability assessment of the trade in farmed aquatic products between Asia and Europe. Until now a range of different sustainability tools has been used to assess aquaculture production systems. Increasingly LCA (Life Cycle Assessment) has been used for industrial and agricultural production systems, and since 2004 LCA has also been increasingly applied to aquaculture systems. Previous LCA studies within the aquaculture sector have mainly focused on production in developed countries, while the sector is dominated by developing countries. Many of the LCA studies focused on just a few emissions and impact categories and were based on limited and sometimes outdated databases and other data sources (Henriksson et al. 2011). The goal of WP3 was to apply LCA to some major aquaculture systems in Asia, collect as representative as feasible data for these systems, and to cover a wide range of emissions and impact categories. During the course of the project it was decided – instead of adapting and applying so-called hybrid LCA - to develop a protocol for horizontal averaging of unit process data including estimates for uncertainty. By the latter, we would be able to present the LCA results as ranges and distributions that much better reflect the status of the data collected and of the background databases than point value results that are generally presented by LCA practitioners. Although LCA is a quite well-developed and ISO-standardized tool (ISO 2006), LCA is not a “silver bullet”. It focuses on an environmental analyses of an as broad as possible range of impact over the whole life-cycle of the aquaculture systems considered. It however does not address all sustainability dimensions and it even cannot address all environmental impacts properly. It is therefore explicitly placed among a portfolio of other tools, like risk assessment (RA), life cycle costing (LCC), global value chain (GVC), social, and ethical analyses. This document is deliverable D3.5 of work package 3 (WP3) on LCA of the SEAT project. D3.4 concerns the final LCA case study report and presents the results of the LCA studies performed for the aquatic species selected for each of the four countries. This is also the final report of WP3 as part of the SEAT project. In this report we present the final results of the LCA studies. D3.5 is drafted as what ISO calls a ‘third party report’. All data, assumptions, results and analyses are included following the ISO third party reporting requirements. In Chapter 2 the goal and scope of the LCAs is summarized. This chapter is largely based on Deliverable 2.4 (Guinée et al. 2010) but adapted for the changed that occurred during the course of the SEAT project and during the development of the LCA studies. In Chapter 3 the life cycle inventory analysis is reported including the life cycle inventory (LCI) results. In Chapter 4 the impact assessment methods and the result of the application of these methods to the LCI results is reported. Chapter 5 presents the Interpretation results. The results of identifying major contributions, of sensitivity and uncertainty analysis, contributions to uncertainty, ranking of relative importance of different life cycle steps, and a limited number of improvement scenario calculations are presented. Finally, Chapter 6 presents the overall conclusions, improvement and research recommendations of WP3.

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2 Goal and scope definition The Goal and Scope Definition (GSD) phase is the first phase of an LCA, establishing the aim of the intended study, the functional unit, the reference flow, the product system(s) under study and the breadth and depth of the study in relation to this aim. It merely covers qualitative descriptions of the issues raised by aquatic product life cycles, identifying the intended goals of the study and stakeholder needs and relating these to clear system definitions, (methodological) choices and assumptions, and data quality and availability with respect to the LCAs that will afterwards be performed. Below, first the Goal definition of the LCAs on Asian aquaculture production systems, and then the Scope definition of the studies are discussed.

2.1 Goal definition The ISO Standards require that the Goal definition of an LCA "shall unambiguously state the intended application, including the reasons for carrying out the study and the intended audience" (ISO 2006).

2.1.1 Determining the goal, application, initiator, performer and target group

This first step of the Goal definition includes the following topics: the definition of the goal of the LCA, the use of the results, the initiator, and the performer of the study and for whom the results are meant.

2.1.1.1 Goal of the study

The SEAT project aims to establish an evidence-based framework to support current and future stakeholder dialogues organised by third party certifiers. For this, the ‘Ethical Aquatic Food Index’ (EAFI), a qualitative holistic measure of overall sustainability intended to support consumers’ purchasing decisions, will be developed. The EAFI should be based on detailed research including Life Cycle Assessment (LCA) studies. LCAs of aquatic production and processing systems should support prioritisation of critical issues and supportive action research (WP9). LCAs should thus support identifying critical issues within these systems and starting points for improvement options. Therefore, the main goal of this LCA study was formulated as getting insight in: the environmental impact and its causes of aquaculture systems for Tilapia, Catfish,

Shrimp and Prawns in Bangladesh, China, Thailand and Vietnam. starting points (“hot spot identification”) for improving the environmental performance of

aquaculture systems for Tilapia, Catfish, Shrimp and Prawns in Bangladesh, China, Thailand and Vietnam, which includes insight into the effects of choices in methods and data on the outcomes.

On top of these main goals, learning (of the environmental ins and outs of aquaculture systems) was another important goal of this study (cf. Baumann and Tillman 2004). It was thus not the intention to draw general conclusions for any of these aquaculture systems in relation to alternative aquaculture system per country, neither to compare between similar aquaculture systems between countries on a nationally averaged basis. For

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that we would need to make a statistical representative sample of LCAs for each fish species representing all different national regions and practices for each of the four countries, which was not feasible within this project. The intention of this study is rather to get a first understanding of critical environmental issues for each fish species and each country (focusing on selected regions and practices, and limited sample sizes) on a life cycle basis, to highlight areas of concern and contribute to development towards best practice. The focus of the LCAs has been on processed products covering the whole life cycle and ready for consumption in the EU; the focus was thus not just on products from cradle to farm gate. Based on the outcomes of WP2 and as reported in Chapter 0, the LCA studies focused on one major and one minor species for each of the four Asian countries (Table 1) and a varying number of farming practices. Table 1: Major and minor species analysed in LCA studies for each of the four Asian countries.

Country Bangladesh China Thailand Vietnam

Major species Prawn Tilapia Shrimp Pangasius

Minor species Shrimp Shrimp Tilapia Shrimp

2.1.1.2 Intended application of the study results

The intended application mainly determines the nature of the study. An LCA to be used for ecolabeling or certification may have to fulfil other quality criteria than an LCA intended to be used by the commissioner for internal purposes only, such as product innovation or learning. Together with the results of other WPs within the SEAT project, the LCA studies should support a more holistic sustainability assessment of Asian aquaculture systems brought together in the ethical aquatic food index (EAFI) mentioned before. The results of the LCA studies should predominantly be used as input to discussions between stakeholders and for improving existing aquaculture practices. In future, the LCAs may also start supporting criteria setting for a next generation of the EAFI but that is outside the scope of the current project and has its own specific problems (cf. Mungkung et al. 2005). As explained above, the LCAs have not been used to directly compare different aquaculture systems amongst each other on a nationally averaged basis. Nevertheless, as the EAFI may in future be used for public assertions, the ISO requirements with respect to “comparative assertions” have been adopted in this study as far as feasible, but as no independent, external expert review was planned, these requirements have not been entirely met (see section 2.3).

2.1.1.3 Initiator

The initiator and commissioner of this LCA study is the European Commission through the Seventh Framework Programme - Sustainable Development Global Change and Ecosystem, project no. 222889.

2.1.1.4 Performer

The performer of this LCA study is the Institute of Environmental Sciences (CML), department of Industrial Ecology, Universiteit Leiden, the Netherlands.

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2.1.1.5 Target group

The target groups for this study are the EU, Asian farmers, producers, processors and traders – both small and medium sized enterprises (SMSEs) and bigger enterprises - , NGOs, policy-makers and other parties interested in the environmental performance of the Asian aquaculture products analysed in this report.

2.1.2 Type of analysis: attributional and consequential

In literature, two modes of LCA can be found:

attributional LCA (ALCA);

consequential LCA (CLCA). Attributional LCA is defined by its focus on describing the environmentally relevant physical flows to and from a life cycle and its subsystems. Consequential LCA is defined by its aim to describe how environmentally relevant flows will change in response to possible decisions. Attributional LCA, also referred to as status-quo or descriptive LCA, addresses questions such as:

Which environmental impacts can be attributed to a certain product?

What is the share of a certain product in global environmental impacts?

What are the “hot spots” (processes or interventions with relatively high impacts) of a certain product system?

Consequential LCA, also referred to as prospective or change-oriented LCA, addresses questions such as:

What changes in environmental impacts occur if product A is replaced by product B?

What are the environmental impacts of choosing product A rather than product B for fulfilling a certain function?

What changes in environmental impacts occur when demand of an existing product changes?

ALCA assumes ceteris paribus ("all other things being equal or held constant"). This assumption may be valid for decision situations causing only marginal changes in product systems and related markets. ALCA does thus generally (not principally) not take into account indirect effects of product systems changes and decisions, e.g., if aquaculture industry uses more soy, the production of soy will need to increase and this may eventually lead to more destruction of rainforests in Brazil. The mapping of such indirect effects is the aim and strong point of CLCA. However, there is a subtle difference between consequential LCA (CLCA) and consequential modelling. Consequential LCA is basically about modelling and evaluating life-cycle based ‘what-if’ scenarios (e.g., what if corn is used for biofuel instead of for taco’s? How will taco’s then be produced, or where will the corn for taco’s then be sourced from, or where will additional corn be sourced from? etc.) for the future. How such scenarios are modelled, is basically an open issue: consequential modelling is just one way. For example, whereas CLCA focuses on modelling future scenarios or consequences bottom-up from product to consequences on other products or sectors (mainstream of CLCA today), an alternative approach recently developed is back-casting life cycle sustainability assessment (BLCSA), modelling and evaluating (top-down) scenarios to stay within planetary boundaries from a life-cycle perspective (Guinée and Heijungs 2011; Heijungs et al. in review), which then can be transposed into improvement requirements to sectors and products. On top of that, current main stream consequential modelling is also not one methodology. According to (Weidema 2000) and Weidema et al. (2009), consequential modelling differs in two ways from attributional modelling:

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1. co-product allocation is avoided by system expansion (‘avoided burdens’) instead of applying allocation factors (semi-consequential modelling);

2. consequential LCA includes the suppliers actually affected by a change in demand instead of averages as in attributional LCA (“full” consequential modelling).

Many studies that characterize themselves as CLCAs, however, only apply the first difference. Schmidt refers to these approaches as semi-consequential (Schmidt 2010). Moreover, it appears that for the avoided burdens approach, the number of ‘what-if’ assumptions is so large that LCAs on the same topic lead to quite diverging results. Since ‘what-if’ questions cannot be answered in an unambiguous way, Heijungs and Guinée (2007) argued that such questions should preferably be left outside of a primarily scientific tool. They don’t argue that in an analysis of future systems, certain processes will be replaced by other processes. But, they do not wish to give it a place in the modelling framework itself, and hide it in an allocation step. “Future systems are by definition unknown, and hence all statements concerning are speculative and contingent. The goal and scope definition phase provides an excellent place to define scenarios with respect to technology development, market shifts, etc. With appropriate techniques (Spielmann et al. 2005), systems analysis, including LCA, can incorporate such speculations in a transparent and explicit way”. Since the primary goals of the SEAT LCA studies are getting insight into:

the environmental impact and its causes; and

the identification of hot spots and starting points for improving the environmental performance

of aquaculture systems for Tilapia, Catfish, Shrimp and Prawns in Bangladesh, China, Thailand and Vietnam, the LCAs reported here are of an attributional nature. Considering the goals of this study and the various drawbacks mentioned above, we have not adopted consequential modelling as an alternative approach.

2.2 Scope definition In the Scope definition the subject and the depth and breadth of the study are established in relation to the stated reasons for performing the study in the Goal definition. According to ISO the “following items shall be considered and clearly described: the function of the system(s), the functional unit, the system to be studied, the system boundaries, allocation procedures, types of impacts considered and the methodology of impact assessment and interpretation, and impact, data requirements, assumptions, limitations, initial data quality requirements, the type of critical review, the type and format of the report" (ISO 2006). In this section we add a discussion on the level of sophistication of the LCA studies and on the sampling design and data collection procedure to this, and then we discuss the function of the systems, the functional unit, and systems studied. Subsequently, we also discuss the type of critical review that has been made. The other items will be reported in Chapter 3 (system boundaries, data requirements, initial data quality requirements, and allocation procedures), Chapter 4 (types of impacts considered and the methodology of impact assessment), and Chapter 5 (interpretation, assumptions, and limitations).

2.2.1 Level of sophistication

According to Guinée et al. (2002), an LCA can be performed at different levels of sophistication:

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The first is a detailed LCA, which is believed to be representative for studies typically requiring between 20 and 200 days of work. The detailed LCA is the baseline LCA elaborated in Guinée et al. (2002). The second is a simplified version of LCA, typically requiring between 1 and 20 days of work, and not completely following the ISO-guidelines. One may, for example, deviate from the economy-environment system boundary specified for detailed LCA, or choose a different time horizon for leaching of landfill. The choice of deviations is entirely the responsibility of the LCA practitioner. For the SEAT project detailed LCAs have been performed for a selected number of systems (see Table 1). Basically, we have done more than detailed LCAs since we have also developed and applied a protocol for horizontal averaging of unit process data including estimates for uncertainty (Henriksson et al. 2013).

2.2.2 Sampling design and data collection

As part of the SEAT project, primary data have been collected for a number of unit processes of the Asian part of the various value chains. The sampling design for this data collection process has determined the selection of systems for which LCA studies have been performed in the SEAT project. In this way, it has determined the scope of WP3 and it has defined the systems of our LCAs for each country, species and farming practice. As part of the sampling design, a project-wide scoping and integrated survey has been performed (WP2; Murray et al. in prep.). Subsequently, an in-depth survey by WP3 and WP5 (Kruijssen et al. in prep.) has been performed. Both these surveys are briefly summarized below as they constituted the basis of the alternative systems for which LCAs have been performed. From a life-cycle perspective, much of the information relevant for a detailed Life-Cycle Assessment (LCA) starts at the farm level. From the scoping work performed by the Asian partners in 2010 (Zhang et al.; Nietes-Satapornvanit et al. 2011; Phan et al. 2011; Haque et al. 2012), a random sampling framework was produced for the subsequent integrated survey in 2011 (Murray et al. 2014). During the integrated survey, basic data was collected on economic flows to and from 1600 grow-out farms (200 samples for two species in four countries), including: feed use, diesel use, electricity use, stocking density, etc. From these parameters, key farming practices were identified and additional data needed for LCI and LCC modelling was collected (Kruijssen et al. in prep.). This data was collected for a minimum of five farms per key practice, in order to allow for estimates of dispersion to be made. Figure 1 summarizes the WP3 data collection procedure.

WP3 data collection

ScopingAsian partners

Integrated surveyMurray et al. in prep. In-depth survey

Kruijsen et al. in prep.

Unit process data

2010 2011 2012 2013

Figure 1: Data collection and sourcing for WP3 throughout the project.

2.2.2.1 Grow-out farm selection criteria for in-depth survey

The farms visited during the in-depth survey were a sub-selection of the previously visited farms during the integrated survey. This meant that only additional data, beyond that

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collected in the integrated survey, needed to be collected. An obvious shortcoming of this was a temporal mismatch between some datasets. The farms were randomly selected from relevant groups identified from the following sets of screening criteria:

Represents a significant market share of the average farm for export to the EU

Different scales of production

Different upstream chains (e.g. different feed producers)

Commonly operating polyculture or integrated farming practices

Distinctly different geographical regions within countries

Different farming facilities (e.g. ponds or reservoirs)

The different systems identified differed in many different ways. For example, some farms only stocked one species (monoculture), while other farms stocked additional species in the same pond (polyculture). Other differences were related to the containment system, such as for Chinese shrimp farms which either were produced in low level ponds, relying upon passive water exchange, or in high level ponds which had lining and active water exchange. Farming intensities could also differ greatly for any species, from intensive (high stocking densities and full reliance on feed inputs) to extensive (no feed inputs). Given many different . For a full listing on the exact definitions of the different classifications, including semi-intensive and improved extensive, please see Murray et al. (2014). Beyond this, in order to maximise resource investments, the selection was made as a sub-sample of WP5’s livelihood sample. The selection was also made to overlap with WP4 and WP7’s samples as far as possible, in order to allow for more detailed data on eutrophying emissions and chemical use (Rico et al. 2013). Certain geographical areas (e.g. Cox’s Bazaar) were also dropped as a result of a lack of differentiation and logistical issues. The final selection of farming practices is defined below. From this selection a random sample of farms was generated using a random sequence generator from (www.random.org/sequences/; accessed 26-August-2011). Two additional farms were randomly added as backups, in order to reach the desired sample size. China (Guangdong) – Shrimp (n = 37)

All China Inspection & Quarantine Services (CIQ) certified farms in the integrated survey (n =19)

Low-level ponds o All large (n = 3 (all)) o Small and medium non-CIQ certified (n=5)

High-level ponds o Large non-CIQ certified (n=5) o Small and medium non-CIQ certified (n=5)

China (Guangdong, Hainan) – Tilapia (n=43)

All CIQ certified farms (n=25)

Non-integrated o Large non-CIQ certified farms in Maoming and Hainan (n=5) o Small and medium non-CIQ certified farms in Maoming and Hainan (n=5)

Integrated with pigs o Large (n=3 (all)) o Small and medium (n=5)

Vietnam (Mekong Delta) – Pangasius (n=20)

Large (n=5)

Medium (n=5)

Small (n=5)

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Vietnam (Mekong Delta) – Shrimp P. monodon (n=25)

Improved extensive (n=5)

Improved extensive alternate (n=5)

Intensive monoculture (n=5)

Mixed mangrove concurrent (n=5)

Semi-intensive monoculture (n=5)

Vietnam (Mekong Delta) – Shrimp L. vannamei (n=5)

Intensive monoculture (n=5)

Bangladesh (Bagerat, Cox’s Bazar, Khulna, Satkhira) (n=20)

Shrimp o Large (n=5) o Small and medium (n=5)

Shrimp and prawn (n=5)

Prawn (n=5)

Thailand (Surath Thani, Chanthaburi) – Shrimp (total of 20)

Surath Thani o Large (n=5) o Small & medium (n=5)

Chanthaburi o Large (n=5) o Small & medium (n=5)

Thailand (Chachoengsao, Nakhon Patom, Petchburi, Suphanburi) – Tilapia (n=18)

All large farms (n=3 (all))

Intensive cage culture o Small (n=5)

Semi-intensive pond culture o Polyculture (n=5) o Monoculture (n=5)

2.2.2.2 Other actors addressed by the in-depth survey

From the desired selection of farms, roughly 75% were available and willing to participate in the in-depth survey. Alongside additional data collection on grow-out farms, the in-depth survey also included data collection on other actors in the value chain. The selection of actors was based upon conclusions from previous LCAs investigated as part of our literature review (Henriksson et al. 2012) and available data. The identified actors and target sample sizes included:

Hatcheries (n=5 per species and country)

Nurseries (n=5 per species and country, if relevant)

Feed mills (n=10 per country)

Capture fisheries (n=5 per country)

Agricultural farmers (n=5 if relevant)

Processing plants (n=5 per species)

Middlemen/traders (depending upon practice and availability)

The collection of data for these actors was conducted using the questionnaires presented in Kruijssen et al. (in prep.), and data was organized in excel templates using dropbox (www.dropbox.com). Questions were, to the extent possible, constructed in a way so that triangulations could be conducted during data evaluation. For example, cross-checks could

9

be made by entailing questions on the economic feed conversion ratio (eFCR), feed use and total marketed harvest (eFCR=feed use/total marketed harvest), or stocking density, farm area, fry price and amount spent on fry (stocking density*area*cost per fry = amount spent on fry). Any grossly abnormal parameters would consequently be removed from the sample. The reference periods of the surveys were left open due to very different characteristics of practices and systems amongst countries and accounting frames. The temporal overlap, therefore, doesn’t always coincide with that of the integrated survey or other surveys. Alongside physical flows, also monetary values were recorded in order to supply bases for allocation and support WP5’s Life Cycle Costing (LCC) work. Monetary data was, moreover, used to evaluate the accuracy of the inventory data, using value additions of unit processes as an indicator. Of the actors in the Asian part of the value chain, feed processors and processing plants were the hardest to get access to. Processing plants also posed a challenge due to their wide range of products and their share scale. Scale became a challenge as individual respondents rarely knew all the relevant information needed for the LCAs. For example, a technician would maybe know the electricity consumption of a processing line, but not the origin of the raw materials. Additional efforts were made to collect data on the transportation route from Asia to Europe, re-processing in Europe and retailing in Europe with little/no success. The real sample sizes did therefore not always meet the target set.

2.2.3 Functional unit

The functional unit describes the main function(s) fulfilled by a product system and indicates how much of this function is considered. In comparative LCAs the functional unit forms the basis for the comparison, but also without any comparison a functional unit is required in order to have something to scale calculations to. For the definition of the functional unit one has to take into account the following elements:

function,

consumer’s behaviour,

quality,

unit, and

quantity.

These elements are being discussed hereafter in relation to the LCA of the aquaculture systems for Tilapia, Catfish, Shrimp and Prawns in Bangladesh, China, Thailand and Vietnam. Function The function of aquaculture products has been defined as clearly and accurately as possible. One might say that aquaculture products fulfil just one function – providing food – but this function can be expressed in various terms: frozen, edible yield, gross energy content, protein content, nutritional value, moisture content, good taste, etc. The most commonly used functional unit in aquaculture LCA literature is a given mass of live fish at the farm gate (Aubin et al. 2009; Ayer and Tyedmers 2009; Pelletier et al. 2009) for cradle-to-gate analyses and a given mass of frozen, edible yield for cradle-to-grave analyses (Mungkung 2005; Ellingsen and Aanondsen 2006; Iribarren et al. 2010; Pelletier and Tyedmers 2010). Since aquaculture products fulfil more than one function, it has to be established which functions aquaculture systems fulfil and whether only the main function is considered or also the alternative ones.

10

For this study we adopted the function “frozen, edible yield” since we were aiming for a cradle-to-consumption analysis. The analysis is thus “cradle-to-consumption” and not “cradle-to-grave”. In case of a “cradle-to-grave” analysis we would have to model different modes of preparing the fish and human consumption. The latter requires modelling of human beings as “economic processes”, which would make up a real cradle-to grave analysis. However, any useful data sets for different modes of preparing and for modelling human beings are currently lacking as far as known to the authors and therefore these processes are excluded from our analyses. As mentioned, we haven’t made comparisons between different fish species with possibly different calorific and protein values. Since the farmed fish are exported to the EU, they have to be frozen or otherwise packed. Therefore, we have focused on ‘frozen’ fish in this study. Consumer’s behaviour Consumer’s behaviour refers to the way the consumer prepares and consumes aquaculture products and how that influences its environmental life cycle performance. Consumer’s behaviour may influence the functioning of a product system (food waste, frying, cooking, baking practices, etc.) but has not been included in this study. Quality Comparable products may differ in quality, and thus the target market may differ substantially. Some examples include: Pangasius fillets that are discoloured are not desirable on European markets and are therefore sold on other markets; Tilapia which have attained an off-flavour; or shrimp of varying quality are sold on the local markets where the best quality products end up in top-end Asian restaurants and on the Western (e.g., European) markets. Unit The unit of frozen, edible yield is taken as kg. Quantity The amount considered was set to 1000 kg. The resulting functional unit then is: 1000 kg of frozen, edible yield of frozen species X produced on farm type Y in country Z for consumption in the EU. Reference flow On the basis of this functional unit, the alternative (product) systems that can provide this functional unit were selected and their system performance was quantified. System performance was quantified by means of a so-called reference flow, i.e. “a measure of the needed outputs from processes in a given product system required to fulfil the function expressed by the functional unit” (ISO 1998). The reference flow is the connecting flow between the physical output of a system and the amount of function delivered by that system as quantified in the functional unit. It is the flow upon which the whole LCA is based, for example the amount of detergent required (= reference flow) for washing a certain amount of clothes optically white (= functional unit)1. The following definitions aim to clarify the difference between functional unit and reference flow:

1 Note that the functional unit and reference flow are different quantities. On the basis of one functional unit,

different reference flows will usually be quantified for each (product) system analysed. Only in exceptional cases will reference flows and functional unit be the same, but this will then generally limit the number of products that can be compared.

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Functional unit: quantified service provided by the product system(s) under study for

use as a reference basis in a life cycle assessment study.

Reference flow: quantified flow generally associated with the use phase of a product

system and representing one way (i.e. by a specific product alternative) of obtaining

the functional unit.

As mentioned above, this study is limited to a cradle-to-consumption analysis and doesn’t include the use phase. In the next section all alternative aquaculture systems and their respective reference flows are provided.

2.2.4 Functionally equivalent alternative systems and their reference flows

Taking into account the major and minor species-countries matrix in Table 1 and the functional unit defined above, detailed LCAs will be performed with the following reference flows: Bangladesh, Prawn: 1. 1000 kg of frozen, head-less shell-on M. rosenbergii prawns produced in improved

extensive systems in Khulna, Bangladesh for consumption in the EU (reference period 2010-2011);

2. 1000 kg of frozen, head-less shell-on M. rosenbergii prawns produced in improved extensive systems in Bagerhat, Bangladesh for consumption in the EU (reference period 2010-2011).

Thailand, Shrimp: 3. 1000 kg of frozen, peeled tail-on L. Vannamei shrimp produced in intensive systems in

Eastern Thailand for consumption in the EU (reference period 2010-2011); 4. 1000 kg of frozen, peeled tail-on L. Vannamei shrimp produced in intensive systems in

Southern Thailand for consumption in the EU (reference period 2010-2011). Vietnam, Pangasius: 5. 1000 kg of frozen, edible yield of Pangasius produced in small systems in the Mekong

Delta, Vietnam for consumption in the EU (reference period 2010-2011); 6. 1000 kg of frozen, edible yield of Pangasius produced in medium systems in Mekong

Delta, Vietnam for consumption in the EU (reference period 2010-2011); 7. 1000 kg of frozen, edible yield of Pangasius produced in large systems in Mekong Delta,

Vietnam for consumption in the EU (reference period 2010-2011). China, Shrimp: 8. 1000 kg of frozen, peeled tail-on L. Vannamei shrimp produced in low-level pond systems

in Guangdong, China for consumption in the EU (reference period 2010-2011); 9. 1000 kg of frozen, peeled tail-on L. Vannamei shrimp produced in high-level pond

systems in Guangdong, China for consumption in the EU (reference period 2010-2011). Vietnam, Shrimp: 10. 1000 kg of frozen, peeled tail-on P. monodon shrimp produced in intensive systems in

the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); 11. 1000 kg of frozen, peeled tail-on L. Vannamei shrimp produced in intensive systems in

the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); 12. 1000 kg of frozen, peeled tail-on P. monodon shrimp produced in semi-intensive systems

in the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011).

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China, Tilapia: 13. 1000 kg of frozen, edible yield of Tilapia produced in polyculture farms in Guangdong,

China for consumption in the EU (reference period 2010-2011); 14. 1000 kg of frozen, edible yield of Tilapia produced in polyculture farms in Hainan, China

for consumption in the EU (reference period 2010-2011); 15. 1000 kg of frozen, edible yield of Tilapia produced in polyculture reservoirs in

Guangdong/Hainan, China for consumption in the EU (reference period 2010-2011); 16. 1000 kg of frozen, edible yield of Tilapia produced in ponds integrated with pigs in

Guangdong, China for consumption in the EU (reference period 2010-2011). Thailand, Tilapia: 17. 1000 kg of frozen, edible yield of Tilapia produced in pond systems in

Chachoengsao/Nakhon Patom/Petchburi, Thailand for consumption in the EU (reference period 2010-2011);

18. 1000 kg of frozen, edible yield of Tilapia produced in intensive cages systems in Suphanburi, Thailand for consumption in the EU (reference period 2010-2011).

Bangladesh, Shrimp: 19. 1000 kg of frozen, peeled tail-on P. monodon shrimp produced in small-medium

improved extensive systems in Bagerhat/Khulna/Satkhira, Bangladesh for consumption in the EU (reference period 2010-2011);

20. 1000 kg of frozen, peeled tail-on P. monodon shrimp produced in large improved extensive systems in Cox’s Bazar, Bangladesh for consumption in the EU (reference period 2010-2011).

Bangladesh, Shrimp & Prawn: 21. 1000 kg of frozen, peeled tail-on P. monodon shrimp produced in shrimp and prawn

polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011);

22. 1000 kg of frozen, head-less shell-on M. rosenbergii prawns produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011).

2.3 Review No independent external review of the LCA case study results has been made. An internal review of the case study results has been made by the SEAT partners only ensuring that (ISO, 2006b):

the data used are appropriate and reasonable in relation to the goal of the study;

the interpretations reflect the limitations identified and the goal of the study, and

the study report is transparent and consistent. As the SEAT partners are no LCA experts they could not ensure that:

the methods used to carry out the LCA are consistent with this ISO 14040 and 14044;

the methods used to carry out the LCA are scientifically and technically valid. This internal review does not comply with ISO requirements on comparative assertions. However, as explained in section 2.1.1.2, the LCAs performed as part of the SEAT project will not be used to directly compare different aquaculture systems amongst each other. In this project comparative assertion is not aimed for. If, after the SEAT project, the results are used for this, an external panel of interested parties should review the case study results as yet to ensure compliance with ISO comparative assertion requirements.

13

3 Inventory analysis

3.1 Introduction In this Chapter the choices and assumptions made in, and results of the inventory analysis are reported. The structure of this reporting is again largely based on the steps distinguished by Guinée et al. (2002) and includes sections on the following topics:

System boundaries (section 3.2)

Drawing up the flow charts (section 3.3)

Process data and cut-off (section 3.4)

Allocation (section 3.5)

Scaling and aggregation (section 3.7)

Inventory results (section 3.8) The inventory tables are the eventual result of the inventory analysis. As these are big tables and as 22 LCA studies needed to be reported, inventory tables are supplied to this report as Supporting Information (MS-Excel files) to this report.

3.2 System boundaries As discussed above, the SEAT aquaculture LCAs looked from cradle (egg, seed) to consumption in the EU (finished, frozen consumable product). This constitutes one part of the boundaries of the aquaculture systems that were analysed. However, there are three additional types of boundaries that needed boundary decisions too:

1. the boundary between the product system and the environment; 2. the boundary between the processes that are part and are not part of the system; 3. the boundary between the product system under investigation and other product

systems. Boundaries of type 1 (between the product system and the environment) are important for wild fisheries in particular. If fish is caught from nature, it is a natural resource and an LCA will consider this as a contribution to the depletion of a biotic resource. If captured fish are used as human food or fish feed, then the feed is an economic input. Put in other terms: it has to be defined which flows cross this boundary and are environmental interventions. Well-known examples of confusion on this point are forests and other biological production systems (including aquaculture systems). Do they belong to the environment and is wood a resource coming into the physical economy (natural forest)? Or is the forest already part of the economy and are solar energy, CO2, water and minerals to be regarded as the environmental interventions passing the boundary between environment and economy (forestry)? Another example concerns the other end of the life cycle: is a landfill to be regarded as part of the environment or still as part of the physical economy? In the first case all materials which are brought to the landfill have to be regarded as emissions into the environment; in the latter case this will only hold for the emissions from the landfill to air and groundwater. Our lead in solving this boundary problem has been the degree to which the processes involved are steered by human activities. Forestry can be regarded as part of the socio-economic system, while wood extracted from a natural forest will have to be regarded as a critical resource taken from the environment. Likewise a landfill, managed without any control measures is regarded as part of the environment, with all discarded materials to be regarded as emissions. If the landfill is a well-controlled site, separated from groundwater and with cleaning of the percolation water, one may well regard this as part of the product

14

system with only the emissions from the landfill to be considered as burdens to the environment. For the aquaculture LCAs reported here, several type 1 boundary situations popped up and were decided as follows. As for agricultural run-off, the models adopted (Nemecek and Schnetzer 2011) did not seek net balances in nutrients between the inputs and the consequent emissions. Needless to say, this is an undesired trait in an LCI model but the suggested models were still selected in order to be in-line with the ecoinvent database from which some of the pig feeds were sourced. Another nutrient trade-off was that between prawns and rice in Bangladeshi ponds. Since the flow of nutrient was too uncertain to determine and the agricultural yields not always clear, the fertilizer inputs were accounted the plants and the feed the shrimp. Naturally, in the interwoven network of ponds, much of these nutrients are utilized at by other farmers. The faith of run-off water and sediments in general, was very hard to determine. An arbitrary assumption was therefore made, assuming that nutrient pumped into agricultural fields were utilized, half of the nutrients to pond dikes and sediment ponds were lost, and all nutrients pumped into wasteland were lost. Boundaries of type 2 (between processes that are part and processes that are not part of the study) will be discussed in section 3.4 under the heading “cut-off”. Boundaries of type 3 (between the product system under investigation and other product systems) will be discussed in section 3.5 under the heading “allocation”.

3.2.1 Temporary carbon storage and biogenic carbon emissions

According to the ILCD guidelines (ILCD 2010), uptake of atmospheric CO2 by photosynthesis and other chemical processes should be inventoried as inflows of “resources from air”. Meanwhile, the release of carbon bound to biomass and litter should be inventoried as biogenic emissions (ILCD 2010). The most common forms of biogenic carbon emissions are carbon dioxide (CO2) and methane (CH4). As both are classified as greenhouse gases, their correct accounting becomes relevant. In the ecoinvent v2.2 database, flows of “resources from air”, and biogenic carbon dioxide and methane are accounted for. For example, in the process “rape seed IP, at farm” [221] an uptake of 2.68 kg “carbon dioxide, in air [resource]” is connected per kg rape seeds. In the consequent step of the rape seed value chain, rapeseed meal and oil is produced in an oil mill [6108] and an additional 1.47 kg of “carbon dioxide, in air [resource]” is connected as an environmental inflow per kg rape seed meal. Any modeller who connects the product “Rape seed meal, at oil mill” to their production system as e.g. an animal feed, is therefore expected to model metabolic carbon emissions and keep track of any carbon embodied in the animals, which in turn would be emitted when the animals are consumed. In the case of rape seed meal, an uptake of 2.59 kg “carbon dioxide, in air [resource]” is embodied in the product and will result in a global warming potential of -2.59 kg CO2-eq. This negative flow is larger than the 0.402 kg of CO2-eq. emitted during the whole production chain of rape seed oil and therefore results in a negative value for aggregated global warming emissions. Apart from accounting mistakes easily being made, allocation will influence carbon fixated from the atmosphere. The influence of allocation can be exemplified by the large amounts of straw co-produced alongside many crops. If one tonne of straw is produced alongside an equal amount of seed or grain, and the straw is sold for a negligible price, most of the carbon dioxide fixated by the crop will be related to the main product (the seed or grain) if economic allocation is applied. Meanwhile, the carbon content of the product is only about half that fixated by the whole crop, resulting in much more reduced GHG emissions for the product than the straw over their whole lifecycles (incl. consumption and landfilling). Similar strange

15

scenarios exist for mass allocation, where carbon dioxide uptake would be equally divided between e.g. one kg of snail meat and snail shells, while shells are made up of calcium carbonate (CaCO3) and 12% carbon, snail meat consists of 85.5% water and only about 5.7% carbon (wet-weight). In the present study, given the complex flows of carbon from plants to feed to fish to products, and a functional unit (product) defined before final consumption, uptake of atmospheric carbon dioxide and emissions of biogenic carbon dioxide were neglected. We hereby assume that uptake and emission of biogenic CO2 balance over the timeframe relevant for the impact category global warming (100 years) (Lackner 2003), and we avoid arbitrary bias from allocation decisions. However, biogenic methane has still been taken into account, as the radioactive forcing of CH4 is 25 times greater than that of the CO2 fixated.

3.3 Drawing up the flow charts In this section a general flow chart for the 22 aquaculture systems listed in section 2.2.4 is provided as Figure 2. It is impossible and also not very useful, to show complete and more detailed flow charts of the 22 systems here, whereas it makes much more sense to provide flow charts when reporting primary and secondary data in section 3.1.1.3. Therefore, detailed flow charts will be presented in section for each (category of) unit processes for which data are reported.

Hatchery

Nursery

Aquaculture

production

Fisheries

Production of

diesel

Feed processing

Processing

Agriculture

Pesticide

production

Fertilizer

production

Seeds

Packaging

Distribution

Retailing

Consumption

Electricity

productionHeat

production

Extraction of

oil, gas & coal

Chemicals &

therapeutants

production

Offal processing

Livestock

farming

Biomass

incineration

Nuclear plant

Hydro power

Retailing

ConsumptionProduction of

packaging material

Waste

Figure 2: Generalized flow chart for aquaculture systems

16

3.4 Process data and cut-off All unit process data collected (primary data) and sourced from databases and/or literature (secondary data), from which the 22 LCAs listed in section 2.2.4 are built and modelled, are reported including a general data quality discussion in a separate Annex report ”Primary data and literature sources adopted in the SEAT LCA studies” (Henriksson et al. 2014). For each process all economic and environmental in- and outputs (flows) were mapped and quantified according to Figure 3.

Figure 3 : Data categories distinguished when collecting unit process data (Guinée et al., 2002). For each of the four countries (China, Vietnam, Thailand and Bangladesh) and each species primary data collected through the integrated survey and the in-depth survey are reported in the Annex report (Henriksson et al. 2014). for the following (categories of) unit processes): hatcheries, nurseries, feed mills, capture fisheries, agricultural practices, processing plants and traders. Secondary data are reported for each country as these data are similar for all species cultured within a country unless mentioned otherwise. These data comprise of data for petroleum products, coal, natural gas, biomass, transport, imported feed commodities, waste, etc. Secondary data adopted from the ecoinvent v2.2 database couldn’t be reported for proprietary reasons. Whenever ecoinvent data are used, ecoinvent process IDs are provided in hard brackets, thus enabling readers of this report with access to ecoinvent to relate to the correct data. In a next step all primary and part of the secondary data (particularly those sourced from literature) were imported into excel spreadsheets to which the protocol for horizontal averaging of unit process data including estimates for uncertainty (Henriksson et al. 2012; Henriksson et al. 2013) was applied. These spreadsheets thus contain all data including detailed uncertainty information and will be made available as Supporting Information to upcoming articles in international scientific journals. In principle, an LCA should track all the processes in the life cycle of a given product system, from the cradle to the grave, and collect full data sets on each one of these. In practice this is

goods

services

materials

energy

waste* (for treatment)

goods

services

materials

energy

waste (for treatment)

environmental

interventions

economic

flows

chemicals to the air

chemicals to water

chemicals to the soil

radionuclides

sound

waste heat

casualties

abiotic resources

biotic resources

land occupation

products products * economic

flows

environmental

interventions

UNIT PROCESS /

PRODUCT SYSTEM

* the functional flows of the process

OUTPUTSINPUTS

land transformation

etc.

17

impossible, however, and a number of flows must be either roughly estimated or cut-off and subsequently ignored. This often is the case for the production of capital goods. The root problem behind the cut-off issue is a lack of readily accessible data, implying disproportionate expenditure of funds and effort on data collection. Cut-off may substantially influence the outcome of an LCA study, however, and means that ‘easy’ LCAs come at a price. In the past decade techniques have become available to quantitatively estimate the importance of flows for which no readily accessible process data are available by combining LCA with environmentally extended Input-Output Analysis (EIOA). This combination of LCA and EIOA is called hybrid LCA. The original intention was to estimate the potential importance of flows for which data were lacking and that could not be collected with reasonable efforts, by EIOA. However, during the course of the project it was decided – instead of adapting and applying EIOA - to develop a protocol for horizontal averaging of unit process data including estimates for uncertainty. By the latter, we would be able to present the LCA results as ranges and distributions that much better reflect the status of the data collected and of the background databases than point value results that are generally presented by LCA practitioners. We considered putting efforts into this subject more important than putting efforts into applying and adapting EIOA for estimating the importance of missing flow data.

3.5 Allocation When processes deliver more than one valuable product, the interventions of these multiple processes should be divided over the production system under investigation and other systems. This is called allocation. There are three kinds of multiple processes: co-production: simultaneous production of economical valuable products, combined waste disposal: simultaneous processing of more than one stream of waste,

with a negative value and' open loop recycling: processing a waste stream of one production process such that it

can be reused in/for another useful material. According to ISO, the following preference order of options for addressing the problem of multiple processes should be applied: Step 1 If it is possible, one should try to avoid allocation by dividing the multiple processes in sub-processes or to enlarge the system under investigation such that also the co-products are involved. Step 2 If it is not possible to do so, an allocation based upon causal physical relations is preferred; e.g. the amount of mercury (Hg) in the emission of a waste combustion installation can be attributed to every mercury containing product to be burned according to its content; the carbon dioxide emission can be attributed to a product according to the caloric value of the product. Step 3 If it is not possible to attribute on the basis of a causal physical relationship, then other relations should be used such as: an allocation proportional to the economic value of the products.

18

In this study we followed the ISO steps as closely as possible with some adaptations as documented in Guinée et al. (2002) and Guinée et al. (2004). If allocation could be avoided, we did so. However, system expansion has not been applied as the focus was on the aquaculture products exported to the EU, and not on the whole basket of products possible. As a baseline economic allocation (step 3) has been applied Guinée et al. (2004). On top of that, sensitivity analyses have been performed since there is in our view not one ultimate best solution accepted by everybody for a problem that is an artefact of wishing to isolate one function out of a network of functions. Allocation choices were thus consistently applied to the extent possible with regards to pre-made decisions in the ecoinvent database. Two allocation principles were selected: mass and economic basis. No changes were made in ecoinvent allocations as that was beyond the project’s possibilities. The two allocation principles were thus only applied to foreground processes. In the present study, the following allocation scenarios were encountered:

Soybean processing (Brazilian soybeans)[TH, 2011]

Soybean processing[CN, 2011]

Processing, Peruvian anchoveta

Tuna processing[TH, 2011]

Reduction of tuna by-products[TH, 2011]

Maize, wet-milling[US, 2011]

Wheat processing, global temp[GLO, 2011]

Trawling mixed catch[TH, 2011]

Wheat processing[AU, 2011]

Maize farming[BD, 2011]

Maize grinding[BD, 2011]

Rice farming[VN, 2011]

Dehusking of paddy rice, at farmer[VN, 2011]

Small mixed fishery[VN, 2011]

Brood stock and trash fish fishery[VN, 2011]

Large mixed fishery[VN, 2011]

Rice milling[VN, 2011]

Ethanol production from maize[BD, 2011]

Soybean processing[VN, 2011]

Wheat processing[US, 2011]

Soybean processing (US soybeans)[TH, 2011]

Soybean processing[RER, 2011]

Pig slaughtering[RER, 2011]

Meat and bone meal production[RER, 2011]

Processed animal protein (PAP) production[RER]

Shrimp meal manufacture[TH, 2011]

Capture fishery, averaged[VN]

Major rice farming[TH, 2011]

Dehusking of paddy rice, at farmer[TH, 2011]

Rice milling[TH, 2011]

Maize grinding[TH, 2011]

Fishing, mixed catch[CN, 2011]

Processing of Pangasius[VN, 2011]

Wheat processing[CN, 2011]

Maize, wet-milling[CN, 2011]

Maize grinding[CN, 2011]

Ethanol production from maize[CN, 2011]

Processing of groundnuts[CN, 2011]

Shrimp processing in Eastern Thailand[TH, 2011]

19

Tilapia processing[TH, 2011]

Shrimp from low-level ponds processing[GD, CN, 2011]

Tilapia processing, Guangdong[GD, CN, 2011]

Fish processing, global fisheries[CN, 2011]

Tilapia by-product reduction[GD, CN, 2011]

Shrimp meal manufacture[CN, 2011]

Fish reduction, small pelagics, global fisheries[GLO, 2011]

Processing of Menhaden fishmeal[US, 2011]

Rice farming[CN, 2011]

Dehusking of paddy rice, at farmer[TH, 2011]

Rice milling[CN, 2011]

Rapeseed oil extraction[CN, 2011]

Tilapia farming, integrated system[GD, CN, 2011]

Tilapia farming, non-integrated polyculture[GD, CN, 2011]

Tilapia farming, non-integrated polyculture Hainan[HI, CN, 2011]

Tilapia farming, reservoir[CN, 2011]

Tilapia processing, Hainan[HI, CN, 2011]

Tilapia by-product reduction[HI, CN, 2011]

Polyculture farming, Southern China[CN, 2011]

Boro rice farming[BD, 2011]

Aman rice farming[BD, 2011]

Rice milling[BD, 2011]

Freshwater Apple Snail de-shelling[BD, 2011]

Capture fishery, Hatiya Island

Rice bran de-oiling[BD, 2011]

Soybean processing[BD, 2011]

Mustard seed mill[BD, 2011]

Wheat farming[BD, 2011]

Wheat milling[BD, 2011]

Prawn farming Khulna[BD, 2011]

Prawn farming Bagerat[BD, 2011]

Processing, prawn from Khulna[BD, 2011]

Processing, prawn from Bagerat[BD, 2011]

Shrimp processing in Southern Thailand[TH, 2011]

Tilapia from integrated farm processing, Guangdong[GD, CN, 2011]

Tilapia from reservoir processing[CN, 2011]

Shrimp from high-level ponds processing[GD, CN, 2011]

Processing of Pangasius from small scale farm[VN, 2011]

Processing of Pangasius from medium scale farm[VN, 2011]

Processing of Pangasius from large scale farm[VN, 2011]

Processing of P. monodon from intensive farms[VN, 2011]

Processing of L. vannamei from intensive farms[VN, 2011]

Processing of P. monodon from semi-intensive farms[VN, 2011]

Processing of Tilapia from ponds[TH, 2011]

Processing of Tilapia from cages[TH, 2011]

Shrimp farming West[BD, 2011]

Shrimp farming East[BD]

Processing, shrimps from Western BD[BD, 2011]

Processing, shrimps from Eastern BD[BD, 2011]

Shrimp & Prawn farming[BD]

Processing, P. monodon from shrimp & prawn farms[BD, 2011]

Processing, Prawn from shrimp & prawn farms[BD, 2011]

20

The allocation procedure can be exemplified by the example in Figure 4.

Figure 4: Example of applying different allocation principles.

3.6 Quantification and propagation of overall dispersions The quantification of unit process parameters is presented in Henriksson et al. (2013). Summarizing, overall dispersions were defined as the sum of inherent uncertainty, spread and unrepresentativeness. Inherent uncertainties were derived from literature where available, but most often assumed adopting the default values presented by Frischknecht et al. (2007). For economic flows, a coefficient of variation (CV) of 0.05 (5%) was assumed as a default. The coefficient of variation is a normalized measure of dispersion, defined as the arithmetic standard deviation divided by the arithmetic mean (C x ). Spread was either calculated amongst primary data points or amongst literature values. Thus, much effort went into finding at least two sources for each parameter. Unrepresentativeness was defined according to the pedigree and uncertainty factors presented in Frischknecht et al. (2007). Where sufficient data were available (n>8), the best-fit distribution was defined using the EasyFit software (www.mathwave.com/easyfit-distribution-fitting.html). Where only a few values were available (n<8), a lognormal distribution was assumed. The data were entered in the spreadsheet available as online resources to Henriksson et al. (2013). In this spreadsheet, the three sources of overall dispersions are summed as the square of CVs. In case of normal distributions, the CV was multiplied by the weighted mean to produce the SD, which is equal to sigma, the input parameter in CMLCA (Heijungs and Frischknecht 2005). Where data were lognormally distributed, the overall CV was converted into the input parameter Phi, as described in (Heijungs and Frischknecht 2005). Propagation of uncertainties was done by running Monte Carlo simulations in the freely available CMLCA©2 software. Monte Carlo is a method which utilizes random samples from

2 The CMLCA

© software tool program is publicly available, see: http://www.cmlca.eu/

21

individual unit processes and then aggregates these into possible outcomes. As the number of iterations increase, a large number of possible outcomes become available. From these outcomes, new parameters can be defined to define outcomes as ranges, rather than point values.

3.6.1 Monte Carlo simulations

Random sampling was used in the Monte Carlo simulations. Given that the reproducibility of results depends upon the number of parameters randomised, their width and the level of reproducibility, some preliminary evaluations were made (Figure 5). From 1 000 iterations (runs), the average and standard deviation was defined for the growing sample. This made evident that for the present model the average and standard deviation reached a steady state around 300 iterations with only minor changes (around 10%) in the standard deviation up to 1 000 iterations. Therefore, 1 000 iterations were deemed sufficient to provide the accuracy needed in the present study. With the computer power at our disposal, this took roughly 8 hours per alternative (reference flow) or impact category.

Figure 5: Example of the relationship between number of iterations and reproducability of results produced from the present model.

3.7 Scaling and aggregation The result of our data collection efforts is a huge database with unit processes and data on economic and environmental inputs and outputs. These processes were connected to each other on the basis of the flow charts and the reference flows presented in section 2.2.4. For all LCA calculations the freely available CMLCA© software was used. CMLCA© is largely based on the ISO Standards and Guinée et al. (2002) allowing, amongst others, allocation scenario calculations, the use of different impact assessment methods and several types of interpretation analyses including contribution analyses and propagation of uncertainty data.

0.00E+00

5.00E+03

1.00E+04

1.50E+04

2.00E+04

2.50E+04

3.00E+04

3.50E+04

4.00E+04

4.50E+04

0 200 400 600 800 1000

Val

ue

Iterations

Average

Standard deviation

22

3.8 Inventory results Inventory tables including uncertainty ranges for each inventory item are provided as Supporting Information (as MS-Excel files) to this report for all 22 country-species-region/farming practice combinations for which LCAs have been performed in the SEAT project (see section 2.2.4): The results will be discussed in Chapter 5 on Interpretation.

23

4 Impact assessment In this chapter the results of the life cycle impact assessment phase for the 22 LCAs are presented. In section 4.1 methods adopted for the impact assessment will be discussed. In section 4.2 characterisation results and in section 4.3 normalisation results are presented. Weighting was not conducted. Besides the fact that ISO does not permit weighting in case the results of an LCA will be used for comparative assertion, our results particularly aimed at mapping the aquaculture value chains while identifying the hot spots of these chains. Assuming the present results to be absolute in any way is highly misleading as system boundaries inevitable exclude process and data are always a limitation. Moreover, we choose not to weight the results in the fear of oversimplifying results, especially since the outcomes of many impact categories were unreliable.

4.1 Selection and definition of impact categories, models and indicators

We have applied two impact assessment methods on our 22 LCAs for the aquaculture systems specified in section 2.2.4. The first one is the updated impact assessment method of Guinée et al (2002) as available from the CML website (http://www.leidenuniv.nl/cml/ssp/databases/cmlia/cmlia.zip; version 4.1, released October 2012) as baseline excluding impacts of land use. This baseline list comprises of the impact categories listed in Table 2 and their baseline characterisation methods. Indicator results have been calculated for each of these categories. Table 2: CML baseline impact categories

Impact category Baseline characterisation method

Depletion of abiotic resources

Updated Guinée et al. (2002) baseline method based on ultimate reserves and extraction rates (Guinée & Heijungs 1995), but distinguishing between depletion of fossil resources and depletion of minerals

Climate change GWP100 (Houghton et al. 2001) Stratospheric ozone depletion

ODPsteady state (WMO 1999)

Human toxicity HTPsteady state, global (Huijbregts 1999) and HTP (Hauschild et al. 2008; Rosenbaum et al. 2008)

Ecotoxicity freshwater aquatic ecotoxicity

FAETPsteady state, global (Huijbregts 1999) and FAETP (Hauschild et al. 2008; Rosenbaum et al. 2008)

marine aquatic ecotoxicity

MAETPsteady state, global (Huijbregts 1999)

terrestrial ecotoxicity

TETPsteady state, global (Huijbregts 1999)

Photo-oxidant formation

High NOx POCP (Derwent et al. 1996; Derwent et al. 1998; Jenkin and Hayman 1999)

Acidification Average European AP (Huijbregts et al. 2000) Eutrophication Generic EP (Heijungs et al. 1992)

24

Although it was proposed in D3.2 (Henriksson et al. 2011) to also include CED as indicator, we have not done this as the CED results would be similar as the fossil resources depletion category. At the time of writing D3.2, this fossil resources depletion category was not yet available. POCP values for China, inclusion of biotic resource use and water use impacts have not been included as time was simply lacking for this in the end of the project. Attention has been focused on developing a fully practical protocol for horizontal averaging of unit process data including estimates for uncertainty. As a consequence the planned scientific paper reviewing the inter-relations between biotic resource use, resource depletion, and land use and biodiversity impacts could also not be produced anymore. A framework for addressing these impacts within LCA (or outside LCA) will thus have to be developed outside the SEAT project. Finally, land competition was excluded as impact category because data on just “occupying space” have little meaning for our case studies. Instead, a separate study has been done by Schoon (master thesis, publication in preparation) on estimating CO2 emissions from land use (LU) and land use change (LUC) of mangrove forests due to shrimp aquaculture practices. The results of this study will be published later separately. The second impact assessment method applied is the midpoint methods recommended by the ILCD (ILCD 2010; Hauschild et al. 2012). This ILCD recommended list of best available characterization methods for midpoint impact categories is given in Table 3. Indicator results have been calculated for each of these categories. Table 3: Best available characterization methods at midpoint. Methods that are classified as level I, II or III are recommended under the ILCD and these are only included here.

Impact category Best among existing

characterization models

Indicator Classification

Climate change Baseline model of 100 years of

the IPCC (Forster et al. 2007)

Radiative forcing as

Global Warming

Potential (GWP100)

I

Ozone depletion Steady-state ODPs from the

WMO assessment (Montzka

and Fraser 1999)

Ozone Depletion

Potential (ODP)

I

Human toxicity,

cancer effects

USEtox model (Rosenbaum et

al. 2008)

Comparative Toxic Unit

for humans (CTUh)

II/III

Human toxicity, non-

cancer effects

USEtox model (Rosenbaum et

al. 2008)

Comparative Toxic Unit

for humans (CTUh)

II/III

Particulate

matter/Respiratory

inorganics

Compilation in Humbert (2009)

based upon Rabl and Spadaro

(2004) and Greco et al. (2007)

Intake fraction for fine

particles (kg PM2.5-

eq/kg)

I/II

Ionising radiation,

human health

Human health effect model as

developed by Dreicer et al.

(1995) (Frischknecht et al.

2000)

Human exposure

efficiency relative to U235

II

Photochemical ozone

formation

LOTOS-EUROS as applied in

ReCiPe (van Zelm et al. 2008)

Tropospheric ozone

concentration increase

II

Acidification Accumulated Exceedance

(Seppälä et al. 2006; Posch et

al. 2008)

Accumulated

Exceedance (AE)

II

25

Impact category Best among existing

characterization models

Indicator Classification

Eutrophication,

terrestrial

Accumulated Exceedance

(Seppälä et al. 2006; Posch et

al. 2008)

Accumulated

Exceedance (AE)

II

Eutrophication,

aquatic

EUTREND model as

implemented in ReCiPe (Struijs

et al. 2009)

Residence time of

nutrients in freshwater

(P) or marine end

compartment (N)

II

Ecotoxicity, freshwater USEtox model, (Rosenbaum et

al. 2008)

Comparative Toxic Unit

for ecosystems (CTUe)

II/III

Land use Model based on Soil Organic

Matter (SOM) (Milà i Canals et

al. 2007)(Milà i Canals et al.,

2007)

Soil Organic Matter III

Resource depletion,

mineral and fossil

CML 2002 (Guinée et al. 2002) Scarcity II

Land use according to the SOM method could not be applied since case-specific characterisation factors had to be produced for which considerable information was needed. Water use data have been collected but in order to characterize them additional work is needed, which was not feasible anymore within the scope of this deliverable. The ILCD method for resource depletion is conceptually the same as for CML, but the ILCD method adopts economic reserves as indicator of depletion whereas CML adopted ultimate reserves as indicator of depletion (cf. van Oers et al. 2002). ILCD LCIA methods and data are not available for all possible impact categories. For some categories it was concluded that additional research was necessary before a best practice could be identified and adopted. Only the characterisation factors of the above listed ILCD recommended midpoint methods for compartments “unspecified” were imported into the CMLCA software. This is because in the SEAT data collection process it was not possible to specify more detailed compartments that are compatible with ecoinvent v2.2 and the ILCD recommended midpoint methods. Note, however, that the ILCD recommended characterisation factors are only for sub-compartments of air emissions in the impact categories human toxicity and particulate matter. In the specified compartments brackish water is missing as compartment while this compartment may be the most important one for aquaculture studies in the areas that the SEAT project focused on. As these environments can inhibit species from both marine and limnic environments, they build an interesting area for further research.

4.2 Characterisation results In Table 4 and Table 5 the CML summary baseline characterisation results (absolute and relative to alternative [A1]) and in Table 6 and Table 7 the ILCD summary characterisation results (absolute and relative to alternative [A1]) are shown for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation. The results shown represent baseline values for each impact category, so without any uncertainty analyses.

26

In Table 8 and Table 9 the CML summary baseline characterisation results (absolute and relative to alternative [A1]) and in Table 10 and Table 11 the ILCD summary characterisation results (absolute and relative to alternative [A1]) are shown for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation. The results shown represent baseline values for each impact category, so without any uncertainty analyses. In Appendix 1 characterisation results including uncertainty ranges are provided for all reference flows and all CML baseline impact categories. In Appendix 2 contribution analyses for the baseline characterisation results for global warming results are provided for all 22 LCAs (see sections 2.2.4 and 3.8) for both CML and ILCD characterization methods, and for both economic and mass allocation. It is impossible to include also the contribution analyses results for all other impact categories in this report as appendix and these results are therefore provided as Supporting Information (as MS-Excel files) to this report. The results will be further discussed in Chapter 5 on Interpretation.

4.3 Normalisation results In this step the characterisation (or indicator) results for each impact category are provided as a fraction of its contribution to the total characterisation results for that impact category of a reference area for a certain reference time interval (mostly a year). Among these reference contributions one might find: the annual contribution of the whole world, or only the Netherlands, to the impact categories under consideration for a certain reference year (e.g., 2000)3. This step enables comparing the contributions of the different impact categories, because now they are in the same dimensions: e.g., the fraction of the annual world-wide contribution to this category in 20004. Normalisation has been performed adopting the most recent normalisation data published by Sleeswijk et al. (2008) for all impact assessment methods discussed in section 4.2. We have thus always calculated normalisation results for the global level for the base year 2000 (for all basic data and assumptions, see http://www.leidenuniv.nl/cml/ssp/databases/cmlia/cmlia.zip). In Table 12 and Table 13 the CML summary baseline normalisation results (absolute and relative to alternative [A1]) and in Table 14 and Table 15 the ILCD summary normalisation results (absolute and relative to alternative [A1]) are shown for all 22 LCAs adopting economic allocation. The results shown represent baseline values for each impact category, so without any uncertainty analyses. In Table 16 and Table 17 the CML summary baseline normalisation results (absolute and relative to alternative [A1]) and in Table 18 and Table 19 the ILCD summary normalisation results (absolute and relative to alternative [A1]) are shown for all 22 LCAs adopting mass allocation. The results shown represent baseline values for each impact category, so without any uncertainty analyses. Contribution analyses for the baseline normalised results and normalized results including uncertainty ranges for each impact category are not provided here, since they will not add any information to the corresponding characterization results. Normalisation is the result of dividing/multiplying the characterization results by a constant factor. Moreover, we have not

3 This annual contribution is simply calculated by the aggregating the multiplication results, economic

allocation, for of the annual global emissions times the corresponding characterisation factors for a given (emission-related) impact category. 4 Note, however, that this doesn’t allow yet for further aggregation of results cross impact categories!!

27

quantified uncertainty data for the normalization factors yet and thus further contribution and uncertainty analysis will give no new insights. The results will be further discussed in Chapter 5 on Interpretation.

28

Table 4: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation

abiotic depletion (minerals; kg Sb-eq.)

abiotic depletion (fossil fuels; MJ)

Land competition (m2.yr)

global warming (kg CO2-eq.)

ozone layer depletion (kg CFC11-eq.)

human toxicity (kg 1,4-DCB eq.)

Freshwater aquatic ecotoxicity (kg 1,4-DCB eq.)

Marine aquatic ecotoxicity (kg 1,4-DCB eq.)

Terrestrial ecotoxicity (kg 1,4-DCB eq.)

photochemical ozone formation (kg C2H4-eq.)

acidification (kg SO2-eq.)

eutrophication (kg PO4-eq.)

[A1] 1.69E-02 2.47E+05 1.31E+03 3.50E+04 2.34E-03 2.97E+03 1.17E+03 4.49E+06 1.32E+02 4.91E+00 2.94E+02 6.77E+02

[A2] 2.17E-02 2.85E+05 1.26E+03 3.95E+04 2.82E-03 3.48E+03 1.32E+03 5.60E+06 1.35E+02 4.74E+00 3.65E+02 8.63E+02

[A3] 4.52E-03 1.79E+05 5.53E+02 1.44E+04 1.54E-03 1.68E+03 1.03E+03 4.57E+06 3.86E+01 3.35E+00 8.48E+01 1.48E+02

[A4] 4.09E-03 1.43E+05 4.53E+02 1.23E+04 1.29E-03 1.42E+03 7.49E+02 3.08E+06 3.29E+01 3.23E+00 8.39E+01 1.45E+02

[A5] 3.18E-03 6.89E+04 1.49E+02 7.34E+03 5.68E-04 9.63E+02 5.09E+02 2.20E+06 2.20E+01 3.77E+00 5.51E+01 9.64E+01

[A6] 3.61E-03 7.23E+04 1.81E+02 7.65E+03 5.74E-04 1.04E+03 5.66E+02 2.42E+06 2.52E+01 3.71E+00 5.84E+01 9.89E+01

[A7] 3.24E-03 6.33E+04 1.63E+02 6.71E+03 4.99E-04 9.32E+02 5.03E+02 2.14E+06 2.25E+01 3.29E+00 5.20E+01 8.53E+01

[A8] 8.36E-03 1.13E+05 3.71E+02 1.07E+04 3.60E-04 3.55E+03 1.63E+03 1.13E+07 8.49E+01 1.97E+00 7.02E+01 1.16E+02

[A9] 9.23E-03 1.20E+05 4.13E+02 1.12E+04 3.36E-04 3.91E+03 1.80E+03 1.26E+07 9.43E+01 2.09E+00 7.37E+01 1.20E+02

[A10] 3.25E-03 1.29E+05 3.34E+02 1.06E+04 1.09E-03 1.34E+03 8.19E+02 3.60E+06 4.62E+01 2.72E+00 6.70E+01 8.98E+01

[A11] 2.39E-03 1.00E+05 2.45E+02 8.42E+03 1.03E-03 9.88E+02 5.31E+02 2.20E+06 3.30E+01 2.23E+00 5.96E+01 6.93E+01

[A12] 3.08E-03 1.17E+05 3.27E+02 1.01E+04 1.13E-03 1.21E+03 6.79E+02 2.83E+06 4.32E+01 2.75E+00 6.98E+01 9.85E+01

[A13] 6.07E-03 5.97E+04 2.19E+03 7.03E+03 3.60E-04 1.68E+03 7.58E+02 4.61E+06 5.84E+01 1.41E+00 6.53E+01 1.27E+02

[A14] 6.83E-03 6.59E+04 2.65E+03 8.08E+03 4.24E-04 1.80E+03 8.09E+02 4.82E+06 6.52E+01 1.66E+00 7.80E+01 1.68E+02

[A15] 5.92E-03 5.65E+04 2.42E+03 7.03E+03 3.91E-04 1.49E+03 6.63E+02 3.83E+06 5.54E+01 1.44E+00 6.93E+01 1.41E+02

[A16] 6.84E-03 6.90E+04 2.19E+03 7.85E+03 3.65E-04 2.03E+03 9.30E+02 5.87E+06 6.74E+01 1.51E+00 6.78E+01 1.40E+02

[A17] 3.93E-03 1.49E+05 5.12E+02 1.17E+04 1.07E-03 1.94E+03 1.13E+03 5.00E+06 4.41E+01 3.09E+00 8.15E+01 1.11E+02

[A18] 4.47E-03 1.01E+05 5.58E+02 9.46E+03 6.06E-04 2.03E+03 1.08E+03 4.58E+06 4.80E+01 3.34E+00 8.50E+01 1.75E+02

[A19] 2.99E-03 6.55E+04 1.44E+02 8.04E+03 5.04E-04 5.97E+02 2.11E+02 9.28E+05 1.84E+01 1.46E+00 7.74E+01 9.86E+01

[A20] 4.35E-03 7.38E+04 4.14E+01 1.21E+04 5.93E-04 9.07E+02 2.30E+02 1.23E+06 2.31E+01 2.97E+00 1.23E+02 1.88E+02

[A21] 9.53E-03 1.36E+05 7.36E+02 1.86E+04 1.17E-03 1.64E+03 6.60E+02 2.57E+06 7.24E+01 2.49E+00 1.62E+02 3.71E+02

[A22] 1.35E-02 1.86E+05 1.05E+03 2.60E+04 1.63E-03 2.28E+03 9.24E+02 3.61E+06 1.03E+02 3.46E+00 2.29E+02 5.29E+02

29

Table 5: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] abiotic

depletion (minerals; kg Sb-eq.)

abiotic depletion (fossil fuels; MJ)

Land competition (m2.yr)

global warming (kg CO2-eq.)

ozone layer depletion (kg CFC11-eq.)

human toxicity (kg 1,4-DCB eq.)

Freshwater aquatic ecotoxicity (kg 1,4-DCB eq.)

Marine aquatic ecotoxicity (kg 1,4-DCB eq.)

Terrestrial ecotoxicity (kg 1,4-DCB eq.)

photochemical ozone formation (kg C2H4-eq.)

acidification (kg SO2-eq.)

eutrophication (kg PO4-eq.)

[A1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

[A2] 1.28 1.15 0.96 1.13 1.21 1.17 1.13 1.25 1.02 0.97 1.24 1.27

[A3] 0.27 0.72 0.42 0.41 0.66 0.57 0.88 1.02 0.29 0.68 0.29 0.22

[A4] 0.24 0.58 0.35 0.35 0.55 0.48 0.64 0.69 0.25 0.66 0.29 0.21

[A5] 0.19 0.28 0.11 0.21 0.24 0.32 0.44 0.49 0.17 0.77 0.19 0.14

[A6] 0.21 0.29 0.14 0.22 0.25 0.35 0.48 0.54 0.19 0.76 0.20 0.15

[A7] 0.19 0.26 0.12 0.19 0.21 0.31 0.43 0.48 0.17 0.67 0.18 0.13

[A8] 0.49 0.46 0.28 0.31 0.15 1.20 1.39 2.52 0.64 0.40 0.24 0.17

[A9] 0.55 0.49 0.32 0.32 0.14 1.32 1.54 2.81 0.71 0.43 0.25 0.18

[A10] 0.19 0.52 0.25 0.30 0.47 0.45 0.70 0.80 0.35 0.55 0.23 0.13

[A11] 0.14 0.40 0.19 0.24 0.44 0.33 0.45 0.49 0.25 0.45 0.20 0.10

[A12] 0.18 0.47 0.25 0.29 0.48 0.41 0.58 0.63 0.33 0.56 0.24 0.15

[A13] 0.36 0.24 1.67 0.20 0.15 0.57 0.65 1.03 0.44 0.29 0.22 0.19

[A14] 0.40 0.27 2.02 0.23 0.18 0.61 0.69 1.07 0.49 0.34 0.27 0.25

[A15] 0.35 0.23 1.85 0.20 0.17 0.50 0.57 0.85 0.42 0.29 0.24 0.21

[A16] 0.40 0.28 1.67 0.22 0.16 0.68 0.79 1.31 0.51 0.31 0.23 0.21

[A17] 0.23 0.60 0.39 0.33 0.46 0.65 0.97 1.11 0.33 0.63 0.28 0.16

[A18] 0.26 0.41 0.43 0.27 0.26 0.68 0.92 1.02 0.36 0.68 0.29 0.26

[A19] 0.18 0.27 0.11 0.23 0.22 0.20 0.18 0.21 0.14 0.30 0.26 0.15

[A20] 0.26 0.30 0.03 0.35 0.25 0.31 0.20 0.27 0.18 0.60 0.42 0.28

[A21] 0.56 0.55 0.56 0.53 0.50 0.55 0.56 0.57 0.55 0.51 0.55 0.55

[A22] 0.80 0.75 0.80 0.74 0.70 0.77 0.79 0.80 0.78 0.71 0.78 0.78

30

Table 6: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation Climate

change (kg CO2-eq.)

Ozone depletion (kg CFC11-eq.)

Human toxicity, cancer effects (CTUh)

Human toxicity, non-cancer effects (CTUh)

Particulate matter/Respiratory inorganics (kg PM2.5-eq.)

Ionizing radiation, human health

Photochemical ozone formation, human health (kg C2H4-eq.)

Acidification (AE)

Eutrophication terrestrial (AE)

Eutrophication freshwater (kg P-eq.)

Eutrophication marine (kg N-eq.)

Ecotoxicity freshwater (CTUe)

Resource depletion, mineral+fossils (kg Sb-eq.)

[A1] 3.50E+04 2.32E-03 3.41E-04 2.10E-03 1.90E+01 9.26E+02 2.64E+01 2.32E+02 8.14E+02 4.85E+00 6.79E+00 5.97E+04 6.48E-01

[A2] 3.95E+04 2.80E-03 3.94E-04 3.24E-03 2.31E+01 1.05E+03 2.85E+01 3.15E+02 1.16E+03 5.75E+00 9.68E+00 6.31E+04 8.16E-01

[A3] 1.44E+04 1.53E-03 2.43E-04 5.70E-04 9.35E+00 3.90E+02 1.41E+01 5.09E+01 9.40E+01 4.84E+00 7.97E-01 3.12E+04 2.00E-01

[A4] 1.23E+04 1.28E-03 1.87E-04 3.64E-04 7.33E+00 3.92E+02 1.22E+01 4.87E+01 9.43E+01 3.41E+00 8.08E-01 2.69E+04 1.90E-01

[A5] 7.34E+03 5.66E-04 1.21E-04 5.55E-04 4.98E+00 1.72E+02 1.46E+01 4.28E+01 1.07E+02 2.58E+00 8.93E-01 9.43E+03 1.33E-01

[A6] 7.65E+03 5.71E-04 1.34E-04 6.05E-04 4.85E+00 1.91E+02 1.37E+01 4.55E+01 1.11E+02 2.90E+00 9.32E-01 1.05E+04 1.50E-01

[A7] 6.71E+03 4.96E-04 1.19E-04 5.38E-04 4.24E+00 1.74E+02 1.20E+01 4.06E+01 9.85E+01 2.59E+00 8.24E-01 9.38E+03 1.34E-01

[A8] 1.07E+04 3.58E-04 3.60E-04 1.20E-03 4.98E+00 3.24E+02 5.81E+00 5.57E+01 7.46E+01 4.31E+00 6.25E-01 4.45E+04 2.40E-01

[A9] 1.12E+04 3.34E-04 3.98E-04 1.36E-03 5.09E+00 3.49E+02 5.78E+00 5.93E+01 7.87E+01 4.76E+00 6.58E-01 4.84E+04 2.62E-01

[A10] 1.06E+04 1.09E-03 1.80E-04 9.09E-04 6.68E+00 2.21E+02 1.14E+01 4.50E+01 8.58E+01 3.85E+00 7.25E-01 1.41E+04 1.51E-01

[A11] 8.42E+03 1.03E-03 1.20E-04 6.41E-04 6.55E+00 1.88E+02 1.11E+01 3.44E+01 6.40E+01 2.44E+00 5.45E-01 9.34E+03 1.14E-01

[A12] 1.01E+04 1.12E-03 1.51E-04 8.28E-04 7.25E+00 2.24E+02 1.25E+01 4.39E+01 8.88E+01 3.14E+00 7.51E-01 1.19E+04 1.43E-01

[A13] 7.03E+03 3.57E-04 1.81E-04 -6.44E-04 3.75E+00 2.73E+02 4.57E+00 5.94E+01 1.52E+02 2.43E+00 1.27E+00 4.45E+04 2.32E-01

[A14] 8.08E+03 4.20E-04 1.94E-04 -9.21E-04 4.40E+00 3.10E+02 5.37E+00 7.22E+01 1.89E+02 2.66E+00 1.57E+00 5.23E+04 2.69E-01

[A15] 7.03E+03 3.88E-04 1.62E-04 -9.36E-04 3.89E+00 2.77E+02 4.81E+00 6.41E+01 1.72E+02 2.25E+00 1.44E+00 4.66E+04 2.38E-01

[A16] 7.85E+03 3.61E-04 2.17E-04 -4.20E-04 4.00E+00 2.94E+02 4.76E+00 6.10E+01 1.51E+02 2.86E+00 1.26E+00 4.71E+04 2.50E-01

[A17] 1.17E+04 1.05E-03 2.39E-04 5.00E-04 7.79E+00 4.89E+02 1.20E+01 5.40E+01 7.83E+01 5.03E+00 6.62E-01 3.30E+04 1.74E-01

[A18] 9.46E+03 5.84E-04 2.23E-04 3.18E-04 6.96E+00 5.73E+02 1.06E+01 6.34E+01 1.09E+02 4.63E+00 9.17E-01 3.77E+04 1.90E-01

[A19] 8.04E+03 5.01E-04 6.29E-05 5.63E-04 4.50E+00 1.58E+02 6.77E+00 7.89E+01 3.03E+02 9.30E-01 2.52E+00 8.26E+03 1.17E-01

[A20] 1.21E+04 5.91E-04 6.89E-05 1.97E-03 8.54E+00 1.58E+02 1.25E+01 1.24E+02 5.05E+02 1.10E+00 4.20E+00 7.62E+03 1.65E-01

[A21] 1.85E+04 1.66E-03 2.76E-04 1.72E-03 1.36E+01 7.59E+02 1.84E+01 2.09E+02 7.50E+02 4.06E+00 6.25E+00 5.02E+04 5.47E-01

[A22] 2.60E+04 4.25E-04 5.88E-05 2.96E-04 2.62E+00 1.47E+02 3.79E+00 3.37E+01 1.04E+02 8.14E-01 8.68E-01 8.21E+03 9.42E-02

31

Table 7: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] Climate

change (kg CO2-eq.)

Ozone depletion (kg CFC11-eq.)

Human toxicity, cancer effects (CTUh)

Human toxicity, non-cancer effects (CTUh)

Particulate matter/Respiratory inorganics (kg PM2.5-eq.)

Ionizing radiation, human health

Photochemical ozone formation, human health (kg C2H4-eq.)

Acidification (AE)

Eutrophication terrestrial (AE)

Eutrophication freshwater (kg P-eq.)

Eutrophication marine (kg N-eq.)

Ecotoxicity freshwater (CTUe)

Resource depletion, mineral+fossils (kg Sb-eq.)

[A1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

[A2] 1.13 1.21 1.16 1.54 1.22 1.13 1.08 1.36 1.43 1.19 1.43 1.06 1.26

[A3] 0.41 0.66 0.71 0.27 0.49 0.42 0.53 0.22 0.12 1.00 0.12 0.52 0.31

[A4] 0.35 0.55 0.55 0.17 0.39 0.42 0.46 0.21 0.12 0.70 0.12 0.45 0.29

[A5] 0.21 0.24 0.35 0.26 0.26 0.19 0.55 0.18 0.13 0.53 0.13 0.16 0.21

[A6] 0.22 0.25 0.39 0.29 0.26 0.21 0.52 0.20 0.14 0.60 0.14 0.18 0.23

[A7] 0.19 0.21 0.35 0.26 0.22 0.19 0.45 0.18 0.12 0.53 0.12 0.16 0.21

[A8] 0.31 0.15 1.06 0.57 0.26 0.35 0.22 0.24 0.09 0.89 0.09 0.75 0.37

[A9] 0.32 0.14 1.17 0.65 0.27 0.38 0.22 0.26 0.10 0.98 0.10 0.81 0.40

[A10] 0.30 0.47 0.53 0.43 0.35 0.24 0.43 0.19 0.11 0.79 0.11 0.24 0.23

[A11] 0.24 0.44 0.35 0.31 0.34 0.20 0.42 0.15 0.08 0.50 0.08 0.16 0.18

[A12] 0.29 0.48 0.44 0.39 0.38 0.24 0.47 0.19 0.11 0.65 0.11 0.20 0.22

[A13] 0.20 0.15 0.53 -0.31 0.20 0.29 0.17 0.26 0.19 0.50 0.19 0.75 0.36

[A14] 0.23 0.18 0.57 -0.44 0.23 0.33 0.20 0.31 0.23 0.55 0.23 0.88 0.42

[A15] 0.20 0.17 0.48 -0.45 0.20 0.30 0.18 0.28 0.21 0.46 0.21 0.78 0.37

[A16] 0.22 0.16 0.64 -0.20 0.21 0.32 0.18 0.26 0.19 0.59 0.19 0.79 0.39

[A17] 0.33 0.45 0.70 0.24 0.41 0.53 0.45 0.23 0.10 1.04 0.10 0.55 0.27

[A18] 0.27 0.25 0.65 0.15 0.37 0.62 0.40 0.27 0.13 0.95 0.14 0.63 0.29

[A19] 0.23 0.22 0.18 0.27 0.24 0.17 0.26 0.34 0.37 0.19 0.37 0.14 0.18

[A20] 0.35 0.25 0.20 0.94 0.45 0.17 0.47 0.53 0.62 0.23 0.62 0.13 0.25

[A21] 0.53 0.72 0.81 0.82 0.72 0.82 0.70 0.90 0.92 0.84 0.92 0.84 0.84

[A22] 0.74 0.18 0.17 0.14 0.14 0.16 0.14 0.15 0.13 0.17 0.13 0.14 0.15

32

Table 8: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation

abiotic depletion (minerals; kg Sb-eq.)

abiotic depletion

(fossil fuels; MJ)

global warming (kg CO2-

eq.)

ozone layer

depletion (kg

CFC11-eq.)

human toxicity (kg 1,4-

DCB eq.)

Freshwater aquatic

ecotoxicity (kg 1,4-

DCB eq.)

Marine aquatic

ecotoxicity (kg 1,4-

DCB eq.)

Terrestrial ecotoxicity

(kg 1,4-DCB eq.)

photochemical ozone

formation (kg C2H4-eq.)

acidification (kg SO2-

eq.)

eutrophication (kg PO4-eq.)

[A1] 7.20E-03 8.81E+04 1.26E+04 7.90E-04 1.50E+03 1.19E+03 1.83E+06 3.87E+02 1.85E+00 9.72E+01 1.85E+02

[A2] 5.76E-03 7.51E+04 9.90E+03 6.69E-04 1.14E+03 8.48E+02 1.46E+06 2.63E+02 1.38E+00 7.88E+01 1.61E+02

[A3] 4.16E-03 1.72E+05 1.41E+04 1.76E-03 1.57E+03 9.97E+02 3.50E+06 1.23E+02 3.00E+00 8.78E+01 1.11E+02

[A4] 3.89E-03 1.49E+05 1.27E+04 1.60E-03 1.40E+03 8.15E+02 2.51E+06 1.20E+02 2.92E+00 8.76E+01 1.09E+02

[A5] 2.65E-03 8.17E+04 7.82E+03 8.88E-04 9.27E+02 5.57E+02 1.76E+06 8.84E+01 2.93E+00 5.78E+01 6.24E+01

[A6] 3.05E-03 7.77E+04 7.71E+03 8.12E-04 9.80E+02 6.31E+02 1.84E+06 1.14E+02 2.82E+00 5.78E+01 6.40E+01

[A7] 2.75E-03 6.77E+04 6.75E+03 7.02E-04 8.75E+02 5.65E+02 1.63E+06 1.03E+02 2.49E+00 5.12E+01 5.55E+01

[A8] 6.62E-03 8.83E+04 8.46E+03 3.02E-04 2.76E+03 1.26E+03 8.70E+06 6.61E+01 1.61E+00 5.85E+01 9.07E+01

[A9] 7.25E-03 9.42E+04 8.91E+03 2.88E-04 3.02E+03 1.38E+03 9.60E+06 7.29E+01 1.70E+00 6.13E+01 9.40E+01

[A10] 4.80E-03 1.84E+05 1.60E+04 2.00E-03 1.91E+03 1.39E+03 3.77E+06 2.96E+02 3.68E+00 1.07E+02 8.81E+01

[A11] 3.75E-03 1.47E+05 1.30E+04 1.72E-03 1.49E+03 1.04E+03 2.61E+06 2.35E+02 3.02E+00 9.06E+01 7.01E+01

[A12] 4.82E-03 1.81E+05 1.62E+04 2.09E-03 1.88E+03 1.34E+03 3.33E+06 3.08E+02 3.79E+00 1.12E+02 9.53E+01

[A13] 3.88E-03 3.77E+04 4.41E+03 2.40E-04 1.04E+03 4.81E+02 2.73E+06 3.33E+01 9.13E-01 4.26E+01 6.82E+01

[A14] 4.43E-03 4.21E+04 5.10E+03 2.79E-04 1.14E+03 5.29E+02 2.95E+06 3.76E+01 1.06E+00 5.04E+01 8.91E+01

[A15] 3.92E-03 3.71E+04 4.52E+03 2.59E-04 9.82E+02 4.52E+02 2.45E+06 3.26E+01 9.47E-01 4.56E+01 7.59E+01

[A16] 4.20E-03 4.17E+04 4.75E+03 2.40E-04 1.20E+03 5.56E+02 3.29E+06 3.72E+01 9.52E-01 4.36E+01 7.39E+01

[A17] 3.62E-03 1.14E+05 9.90E+03 1.03E-03 1.60E+03 1.11E+03 3.29E+06 1.97E+02 2.36E+00 7.29E+01 6.85E+01

[A18] 4.51E-03 1.08E+05 1.05E+04 1.01E-03 1.89E+03 1.30E+03 3.45E+06 2.64E+02 2.79E+00 8.67E+01 1.04E+02

[A19] 2.32E-03 4.25E+04 5.53E+03 3.17E-04 4.98E+02 2.75E+02 6.99E+05 6.90E+01 1.03E+00 5.02E+01 5.81E+01

[A20] 2.51E-03 4.71E+04 6.91E+03 3.72E-04 5.54E+02 1.50E+02 7.09E+05 1.69E+01 1.70E+00 6.92E+01 1.04E+02

[A21] 6.52E-03 7.72E+04 1.10E+04 6.51E-04 1.34E+03 1.08E+03 1.67E+06 3.52E+02 1.62E+00 8.65E+01 1.64E+02

[A22] 6.29E-03 7.45E+04 1.06E+04 6.28E-04 1.29E+03 1.04E+03 1.61E+06 3.39E+02 1.57E+00 8.35E+01 1.58E+02

33

Table 9: CML baseline characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1]

abiotic depletion (minerals; kg Sb-eq.)

abiotic depletion

(fossil fuels; MJ)

global warming (kg CO2-

eq.)

ozone layer

depletion (kg

CFC11-eq.)

human toxicity

(kg 1,4-DCB eq.)

Freshwater aquatic

ecotoxicity (kg 1,4-

DCB eq.)

Marine aquatic

ecotoxicity (kg 1,4-

DCB eq.)

Terrestrial ecotoxicity

(kg 1,4-DCB eq.)

photochemical ozone

formation (kg C2H4-eq.)

acidification (kg SO2-

eq.)

eutrophication (kg PO4-eq.)

[A1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

[A2] 0.80 0.85 0.79 0.85 0.76 0.71 0.80 0.68 0.75 0.81 0.87

[A3] 0.58 1.96 1.12 2.23 1.05 0.84 1.91 0.32 1.62 0.90 0.60

[A4] 0.54 1.69 1.01 2.03 0.93 0.68 1.37 0.31 1.58 0.90 0.59

[A5] 0.37 0.93 0.62 1.12 0.62 0.47 0.96 0.23 1.59 0.59 0.34

[A6] 0.42 0.88 0.61 1.03 0.65 0.53 1.00 0.29 1.52 0.59 0.35

[A7] 0.38 0.77 0.54 0.89 0.58 0.47 0.89 0.27 1.35 0.53 0.30

[A8] 0.92 1.00 0.67 0.38 1.84 1.05 4.75 0.17 0.87 0.60 0.49

[A9] 1.01 1.07 0.71 0.36 2.01 1.16 5.24 0.19 0.92 0.63 0.51

[A10] 0.67 2.09 1.27 2.53 1.27 1.17 2.06 0.77 1.99 1.10 0.48

[A11] 0.52 1.67 1.03 2.18 1.00 0.87 1.42 0.61 1.64 0.93 0.38

[A12] 0.67 2.05 1.28 2.64 1.25 1.13 1.82 0.80 2.05 1.15 0.52

[A13] 0.54 0.43 0.35 0.30 0.69 0.40 1.49 0.09 0.49 0.44 0.37

[A14] 0.62 0.48 0.40 0.35 0.76 0.44 1.61 0.10 0.57 0.52 0.48

[A15] 0.54 0.42 0.36 0.33 0.66 0.38 1.34 0.08 0.51 0.47 0.41

[A16] 0.58 0.47 0.38 0.30 0.80 0.47 1.80 0.10 0.52 0.45 0.40

[A17] 0.50 1.29 0.79 1.30 1.07 0.93 1.80 0.51 1.28 0.75 0.37

[A18] 0.63 1.23 0.83 1.28 1.26 1.09 1.88 0.68 1.51 0.89 0.56

[A19] 0.32 0.48 0.44 0.40 0.33 0.23 0.38 0.18 0.56 0.52 0.31

[A20] 0.35 0.53 0.55 0.47 0.37 0.13 0.39 0.04 0.92 0.71 0.56

[A21] 0.91 0.88 0.87 0.82 0.89 0.91 0.91 0.91 0.88 0.89 0.89

[A22] 0.87 0.85 0.84 0.80 0.86 0.88 0.88 0.88 0.85 0.86 0.86

34

Table 10: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation Climate

change (kg CO2-

eq.)

Ozone depletion

(kg CFC11-

eq.)

Human toxicity, cancer effects (CTUh)

Human toxicity,

non-cancer effects (CTUh)

Particulate matter/Respiratory

inorganics (kg PM2.5-eq.)

Ionizing radiation, human health

Photochemical ozone

formation, human health (kg C2H4-eq.)

Acidification (AE)

Eutrophication terrestrial (AE)

Eutrophication freshwater (kg

P-eq.)

Eutrophication marine (kg N-

eq.)

Ecotoxicity freshwater

(CTUe)

Resource depletion,

mineral+fossils (kg Sb-eq.)

[A1] 1.26E+04 7.81E-04 1.41E-04 2.10E-03 6.27E+00 516.8 8.567 8.89E+01 3.16E+02 2.54E+00 2.63E+00 2.75E+04 0.3232

[A2] 9.89E+03 6.63E-04 1.12E-04 1.60E-03 5.19E+00 387.3 6.82 7.34E+01 2.62E+02 1.93E+00 2.18E+00 1.99E+04 0.2503

[A3] 1.41E+04 1.75E-03 2.01E-04 8.44E-04 1.05E+01 398.1 16.21 4.82E+01 9.54E+01 3.88E+00 8.14E-01 2.72E+04 0.2018

[A4] 1.27E+04 1.60E-03 1.64E-04 7.12E-04 9.23E+00 400.7 15.04 4.69E+01 9.60E+01 2.93E+00 8.24E-01 2.44E+04 1.96E-01

[A5] 7.82E+03 8.85E-04 1.03E-04 7.48E-04 6.42E+00 202.5 14.25 3.70E+01 9.00E+01 2.12E+00 7.59E-01 9056 0.1265

[A6] 7.71E+03 8.08E-04 1.09E-04 8.78E-04 5.89E+00 225.2 12.93 3.97E+01 1.01E+02 2.31E+00 8.46E-01 1.01E+04 0.1434

[A7] 6.75E+03 6.99E-04 9.74E-05 7.89E-04 5.12E+00 204.3 11.28 3.57E+01 8.98E+01 2.06E+00 7.55E-01 9020 0.1289

[A8] 8.46E+03 3.00E-04 2.81E-04 8.10E-04 4.03E+00 266.4 4.787 4.64E+01 6.66E+01 3.37E+00 5.58E-01 3.72E+04 0.193

[A9] 8.91E+03 2.86E-04 3.08E-04 9.21E-04 4.14E+00 284.5 4.797 4.92E+01 7.02E+01 3.69E+00 5.88E-01 4.02E+04 0.2094

[A10] 1.60E+04 2.00E-03 2.08E-04 2.05E-03 1.26E+01 432.6 21.09 6.64E+01 1.64E+02 4.36E+00 1.39E+00 2.06E+04 0.2543

[A11] 1.29E+04 1.72E-03 1.53E-04 1.59E-03 1.10E+01 358.2 18.39 5.28E+01 1.29E+02 3.12E+00 1.10E+00 1.54E+04 0.2008

[A12] 1.62E+04 2.08E-03 1.93E-04 2.07E-03 1.34E+01 448.9 22.48 6.75E+01 1.71E+02 3.99E+00 1.45E+00 1.97E+04 0.2569

[A13] 4.41E+03 2.37E-04 1.11E-04 -5.43E-04 2.49E+00 186.4 3.082 3.91E+01 1.01E+02 1.60E+00 8.44E-01 2.65E+04 0.1599

[A14] 5.10E+03 2.76E-04 1.22E-04 -7.26E-04 2.90E+00 211.2 3.579 4.70E+01 1.24E+02 1.79E+00 1.04E+00 3.13E+04 0.1865

[A15] 4.52E+03 2.57E-04 1.05E-04 -7.07E-04 2.62E+00 192.7 3.267 4.24E+01 1.14E+02 1.56E+00 9.50E-01 2.81E+04 0.168

[A16] 4.75E+03 2.38E-04 1.27E-04 -4.36E-04 2.59E+00 194.7 3.151 3.97E+01 9.97E+01 1.78E+00 8.32E-01 2.75E+04 0.1667

[A17] 9.90E+03 1.02E-03 1.69E-04 1.13E-03 7.28E+00 462.6 11.14 5.01E+01 1.06E+02 3.59E+00 8.91E-01 2.46E+04 0.1809

[A18] 1.05E+04 9.90E-04 1.81E-04 1.37E-03 8.22E+00 583.1 12.49 6.31E+01 1.46E+02 3.86E+00 1.22E+00 3.02E+04 0.2243

[A19] 5.53E+03 3.15E-04 4.68E-05 6.51E-04 2.82E+00 142 4.414 4.95E+01 1.85E+02 7.93E-01 1.54E+00 6552 0.09813

[A20] 6.91E+03 3.70E-04 4.59E-05 1.14E-03 5.15E+00 108 7.201 6.99E+01 2.77E+02 6.66E-01 2.31E+00 4695 0.09544

[A21] 1.10E+04 6.43E-04 1.27E-04 1.89E-03 5.08E+00 465.8 7.053 8.33E+01 2.98E+02 2.31E+00 2.48E+00 2.50E+04 0.2938

[A22] 1.06E+04 6.20E-04 1.22E-04 1.82E-03 4.90E+00 449.8 6.812 8.03E+01 2.87E+02 2.23E+00 2.39E+00 2.41E+04 0.2833

35

Table 11: ILCD characterisation results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1] Climate

change (kg

CO2-eq.)

Ozone depletion

(kg CFC11-

eq.)

Human toxicity, cancer effects (CTUh)

Human toxicity,

non-cancer effects (CTUh)

Particulate matter/Respiratory

inorganics (kg PM2.5-eq.)

Ionizing radiation, human health

Photochemical ozone formation, human health (kg

C2H4-eq.)

Acidification (AE)

Eutrophication terrestrial (AE)

Eutrophication freshwater (kg

P-eq.)

Eutrophication marine (kg N-

eq.)

Ecotoxicity freshwater

(CTUe)

Resource depletion,

mineral+fossils (kg Sb-eq.)

[A1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

[A2] 0.78 0.85 0.79 0.76 0.83 0.75 0.80 0.83 0.83 0.76 0.83 0.72 0.77

[A3] 1.12 2.24 1.42 0.40 1.68 0.77 1.89 0.54 0.30 1.52 0.31 0.99 0.62

[A4] 1.01 2.04 1.16 0.34 1.47 0.78 1.76 0.53 0.30 1.15 0.31 0.88 0.60

[A5] 0.62 1.13 0.73 0.36 1.02 0.39 1.66 0.42 0.29 0.83 0.29 0.33 0.39

[A6] 0.61 1.03 0.77 0.42 0.94 0.44 1.51 0.45 0.32 0.91 0.32 0.37 0.44

[A7] 0.54 0.89 0.69 0.38 0.82 0.40 1.32 0.40 0.28 0.81 0.29 0.33 0.40

[A8] 0.67 0.38 1.99 0.39 0.64 0.52 0.56 0.52 0.21 1.32 0.21 1.35 0.60

[A9] 0.71 0.37 2.18 0.44 0.66 0.55 0.56 0.55 0.22 1.45 0.22 1.46 0.65

[A10] 1.27 2.55 1.47 0.98 2.02 0.84 2.46 0.75 0.52 1.71 0.53 0.75 0.79

[A11] 1.02 2.20 1.08 0.76 1.76 0.69 2.15 0.59 0.41 1.23 0.42 0.56 0.62

[A12] 1.29 2.67 1.37 0.99 2.14 0.87 2.62 0.76 0.54 1.57 0.55 0.72 0.79

[A13] 0.35 0.30 0.79 -0.26 0.40 0.36 0.36 0.44 0.32 0.63 0.32 0.96 0.49

[A14] 0.40 0.35 0.86 -0.35 0.46 0.41 0.42 0.53 0.39 0.70 0.39 1.14 0.58

[A15] 0.36 0.33 0.74 -0.34 0.42 0.37 0.38 0.48 0.36 0.61 0.36 1.02 0.52

[A16] 0.38 0.30 0.90 -0.21 0.41 0.38 0.37 0.45 0.32 0.70 0.32 1.00 0.52

[A17] 0.79 1.30 1.19 0.54 1.16 0.90 1.30 0.56 0.33 1.41 0.34 0.89 0.56

[A18] 0.83 1.27 1.28 0.65 1.31 1.13 1.46 0.71 0.46 1.52 0.47 1.10 0.69

[A19] 0.44 0.40 0.33 0.31 0.45 0.27 0.52 0.56 0.59 0.31 0.59 0.24 0.30

[A20] 0.55 0.47 0.32 0.55 0.82 0.21 0.84 0.79 0.88 0.26 0.88 0.17 0.30

[A21] 0.87 0.82 0.90 0.90 0.81 0.90 0.82 0.94 0.94 0.91 0.94 0.91 0.91

[A22] 0.84 0.79 0.87 0.87 0.78 0.87 0.80 0.90 0.91 0.88 0.91 0.87 0.88

36

Table 12: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation

abiotic depletion (minerals;

yr)

abiotic depletion

(fossil fuels; yr))

land competition

(yr)

global warming

(yr)

ozone layer

depletion (yr)

human toxicity

(yr)

Freshwater aquatic

ecotoxicity (yr)

Marine aquatic

ecotoxicity (yr)

Terrestrial ecotoxicity

(yr)

photochemical ozone

formation (yr)

acidification (yr)

eutrophication (yr)

[A1] 8.09E-11 6.51E-10 0 8.42E-10 1.15E-11 1.24E-09 4.99E-10 2.30E-08 1.21E-10 1.34E-10 1.23E-09 4.27E-09

[A2] 1.04E-10 7.50E-10 0 9.51E-10 1.39E-11 1.45E-09 5.64E-10 2.87E-08 1.24E-10 1.29E-10 1.53E-09 5.45E-09

[A3] 2.16E-11 4.71E-10 0 3.47E-10 7.59E-12 7.01E-10 4.39E-10 2.34E-08 3.53E-11 9.11E-11 3.55E-10 9.37E-10

[A4] 1.96E-11 3.76E-10 0 2.95E-10 6.38E-12 5.90E-10 3.20E-10 1.58E-08 3.01E-11 8.77E-11 3.51E-10 9.15E-10

[A5] 1.52E-11 1.81E-10 0 1.77E-10 2.80E-12 4.01E-10 2.17E-10 1.13E-08 2.01E-11 1.03E-10 2.31E-10 6.09E-10

[A6] 1.73E-11 1.90E-10 0 1.84E-10 2.83E-12 4.35E-10 2.42E-10 1.24E-08 2.31E-11 1.01E-10 2.45E-10 6.25E-10

[A7] 1.55E-11 1.67E-10 0 1.62E-10 2.46E-12 3.88E-10 2.15E-10 1.10E-08 2.06E-11 8.94E-11 2.18E-10 5.39E-10

[A8] 4.00E-11 2.96E-10 0 2.57E-10 1.78E-12 1.48E-09 6.95E-10 5.81E-08 7.77E-11 5.37E-11 2.94E-10 7.35E-10

[A9] 4.42E-11 3.17E-10 0 2.70E-10 1.66E-12 1.63E-09 7.70E-10 6.46E-08 8.64E-11 5.68E-11 3.09E-10 7.61E-10

[A10] 1.55E-11 3.40E-10 0 2.54E-10 5.39E-12 5.58E-10 3.50E-10 1.85E-08 4.23E-11 7.41E-11 2.80E-10 5.67E-10

[A11] 1.14E-11 2.64E-10 0 2.03E-10 5.08E-12 4.12E-10 2.27E-10 1.13E-08 3.02E-11 6.07E-11 2.49E-10 4.38E-10

[A12] 1.47E-11 3.07E-10 0 2.43E-10 5.55E-12 5.05E-10 2.90E-10 1.45E-08 3.95E-11 7.47E-11 2.93E-10 6.22E-10

[A13] 2.91E-11 1.57E-10 0 1.69E-10 1.78E-12 6.99E-10 3.24E-10 2.36E-08 5.34E-11 3.83E-11 2.73E-10 8.00E-10

[A14] 3.27E-11 1.73E-10 0 1.95E-10 2.09E-12 7.51E-10 3.46E-10 2.47E-08 5.97E-11 4.52E-11 3.27E-10 1.06E-09

[A15] 2.83E-11 1.49E-10 0 1.69E-10 1.93E-12 6.22E-10 2.83E-10 1.97E-08 5.07E-11 3.93E-11 2.90E-10 8.92E-10

[A16] 3.27E-11 1.82E-10 0 1.89E-10 1.80E-12 8.47E-10 3.98E-10 3.01E-08 6.17E-11 4.09E-11 2.84E-10 8.84E-10

[A17] 1.88E-11 3.91E-10 0 2.82E-10 5.29E-12 8.08E-10 4.82E-10 2.56E-08 4.04E-11 8.39E-11 3.41E-10 7.00E-10

[A18] 2.14E-11 2.65E-10 0 2.28E-10 2.99E-12 8.44E-10 4.62E-10 2.35E-08 4.39E-11 9.08E-11 3.56E-10 1.10E-09

[A19] 1.43E-11 1.72E-10 0 1.94E-10 2.49E-12 2.49E-10 9.00E-11 4.76E-09 1.68E-11 3.97E-11 3.24E-10 6.23E-10

[A20] 2.08E-11 1.94E-10 0 2.92E-10 2.93E-12 3.78E-10 9.84E-11 6.31E-09 2.12E-11 8.07E-11 5.15E-10 1.18E-09

[A21] 4.56E-11 3.58E-10 0 4.46E-10 5.77E-12 6.82E-10 2.82E-10 1.32E-08 6.63E-11 6.77E-11 6.80E-10 2.34E-09

[A22] 6.44E-11 4.90E-10 0 6.26E-10 8.03E-12 9.52E-10 3.95E-10 1.85E-08 9.38E-11 9.41E-11 9.59E-10 3.34E-09

37

Table 13: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] abiotic

depletion (minerals; yr)

abiotic depletion (fossil fuels; yr))

land competition (yr)

global warming (yr)

ozone layer depletion (yr)

human toxicity (yr)

Freshwater aquatic ecotoxicity (yr)

Marine aquatic ecotoxicity (yr)

Terrestrial ecotoxicity (yr)

photochemical ozone formation (yr)

acidification (yr)

eutrophication (yr)

[A1] 1.00 1.00 n/a 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

[A2] 1.29 1.15 n/a 1.13 1.20 1.17 1.13 1.25 1.03 0.97 1.24 1.28

[A3] 0.27 0.72 n/a 0.41 0.66 0.57 0.88 1.02 0.29 0.68 0.29 0.22

[A4] 0.24 0.58 n/a 0.35 0.55 0.48 0.64 0.68 0.25 0.66 0.29 0.21

[A5] 0.19 0.28 n/a 0.21 0.24 0.32 0.44 0.49 0.17 0.77 0.19 0.14

[A6] 0.21 0.29 n/a 0.22 0.25 0.35 0.48 0.54 0.19 0.76 0.20 0.15

[A7] 0.19 0.26 n/a 0.19 0.21 0.31 0.43 0.48 0.17 0.67 0.18 0.13

[A8] 0.49 0.45 n/a 0.30 0.15 1.19 1.39 2.52 0.64 0.40 0.24 0.17

[A9] 0.55 0.49 n/a 0.32 0.14 1.32 1.54 2.80 0.72 0.43 0.25 0.18

[A10] 0.19 0.52 n/a 0.30 0.47 0.45 0.70 0.80 0.35 0.55 0.23 0.13

[A11] 0.14 0.40 n/a 0.24 0.44 0.33 0.45 0.49 0.25 0.45 0.20 0.10

[A12] 0.18 0.47 n/a 0.29 0.48 0.41 0.58 0.63 0.33 0.56 0.24 0.15

[A13] 0.36 0.24 n/a 0.20 0.15 0.57 0.65 1.03 0.44 0.29 0.22 0.19

[A14] 0.40 0.27 n/a 0.23 0.18 0.61 0.69 1.07 0.49 0.34 0.27 0.25

[A15] 0.35 0.23 n/a 0.20 0.17 0.50 0.57 0.85 0.42 0.29 0.24 0.21

[A16] 0.40 0.28 n/a 0.22 0.16 0.68 0.80 1.31 0.51 0.31 0.23 0.21

[A17] 0.23 0.60 n/a 0.34 0.46 0.65 0.96 1.11 0.33 0.63 0.28 0.16

[A18] 0.26 0.41 n/a 0.27 0.26 0.68 0.93 1.02 0.36 0.68 0.29 0.26

[A19] 0.18 0.26 n/a 0.23 0.22 0.20 0.18 0.21 0.14 0.30 0.26 0.15

[A20] 0.26 0.30 n/a 0.35 0.25 0.31 0.20 0.27 0.18 0.60 0.42 0.28

[A21] 0.56 0.55 n/a 0.53 0.50 0.55 0.57 0.57 0.55 0.51 0.55 0.55

[A22] 0.80 0.75 n/a 0.74 0.70 0.77 0.79 0.80 0.78 0.70 0.78 0.78

38

Table 14: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation Climate

change (yr)

Ozone depletion (yr)

Human toxicity, cancer effects (yr)

Human toxicity, non-cancer effects (yr)

Particulate matter/Respiratory inorganics (yr)

Ionizing radiation, human health (yr)

Photochemical ozone formation, human health (yr)

Acidification (yr)

Eutrophication terrestrial (yr)

Eutrophication freshwater (yr)

Eutrophication marine (yr)

Ecotoxicity freshwater (yr)

Resource depletion, mineral+fossils (yr)

[A1] 8.42E-10 1.18E-11 1.57E-08 7.06E-11 2.03E-09 1.57E-10 4.25E-10 1.07E-09 3.96E-09 n/a 3.98E-09 1.78E-09 2.60E-10

[A2] 9.51E-10 1.42E-11 1.82E-08 1.09E-10 2.48E-09 1.77E-10 4.58E-10 1.45E-09 5.65E-09 n/a 5.67E-09 1.88E-09 3.28E-10

[A3] 3.47E-10 7.77E-12 1.12E-08 1.92E-11 1.00E-09 6.61E-11 2.27E-10 2.35E-10 4.57E-10 n/a 4.67E-10 9.29E-10 8.04E-11

[A4] 2.95E-10 6.53E-12 8.61E-09 1.23E-11 7.84E-10 6.64E-11 1.97E-10 2.25E-10 4.59E-10 n/a 4.73E-10 8.01E-10 7.62E-11

[A5] 1.77E-10 2.88E-12 5.56E-09 1.87E-11 5.33E-10 2.91E-11 2.35E-10 1.97E-10 5.19E-10 n/a 5.23E-10 2.81E-10 5.35E-11

[A6] 1.84E-10 2.90E-12 6.16E-09 2.03E-11 5.19E-10 3.24E-11 2.20E-10 2.10E-10 5.42E-10 n/a 5.46E-10 3.13E-10 6.02E-11

[A7] 1.61E-10 2.52E-12 5.50E-09 1.81E-11 4.54E-10 2.94E-11 1.93E-10 1.88E-10 4.79E-10 n/a 4.83E-10 2.79E-10 5.40E-11

[A8] 2.56E-10 1.82E-12 1.66E-08 4.02E-11 5.32E-10 5.50E-11 9.36E-11 2.57E-10 3.63E-10 n/a 3.66E-10 1.32E-09 9.63E-11

[A9] 2.70E-10 1.70E-12 1.83E-08 4.58E-11 5.45E-10 5.91E-11 9.30E-11 2.74E-10 3.82E-10 n/a 3.86E-10 1.44E-09 1.05E-10

[A11] 2.03E-10 5.21E-12 5.55E-09 2.16E-11 7.01E-10 3.19E-11 1.79E-10 1.59E-10 3.11E-10 n/a 3.19E-10 2.78E-10 4.57E-11

[A12] 2.43E-10 5.70E-12 6.97E-09 2.78E-11 7.75E-10 3.80E-11 2.01E-10 2.03E-10 4.32E-10 n/a 4.40E-10 3.55E-10 5.77E-11

[A13] 1.69E-10 1.82E-12 8.32E-09 -2.17E-11 4.01E-10 4.63E-11 7.36E-11 2.74E-10 7.41E-10 n/a 7.44E-10 1.32E-09 9.34E-11

[A14] 1.95E-10 2.13E-12 8.94E-09 -3.10E-11 4.71E-10 5.25E-11 8.65E-11 3.33E-10 9.17E-10 n/a 9.21E-10 1.56E-09 1.08E-10

[A15] 1.69E-10 1.97E-12 7.45E-09 -3.15E-11 4.16E-10 4.70E-11 7.75E-11 2.96E-10 8.38E-10 n/a 8.42E-10 1.39E-09 9.57E-11

[A16] 1.89E-10 1.84E-12 1.00E-08 -1.41E-11 4.28E-10 4.98E-11 7.66E-11 2.82E-10 7.32E-10 n/a 7.36E-10 1.40E-09 1.01E-10

[A17] 2.82E-10 5.36E-12 1.10E-08 1.68E-11 8.34E-10 8.28E-11 1.93E-10 2.49E-10 3.81E-10 n/a 3.88E-10 9.83E-10 6.98E-11

[A18] 2.28E-10 2.97E-12 1.03E-08 1.07E-11 7.45E-10 9.71E-11 1.70E-10 2.93E-10 5.32E-10 n/a 5.37E-10 1.12E-09 7.64E-11

[A19] 1.94E-10 2.55E-12 2.90E-09 1.89E-11 4.82E-10 2.68E-11 1.09E-10 3.64E-10 1.48E-09 n/a 1.48E-09 2.46E-10 4.69E-11

[A20] 2.92E-10 3.00E-12 3.18E-09 6.64E-11 9.14E-10 2.67E-11 2.01E-10 5.74E-10 2.46E-09 n/a 2.46E-09 2.27E-10 6.64E-11

[A21] 4.46E-10 5.89E-12 8.72E-09 3.89E-11 9.85E-10 8.74E-11 2.03E-10 6.43E-10 2.41E-09 n/a 2.42E-09 9.97E-10 1.48E-10

[A22] 6.26E-10 8.19E-12 1.22E-08 5.49E-11 1.38E-09 1.22E-10 2.83E-10 9.09E-10 3.43E-09 n/a 3.45E-09 1.41E-09 2.09E-10

39

Table 15: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting economic allocation, relative to alternative [A1] Climate

change (yr)

Ozone depletion (yr)

Human toxicity, cancer effects (yr)

Human toxicity, non-cancer effects (yr)

Particulate matter/Respiratory inorganics (yr)

Ionizing radiation, human health (yr)

Photochemical ozone formation, human health (yr)

Acidification (yr)

Eutrophication terrestrial (yr)

Eutrophication freshwater (yr)

Eutrophication marine (yr)

Ecotoxicity freshwater (yr)

Resource depletion, mineral+fossils (yr)

[A1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 n/a 1.00 1.00 1.00

[A2] 1.13 1.21 1.16 1.55 1.22 1.13 1.08 1.36 1.43 n/a 1.43 1.06 1.26

[A3] 0.41 0.66 0.71 0.27 0.49 0.42 0.53 0.22 0.12 n/a 0.12 0.52 0.31

[A4] 0.35 0.55 0.55 0.17 0.39 0.42 0.46 0.21 0.12 n/a 0.12 0.45 0.29

[A5] 0.21 0.24 0.35 0.26 0.26 0.19 0.55 0.18 0.13 n/a 0.13 0.16 0.21

[A6] 0.22 0.25 0.39 0.29 0.26 0.21 0.52 0.20 0.14 n/a 0.14 0.18 0.23

[A7] 0.19 0.21 0.35 0.26 0.22 0.19 0.45 0.18 0.12 n/a 0.12 0.16 0.21

[A8] 0.30 0.15 1.06 0.57 0.26 0.35 0.22 0.24 0.09 n/a 0.09 0.74 0.37

[A9] 0.32 0.14 1.17 0.65 0.27 0.38 0.22 0.26 0.10 n/a 0.10 0.81 0.40

[A10] 0.30 0.47 0.53 0.43 0.35 0.24 0.43 0.19 0.11 n/a 0.11 0.24 0.23

[A11] 0.24 0.44 0.35 0.31 0.35 0.20 0.42 0.15 0.08 n/a 0.08 0.16 0.18

[A12] 0.29 0.48 0.44 0.39 0.38 0.24 0.47 0.19 0.11 n/a 0.11 0.20 0.22

[A13] 0.20 0.15 0.53 -0.31 0.20 0.30 0.17 0.26 0.19 n/a 0.19 0.75 0.36

[A14] 0.23 0.18 0.57 -0.44 0.23 0.33 0.20 0.31 0.23 n/a 0.23 0.88 0.41

[A15] 0.20 0.17 0.47 -0.45 0.20 0.30 0.18 0.28 0.21 n/a 0.21 0.78 0.37

[A16] 0.22 0.16 0.64 -0.20 0.21 0.32 0.18 0.26 0.18 n/a 0.18 0.79 0.39

[A17] 0.33 0.46 0.70 0.24 0.41 0.53 0.45 0.23 0.10 n/a 0.10 0.55 0.27

[A18] 0.27 0.25 0.66 0.15 0.37 0.62 0.40 0.27 0.13 n/a 0.14 0.63 0.29

[A19] 0.23 0.22 0.18 0.27 0.24 0.17 0.26 0.34 0.37 n/a 0.37 0.14 0.18

[A20] 0.35 0.25 0.20 0.94 0.45 0.17 0.47 0.54 0.62 n/a 0.62 0.13 0.25

[A21] 0.53 0.50 0.56 0.55 0.49 0.56 0.48 0.60 0.61 n/a 0.61 0.56 0.57

[A22] 0.74 0.70 0.77 0.78 0.68 0.78 0.67 0.85 0.87 n/a 0.87 0.80 0.80

40

Table 16: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation

abiotic depletion (minerals;

yr)

abiotic depletion

(fossil fuels; yr))

global warming

(yr)

ozone layer

depletion (yr)

human toxicity

(yr)

Freshwater aquatic

ecotoxicity (yr)

Marine aquatic

ecotoxicity (yr)

Terrestrial ecotoxicity

(yr)

photochemical ozone

formation (yr)

acidification (yr)

eutrophication (yr)

[A1] 3.44E-11 2.32E-10 3.03E-10 3.90E-12 6.24E-10 5.09E-10 9.39E-09 3.54E-10 5.02E-11 4.07E-10 1.17E-09

[A2] 2.75E-11 1.98E-10 2.38E-10 3.30E-12 4.76E-10 3.63E-10 7.50E-09 2.41E-10 3.75E-11 3.30E-10 1.02E-09

[A3] 1.99E-11 4.53E-10 3.40E-10 8.68E-12 6.54E-10 4.26E-10 1.80E-08 1.12E-10 8.14E-11 3.68E-10 7.04E-10

[A4] 1.86E-11 3.91E-10 3.06E-10 7.91E-12 5.82E-10 3.49E-10 1.29E-08 1.10E-10 7.94E-11 3.67E-10 6.89E-10

[A5] 1.27E-11 2.15E-10 1.88E-10 4.38E-12 3.86E-10 2.38E-10 9.00E-09 8.09E-11 7.96E-11 2.42E-10 3.94E-10

[A6] 1.46E-11 2.05E-10 1.86E-10 4.00E-12 4.08E-10 2.70E-10 9.42E-09 1.04E-10 7.65E-11 2.42E-10 4.04E-10

[A7] 1.31E-11 1.78E-10 1.63E-10 3.46E-12 3.65E-10 2.42E-10 8.34E-09 9.43E-11 6.77E-11 2.14E-10 3.50E-10

[A8] 3.17E-11 2.32E-10 2.04E-10 1.49E-12 1.15E-09 5.37E-10 4.46E-08 6.05E-11 4.37E-11 2.45E-10 5.73E-10

[A9] 3.47E-11 2.48E-10 2.14E-10 1.42E-12 1.26E-09 5.90E-10 4.92E-08 6.67E-11 4.61E-11 2.57E-10 5.94E-10

[A10] 2.30E-11 4.84E-10 3.86E-10 9.87E-12 7.95E-10 5.95E-10 1.93E-08 2.71E-10 9.99E-11 4.47E-10 5.56E-10

[A11] 1.79E-11 3.87E-10 3.12E-10 8.49E-12 6.22E-10 4.46E-10 1.34E-08 2.15E-10 8.21E-11 3.79E-10 4.43E-10

[A12] 2.31E-11 4.76E-10 3.89E-10 1.03E-11 7.82E-10 5.75E-10 1.71E-08 2.82E-10 1.03E-10 4.68E-10 6.02E-10

[A13] 1.86E-11 9.92E-11 1.06E-10 1.18E-12 4.34E-10 2.06E-10 1.40E-08 3.05E-11 2.48E-11 1.79E-10 4.30E-10

[A14] 2.12E-11 1.11E-10 1.23E-10 1.38E-12 4.76E-10 2.26E-10 1.51E-08 3.44E-11 2.89E-11 2.11E-10 5.62E-10

[A15] 1.88E-11 9.78E-11 1.09E-10 1.28E-12 4.09E-10 1.93E-10 1.26E-08 2.99E-11 2.58E-11 1.91E-10 4.79E-10

[A16] 2.01E-11 1.10E-10 1.14E-10 1.19E-12 4.98E-10 2.38E-10 1.69E-08 3.41E-11 2.59E-11 1.82E-10 4.67E-10

[A17] 1.73E-11 2.99E-10 2.38E-10 5.07E-12 6.68E-10 4.74E-10 1.69E-08 1.80E-10 6.40E-11 3.05E-10 4.32E-10

[A18] 2.16E-11 2.85E-10 2.53E-10 4.97E-12 7.87E-10 5.57E-10 1.77E-08 2.42E-10 7.58E-11 3.63E-10 6.57E-10

[A19] 1.11E-11 1.12E-10 1.33E-10 1.57E-12 2.08E-10 1.17E-10 3.58E-09 6.32E-11 2.81E-11 2.10E-10 3.67E-10

[A20] 1.20E-11 1.24E-10 1.66E-10 1.83E-12 2.31E-10 6.40E-11 3.64E-09 1.54E-11 4.61E-11 2.90E-10 6.56E-10

[A21] 3.12E-11 2.03E-10 2.65E-10 3.21E-12 5.58E-10 4.63E-10 8.56E-09 3.22E-10 4.41E-11 3.62E-10 1.04E-09

[A22] 3.01E-11 1.96E-10 2.55E-10 3.10E-12 5.39E-10 4.46E-10 8.26E-09 3.10E-10 4.26E-11 3.50E-10 9.98E-10

41

Table 17: CML baseline normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1]

abiotic depletion (minerals;

yr)

abiotic depletion

(fossil fuels; yr))

global warming

(yr)

ozone layer

depletion (yr)

human toxicity

(yr)

Freshwater aquatic

ecotoxicity (yr)

Marine aquatic

ecotoxicity (yr)

Terrestrial ecotoxicity

(yr)

photochemical ozone

formation (yr)

acidification (yr)

eutrophication (yr)

[A1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

[A2] 0.80 0.85 0.79 0.85 0.76 0.71 0.80 0.68 0.75 0.81 0.87

[A3] 0.58 1.95 1.12 2.23 1.05 0.84 1.91 0.32 1.62 0.90 0.60

[A4] 0.54 1.69 1.01 2.03 0.93 0.68 1.37 0.31 1.58 0.90 0.59

[A5] 0.37 0.93 0.62 1.12 0.62 0.47 0.96 0.23 1.59 0.59 0.34

[A6] 0.42 0.88 0.61 1.03 0.65 0.53 1.00 0.29 1.53 0.59 0.35

[A7] 0.38 0.77 0.54 0.89 0.58 0.47 0.89 0.27 1.35 0.53 0.30

[A8] 0.92 1.00 0.67 0.38 1.84 1.05 4.75 0.17 0.87 0.60 0.49

[A9] 1.01 1.07 0.71 0.36 2.01 1.16 5.24 0.19 0.92 0.63 0.51

[A10] 0.67 2.09 1.27 2.53 1.27 1.17 2.06 0.77 1.99 1.10 0.48

[A11] 0.52 1.67 1.03 2.18 1.00 0.88 1.42 0.61 1.64 0.93 0.38

[A12] 0.67 2.05 1.28 2.64 1.25 1.13 1.82 0.80 2.05 1.15 0.52

[A13] 0.54 0.43 0.35 0.30 0.69 0.40 1.49 0.09 0.49 0.44 0.37

[A14] 0.62 0.48 0.40 0.35 0.76 0.44 1.61 0.10 0.57 0.52 0.48

[A15] 0.54 0.42 0.36 0.33 0.66 0.38 1.34 0.08 0.51 0.47 0.41

[A16] 0.58 0.47 0.38 0.30 0.80 0.47 1.80 0.10 0.52 0.45 0.40

[A17] 0.50 1.29 0.79 1.30 1.07 0.93 1.80 0.51 1.28 0.75 0.37

[A18] 0.63 1.23 0.83 1.28 1.26 1.09 1.88 0.68 1.51 0.89 0.56

[A19] 0.32 0.48 0.44 0.40 0.33 0.23 0.38 0.18 0.56 0.52 0.31

[A20] 0.35 0.53 0.55 0.47 0.37 0.13 0.39 0.04 0.92 0.71 0.56

[A21] 0.91 0.88 0.87 0.82 0.89 0.91 0.91 0.91 0.88 0.89 0.89

[A22] 0.87 0.85 0.84 0.79 0.86 0.88 0.88 0.88 0.85 0.86 0.86

42

Table 18: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation Climate

change (yr)

Ozone depletion

(yr)

Human toxicity, cancer effects

(yr)

Human toxicity,

non-cancer effects (yr)

Particulate matter/Respiratory

inorganics (yr)

Ionizing radiation, human

health (yr)

Photochemical ozone formation,

human health (yr)

Acidification (yr)

Eutrophication terrestrial (yr)

Eutrophication freshwater (yr)

Eutrophication marine (yr)

Ecotoxicity freshwater

(yr)

Resource depletion,

mineral+fossils (yr)

[A1] 3.03E-10 3.97E-12 6.51E-09 7.05E-11 6.70E-10 8.76E-11 1.38E-10 4.10E-10 1.54E-09 0.00E+00 1.54E-09 8.19E-10 1.30E-10

[A2] 2.38E-10 3.37E-12 5.15E-09 5.37E-11 5.55E-10 6.56E-11 1.10E-10 3.39E-10 1.27E-09 0.00E+00 1.28E-09 5.91E-10 1.01E-10

[A3] 3.40E-10 8.90E-12 9.27E-09 2.84E-11 1.13E-09 6.75E-11 2.61E-10 2.23E-10 4.64E-10 0.00E+00 4.77E-10 8.08E-10 8.11E-11

[A4] 3.06E-10 8.11E-12 7.57E-09 2.39E-11 9.88E-10 6.79E-11 2.42E-10 2.17E-10 4.67E-10 0.00E+00 4.83E-10 7.24E-10 7.86E-11

[A5] 1.88E-10 4.50E-12 4.73E-09 2.52E-11 6.87E-10 3.43E-11 2.29E-10 1.71E-10 4.38E-10 0.00E+00 4.45E-10 2.69E-10 5.08E-11

[A6] 1.86E-10 4.11E-12 5.03E-09 2.95E-11 6.30E-10 3.82E-11 2.08E-10 1.84E-10 4.89E-10 0.00E+00 4.96E-10 3.00E-10 5.76E-11

[A7] 1.62E-10 3.55E-12 4.49E-09 2.65E-11 5.48E-10 3.46E-11 1.82E-10 1.65E-10 4.36E-10 0.00E+00 4.42E-10 2.68E-10 5.18E-11

[A8] 2.04E-10 1.52E-12 1.29E-08 2.72E-11 4.32E-10 4.51E-11 7.71E-11 2.14E-10 3.24E-10 0.00E+00 3.27E-10 1.11E-09 7.76E-11

[A9] 2.14E-10 1.45E-12 1.42E-08 3.10E-11 4.43E-10 4.82E-11 7.72E-11 2.27E-10 3.41E-10 0.00E+00 3.44E-10 1.20E-09 8.42E-11

[A10] 3.86E-10 1.01E-11 9.56E-09 6.89E-11 1.35E-09 7.33E-11 3.39E-10 3.06E-10 7.97E-10 0.00E+00 8.12E-10 6.11E-10 1.02E-10

[A11] 3.12E-10 8.72E-12 7.06E-09 5.36E-11 1.18E-09 6.07E-11 2.96E-10 2.44E-10 6.28E-10 0.00E+00 6.41E-10 4.57E-10 8.07E-11

[A12] 3.89E-10 1.06E-11 8.90E-09 6.96E-11 1.44E-09 7.60E-11 3.62E-10 3.12E-10 8.33E-10 0.00E+00 8.49E-10 5.86E-10 1.03E-10

[A13] 1.06E-10 1.21E-12 5.12E-09 -1.83E-11 2.66E-10 3.16E-11 4.96E-11 1.81E-10 4.92E-10 0.00E+00 4.95E-10 7.89E-10 6.43E-11

[A14] 1.23E-10 1.40E-12 5.61E-09 -2.44E-11 3.11E-10 3.58E-11 5.76E-11 2.17E-10 6.04E-10 0.00E+00 6.07E-10 9.30E-10 7.49E-11

[A15] 1.09E-10 1.31E-12 4.84E-09 -2.38E-11 2.80E-10 3.27E-11 5.26E-11 1.96E-10 5.54E-10 0.00E+00 5.57E-10 8.37E-10 6.75E-11

[A16] 1.14E-10 1.21E-12 5.85E-09 -1.47E-11 2.77E-10 3.30E-11 5.07E-11 1.83E-10 4.85E-10 0.00E+00 4.87E-10 8.19E-10 6.70E-11

[A17] 2.38E-10 5.16E-12 7.77E-09 3.79E-11 7.79E-10 7.84E-11 1.79E-10 2.31E-10 5.14E-10 0.00E+00 5.22E-10 7.32E-10 7.27E-11

[A18] 2.53E-10 5.03E-12 8.36E-09 4.61E-11 8.79E-10 9.88E-11 2.01E-10 2.91E-10 7.08E-10 0.00E+00 7.17E-10 8.97E-10 9.02E-11

[A19] 1.33E-10 1.60E-12 2.15E-09 2.19E-11 3.02E-10 2.41E-11 7.10E-11 2.29E-10 9.01E-10 0.00E+00 9.04E-10 1.95E-10 3.94E-11

[A20] 1.66E-10 1.88E-12 2.11E-09 3.84E-11 5.51E-10 1.83E-11 1.16E-10 3.23E-10 1.35E-09 0.00E+00 1.35E-09 1.40E-10 3.84E-11

[A21] 2.65E-10 3.27E-12 5.83E-09 6.35E-11 5.43E-10 7.89E-11 1.14E-10 3.84E-10 1.45E-09 0.00E+00 1.46E-09 7.43E-10 1.18E-10

[A22] 2.55E-10 3.15E-12 5.64E-09 6.12E-11 5.25E-10 7.62E-11 1.10E-10 3.71E-10 1.40E-09 0.00E+00 1.40E-09 7.16E-10 1.14E-10

43

Table 19: ILCD normalisation (World, 2000) results for all 22 aquaculture systems listed in section 2.2.4 adopting mass allocation, relative to alternative [A1] Climate

change (yr)

Ozone depletion

(yr)

Human toxicity, cancer effects

(yr)

Human toxicity,

non-cancer effects

(yr)

Particulate matter/Respiratory

inorganics (yr)

Ionizing radiation, human

health (yr)

Photochemical ozone formation,

human health (yr)

Acidification (yr)

Eutrophication terrestrial (yr)

Eutrophication freshwater (yr)

Eutrophication marine (yr)

Ecotoxicity freshwater

(yr)

Resource depletion,

mineral+fossils (yr)

[A1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 n/a 1.00 1.00 1.00

[A2] 0.79 0.85 0.79 0.76 0.83 0.75 0.80 0.83 0.83 n/a 0.83 0.72 0.77

[A3] 1.12 2.24 1.42 0.40 1.68 0.77 1.89 0.54 0.30 n/a 0.31 0.99 0.62

[A4] 1.01 2.04 1.16 0.34 1.47 0.78 1.76 0.53 0.30 n/a 0.31 0.88 0.60

[A5] 0.62 1.13 0.73 0.36 1.02 0.39 1.66 0.42 0.29 n/a 0.29 0.33 0.39

[A6] 0.61 1.04 0.77 0.42 0.94 0.44 1.51 0.45 0.32 n/a 0.32 0.37 0.44

[A7] 0.53 0.89 0.69 0.38 0.82 0.40 1.32 0.40 0.28 n/a 0.29 0.33 0.40

[A8] 0.67 0.38 1.99 0.39 0.64 0.52 0.56 0.52 0.21 n/a 0.21 1.35 0.60

[A9] 0.71 0.37 2.18 0.44 0.66 0.55 0.56 0.55 0.22 n/a 0.22 1.46 0.65

[A10] 1.27 2.55 1.47 0.98 2.02 0.84 2.46 0.75 0.52 n/a 0.53 0.75 0.79

[A11] 1.03 2.20 1.08 0.76 1.76 0.69 2.15 0.59 0.41 n/a 0.42 0.56 0.62

[A12] 1.28 2.67 1.37 0.99 2.14 0.87 2.62 0.76 0.54 n/a 0.55 0.72 0.79

[A13] 0.35 0.30 0.79 -0.26 0.40 0.36 0.36 0.44 0.32 n/a 0.32 0.96 0.49

[A14] 0.41 0.35 0.86 -0.35 0.46 0.41 0.42 0.53 0.39 n/a 0.39 1.14 0.58

[A15] 0.36 0.33 0.74 -0.34 0.42 0.37 0.38 0.48 0.36 n/a 0.36 1.02 0.52

[A16] 0.38 0.30 0.90 -0.21 0.41 0.38 0.37 0.45 0.32 n/a 0.32 1.00 0.52

[A17] 0.79 1.30 1.19 0.54 1.16 0.90 1.30 0.56 0.33 n/a 0.34 0.89 0.56

[A18] 0.83 1.27 1.28 0.65 1.31 1.13 1.46 0.71 0.46 n/a 0.47 1.10 0.69

[A19] 0.44 0.40 0.33 0.31 0.45 0.27 0.52 0.56 0.59 n/a 0.59 0.24 0.30

[A20] 0.55 0.47 0.32 0.55 0.82 0.21 0.84 0.79 0.88 n/a 0.88 0.17 0.30

[A21] 0.87 0.82 0.90 0.90 0.81 0.90 0.82 0.94 0.94 n/a 0.94 0.91 0.91

[A22] 0.84 0.79 0.87 0.87 0.78 0.87 0.79 0.90 0.91 n/a 0.91 0.87 0.88

44

General legend: [A1] = 1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Khulna, Bangladesh for consumption in the EU (reference period 2010-2011); [A2] = 1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Bagerhat, Bangladesh for consumption in the EU (reference period 2010-2011); [A3] = 1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011); [A4] = 1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011); [A5] = 1 tonne of frozen, edible yield of Pangasius produced in small systems in the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); [A6] = 1 tonne of frozen, edible yield of Pangasius produced in medium systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); [A7] = 1 tonne of frozen, edible yield of Pangasius produced in large systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); [A8] = 1 tonne of frozen, edible yield of Shrimp produced in low-level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011); [A9] = 1 tonne of frozen, edible yield of Shrimp produced in high level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011); [A10] = 1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); [A11] = 1 tonne of frozen, edible yield of Shrimp (L. Vannamei) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); [A12] = 1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in semi-intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011); [A13] = 1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Guangdong, China for consumption in the EU (reference period 2010-2011); [A14] = 1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Hainan, China for consumption in the EU (reference period 2010-2011); [A15] = 1 tonne of frozen, edible yield of Tilapia produced in polyculture reservoirs in Guangdong/Hainan, China for consumption in the EU (reference period 2010-2011); [A16] = 1 tonne of frozen, edible yield of Tilapia produced in ponds integrated with pigs in Guangdong, China for consumption in the EU (reference period 2010-2011); [A17] = 1 tonne of frozen, edible yield of Tilapia produced in pond systems in Chachoengsao/Nakhon Patom/Petchburi, Thailand for consumption in the EU (reference period 2010-2011); [A18] = 1 tonne of frozen, edible yield of Tilapia produced in intensive cages systems in Suphanburi, Thailand for consumption in the EU (reference period 2010-2011); [A19] = 1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in small-medium improved extensive systems in Bagerhat/Khulna/Satkhira, Bangladesh for consumption in the EU (reference period 2010-2011); [A20] = 1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in large improved extensive systems in Cox’s Bazar, Bangladesh for consumption in the EU (reference period 2010-2011); [A21] = 1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011); [A22] = 1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011).

45

5 Interpretation In this chapter the inventory and impact assessment results of 22 LCAs are discussed and analysed with the original goal and scope in mind. In section 5.1 the inventory results will be briefly discussed, followed by a more elaborate discussion of the impact assessment results in section 5.2. In section 5.3 the hot spots identified by contribution analysis are summarized. In section 5.4 the main results from uncertainty analysis are discussed, followed by a discussion of the main results from selected sensitivity analyses in section 5.5. Finally, the key results from all 22 LCAs will be discussed in a general discussion in section 5.6.

5.1 Discussion of inventory results As overall dispersions only were evaluated at the inventory stage, overall dispersions related to inventory flows were similar to those at the impact assessment stage. Noteworthy is that the uncertainty of inventory flows such as methane and dinitrogen monoxide was higher than that of carbon dioxide. The reason being that carbon dioxide emissions are largely defined by the carbon content of the fuel, while methane and dinitrogen monoxide emissions also are influenced by the type of combustion. As for nutrient run-off, the overall dispersions were highly similar, because that they often built upon the same assumptions. Some inventory results surprisingly came out as negative. The reason for this was that accounted uptakes in the background database were not balanced in later parts of the lifecycle. For example, the emissions of zinc came out as negative in some scenarios as a result of accounted uptakes of zinc in rape seed plants in ecoinvent processes [6957]. This highlights the danger of misinterpretations when temporal fixation of environmental flows are accounted in background data and the system boundary is set at farm-gate rather than grave, or if such negative flows are simply overseen. LCA practitioners are therefore urged to consistently scrutinise their LCA outcomes in light of inventory flows, in order to detect the occurrence of these types of issues.

5.2 Discussion of impact assessment results This section will discuss the outcomes of the impact assessment. The baseline characterisation results without uncertainty ranges are presented in section 4.2 and show relative marginal differences between species and between different characterisation methods (CML baseline, ILCD). For full tables with results, please see appendix 1. Normalisation results will not be further discussed as they do not provide new insights compared to the insights based on the characterisation results. The differences between the results of the 22 LCAs are surprisingly small. As discussed in earlier work, LCA outcomes of different products often differ with more than an order of magnitude. Likewise, different studies of the same product can differ with an order of magnitude. Meanwhile, in the present study, results exceeded not more than 5-fold differences for either global warming, eutrophication or acidification, despite large differences in production practices. Obvious reasons for this include a consistent use of one database for background processes, and a consistent use of emission models. Another reason is the use of averaged data from several studies. This reduces the influence of abnormal parameters, which otherwise could skew the final results in the propagation of LCI results as many parameters act as multipliers. For example, if fishmeal is adopted as a general commodity and only one source is used for the fuel consumption in capture fishing boats, this value

46

could be either 0.01 or 3.9 kg diesel kg-1 fish landed depending upon the study used (Kuldilok 2009; Avadí and Vázquez-Rowe 2014). The overall dispersions differed greatly depending upon the product and impact category, but most 95% confidence intervals were in excess of ±50%. In the present research dispersions were only evaluated for LCI data, neglecting uncertainty related to characterisation factors, while uncertainties can also be very large for certain characterisation factors, such as for toxicity. Future work should, therefore, try and include uncertainty factors around characterisation factors alongside overall dispersions in LCI data. Below, each impact category will be discussed per species, highlighting hot-spots and trends amongst the results. The discussion will focus on three impact categories whose environmental flows were best defined: global warming, acidification and eutrophication. The figures displaying the ranges also give an overview of the different magnitude of uncertainties encountered in the present study. Each figure portrays box-and-whisker plots, indicating the median as the central value, the 25th percentile (lower edge of box) and 75th percentile (upper edge of box), with the whiskers outlining the first (10th percentile) and last (90th percentile) deciles. This in line with Bowley’s seven-figure summary, aimed at representing non-parameterised data.

5.2.1 Global warming

5.2.1.1 L. vannamei

For L. vannamei shrimps, emissions of greenhouse gases were much higher for Thai and Vietnamese production systems compared to the two Chinese systems when mass allocation was considered (Figure 6). However, when economic allocation was considered, the emissions related to L. vannamei shrimp from Vietnam were comparable to those from China. The differences can be explained by differences in fuel consumption in capture fishing boats, catching fish aimed for reduction. In Vietnam, these emissions resulting from local capture fishing boats accounted for 34% of the overall GHG emissions in light of mass allocation. Meanwhile, in Thailand, they only accounted for between 25-29%. Larger reductions in emissions when using economic allocation to fish aimed for reduction can therefore explain these differences. When economic allocation was applied, the emissions from Vietnamese fishing boats only accounted for between 13% and 17% of the overall emissions. In this respect, Chinese systems came out preferable due to their reliance on Peruvian fishmeal, originating from the highly fuel efficient anchoveta fishery (Avadí and Vázquez-Rowe 2014).

47

Figure 6: Global warming, mass allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3) , Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11).

Figure 7: Global warming, economic allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11).

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5.2.1.2 P. monodon

Greenhouse gas emissions from P. monodon production systems were more inconsistent than for P vannamei (Figure 8 and Figure 9). The divergence was more prominent using mass allocation. Particularly the multi-functional process capture fishing boats got allocated higher emissions, thus resulting in higher aggregate emission for Vietnamese shrimp compared to the other shrimp systems. When economic allocation was applied, the largest emissions were related to Bangladeshi P. monodon shrimps due to the higher value of boiled rice used as feed and shrimps compared to fish co-produced in the ponds. In the shrimp and prawn farms, the allocation to P. monodon shrimp at farm-gate increased from 24% to 38% when economic allocation was considered. For the Eastern BD farms, rice production was the dominating hot-spot using both allocation factors, while in the Western BD farms electricity production was the dominating single source of greenhouse gases. Most of this electricity was consumed in processing plants, driven by old inefficient freezers being used in a relatively hot climate. GHG emissions from wood burning at the farms, mainly used to boil the rice, were surprisingly low (<1%) for all the Bangladeshi systems. This possibly because of the adoption of an ecoinvent processes describing burning in industrial furnaces [2425] to represent the burning on farms. In reality, the wood is burned in small stoves, probably resulting in less efficient combustions and more potentially hazardous emissions due to incomplete combustion.

Figure 8: Global warming, mass allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20????) and shrimp & prawn systems in Bangladesh (alternative 21).

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Figure 9: Global warming, economic allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21).

5.2.1.3 Macrobrachium rosenbergii

Naturally, given the large discrepancies of the primary data, the overall dispersions around the Bangladeshi prawn data were also large (Figure 9 and 10). No real differences in performance could therefore be concluded amongst the total emissions from the three LCAs performed in the present study. Meanwhile, the contribution of emissions differed widely amongst farming systems and between allocation factors. GHG emissions from prawn farms in Khulna were dominated by methane emissions from pig manure in EU farms when mass allocation was considered, and by dinitrogen oxide emissions from the ponds when economic allocation was considered. In Bagerhat, dinitrogen monoxide emissions related to domestic wheat farming accounted for most of the emissions (≈20%) when economic allocation was considered, and methane emissions from pig farming together with carbon dioxide emissions from natural gas power plants in the light of mass allocation. Overall greenhouse gas emissions from electricity production accounted for about 10% of total emissions for all three systems. Again, most of it was consumed by the processing plant. Diesel burned on farm similarly resulted in another 10% of the emissions.

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Figure 10: Global warming, mass allocation, per tonne shell-on head-on M rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22).

Figure 11: Global warming, economic allocation, per tonne shell-on head-on M rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2), and shrimp & prawn ponds in Bangladesh (alternative 22).

51

5.2.1.4 Tilapia

GHG emissions from different tilapia production systems were overall pretty uniform in China. Thai production systems compared to Chinese production systems showed larger emissions (Figure 12 and Figure 13). These emissions were again the result of high fishmeal inclusion and emissions resulting from capture fisheries. Thai tilapia feeds not only included fishmeal from more energy intensive sources, but also included 50% more fishmeal compared to their Chinese counterparts. The lack of consistency in the Thai data is made obvious by the large dispersions. Between the two allocation scenarios, differences were more prominent when mass allocation was applied. This was partially due to the strong influence of allocation on fishmeal, but also to the common practice of co-producing carp in Chinese tilapia farms (60±30 kg carp per tonne tilapia). Co-production of carp was also reported by Thai pond farmers, but due to a lack of quantitative data, this co-production was neglected in the present study. Tilapia from Thai farms could therefore actually have slightly lower emissions than reported in the present study.

52

Figure 12: Global warming, mass allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18).

Figure 13: Global warming, economic allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18).

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5.2.1.5 Pangasius

The three different scales of Pangasius farming resulted in relatively similar GHG emissions, even between the two allocation scenarios (Figure 14 and 15). This was rather surprising given that production practices differed greatly between the different scales. For example, 31% of the feed used (by quantity) by small scale farms consisted of farm-made feeds compared to only 6% of the feed used by large scale farms. With regards to the contributing processes, however, the differences were more distinct. When mass allocation was adopted, diesel burned in fishing boats was the dominant source of emissions for all scales of production, despite relatively low fishmeal inclusion in Pangasius feeds (7% on average). When economic allocation was adopted, production of soybeans in the US was the major source of emissions in the medium and large scale farms. In both these cases dinitrogen monoxide from agricultural fields was the dominating source of emissions related to soybean farming. Rice farming accounted for between 4% and 12% amongst all systems and both allocation factors. Electricity production made up roughly 40% of the emission, most of which was used for processing, feed production and grow-out farming. Diesel use on farm accounted for less than 1% of the overall GHG emissions.

5.2.2 Acidification

Similar to global warming, most acidifying emissions were dominated by emissions from burning of diesel on capture fishing boats, as part of fishmeal production. It was also the only impact category where transportation played a greater role, because transoceanic freighter boats often rely upon fuels with high sulphur content.

Figure 15: Global warming, economic allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam.

Figure 15: Global warming, mass allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam.

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5.2.2.1 L. vannamei

The Chinese L. vannamei production systems performed better than the Thai production systems, mainly due to their sourcing of fishmeal (Figure 15 and figure 16). The Vietnamese system, in turn, were again greatly influenced by the allocation method used, but were in the range of the other four systems. In the Chinese farms, acidifying agents mainly originated from coal power plants. Another common source for acidifying agents, and also the only impact where transportation contributed to any greater extent, was transoceanic shipping. This is due to the high sulphur content in fuel used by transoceanic shipping vessels, a consequence of cheaper fuels and the remote location of their combustion. These emissions are thus less likely to have the same negative effects as emissions in close to terrestrial ecosystems (Hicks et al. 2008).

Figure 16: Acidification, mass allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11).

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Figure 17: Acidification, economic allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11).

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5.2.2.2 P. monodon

The emissions related to acidification resulting from the production of P. monodon expressed larger discrepancies than in the L. vannamei scenario. The discrepancies were especially prominent when economic allocation was applied and Vietnamese systems stood out as preferable to the Bangladeshi systems (Figure 17 and 18). Between the two Vietnamese systems, the impacts were similar with slightly higher absolute emissions from a mass allocation perspective. Meanwhile, P. monodon shrimp produced alongside M. rosenbergii resulted in the highest emissions, followed by systems in Eastern Bangladesh. The emissions from the Vietnamese farms primarily originated from diesel burned in fishing boats, followed by either diesel burned on the grow-out farm or pig manure, depending upon the allocation method used. The process “Operation, transoceanic shipping” [1961] accounted for 8% (mass allocation) or 14-15% (economic allocation) of the total acidifying emissions. For the Bangladeshi systems, absolute emissions were up to three times higher when applying economic allocation relative to mass allocation. This difference was again governed by the large mass/value difference between shrimp and carp at farm-gate. The dominating acidifying emission from Bangladeshi shrimp production, indifferent of allocation method, was ammonia from aquaculture ponds followed by mustard seed farming. The strong influence of ammonia emissions from the ponds was due to the large inputs of agricultural products as feed (up to four tonnes per tonne shrimp). As part of this biomass breaks down, nitrogen is released, some of which leaves the pond in the form of ammonia.

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Figure 18: Acidification, mass allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21).

Figure 19: Acidification, economic allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21).

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5.2.2.3 Macrobrachium rosenbergii

Emissions from the production of freshwater prawns were very similar. The overall dispersion around the acidifying emissions from prawn farms in Bagerhat was larger. Between the two different methods of allocation, the absolute results differed greatly while the contributions to these results remained relatively similar. This as the largest influence of allocation was at farm-gate, with prawns being the most valuable items produced in these polyculture systems. The major sources of emissions thus remained relatively similar between the two allocation factors. Ammonia from domestic wheat farming made up the largest share of acidifying emissions (4-24%) amongst all of the different prawn LCAs. This was joined by methane from pig manure storage, transoceanic shipping, mustard seed farming, maize farming and diesel burned at grow-out farm. Overall, agricultural emissions accounted for about half of the emissions, while road transportations surprisingly accounted for only about one percent of the overall emissions.

Figure 21: Acidification, economic allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22).

Figure 21: Acidification, mass allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22).

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5.2.2.4 Tilapia

Acidifying emissions were overall higher for the Thai tilapia production systems when mass allocation was consulted, but extremely similar when economic allocation was applied (Figure 22 and Figure 23). The ranges between the Thai systems were largely overlapping, as were they amongst the Chinese systems. Again, diesel burned in fishing boats was the major source of acidifying emissions with regards to mass allocation. However, in terms of economic allocation, operation of transoceanic freight ships was the main source of SO2-eq. Most of this transportation was the shipping of soybeans from the Americas to Asia and fillets from Asia to Europe.

Figure 22: Acidification, mass allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18).

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Figure 23: Acidification, economic allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18).

5.2.2.5 Pangasius

There were only very subtle trends amongst the three different scales of Pangasius farming (Figure 24 and 25). Similarly, very small differences existed in absolute outcomes between the different allocation methods. Nitrogen oxides (NOx) from diesel burned on fishing boats as part of fishmeal production was the dominating source of acidifying emissions. This was followed by transoceanic shipping and soybean production in the US, the dominant source of soybeans used in Vietnam. About half of the shipping was related to getting the fillets to Europe and roughly a third of the shipping was related to transporting soybeans from the US to Asia. Agricultural production accounted for between 20% and 35% of the emissions related to acidification.

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Figure 25: Acidification, economic allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam.

Figure 25: Acidification, mass allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam.

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5.2.3 Eutrophication

Eutrophication was dominated by nutrient run-off from the farms, generally accounting for roughly half of the emissions. Of these, nitrate, ammonium and phosphorus to freshwater were the main eutrophying substances. Chemical oxygen demand was not estimated for most processes in the present research due to lack of data. Other hot-spots included wastewater from processing plants and fishmeal factories, and agricultural run-off. Emissions from agricultural fields were modelled according to (Nemecek and Schnetzer 2011). Estimates of the emissions from grow out farms were based upon nutrient budgets. The fate of nutrients was, however, very hard to determine and based upon literature values (e.g Funge-smith & Briggs, 1998). For nutrients trapped in the sediments, the fate was equally hard to determine. Based upon farmer testimonies, the sediments were either pumped into agricultural fields, pumped into wasteland, trapped in sediment ponds or used to build pond dikes. For the latter two, however, the final destination of the nutrients embodied in the sediments was next to impossible to foresee. Moreover, processing plants and fishmeal factories reported on wastewater use. However, most of the chemical composition of that wastewater was based upon literature studies. The amount of wastewater leaving the factories is therefore, most likely, directly linked to the concentration of nutrients in that waste water. The eutrophying emissions are therefore based upon many assumptions, all clarified in the separate Annex report ”Primary data and literature sources adopted in the SEAT LCA studies” (Henriksson et al. 2014) to this document.

5.2.3.1 L. vannamei

The eutrophying emissions from L. vannamei farms were slightly higher in Thailand compared to the other countries, both for mass and economic allocation (Figure 26 and Figure 27). This based itself upon the somewhat higher feed utilisation in Thai shrimp farms compared to Chinese farms. Vietnamese L. vannamei farmers performed surprisingly well, given that the shift from P. monodon has occurred only over recent years. The overall dispersion was mainly driven by spread in feed inputs. In this respect, the Vietnamese data were much more consistent compared to the other countries. Between 49-55% of the eutrophying emissions from the Thai farms were due to grow-out farm runoff. This was followed by wastewater from the processing plants (15-17%), diesel burned in fishing boats and on farm (2-8%), and wastewater from fishmeal factories (5-6%). Overall agricultural run-off accounted for between 2-14% of the eutrophying emissions, amongst both Thai systems and both allocation factors. For the Chinese systems, agricultural run-off had similar importance (between 13-14%) while emissions from grow-out were slightly less (around 40%). Emissions from Chinese processing plants were identical to those from Thai processing plants (around 20 kg PO4-eq. tonne-1 of shrimp), because the unit process data were averaged between the two countries. Meanwhile, the emissions from fishmeal factories were roughly twice as high in China compared to Thailand. This was the result of the assumed more efficient wastewater treatment in Thailand. In Vietnam, nutrient losses from grow-out farms were responsible for between 42-63% of the nitrogen (roughly 75-80% of all eutrophying emissions) and phosphorus emissions (20-25% of all emissions).

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. Figure 26: Eutrophication, mass allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11).

Figure 27: Eutrophication, economic allocation, per tonne peeled tail-on L. vannamei shrimp from Eastern Thailand (alternative 3), Southern Thailand (alternative 4), low-level (LL) ponds in China (alternative 8), high-level (HL) ponds in China (alternative 9) and Vietnam (alternative 11).

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5.2.3.2 P. monodon

Apart from the P. monodon produced in the shrimp and prawn farms, most systems preformed comparably with regards to eutrophication and mass allocation (Figure 28). In the economic allocation scenario, however, the two Vietnamese and the Western Bangladeshi systems performed favourably compared to Eastern Bangladesh and shrimp and prawn systems (Figure 29). The bulk (about 60%) of the emissions from the prawn and shrimp farms was due to run-off from the grow-out farm. This was followed by domestic wheat farming, accounting for 7-12% of the overall eutrophying emissions depending upon allocation method. As for the two monoculture systems in Bangladesh, pond effluents only accounted for between 33-43% of the emissions, with the remainder mainly being due to agricultural run-off. The relatively efficient pond budgets can be explained by moderate use of rice and wheat bran, which for the Bangladeshi systems are the only real external feed inputs. Emissions from Vietnamese farms were dominated by pond run-off, constituting 44-46% of the eutrophying emissions when mass allocation and 66-68% when economic allocation was adopted. Other emissions were related to agriculture, fish reduction and capture fisheries. Most of the increase in emissions between the two allocation approaches is related to the higher value of the shrimp tails in comparison to the co-products heads and shells.

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Figure 28: Eutrophication, mass allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21).

Figure 29: Eutrophication, economic allocation, per tonne peeled tail-on P. monodon shrimp from intensive farms in Vietnam (alternative 10), semi-intensive farms in Vietnam (alternative 12), Western Bangladesh (alternative 19), Eastern Bangladesh (alternative 20) and shrimp & prawn systems in Bangladesh (alternative 21).

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5.2.3.3 Macrobrachium rosenbergii

With very large overall dispersions around results, little can be concluded amongst the eutrophying emissions from freshwater prawn farming in Bangladesh (Figure 30 and 31). The only real trend was slightly favourable performance of shrimp and prawn systems. Emissions from the grow-out farms accounted for over half (57-63%) of the eutrophying emissions from prawn farms. Agricultural run-off was, in the meantime, related to between a quarter and a third of the emissions. The over three-fold increase in absolute emissions when economic allocation was adopted is due to the high value of prawns, especially in comparison to carp. Ammonium emissions were again dominating (33% of total eutrophying lifecycle emissions), followed by nitrate (28%) and phosphorus (25%).

Figure 31: Eutrophication, mass allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22).

Figure 31: Eutrophication, economic allocation, per tonne shell-on head-on M. rosenbergii prawns from polyculture systems in Khulna (alternative 1), Bagerhat (alternative 2) and shrimp & prawn ponds in Bangladesh (alternative 22).

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5.2.3.4 Tilapia

Amongst the different tilapia farming systems, cage farms in Thailand were related to the highest emissions (Figure 32 and figure 33), because almost all excess nutrients were lost through the cages to the surrounding ecosystem. A similar scenario was expected for reservoirs in China, but half of these farmers stated the use of sediment ponds. This may of course be the result of misinterpretation or misunderstanding of the difference between a reservoir and sediment ponds. The difference in fates of nutrients entering the both of these systems is, either way, very uncertain as they largely depend upon where the sediments will end up when they finally are removed. Amongst all systems, emissions from the aquaculture farming sites accounted for between 40% and 60% of the lifecycle eutrophying emissions. Effluents from processing plants were the second largest contributor, accounting for roughly 15-20% of emissions.

Figure 32: Eutrophication, mass allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18).

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Figure 33: Eutrophication, economic allocation, per tonne Tilapia fillets from polyculture systems in Guangdong (GD) (alternative 13) and Hainan (HI) (alternative 14), reservoirs (R) in China (alternative 15), Integrated with pigs in China (alternative 16), ponds in Thailand (alternative 17) and cages in Thailand (alternative 18).

5.2.3.5 Pangasius

Again, differences amongst different scales of Pangasius farms were discrete for emissions related to eutrophication. Farm effluents accounted for between 38% and 51% of the lifecycle emissions, with lower shares when economic allocation was adopted. Rice farming was the second largest source of eutrophying emissions (16-20%), with higher contributions in small scale farming. Overall, large farms performed slightly better due to a more efficient conversion of feed to fish, the result of a higher reliance on commercial, rather than farm-made, feeds.

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Figure 35: Eutrophication, mass allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam.

Figure 35: Eutrophication, economic allocation, per tonne Pangasius fillets from small (alternative 5), medium (alternative 6) and large (alternative 7) scale farms in Vietnam.

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5.3 Summary of hot spots identified by contribution analyses Of all the resources used throughout Asian aquaculture chains, regional fishmeal production stood out as especially detrimental. It was a hot-spot for global warming, abiotic resource depletion of fossil fuels, acidification and eutrophication. These impacts were all driven by diesel use in capture fishing boats, with additional eutrophying emissions from fish reduction factories. These local fishing boats mainly rely upon trawl fishing gear and target a mixed catch. Some of the larger fishing boats targeted species such as Japanese anchoveta (Engraulis japonicus) or different kinds of tuna. Overall, however, the fuel efficiency of the different capture methods was very poor, often with fishing boats leaving the docks with more fuel than fish they return with. Central to the problem is poor fishing regulations, which have resulted in many overexploited fish stocks. Fishing boats thus need to travel further and put in more effort per unit of landed fish. Adding to this is a loss of weight in the reduction of whole fish to fishmeal by around 80% (about 5 kg whole fish per kg fishmeal). The reduction process also requires large amounts of steam to dry the fish, with some of the smaller Chinese fishmeal plants reporting the burning of up to 500 kg of coal per tonne of fishmeal produced. This is largely due to the poor technology used, which is reflected by the low quality of fishmeal produced. In addition to this, several other concerns exist, including working conditions on fishing boats, damage to wild fish stocks and the seafloor, competition with direct human consumption, etc. This unavoidably adds up to the conclusion that fish meal is a very poor resource from a sustainability perspective. The grow-out farms themselves represent an area where many improvement options are possible. First of all, the utilisation of feeds could be improved in many farms, which would reduce impacts related to feed production as well as to nutrient run-off from the farms. Better feed management could also reduce the sediment build-up and the use of paddle-wheels. Powering paddle-wheels actually accounted for a substantial part (15-50%) of the emissions from the shrimp production chains. The fate of sediments and run-off water also influences the performance of the farms, where better utilisation of these nutrient flows is recommended. For example, applying sediments and run-off water to agricultural fields rather than into waste land helps closing nutrient flows. More research is, however, needed on the possible build-up of metals and other toxins agricultural soils. Wastewater from fishmeal factories and processing plants is also a substantial source of potentially eutrophying emissions. Main reason for this is that wastewater treatment beyond simple sediment ponds is lacking for these processes. The actual emissions, however, are very hard to estimate as long as data on both the amount of wastewater emitted and its composition are not provided. Unfortunately, we here needed to rely upon estimates on wastewater quantities from interviews, but values on wastewater composition from literature. The mixes of fuels used in the different countries clearly influenced the results. With abundant natural gas available in e.g. Bangladesh and Thailand, emissions were generally lower than in countries reliant on mainly coal (China). As for electricity consumption, the two most energy-consuming practices were paddle-wheels in shrimp farms and freezers at processing plants. Paddle-wheels averagely need of 3-4 GWh per tonne of shrimp produced, while energy needs of processing plants varied much more depending upon the country of production. In Thailand, for example, only 0.4 GWh was needed per tonne of processed product, while in Bangladesh almost 2.5 GWh was used for per tonne comparable product. This could be explained by the poorer technology used in Bangladesh. Despite improvements in flue-gas treatment in Chinese power plants, sulphur dioxide and nitrogen oxide emissions remain substantial. This contributed to elevated emissions related to photochemical ozone formation and acidification.

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Transportation was of relatively little importance, apart from acidifying emissions from transoceanic shipping. Road and sea transportations each accounted for between 2% and 5% of the overall GHG emissions, while shipping alone could be responsible for over 20% of the emissions related to acidification and 26% of the emissions related to photochemical ozone formation. The effects of these emissions on ecosystems are, however, probably limited as they mainly occur far off-shore (Hicks et al. 2008). Lacking environmental regulations or enforcement of tailpipe emissions was prominent in some areas. Interestingly in Bangladesh, where no regulations are in place to limit truck exhaust, emissions were somewhat diluted per tonne transported as overloading was common practice. Meanwhile, in Thailand, the use of trucks powered by compressed natural gas (CNG) is increasing, resulting in slightly reduced emissions. These reductions would, however, be much more prominent in other countries which lack the same strict emissions standard as Thailand (Thailand implements the EURO 4 standard since 2007). Also, more research is needed in order to evaluate the emissions of particles and their influence on human health, a highly relevant topic with regards to the current smog problems in many Asian cities. The practice of burning straw on agricultural fields also had many negative effects. Apart from destroying a resource that could be used as feed, the burning resulted in the release of ammonia, nitrogen oxides, biogenic methane and carbon monoxide, amongst other things. Apart from harming the environment, carbon monoxide and particulates can have severe negative impacts on respiratory systems of humans. Moreover, the burning results in a loss of nutrients and carbon which eventually may result in a loss of soil fertility. Abiotic resource depletion of elements, freshwater toxicity and human toxicity impacts were governed by infrastructure in background processes. The main elements driving these impacts are zinc, copper and lead used in the production of different infrastructure items such as roofs, power cables and circuit boards. Any conclusions on these impact categories are therefore rather useless. The only valid reflection that could be made was the small influence of phosphorus fertilizers, accounting for only about 2% of the abiotic resource depletion. Similarly, emissions related to ozone layer depletion were merely driven by background processes, such as crude oil extraction and transportation of natural gas. This as many of the extensions characterised to contribute to ozone depletion were not defined in our foreground models (e.g. Halon 1211 and Halon 1301). Conclusively, the outcomes for these two impact categories highlights the importance of evaluating all relevant inventory flows before reflecting upon the impact assessment results.

5.4 Uncertainties As defined in the present research, spread had the largest influence on overall dispersion of economic flows, while inherent uncertainty was dominating modelled environmental flows. Foreground data for electricity consumed, water use and wastewater emissions often showed great spread. This is understandable, as making visual estimates of these flows is basically impossible. In terms of electricity, however, many farmers quoted the price of the electricity bill, a very detailed measurement. Part of this dispersion might therefore be related to interlinkages between farms and residential housing in many countries, both of which are specified on the same electricity bill. Feed also displayed large spread, but much of this could be explained by the quality of the feed and mortality rates. This highlights the need to develop covariance in LCA models, where relationships between different uncertainty parameters are taken into account. Two very obviously interlinked parameters are e.g. the amount of feed used and the harvest. Another one could be therapeutant use and mortality rates. In the present research, these interlinkages could, however, not yet be included.

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The scale of inherent uncertainties defined around model outcomes differed greatly amongst the model and the type of emission. For example, the IPCC that is famous for a rigorous uncertainty framework, defines a relatively narrow range around CO2 emissions from combustion, but much larger ranges for N2O and CH4. CO2 emissions are governed by the amount of carbon in the fuel, a parameter often well known, while N2O and CH4 emissions are much more influenced by the characteristics of the combustion (type of boiler, moisture content of fuel, oxygen availability, temperature, etc.). Many of the models used to calculate agricultural run-off were complex, but did not supply any uncertainty ranges for their parameters (Nemecek and Schnetzer 2011). Nevertheless, large uncertainties may be expected around emissions from agricultural run-off related to highly site-specific characteristics such as slope of the field, soil type, size of fields, etc. Better uncertainty estimates in agricultural emissions are therefore needed, and environmental emission models not acknowledging their own inherent uncertainties should be treated with greatest caution. Unrepresentativeness in most cases only had limited influence on the overall dispersions. Unrepresentativeness, however, made a much larger influence on the weighted means, as most studies failed to report on inherent uncertainties, leaving the unrepresentativeness as the sole weighting factor (cf. Henriksson et al, 2013). The lack of reporting on inherent uncertainties in literature was a problem and also unfortunate, as much information describing hard-sought data (such as variances and distributions) currently is being lost by poor reporting. Reporting on uncertainties related to inventory flows should therefore be made mandatory for LCA practitioners in, for example, the ILCD handbook.

5.5 Sensitivity analyses Despite the quantification of inventory data uncertainties in the present study, many methodological choices may also strongly influence the results. Sensitivity analyses were therefore performed on the following topics:

choice of allocation method;

choice of characterisation methods;

choice of method for propagating uncertainties. The choice of allocation method had large influence on the absolute results of the individual LCAs performed, but much less influence on relative differences between the LCAs. This is due to the fact that the system boundary was set beyond the processing plant, where the large influence of allocation on by-products used in feeds are somewhat counter-balanced by the allocation between edible yield and by-products at the processing plant. This topic is more extensively discussed below. Compared to the choice of the allocation method, the choice of characterisation method (CML or ILCD) had less influence on both the absolute as the relative results, except for the toxicity results that are known to be more uncertain compared to other impact categories and are known for their debatable modelling of metals and inorganics (see for example ). For propagating uncertainties to LCI results, First order Taylor expansion was used as a quick alternative. It soon became evident though, that First-order Taylor expansion better deals with smaller variances and not the large ranges encountered in the present research. Thus, Monte Carlo was adopted as the main propagation method.

5.6 General discussion Finally we here discuss a selection of possible improvement options identified from the 22 LCAs performed. These lessons concern the competitiveness of Chinese production

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systems, reducing the use of fishmeal, technology investments in fishmeal factories, improving the aeration of ponds, the use of livestock co-products, practices related to agricultural straw, shifting from farm-made to commercial feeds, intensification rather than expansion of aquaculture, improved freezers, the role of allocation, and finally dealing properly with the limitations of the studies performed.

5.6.1 Competitiveness of Chinese systems

A surprise was the competitiveness of the Chinese systems, despite their large reliance on coal. Two reasons could be found for this. Firstly, electricity and heat production accounted for only a limited amount of the impacts in any of the systems, with fishmeal production being the main hot-spot for most impact categories. Secondly, Chinese power plants have rapidly been upgraded with flue-gas desulphurisation units (FGDs) and low NOx burners (LNBs) (Zhao et al. 2008) over the last decade, reducing emissions (Henriksson et al. in prep.). The two provinces in focus for the present research (Guangdong and Hainan) both use coal of relatively low sulphur content and have modern power plants (Zhao et al. 2008).

5.6.2 Motivate institutions and feed producers to reduce their inclusions of fishmeal

Fishmeal is responsible for many sustainability concerns related to aquaculture, including overfishing, competition with food availability for the poor, physical damage to aquatic habitats, removal of juvenile fish, poor working conditions on fishing boats, etc (Pelletier and Tyedmers 2008; Naylor et al. 2009). The present research adds to this list of concerns by identifying fishmeal as the overall largest single source of GHGs related to the Asian aquaculture sector. Also, the practices used to reduce fish to fishmeal are highly polluting, with emissions related to eutrophication, acidification and global warming. In Bangladesh, where less industrialised production methods were applied (including sun-drying of fish), other impacts related to overexploitation of wild fish stocks had socio-economic related impacts, with decreasing catches and competition with a food source utilized by the country’s poorest. Likewise in Peru, where much more efficient capture practices are in place, the fish stocks are on the edge of overexploitation while malnourishment is still widespread (Jacquet et al. 2009). Fishmeal is also one of the most expensive feed resources, a cost that is transferred to the farmer and finally to the consumer. Despite of this, aquaculture farmers select for feeds with high fishmeal inclusion as it is believed to produce healthier and faster growing fish and shrimps. Some farmers even turned to feeds aimed for other species, such as frog feeds, as they got more protein for their money. This of course has bad trade-offs in that these feeds don’t meet the dietary needs of the fish they are fed to. This negative spiral was most obvious in Thailand where the fishmeal inclusion in feeds, even for omnivorous species such as tilapia, was the highest. As suggested in the SEAT policy brief in Thailand, breaking this bad circle of demand of supply most likely needs to start at the institutional level. Many feed producers have already developed alternative feeds with much lower fishmeal inclusion ratios. However, due to market demands, few of these feeds take-off. This is understandable, given the perception of farmers that productivity is linked to fishmeal inclusion. By imposing maximum levels of fishmeal inclusion in feeds and initiating farmer groups to demonstrate the success of low fishmeal feeds together with feed producers, governmental institutions are the most likely actors to push for such a change. In return, countries reforming their fishmeal inclusion will be able to publicly expose themselves as more sustainable and refrain from the highly

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volatile fishmeal market. Additional measures recommended are better fisheries management, reviewed fuel subsidies and more sustainable fishing practices, all which would help reduce the impacts of fishmeal production.

5.6.3 Technology investments in fishmeal factories

Apart from the capture fisheries providing the resources to the fishmeal factories, the factories themselves generally rely upon old technology. As a result of this, large amounts of fuels are used (especially in China) only to produce a product of fairly low quality. One evident problem was the lack of raw materials for many fishmeal factories which might hamper investments. This also resulted in a very wide range of resources used in the reduction process, including crustaceans, squid and even bivalves. Another issue concerns the fairly long transportation distances from place of capture to docks and from docks to fishmeal factories. For this, lots of ice was used for cooling and the fish sometimes even would get frozen before loaded into trucks. Overall, the fishmeal industry is a highly polluting industry, which needs to be upgraded. In the meantime, the whole domestic fishmeal industry in Asia needs to be critically reviewed with regards to environmental and socioeconomic impacts.

5.6.4 Improving aeration of ponds

Aerating shrimp ponds uses a substantial amount of energy in the life-cycle of these commodities. As stocking densities increase, the metabolic demand of the animals often exceeds the oxygen produced within the ponds. Reduced oxygen levels are especially prominent during warm nights when no photosynthesis occurs and metabolism remains high. Excessive use of feeds and other biomass also increase the oxygen demands within the ponds as oxygen is needed to break down these materials. In order to aerate the ponds, paddle-wheels are commonly used. These paddle-wheels are powered by electricity, diesel or LPG. However, since very few farmers know exactly how much oxygen is present in the ponds, paddle-wheels are often left running throughout the night. The range of different paddle-wheels is great, ranging from home-made designs to commercial products. Identifying the more efficient of these could result in great energy savings for shrimp farmers. This should be done alongside improvements in pond designs, where the number of paddle-wheels may be reduced. Better monitoring of the oxygen levels within the ponds could also help farmers to better manage the use of paddle-wheels. For example, building square ponds leaves many corners where water may be left standing and become hypoxic. In-line with changes to aeration systems, emissions of gases such as methane and dinitrogen monoxide should be considered. Similar to rice paddies, standing nutrient rich bodies of water, such as aquaculture ponds, can have substantial emissions of GHGs and other gases. In the present study a baseline volatilisation rate of 0.7% dinitrogen monoxide and 1% ammonia of excess nitrogen was assumed (Zimmo et al. 2003; Anh et al. 2010). Methane was harder to quantify as little research has gone into it. Unlike rice paddies which are not aerated, the aeration of aquaculture ponds reduces the formation of methane, but with hardly any data on exact reduction percentages. The temperature and aeration of the ponds, however, strongly influences the emissions from the ponds. Thus covering ponds during the day to limit the water temperature might be useful to limit emissions. Overall, more research is suggested on this topic.

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5.6.5 Livestock co-products

As part of the BSE outbreak in Europe in the late 1980s, the use of livestock co-products as feed in Europe and America has basically ceased. Meanwhile, in Asia meat and bone meal, blood meal and feather meal are common ingredients in aquaculture feeds. Most of these meals are imported since most parts of the animals are used for human consumption and not considered as by-products or wastes in Asia. Consequently, the environmental impacts of several Asian aquaculture systems are due to farming of pigs and other livestock in Europe. Much of the emissions stem from the management of manure in Europe, a practice that is far beyond the influence of any aquaculture farmer. However, in our globalised world, European farmers practices could actually improve the environmental performance of Asian aquaculture. By adopting better storage methods for manure, the resulting emissions (especially from manure) could be greatly reduced (De Vries et al. 2013).

5.6.6 Practices related to agricultural straw

Burning of agricultural residues in the fields is an old practiced thought to increase the productivity of the land. While some nutrients stay in the field in this way, most are lost to the atmosphere as chemical emissions contributing to a number of impact categories. Moreover, straw can be used as an animal feed or a fuel if valued (Silalertruksa and Gheewala 2013). Reducing the burning of agricultural residues should therefore be promoted. From a methodological perspective, more extensive utilisation of straw also becomes highly relevant. Given the large mass and energy embodied in it, straw makes a huge difference if mass or energy allocation is applied. It is rather surprising how little attention has been given to straw and allocation in previous studies. In the case of economic allocation, the issue can be rather opposite, as the economic value of straw can be very difficult to determine. Especially in countries like Bangladesh where rice straw is traded/exchanged for other commodities, or is bought for a symbolic sum of money. Better reporting on assumptions made with regards to allocation of agricultural straw is, therefore, promoted.

5.6.7 Shifting from farm-made to commercial feeds

The practice of producing farm-made feeds was still common for Pangasius farmers. Especially small scale farmers still relied on this practice. Chinese farmers also sometimes commissioned feed mills to produce feeds according to the farmers’ formula and/or to use self-sourced ingredients. Common for these feeds is that they are cheaper than commercial feeds but that more is needed. Generally, this trade-off weighted in favour of commercial feeds both economically and environmentally. Not surprisingly, as commercial feed producers spend lots of money on developing better feeds targeting individual species. Larger feed mills also have the capacity to make better feed pellets that float and are less likely to dissolve before being consumed. Paradoxically, farmers making their own feeds in an attempt to save money, often loose money and produce worse farming environments (as uneaten food consumes oxygen in its degradation) in the process.

5.6.8 Aim for intensification rather than expansion of aquaculture

A common notion is that more extensive, low-input systems are better for the environment than more intensive production methods. However, once land-use is taken into account, this is far from the case. Not described in detail here, but presented as part of the work

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Figure 36: Simplified flow-chart of the life cycle of capture fish used in feeds and the related allocation choices.

undertaken by Schoon (master thesis, publication in preparation) and Jonell (2013), systems established in mangrove areas can result in carbon dioxide emissions far beyond those related to conventional production systems. In the mentioned works, extensive shrimp farms in Vietnam were evaluated and the displaced area of mangrove evaluated. In both of these studies, the global warming impacts of land-use and land-use change far exceeded those of production in more intensive systems. As many aquaculture farms compete for coastal areas, expansions in many countries often result in the loss of mangrove. Further expansions in aquaculture production should therefore focus on intensification, rather than expansion.

5.6.9 Improved freezers

Processing plants consumed surprisingly large amounts of energy, especially in Bangladesh. Most of this energy was used to powering the extensive freezers housed by the processing plant. In Bangladesh, the poor performance of these freezers became obvious by the lack of dual doors into the freezers and the large piles of ice accumulated inside the freezers. Moreover, loading and emptying of the freezers was done by hand, resulting in slow operations and lots of energy loss. A common practice in the other countries was to freeze whole fish or shrimp, only for later processing. This of course requires the amount of energy for freezing twice. Ice was also used at large quantities on fishing boats and in markets. Improving logistics and timing of harvests together with improved freezers could therefore reduce the energy need substantially.

5.6.10 Allocation

As discussed above, the choice of allocation method (mass- or economic-based) greatly influenced absolute results, but much less relative results (see above). This goes against the conclusion from previous studies, that the comparative performance is highly influenced by the allocation method used (Henriksson et al. 2011). The reason for this is that previous studies had often limited their system boundaries to farm gate. Once the functional unit is extended beyond the processing plant, the emissions allocated to fish or shrimp by-products will to a large extent equal out those to either trash fish or agricultural by-products (Figure 36). For example, if mass allocation is applied, the emissions to trash fish will be equal that of edible fish, or even brood stock shrimp. However, if economic allocation is applied, more will be allocated to the edible fish and less to the trash fish. In the meantime a similar scenario exists at the processing plant, but here with larger allocation towards the frozen fillet if economic allocation is applied. Thus using mass allocation, the emissions will generally be much greater at farm-gate, but not necessarily at processing gate as much more is allocated towards the by-products (viscera, head, bones, scales, etc.).

Trash fish

Feed

Fish/shrimp

Diesel

Emission

Edible fish

FilletsByproducts

Capture fishery

Feed production

Grow-out farm

Processing plant

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5.6.11 Limitations

Most importantly when interpreting the results of the present study is to understand that the different products are not readily comparable to each other. Shrimps and prawn are sold in European markets at roughly 10-20 € kg-1, while you can get a kilogram of Pangasius fillets for only around 5 euro or a kilogram of Tilapia fillets for less than 10 euro in European supermarkets. Thus, despite a similar typical serving size of about 100-150 grams flesh for either product, consumption patterns and value additions will differ greatly amongst the different products. Moreover, fillets and tails come at many different qualities depending upon their origin, mode of production, feed, etc. Some examples include: off-flavouring of tilapia fillets, discoloured Pangasius fillets and poor texture of shrimp tails. These attributes are highly relevant and each country and production system produces its own level of quality. This is partly the reasoning behind defining the functional unit for prawns with shell-on and for shrimp shell-off (a weight difference of roughly 25%) (Haq and Quddus 1995). Despite the extensive dataset and methodology advances, several other limitations exist in the present study.

Feed inputs to Thai tilapia farmers – Overall, Thai tilapia farmers produced very stochastic data, especially with regards to feed in and product out. As a consequence, only a limited amount of farm data could be used in the present study.

Agricultural models – Most of the emission models for agricultural run-off were relatively complex, often with more than five parameters. However, no uncertainties were presented around these parameters. Overall dispersions from agricultural emissions therefore needed to be estimated in other ways.

Concurrent agriculture – In Bangladesh and Vietnam, it was common practice to produce agricultural crops alongside the aquaculture ponds. The exact level of integration of these crops and total quantities produced were, however, unclear. Thus, the two systems were considered as separate systems. Further research should therefore investigate the level of integration and advantages of such integrated production.

Characterisation factors for toxicity data – While detailed records on chemical use were available in (Rico et al. (2013), many of these chemicals lacked corresponding characterisation factors in both the CML and the ILCD methods. Additional efforts are therefore needed to produce characterisation factors for many of these chemicals.

Infrastructure – Infrastructure was not included in the present models but should be further evaluated. Especially brick and cement production in Asia, which is known to be more polluting than European production.

Land-use and land-use change – With initial efforts made into evaluating the impacts of land-use change in mangrove areas, a more holistic inclusion of land-use and land-use change (LULUC) impacts is needed. Preliminary outcomes, however, show that mangrove deforestation has a huge influence on the resulting CO2 emissions from aquaculture (Schoon 2013, MSc thesis).

Freshwater depletion – The use of groundwater for irrigation and ponds is highly controversial in many parts of Asia where saltwater intrusion is becoming more frequent. The competition for riverine water is also increasing and could become a constraint in the future.

Propagation methods – In the present study, Monte-Carlo simulations and random sampling were used to propagate results. Future research should, however, investigate additional sampling techniques which could reduce the computation times and produce more reproducible results.

Statistical testing of results – As Monte-Carlo outcomes simply are resampled outcomes from predefined ranges (either or not based on field samples), the legitimacy of implementing statistical tests should be evaluated by dedicated

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statisticians. This as an indefinite number of samples, theoretically could be produced, and thus statistical significance could be produced for the most weakest trends.

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6 Overall conclusions and recommendations One of the greatest outcomes of the present work was the confirmation that overall uncertainties actually can be quantified for a wide range of processes with attainable resources. Given that the research was on Asian aquaculture systems for which little representative LCA data are available, much of the data had to be collected from scratch for both foreground and background processes. Organising these in the spreadsheets developed within the project and published as supporting information to Henriksson et al. (2013), the results highlight that the overall dispersions around LCI results are large although highly dependent upon the system under study. The present research also highlights that by using averages, results become more uniform by reducing the influence of abnormal point values selected by chance. As many parameters act as multipliers during the propagation stage, one abnormal value could greatly distort point value results. Methodological choices, however, also appeared to have a great influence on the absolute results of individual LCAs, although it only marginally influenced the relative ranking of all LCAs mutually. Communication of results of uncertainty analyses is of high importance, especially when communicated to an audience not familiar with statistics and uncertainty parameters. We therefore promote the use of both tables and graphs, in order to portray different aspects of the data. Loss of information on additional mathematical moments of data, such as variances (inherent uncertainties), due to poor reporting was actually a major challenge in the present work, and we therefore promote more extensive reporting for future work on uncertainties in LCIs. Given that many LCI and LCA results were neither normally nor lognormally (non-parametric) distributed, the use of box and whisker plots defining the median and quartiles is recommended. More research into different statistical tests on propagated results in relation to the application of the results (learning, ecodesign, ecolabeling, carbon footprints, etc.) would, however, be welcomed. Discussing the LCA results with the dispersion levels in mind, we have come to a carefully considered set of recommendations for industry, for LCA practitioners and for further research.

6.1 Industry recommendations

Fishmeal use in aquaculture is a major environmental and socioeconomic concern and should be reduced to the extent possible.

The edible yield from omnivorous finfish, such as tilapia and Pangasius, overall shows lower impacts than the different crustaceans investigated in the present study. Shrimps are, however, not as environmentally detrimental as sometimes portrayed.

The use of commercial pelleted feeds is most often advantageous compared to farm-made feeds, both economically and environmentally.

Not only collection of sediments but also the use of those sediments should be reviewed in order to close nutrient loops. Sediment ponds should also be promoted.

Investments in better equipment (incl. boilers, freezers, generators, etc.) could improve both the economic and environmental performance of farms. However, it seems that volatile prices and supply of resources lock many aquaculture farmers into short-term planning.

Transportation had small contributions to the overall impacts as long as they go by modern trucks and ships.

The concept of eFCR is rather arbitrary as it does not account for co-produced species, the quality of the feed or supplemental feeds and fertilizers.

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Unlike the outcomes of Bosma et al. (2011) and Huysveld et al. (2013), methane emissions from rice paddies are large, but not the major concern. Researchers are therefore recommended to consider the scaling factors in the IPCC method and to better account for how straw was considered from an allocation perspective, given it makes up around 70% of the produce.

Allocation appeared to have a great influence on the absolute results of individual LCAs, although it only marginally influenced the relative ranking of all LCAs mutually.

Edible yields differed substantially amongst different species and products. Future LCA studies on fish and shrimp commodities are therefore recommended to define a functional unit based upon the edible yield, rather than whole animal.

6.2 LCA relevant methodological recommendations

The field of LCA should amend more rigid uncertainty methodologies as many of the past hurdles excluding its inclusion today have been overcome. More courses, better software solutions, and improved reporting in literature and databases on uncertainties is therefore urged in order to further increase LCA’s credibility.

Comparisons of absolute results amongst studies should not be conducted or accepted in peer-reviewed literature. This is made obvious by the fact that even when a common methodology (methodological choices and emission models) is implemented, simple differences in data sourcing could results in five- fold differences in results (basically the range covered by the whiskers). Neither are meta-analyses of LCA results especially relevant as these results are inherently influenced by individual practitioners and scopes of studies.

The ILCD standard should make it mandatory to either report comprehensively on the background studies from which unit process data have been derived, or on the inherent uncertainty, spread and representativeness following the protocol developed by Henriksson et al. (2013) in order not to lose any valuable information for quantifying unit process data dispersions in future.

Data should be thoroughly evaluated before any horizontal averaging takes place, as e.g. different production methods (e.g., high-level vs. low-level ponds) can have much greater influence than geographical area (e.g., different areas in Vietnam). In the present study this was made evident by the different criteria chosen before any horizontal averaging of data took place.

Spending time in the field collecting data and getting a visual understanding of systems is essential for proper modelling of LCIs.

More effort should be invested in educating students performing LCAs with proper statistics and basic experimental designs (including constructing sample frameworks).

LCAs should not proclaim to be representative of systems wider than that of the primary data. E.g. if a soybean study is based upon data from the Mato Grosso province in Brazil in 2007, it should not claim to be an LCA of Brazilian soybeans. A great example from the present research is that of fishmeal which impacts could differ with two orders of magnitude depending upon its origin.

The more averaged a process is, the greater the underlying spread becomes. Highly generalised processes which only present point-values should therefore be treated with care.

The impact category results that were driven by environmental flows and characterisation factors from available (Western) databases should be interpreted with caution, because the current study was set in Asia.

Rather than including more statistical distributions, software and databases should aim allowing for the implementation of a skewness and kurtosis to describe data. For example, by allowing for the implementation of a third and fourth parameter (the 3rd

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and 4th mathematical moments), greater sets of data could be described by fewer distributions (e.g. the lognormal distribution 3P).

6.3 Research recommendations

More extensive implementation of the protocol suggested in Henriksson et al. (2013) by ecoinvent and other partners is promoted. Inventory data based on this protocol can be easily updated by simply adding new, or removing old or unrepresentative studies in the provided excel file.

In the list of sub-compartments for the ILCD recommended characterisation factors brackish water is missing as compartment while this may the most important one for aquaculture studies in the areas that the SEAT project focused on.

Direct emissions from aquaculture ponds to the atmosphere have so far been poorly researched and more research is promoted.

Additional work on land-use and land-use change is recommended, especially in mangrove areas.

Further work on robust statistical methods designed to identify significance amongst Monte Carlo outcomes is encouraged.

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Acknowledgement This work is part of the Sustaining Ethical Aquaculture Trade (SEAT) project, which is co-funded by the European Commission within the Seventh Framework Programme—Sustainable Development Global Change and Ecosystem (project no. 222889).http:// www.seatglobal.eu The present research could not have been done without the eternal support from our project partners and field staff. We are also ever grateful to Dr. Rattanawan Mungkung for her support. We would also like to thank Reinout Heijungs for his support with mathematical issues and CMLCA, and Lauran van Oers for making the ILCD impact assessment methods available in a CMLCA readable format. This research could neither have been conducted without the support of many industry partners who took the time to meet us and share their knowledge.

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89

Appendix 1: Characterisation results including uncertainty information Table 20: Global warming, mass allocation, Shrimp and prawns. Emissions as kg of CO2-eq. per tonne product at European importer.

Species Litopenaeus vannamei Penaeus monodon Macrobrachium rosenbergii

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on Frozen headless shell-on

Country TH TH CN CN VN VN VN BD BD BD BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West West Khulna Bagerhat

System M M LL HL M M M P P S&P S&P P P

Intensity I I I I I I SI IE IE IE IE IE IE

Scale All All All All All All All All All All All All All

Baseline 1,41E4 1,27E4 8,46E3 8,91E3 1,29E4 1,60E4 1,62E4 5,53E3 6,91E3 1,10E4 1,06E4 1,26E4 9,89E3

Average 14664 13426 8510 8893 13309 16422 16431 5641 6863 11459 11201 13036 10268

Stdev 8784 3972 4530 4635 3480 5039 4551 3035 4332 8637 8048 8349 6429

CV 0,599 0,296 0,532 0,521 0,261 0,307 0,277 0,538 0,631 0,754 0,719 0,640 0,626

Geomean 13429 12885 7690 7940 12900 15743 15868 5137 6059 9753 9515 11348 8919

GeoStdev 1,469 1,330 1,547 1,600 1,280 1,331 1,298 1,504 1,603 1,690 1,709 1,646 1,664

Distr. Br LN(3P) P5 (3P) P5 (3P) LL(3P) JSB GEV F (3P) P6 (4P) F (3P) P5 (3P) F(3P) F(3P)

Best-fit dist LN LN* LN* LN* LN* LN* LN* LN LN LN LN LN LN

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

90

Table 21: Global warming, mass allocation, Tilapia and Pangasius. Emissions as kg of CO2-eq. per tonne product at European importer.

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All Small Medium Large

Baseline 4,41E3 5,10E3 4,52E3 4,75E3 9,90E3 1,05E4 7,82E3 7,71E3 6,75E3

Average 4503 5188 4575 4866 10354 11204 8015 7878 6879

Stdev 1229 1188 1069 1822 4399 5975 2293 2046 1463

CV 0,273 0,229 0,234 0,374 0,425 0,533 0,286 0,260 0,213

Geomean 4338 5053 4453 4605 9546 9961 7732 7635 6733

GeoStdev 1,317 1,260 1,263 1,383 1,493 1,618 1,300 1,282 1,229

Distr. Br GG JSU LL FL (3P) GG (4P) LL(3P) JSB GEV

Best-fit dist LN* LN* LN* LN* LN* LN* LN* LN* LN*

M=Monoculture; P=Polyculture; Ig=Integrated; R=Reservoir; I=Intensive; SI=Semi-Intensive

91

Table 22: Global warming, economic allocation, Shrimp and prawns. Emissions as kg of CO2-eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,44E+04 1,23E+04 1,07E+04 1,12E+04 8,42E+03 1,06E+04 1,01E+04 8,04E+03 1,21E+04 2,70E+04

Average 14818 12793 10850 11083 8671 10754 10359 8335 12546 27900

Stdev 14367 3753 5762 5975 2225 3103 2418 3897 8962 16207

CV 0,970 0,293 0,531 0,539 0,257 0,289 0,233 0,467 0,714 0,581

Geomean 12887 12293 9798 9906 8427 10365 10100 7679 10531 24744

GeoStdev 1,575 1,324 1,536 1,585 1,263 1,305 1,249 1,477 1,752 1,6

Distr. Br P5 (3P) GEV GEV LL (3P) GEV P6 (4P) P6 (4P) P5 (3P) P5

Best-fit dist LN LN* LN LN* LN LN* LN* LN LN LN*

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

92

Table 23: Global warming, economic allocation, Tilapia and Pangasius. Emissions as kg of CO2-eq. per tonne product at European importer.

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 7,03E+03 8,08E+03 7,03E+03 7,85E+03 1,17E+04 9,46E+03 7,34E+03 7,65E+03 6,71E+03

Average 7112 8229 7043 7829 12154 9623 7513 7925 6876

Stdev 2210 2163 1770 2736 6298 4551 1694 1837 1437

CV 0,311 0,263 0,251 0,349 0,518 0,473 0,226 0,232 0,209

Geomean 6805 7976 6841 7417 11038 8697 7336 7728 6737

GeoStdev 1,345 1,282 1,271 1,388 1,527 1,565 1,241 1,250 1,221

Distr. Br GEV GEV Br P5 (3P) GG (4P) GEV P5 (3P) GEV

Best-fit dist LN* LN* LN* LN* LN* LN* LN* LN* LN*

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

93

Table 24: Eutrophication, mass allocation. Shrimp and prawns. Emissions as kg of PO4-eq. per tonne product at European importer.

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,11E+02 1,09E+02 9,07E+01 9,40E+01 7,01E+01 8,81E+01 9,53E+01 5,81E+01 1,04E+02 1,64E+02

Average 116 113 92 97 71 90 98 61 103 193

Stdev 36 33 34 42 11 16 17 52 72 694

CV 0,310 0,288 0,372 0,438 0,158 0,178 0,173 0,865 0,698 3,594

Geomean 112 110 87 90 71 89 97 51 86 140

GeoStdev 1,276 1,275 1,393 1,393 1,166 1,191 1,186 1,724 1,793 1,864

Distr. D(4P) Br D(4P) LL(3P) JSB P5(3P) GG(4P) LG GEV B(4P)

Best-fit dist LN LN LN* LN LN* LN* LN* LN* LN* LN

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

94

Table 25: Eutrophication, mass allocation. Tilapia and Pangasius. Emissions as kg of PO4-eq. per tonne product at European importer.

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 6,81E+01 8,91E+01 7,59E+01 7,39E+01 6,85E+01 1,04E+02 6,24E+01 6,40E+01 5,55E+01

Average 70 90 77 78 72 106 65 67 58

Stdev 28 26 25 50 28 37 14 14 11

CV 0,396 0,293 0,327 0,645 0,383 0,349 0,213 0,203 0,191

Geomean 67 88 74 72 68 100 63 65 57

GeoStdev 1,347 1,268 1,303 1,428 1,394 1,377 1,236 1,223 1,209

Distr. D(4P) D(4P) LL(3P) B(4P) F(3P) LN(3P) P5(3P) P6(4P) JSU

Best-fit dist LN LN LN LN LN* LN* LN* LN* LN*

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

95

Table 26: Eutrophication, economic allocation. Shrimp and prawns. Emissions as kg of PO4-eq. per tonne product at European importer.

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,48E+02 1,45E+02 1,16E+02 1,20E+02 6,93E+01 8,98E+01 9,85E+01 9,86E+01 1,88E+02 5,62E+02

Average 152 146 119 120 71 93 101 101 198 562

Stdev 40 39 49 52 9 15 14 74 176 478

CV 0,266 0,266 0,411 0,435 0,131 0,159 0,143 0,736 0,888 0,851

Geomean 148 142 112 112 71 92 100 87 153 454

GeoStdev 1,266 1,267 1,389 1,419 1,14 1,17 1,151 1,702 1,985 1,837

Distr. F(3P) GL D D(4P) GEV B4(P) P5 D LP3 F(3P) Best-fit dist LN LN LN LN LN* LN* LN* LN* LN* LN

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

96

Table 27: Eutrophication, economic allocation. Tilapia and Pangasius. Emissions as kg of PO4-eq. per tonne product at European importer.

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 1,27E+02 1,68E+02 1,41E+02 1,40E+02 1,11E+02 1,75E+02 9,64E+01 9,89E+01 8,53E+01

Average 127 169 142 139 112 176 100 103 89

Stdev 48 49 61 60 53 65 21 22 17

CV 0,382 0,291 0,426 0,432 0,475 0,370 0,210 0,214 0,195

Geomean 120 164 135 130 104 167 97 101 87

GeoStdev 1,354 1,285 1,333 1,416 1,402 1,388 1,230 1,232 1,215

Distr. B GL D(4P) B(4P) LL(3P) D(4P) JSU P5(3P) LN(3P) Best-fit dist LN LN LN LN* LN LN LN* LN* LN*

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

97

Table 28: Acidification, mass allocation. Shrimp and prawns. Emissions as kg of SO2-eq. per tonne product at European importer.

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 8,78E+01 8,76E+01 5,85E+01 6,13E+01 9,06E+01 1,07E+02 1,12E+02 5,02E+01 6,92E+01 8,65E+01

Average 93 93 60 62 91 108 113 52 72 92

Stdev 33 29 27 27 26 37 34 29 52 70

CV 0,353 0,311 0,443 0,436 0,288 0,344 0,304 0,557 0,723 0,764

Geomean 88 89 56 57 88 103 108 47 61 77

GeoStdev 1,366 1,343 1,469 1,496 1,31 1,354 1,326 1,575 1,741 1,765

Distr. B(4P) F(3P) B(4P) FL(3P) D(4P) LL(3P) GEV D(4P) F(3P) LP3

Best-fit dist LN* LN* LN* LN* LN LN LN* LN LN LN

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

98

Table 29: Acidification, mass allocation. Tilapia and Pangasius. Emissions as kg of SO2-eq. per tonne product at European importer.

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 4,26E+01 5,04E+01 4,56E+01 4,35E+01 7,29E+01 8,67E+01 5,78E+01 5,78E+01 5,12E+01

Average 43 51 46 44 75 91 59 59 52

Stdev 12 13 11 12 29 46 19 15 12

CV 0,275 0,244 0,241 0,282 0,391 0,507 0,316 0,258 0,223

Geomean 41 50 45 42 70 81 57 57 51

GeoStdev 1,317 1,273 1,271 1,343 1,453 1,623 1,297 1,271 1,230

Distr. JSU P(5)3P B(4P) P5(3P) IG GG(4P) B(4P) GEV JSU

Best-fit distr. LN* LN* LN* N* LN* LN* LN LN* LN

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 99

Table 30: Acidification, economic allocation. Shrimp and prawns. Emissions as kg of SO2-eq. per tonne product at European importer.

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 8,48E+01 8,39E+01 7,02E+01 7,37E+01 5,96E+01 6,70E+01 6,98E+01 7,74E+01 1,23E+02 2,42E+02

Average 91 89 72 73 62 69 73 79 122 242

Stdev 42 29 29 33 16 18 18 45 110 130

CV 0,461 0,327 0,399 0,454 0,252 0,258 0,252 0,572 0,898 0,535

Geomean 85 85 67 66 61 67 71 70 98 217

GeoStdev 1,434 1,352 1,442 1,509 1,27 1,275 1,267 1,582 1,854 1,584

Distr. B(4P) D(4P) P5 LN(3P) GEV GG(4P) GEV F(3P) P6(4P) P5(3P)

Best-fit distr. LN LN* LN* LN* LN* LN* LN* LN LN LN*

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 100

Table 31: Acidification, economic allocation. Tilapia and Pangasius. Emissions as kg of SO2-eq. per tonne product at European importer.

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 6,53E+01 7,80E+01 6,93E+01 6,78E+01 8,15E+01 8,50E+01 5,51E+01 5,84E+01 5,20E+01

Average 66 79 70 68 82 88 58 62 55

Stdev 19 20 18 21 35 43 13 16 12

CV 0,288 0,256 0,258 0,303 0,430 0,492 0,220 0,258 0,212

Geomean 64 77 68 65 76 80 56 60 54

GeoStdev 1,339 1,286 1,292 1,357 1,481 1,565 1,237 1,276 1,222

Distr. GEV P6 D(4P) G(3P) LN(3P) LG JSU GEV D

Best-fit distr. LN* LN* LN* LN* LN* LN* LN* LN* LN*

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 101

Table 32: Abiotic depletion (elements, ultimate reserves), mass allocation. Shrimp and prawns. Resource use as kg of antimony-eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline -4,16E-03 -3,89E-03 -6,62E-03 -7,25E-03 -3,75E-03 -4,80E-03 -4,82E-03 -2,32E-03 -2,51E-03 -6,52E-03

Average -4,50E-03 -4,34E-03 -6,70E-03 -7,29E-03 -4,01E-03 -5,13E-03 -5,17E-03 -2,42E-03 -2,60E-03 -6,70E-03

Stdev 2,17E-03 1,97E-03 4,01E-03 4,43E-03 1,43E-03 1,95E-03 1,98E-03 1,91E-03 2,66E-03 6,05E-03

CV -0,483 -0,454 -0,599 -0,607 -0,357 -0,380 -0,383 -0,787 -1,021 -0,902

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 102

Table 33: Abiotic depletion (elements, ultimate reserves), mass allocation. Tilapia and Pangasius. Resource use as kg of antimony-eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline -3,88E-03 -4,43E-03 -3,92E-03 -4,20E-03 -3,62E-03 -4,51E-03 -2,65E-03 -3,05E-03 -2,75E-03

Average -3,99E-03 -4,59E-03 -4,02E-03 -4,33E-03 -3,82E-03 -4,83E-03 -2,87E-03 -3,29E-03 -2,96E-03

Stdev 1,58E-03 1,73E-03 1,49E-03 1,67E-03 2,04E-03 2,83E-03 1,14E-03 1,29E-03 1,11E-03

CV -3,96E-01 -3,77E-01 -3,71E-01 -3,86E-01 -5,34E-01 -5,85E-01 -3,99E-01 -3,92E-01 -3,75E-01

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive Table 34: Abiotic depletion (elements, ultimate reserves), economic allocation. Shrimp and prawns. Resource use as kg of antimony-eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline -4,52E-03 -4,09E-03 -8,36E-03 -9,23E-03 -2,39E-03 -3,25E-03 -3,08E-03 -2,99E-03 -4,35E-03 -1,42E-02

Average -4,90E-03 -4,38E-03 -8,39E-03 -9,46E-03 -2,57E-03 -3,49E-03 -3,33E-03 -3,14E-03 -4,48E-03 -1,46E-02

Stdev 2,99E-03 2,15E-03 5,15E-03 6,52E-03 1,18E-03 1,64E-03 1,56E-03 2,28E-03 7,57E-03 1,11E-02

CV -0,611 -0,492 -0,614 -0,689 -0,458 -0,470 -0,468 -0,725 -1,687 -0,758

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 103

Table 35: Abiotic depletion (elements, ultimate reserves), mass allocation. Tilapia and Pangasius. Resource use as kg of antimony-eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline -6,07E-03 -6,83E-03 -5,92E-03 -6,84E-03 -3,93E-03 -4,47E-03 -3,18E-03 -3,61E-03 -3,24E-03

Average -6,22E-03 -6,95E-03 -6,09E-03 -7,17E-03 -4,14E-03 -4,67E-03 -3,37E-03 -3,84E-03 -3,45E-03

Stdev 2,72E-03 2,78E-03 2,48E-03 3,48E-03 2,29E-03 2,95E-03 1,45E-03 1,80E-03 1,45E-03

CV -4,38E-01 -4,00E-01 -4,07E-01 -4,84E-01 -5,53E-01 -6,32E-01 -4,30E-01 -4,69E-01 -4,20E-01

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive Table 36: Abiotic depletion (fossil fuels), mass allocation. Shrimp and prawns. Resource use as MJ per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline -1,72E+05 -1,49E+05 -8,83E+04 -9,41E+04 -1,47E+05 -1,84E+05 -1,81E+05 -4,25E+04 -4,71E+04 -7,72E+04

Average -1,77E+05 -1,50E+05 -8,84E+04 -9,63E+04 -1,49E+05 -1,86E+05 -1,84E+05 -4,28E+04 -4,62E+04 -7,66E+04

Stdev 1,27E+05 4,92E+04 8,54E+04 8,50E+04 4,60E+04 6,54E+04 6,07E+04 1,60E+04 1,93E+04 4,01E+04

CV 0,716 0,327 0,966 0,883 0,310 0,353 0,330 0,374 0,417 0,524

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 104

Table 37: Abiotic depletion (fossil fuels), mass allocation. Tilapia and Pangasius. Resource use as MJ per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline -3,77E+04 -4,21E+04 -3,71E+04 -4,17E+04 -1,14E+05 -1,08E+05 -8,17E+04 -7,77E+04 -6,76E+04

Average -3,95E+04 -4,40E+04 -3,78E+04 -4,28E+04 -1,17E+05 -1,11E+05 -8,30E+04 -7,89E+04 -6,86E+04

Stdev 2,38E+04 2,26E+04 1,74E+04 3,51E+04 5,41E+04 6,34E+04 3,48E+04 2,56E+04 1,92E+04

CV 6,03E-01 5,14E-01 4,61E-01 8,21E-01 4,63E-01 5,69E-01 4,19E-01 3,25E-01 2,80E-01

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive Table 38: Abiotic depletion (fossil fuels), economic allocation. Shrimp and prawns. Resource use as MJ per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline -1,79E+05 -1,43E+05 -1,13E+05 -1,20E+05 -1,00E+05 -1,29E+05 -1,17E+05 -6,55E+04 -7,38E+04 -1,88E+05

Average -1,80E+05 -1,49E+05 -1,12E+05 -1,16E+05 -1,02E+05 -1,32E+05 -1,18E+05 -6,49E+04 -7,86E+04 -2,01E+05

Stdev 1,63E+05 5,74E+04 1,04E+05 1,06E+05 2,96E+04 4,97E+04 3,04E+04 2,49E+04 5,72E+04 1,37E+05

CV 0,907 0,385 0,927 0,906 0,290 0,377 0,259 0,384 0,729 0,683

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 105

Table 39: Abiotic depletion (fossil fuels), mass allocation. Tilapia and Pangasius. Resource use as MJ per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline -5,97E+04 -6,59E+04 -5,65E+04 -6,90E+04 -1,49E+05 -1,01E+05 -6,89E+04 -7,23E+04 -6,33E+04

Average -5,83E+04 -6,57E+04 -5,71E+04 -6,94E+04 -1,45E+05 -1,01E+05 -6,93E+04 -7,33E+04 -6,44E+04

Stdev 2,92E+04 2,91E+04 2,50E+04 4,77E+04 7,43E+04 5,50E+04 1,84E+04 1,93E+04 1,61E+04

CV 0.501 0.443 0.438 0.687E 0.513 0.542 0.265 0.263 0.250

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive Table 40: Ozone layer depletion, mass allocation. Shrimp and prawns. Emissions as kg CFC-11 eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,76E-03 1,60E-03 3,02E-04 2,88E-04 1,72E-03 2,00E-03 2,09E-03 3,17E-04 3,72E-04 6,51E-04

Average 1,80E-03 1,63E-03 3,09E-04 2,89E-04 1,74E-03 2,00E-03 2,10E-03 3,19E-04 3,79E-04 6,64E-04

Stdev 1,49E-03 8,81E-04 1,98E-04 1,76E-04 1,06E-03 1,23E-03 1,28E-03 2,04E-04 3,21E-04 4,82E-04

CV 0,831 0,540 0,641 0,607 0,610 0,613 0,608 0,638 0,846 0,726

Geomean 1,51E-03 1,44E-03 2,67E-04 2,50E-04 1,51E-03 1,74E-03 1,82E-03 2,82E-04 3,18E-04 5,59E-04

GeoStdev 1,748 1,625 1,679 1,697 1,679 1,676 1,680 1,597 1,721 1,742

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 106

Table 41: Ozone layer depletion, mass allocation. Tilapia and Pangasius. Emissions as kg CFC-11 eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 2,40E-04 2,79E-04 2,59E-04 2,40E-04 1,03E-03 1,01E-03 8,88E-04 8,12E-04 7,02E-04

Average 2,42E-04 2,85E-04 2,66E-04 2,44E-04 1,09E-03 1,06E-03 8,97E-04 8,23E-04 7,11E-04

Stdev 1,11E-04 1,26E-04 1,19E-04 1,10E-04 7,40E-04 8,20E-04 5,76E-04 4,74E-04 3,92E-04

CV 0,461 0,442 0,446 0,451 0,680 0,771 0,642 0,575 0,552

Geomean 2,20E-04 2,62E-04 2,44E-04 2,23E-04 9,19E-04 8,56E-04 7,73E-04 7,25E-04 6,33E-04

GeoStdev 1,532 1,500 1,512 1,525 1,761 1,904 1,689 1,633 1,595

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 107

Table 42: Ozone layer depletion, economic allocation. Shrimp and prawns. Emissions as kg CFC-11 eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,54E-03 1,29E-03 3,60E-04 3,36E-04 1,03E-03 1,09E-03 1,13E-03 5,04E-04 5,93E-04 1,68E-03

Average 1,54E-03 1,32E-03 3,73E-04 3,33E-04 1,06E-03 1,11E-03 1,13E-03 5,20E-04 6,19E-04 1,76E-03

Stdev 1,32E-03 6,89E-04 2,66E-04 1,95E-04 6,46E-04 5,75E-04 6,22E-04 4,64E-04 8,15E-04 1,40E-03

CV 0,854 0,523 0,714 0,587 0,612 0,519 0,549 0,893 1,317 0,799

Geomean 1,24E-03 1,18E-03 3,16E-04 2,88E-04 9,11E-04 9,91E-04 1,00E-03 4,34E-04 4,86E-04 1,44E-03

GeoStdev 1,858 1,585 1,724 1,704 1,694 1,599 1,632 1,712 1,833 1,806

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 108

Table 43: Ozone layer depletion, economic allocation. Tilapia and Pangasius. Emissions as kg CFC-11 eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 3,60E-04 4,24E-04 3,91E-04 3,65E-04 1,07E-03 6,06E-04 5,68E-04 5,74E-04 4,99E-04

Average 3,58E-04 4,24E-04 4,00E-04 3,65E-04 1,10E-03 6,43E-04 5,69E-04 5,83E-04 5,05E-04

Stdev 1,72E-04 2,04E-04 2,01E-04 1,84E-04 7,87E-04 4,38E-04 2,96E-04 2,56E-04 2,17E-04

CV 0,480 0,482 0,503 0,503 0,717 0,681 0,520 0,440 0,429

Geomean 3,22E-04 3,84E-04 3,60E-04 3,27E-04 9,19E-04 5,41E-04 5,14E-04 5,36E-04 4,65E-04

GeoStdev 1,579 1,555 1,569 1,590 1,781 1,782 1,545 1,506 1,496

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 109

Table 44 Photochemical ozone formation (high NOx), mass allocation. Shrimp and prawns. Emissions as kg ethylene eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 3,00E+00 2,92E+00 1,61E+00 1,69E+00 3,02E+00 3,68E+00 3,78E+00 1,03E+00 1,70E+00 1,62E+00

Average 3,26E+00 3,25E+00 1,63E+00 1,74E+00 3,13E+00 3,83E+00 3,96E+00 1,06E+00 1,78E+00 1,70E+00

Stdev 1,61E+00 1,65E+00 7,96E-01 9,36E-01 1,15E+00 1,51E+00 1,53E+00 7,80E-01 1,83E+00 1,24E+00

CV 0,493 0,508 0,487 0,537 0,367 0,395 0,387 0,733 1,029 0,731

Geomean 2,99E+00 2,97E+00 1,49E+00 1,56E+00 2,96E+00 3,59E+00 3,72E+00 9,11E-01 1,37E+00 1,42E+00

GeoStdev 1,493 1,494 1,521 1,583 1,378 1,414 1,407 1,667 1,928 1,763

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 110

Table 45: Photochemical ozone formation (high NOx), mass allocation. Tilapia and Pangasius. Emissions as kg ethylene eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 9,13E-01 1,06E+00 9,47E-01 9,52E-01 2,36E+00 2,79E+00 2,93E+00 2,82E+00 2,49E+00

Average 9,34E-01 1,09E+00 9,58E-01 9,74E-01 2,52E+00 3,00E+00 3,07E+00 2,95E+00 2,63E+00

Stdev 2,73E-01 2,73E-01 2,38E-01 3,05E-01 1,12E+00 1,69E+00 1,84E+00 1,49E+00 1,34E+00

CV 0,293 0,250 0,248 0,313 0,442 0,563 0,600 0,503 0,510

Geomean 8,96E-01 1,06E+00 9,30E-01 9,32E-01 2,30E+00 2,63E+00 2,78E+00 2,71E+00 2,43E+00

GeoStdev 1,334 1,275 1,274 1,345 1,528 1,653 1,516 1,481 1,451

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 111

Table 46: Photochemical ozone formation (high NOx), economic allocation. Shrimp and prawns. Emissions as kg ethylene eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 3,35E+00 3,23E+00 1,97E+00 2,09E+00 2,23E+00 2,72E+00 2,75E+00 1,46E+00 2,97E+00 3,62E+00

Average 3,79E+00 3,64E+00 1,94E+00 2,04E+00 2,46E+00 3,00E+00 3,07E+00 1,53E+00 2,86E+00 3,87E+00

Stdev 2,28E+00 2,09E+00 8,95E-01 1,01E+00 1,05E+00 1,31E+00 1,37E+00 1,07E+00 3,09E+00 2,56E+00

CV 0,601 0,573 0,461 0,494 0,426 0,438 0,446 0,702 1,079 0,663

Geomean 3,34E+00 3,26E+00 1,80E+00 1,85E+00 2,29E+00 2,78E+00 2,85E+00 1,32E+00 2,12E+00 3,29E+00

GeoStdev 1,616 1,554 1,464 1,542 1,425 1,448 1,444 1,669 2,034 1,731

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 112

Table 47: Photochemical ozone formation (high NOx), economic allocation. Tilapia and Pangasius. Emissions as kg ethylene eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 1,41E+00 1,66E+00 1,44E+00 1,51E+00 3,09E+00 3,34E+00 3,77E+00 3,71E+00 3,29E+00

Average 1,41E+00 1,68E+00 1,45E+00 1,50E+00 3,32E+00 3,75E+00 4,19E+00 4,14E+00 3,68E+00

Stdev 4,15E-01 4,61E-01 3,95E-01 5,21E-01 1,59E+00 2,14E+00 2,48E+00 2,34E+00 1,91E+00

CV 0,295 0,275 0,273 0,347 0,478 0,570 0,592 0,565 0,520

Geomean 1,35E+00 1,62E+00 1,40E+00 1,43E+00 3,02E+00 3,26E+00 3,71E+00 3,69E+00 3,32E+00

GeoStdev 1,337 1,297 1,299 1,377 1,541 1,694 1,604 1,588 1,542

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 113

Table 48: Human toxicity HTP inf, mass allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,68E+03 1,42E+03 3,55E+03 3,91E+03 9,88E+02 1,34E+03 1,21E+03 5,97E+02 9,07E+02 2,39E+03

Average 1,79E+03 1,46E+03 3,66E+03 4,04E+03 1,01E+03 1,36E+03 1,24E+03 5,97E+02 9,14E+02 2,46E+03

Stdev 1,46E+03 5,03E+02 2,60E+03 3,03E+03 2,82E+02 4,73E+02 3,60E+02 2,72E+02 6,34E+02 1,69E+03

CV 0,816 0,344 0,710 0,749 0,280 0,347 0,290 0,455 0,694 0,688

Geomean 1,57E+03 1,39E+03 3,08E+03 3,36E+03 9,78E+02 1,30E+03 1,20E+03 5,49E+02 7,79E+02 2,09E+03

GeoStdev 1,573 1,369 1,759 1,789 1,281 1,353 1,295 1,488 1,715 1,729

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 114

Table 49: Human toxicity HTP inf, mass allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 1,68E+03 1,80E+03 1,49E+03 2,03E+03 1,94E+03 2,03E+03 9,63E+02 1,04E+03 9,32E+02

Average 1,69E+03 1,80E+03 1,50E+03 2,01E+03 1,94E+03 2,11E+03 9,91E+02 1,07E+03 9,62E+02

Stdev 7,05E+02 5,97E+02 4,71E+02 1,07E+03 9,93E+02 1,31E+03 2,81E+02 3,33E+02 2,78E+02

CV 0,417 0,331 0,314 0,531 0,512 0,620 0,284 0,310 0,289

Geomean 1,57E+03 1,71E+03 1,43E+03 1,81E+03 1,74E+03 1,81E+03 9,57E+02 1,03E+03 9,28E+02

GeoStdev 1,458 1,369 1,352 1,551 1,576 1,714 1,297 1,320 1,301

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 115

Table 50: Human toxicity HTP inf, economic allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD

Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,68E+03 1,42E+03 3,55E+03 3,91E+03 9,88E+02 1,34E+03 1,21E+03 5,97E+02 9,07E+02 2,39E+03

Average 1,79E+03 1,46E+03 3,66E+03 4,04E+03 1,01E+03 1,36E+03 1,24E+03 5,97E+02 9,14E+02 2,46E+03

Stdev 1,46E+03 5,03E+02 2,60E+03 3,03E+03 2,82E+02 4,73E+02 3,60E+02 2,72E+02 6,34E+02 1,69E+03

CV 0,816 0,344 0,710 0,749 0,280 0,347 0,290 0,455 0,694 0,688

Geomean 1,57E+03 1,39E+03 3,08E+03 3,36E+03 9,78E+02 1,30E+03 1,20E+03 5,49E+02 7,79E+02 2,09E+03

GeoStdev 1,573 1,369 1,759 1,789 1,281 1,353 1,295 1,488 1,715 1,729

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 116

Table 51: Human toxicity HTP inf, economic allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 1,68E+03 1,80E+03 1,49E+03 2,03E+03 1,94E+03 2,03E+03 9,63E+02 1,04E+03 9,32E+02

Average 1,69E+03 1,80E+03 1,50E+03 2,01E+03 1,94E+03 2,11E+03 9,91E+02 1,07E+03 9,62E+02

Stdev 7,05E+02 5,97E+02 4,71E+02 1,07E+03 9,93E+02 1,31E+03 2,81E+02 3,33E+02 2,78E+02

CV 0,417 0,331 0,314 0,531 0,512 0,620 0,284 0,310 0,289

Geomean 1,57E+03 1,71E+03 1,43E+03 1,81E+03 1,74E+03 1,81E+03 9,57E+02 1,03E+03 9,28E+02

GeoStdev 1,458 1,369 1,352 1,551 1,576 1,714 1,297 1,320 1,301

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 117

Table 52: Freshwater aquatic ecotoxicity, mass allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 9,97E+02 8,15E+02 1,25E+03 1,38E+03 1,04E+03 1,39E+03 1,34E+03 2,75E+02 1,50E+02 1,08E+03

Average 1,08E+03 8,88E+02 1,36E+03 1,43E+03 1,08E+03 1,42E+03 1,40E+03 2,88E+02 1,57E+02 1,07E+03

Stdev 8,15E+02 4,76E+02 1,69E+03 1,34E+03 3,99E+02 5,06E+02 5,30E+02 2,47E+02 1,04E+02 1,06E+03

CV 0,755 0,535 1,240 0,938 0,369 0,355 0,378 0,855 0,663 0,996

Geomean 9,38E+02 8,30E+02 9,89E+02 1,09E+03 1,04E+03 1,35E+03 1,34E+03 2,32E+02 1,37E+02 7,76E+02

GeoStdev 1,614 1,418 2,051 2,004 1,32 1,367 1,344 1,861 1,632 2,153

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 118

Table 53: Freshw. aquatic ecotoxicity, mass allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 4,81E+02 5,29E+02 4,52E+02 5,56E+02 1,11E+03 1,30E+03 5,57E+02 6,31E+02 5,65E+02

Average 4,97E+02 5,46E+02 4,69E+02 5,74E+02 1,19E+03 1,35E+03 5,80E+02 6,55E+02 5,89E+02

Stdev 3,21E+02 2,95E+02 2,38E+02 4,23E+02 7,12E+02 8,52E+02 2,35E+02 2,67E+02 2,54E+02

CV 0,646 0,541 0,507 0,737 0,600 0,630 0,405 0,408 0,431

Geomean 4,38E+02 4,93E+02 4,28E+02 4,95E+02 1,05E+03 1,16E+03 5,51E+02 6,22E+02 5,65E+02

GeoStdev 1,605 1,535 1,509 1,673 1,620 1,721 1,355 1,360 1,305

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

Page 119

Table 54: Freshwater aquatic ecotoxicity, eco. allocation. Shrimp and prawns. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Litopenaeus vannamei Penaeus monodon

Product Individually quick frozen peeled tail-on Individually quick frozen peeled tail-on

Country TH TH CN CN VN VN VN BD BD BD Region East South Guangdong Guangdong Mekong Mekong Mekong West East West

System M M LL HL M M M P P S&P

Intensity I I I I I I SI IE IE IE

Scale All All All All All All All All All All

Baseline 1,03E+03 7,49E+02 1,63E+03 1,80E+03 5,31E+02 8,19E+02 6,79E+02 2,11E+02 2,30E+02 9,68E+02

Average 1,04E+03 7,71E+02 1,63E+03 1,76E+03 5,44E+02 8,31E+02 6,99E+02 2,14E+02 2,34E+02 9,91E+02

Stdev 9,50E+02 3,58E+02 1,53E+03 1,64E+03 1,76E+02 3,89E+02 2,43E+02 1,27E+02 1,68E+02 8,66E+02

CV 0,914 0,465 0,936 0,929 0,324 0,468 0,348 0,594 0,717 0,874

Geomean 8,60E+02 7,11E+02 1,26E+03 1,35E+03 5,19E+02 7,66E+02 6,63E+02 1,91E+02 1,99E+02 8,03E+02

GeoStdev 1,739 1,473 1,963 2,006 1,356 1,475 1,382 1,587 1,721 1,839

M=Monoculture; P=Polyculture; LL=Low level; HL=High level; S&P= Shrimp & Prawn; I=Intensive; SI=Semi-Intensive; IE=Improved Extensive

Page 120

Table 55: Freshw. aquatic ecotoxicity, eco. allocation. Tilapia and Pangasius. Emissions as kg 1,4-dichlorobenzene eq. per tonne product at European importer

Species Tilapia Pangasius

Product Frozen fillet Frozen fillet

Country CN CN CN CN TH TH VN VN VN

Region Guangdong Hainan GD & HI Guangdong Central Suphanburi Mekong Mekong Mekong

System P P R Ig Ponds Cages M M M

Intensity SI/I SI/I SI/I SI/I SI I I I I

Scale All All All All All All S M L

Baseline 7,58E+02 8,09E+02 6,63E+02 9,30E+02 1,13E+03 1,08E+03 5,09E+02 5,66E+02 5,03E+02

Average 7,57E+02 8,11E+02 6,69E+02 9,66E+02 1,15E+03 1,07E+03 5,24E+02 5,84E+02 5,17E+02

Stdev 4,49E+02 4,30E+02 3,42E+02 1,53E+03 7,74E+02 7,92E+02 2,01E+02 2,32E+02 1,90E+02

CV 0,593 0,530 0,512 1,585 0,675 0,743 0,385 0,397 0,368

Geomean 6,71E+02 7,36E+02 6,14E+02 7,94E+02 9,77E+02 8,84E+02 4,92E+02 5,46E+02 4,88E+02

GeoStdev 1,597 1,529 1,488 1,726 1,722 1,805 1,410 1,427 1,394

M=Monoculture; P=Polyculture; Ig=Integrated; I=Intensive; R=Reservoir; SI=Semi-Intensive; IE=Improved Extensive

.......

Page 121

Appendix 2: Contribution analyses Table 56: Contribution analysis for CMLCML and ILCD global warming results, economic allocation, for [A1] (1 tonne of frozen, head-less shell-

on Macrobrachium Prawns produced in improved extensive systems in Khulna, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4697.1] Prawn farming Khulna[BD, 2011]; input of [G4821] Prawn, Khulna, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 4.74E+03 14

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 3.44E+03 10

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 2.97E+03 8

[P4654] Pulse farming[BD, 2011] [E44] Dinitrogen monoxide[air] 2.03E+03 6

[P4691.1] Wheat farming[BD, 2011]; input of [G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 2.13E+03 6

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 1.64E+03 5

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 1.52E+03 4

[P4229.1] Maize farming[BD, 2011]; input of [G4248] Maize, fresh grain[BD, 2011] [E44] Dinitrogen monoxide[air] 1.32E+03 4

[P4642.1] Aman rice farming[BD, 2011]; input of [G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 8.89E+02 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 909 3

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 1.10E+03 3

[P4641.1] Boro rice farming[BD, 2011]; input of [G4746] Boro rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 560 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 527 2

[P4642.1] Aman rice farming[BD, 2011]; input of [G4747] Aman rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 636 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 7.20E+02 2

[P4641.1] Boro rice farming[BD, 2011]; input of [G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 687 2

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 7.71E+02 2

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 466 1

[P4684] Diesel, burned at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 3.94E+02 1

[P4668] Natural gas, burned in boiler[BD, 2011] [E11] Carbon dioxide, fossil[air] 4.38E+02 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 4.07E+02 1

Sum Sum 2.83E+04 81

All All 3.50E+04 100

Page 122

Table 57: Contribution analysis for CMLCML and ILCD global warming results, economic allocation, for [A2] (1 tonne of frozen, head-less shell-

on Macrobrachium Prawns produced in improved extensive systems in Bagerhat, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4691.1] Wheat farming[BD, 2011]; input of [G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 7.76E+03 20

[P4702.1] Prawn farming Bagerat[BD, 2011]; input of [G4823] Prawn, Bagerat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 5.40E+03 14

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 3.98E+03 10

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 3.01E+03 8

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 2.14E+03 5

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 1.42E+03 4

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 1.01E+03 3

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 718 2

[P4229.1] Maize farming[BD, 2011]; input of [G4248] Maize, fresh grain[BD, 2011] [E44] Dinitrogen monoxide[air] 621 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 671 2

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 971 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 866 2

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 869 2

[P4668] Natural gas, burned in boiler[BD, 2011] [E11] Carbon dioxide, fossil[air] 408 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 502 1

[P4228] Application of urea, at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 474 1

[P4642.1] Aman rice farming[BD, 2011]; input of [G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 428 1

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 414 1

[P4684] Diesel, burned at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 456 1

Sum Sum 3.21E+04 81

All All 3.95E+04 100

Page 123

Table 58: Contribution analysis for CMLCML and ILCD global warming results, economic allocation, for [A3] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 3.13E+03 22

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 1.45E+03 10

[P4514] Diesel, burned at farm[TH, 2011] [E11] Carbon dioxide, fossil[air] 1.31E+03 9

[P4517] Shrimp farming in Eastern Thailand[TH, 2011] [E44] Dinitrogen monoxide[air] 794 6

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 743 5

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 537 4

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 447 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 387 3

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 295 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 300 2

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 238 2

[P4514] Diesel, burned at farm[TH, 2011] [E44] Dinitrogen monoxide[air] 151 1

[P4187] LPG, burned in gas motor[TH] [E11] Carbon dioxide, fossil[air] 164 1

[P1369] natural gas, burned in gas turbine, for compressor station[RU, 1990-2000] [E11] Carbon dioxide, fossil[air] 174 1

[P847] hard coal, burned in power plant[FR, 1993-2000] [E11] Carbon dioxide, fossil[air] 146 1

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 175 1

Sum Sum 1.04E+04 72

All All 1.44E+04 100

Page 124

Table 59: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A4] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4187] LPG, burned in gas motor[TH] [E11] Carbon dioxide, fossil[air] 2.37E+03 19

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 1.46E+03 12

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 1.29E+03 11

[P4518] Shrimp farming in Southern Thailand[TH, 2011] [E44] Dinitrogen monoxide[air] 804 7

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 542 4

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 452 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 369 3

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 324 3

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 266 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 299 2

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 255 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 133 1

[P1490] heavy fuel oil, burned in refinery furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 123 1

[P1493] light fuel oil, burned in boiler 100kW, non-modulating[CH, 1991-2000] [E11] Carbon dioxide, fossil[air] 129 1

[P4514] Diesel, burned at farm[TH, 2011] [E11] Carbon dioxide, fossil[air] 137 1

Sum Sum 8.95E+03 73

All All 1.23E+04 100

Page 125

Table 60: Contribution analysis for CML and ILCD global warming results, economic allocation, for, economic allocation, for [A5] (1 tonne of frozen, edible yield of Pangasius produced in small systems in the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 871 12

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 719 10

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 756 10

[P4486] Pangasius farming, small scale[VN, 2011] [E44] Dinitrogen monoxide[air] 678 9

[P4252.1] Rice farming[VN, 2011]; input of [G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 624 8

[P4492] Hard coal, at feed mill, burned in industrial furnace 1-10MW[VN] [E11] Carbon dioxide, fossil[air] 379 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 308 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 267 4

[P4252.1] Rice farming[VN, 2011]; input of [G4267] Paddy rice, at farm[VN, 2011] [E44] Dinitrogen monoxide[air] 288 4

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 237 3

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 114 2

[P4240] operation, lorry >16t, fleet average[VN, 2011] [E11] Carbon dioxide, fossil[air] 106 1

[P4361] operation, lorry >32t, EURO4[US, 2011] [E11] Carbon dioxide, fossil[air] 91.4 1

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 108 1

Sum Sum 5.55E+03 76

All All 7.34E+03 100

Page 126

Table 61: Contribution analysis for CML and ILCD global warming results, economic allocation, for, economic allocation, for [A6] (1 tonne of frozen, edible yield of Pangasius produced in medium systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 886 12

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 806 11

[P4489] Pangasius farming, medium scale[VN, 2011] [E44] Dinitrogen monoxide[air] 690 9

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 725 9

[P4252.1] Rice farming[VN, 2011]; input of [G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 515 7

[P4492] Hard coal, at feed mill, burned in industrial furnace 1-10MW[VN] [E11] Carbon dioxide, fossil[air] 406 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 343 4

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 315 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 284 4

[P4252.1] Rice farming[VN, 2011]; input of [G4267] Paddy rice, at farm[VN, 2011] [E44] Dinitrogen monoxide[air] 238 3

[P4240] operation, lorry >16t, fleet average[VN, 2011] [E11] Carbon dioxide, fossil[air] 118 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 115 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 135 2

[P4361] operation, lorry >32t, EURO4[US, 2011] [E11] Carbon dioxide, fossil[air] 113 1

Sum Sum 5.69E+03 74

All All 7.65E+03 100

Page 127

Table 62: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A7] (1 tonne of frozen, edible yield of Pangasius produced in large systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 799 12

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 699 10

[P4490] Pangasius farming, large scale[VN, 2011] [E44] Dinitrogen monoxide[air] 583 9

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 618 9

[P4252.1] Rice farming[VN, 2011]; input of [G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 447 7

[P4492] Hard coal, at feed mill, burned in industrial furnace 1-10MW[VN] [E11] Carbon dioxide, fossil[air] 360 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 325 5

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 287 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 247 4

[P4252.1] Rice farming[VN, 2011]; input of [G4267] Paddy rice, at farm[VN, 2011] [E44] Dinitrogen monoxide[air] 207 3

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 121 2

[P4361] operation, lorry >32t, EURO4[US, 2011] [E11] Carbon dioxide, fossil[air] 102 2

[P4240] operation, lorry >16t, fleet average[VN, 2011] [E11] Carbon dioxide, fossil[air] 109 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 100 1

Sum Sum 5.00E+03 75

All All 6.71E+03 100

Page 128

Table 63: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A8] (1 tonne of frozen, edible yield of Shrimp produced in low-level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011)

Process Extension kg SO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E64] Sulfur dioxide[air] 13.9 20

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E64] Sulfur dioxide[air] 7.45 11

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E13] Nitrogen oxides[air] 7.49 11

[P4519] Wheat farming, Northern China[CN, 2011] [E12] Ammonia[air] 5.25 7

[P3856] natural gas, at production[RNA, 2000-2005] [E64] Sulfur dioxide[air] 3.5 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E13] Nitrogen oxides[air] 3.52 5

[P4519] Wheat farming, Northern China[CN, 2011] [E13] Nitrogen oxides[air] 2.67 4

[P4102] Soybeans, at farm[BR, 2011] [E13] Nitrogen oxides[air] 2.72 4

[P4611] Diesel, burned at farm[CN, 2011] [E13] Nitrogen oxides[air] 2.31 3

[P4626] Shrimp low-level farming[GD, CN, 2011] [E12] Ammonia[air] 1.81 3

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E64] Sulfur dioxide[air] 1.2 2

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E13] Nitrogen oxides[air] 1.19 2

[P4401] hard coal, burned in boiler[CN, 2011] [E64] Sulfur dioxide[air] 1.44 2

[P4577] hard coal, burned at fish reduction plant[CN] [E64] Sulfur dioxide[air] 1.53 2

[P4561] Groundnut farming[CN, 2011] [E12] Ammonia[air] 0.718 1

[P4395] hard coal, burned in power plant[CN, 2011] [E64] Sulfur dioxide[air] 0.784 1

[P4133] Diesel, burned in fishing boats[PE, 2011] [E13] Nitrogen oxides[air] 1.03 1

Sum Sum 58.5 83

All All 70.2 100

Page 129

Table 64: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A9] (1 tonne of frozen, edible yield of Shrimp produced in high level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011) Process Extension kg SO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E64] Sulfur dioxide[air] 15.7 21

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E13] Nitrogen oxides[air] 8.43 11

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E64] Sulfur dioxide[air] 7.66 10

[P4519] Wheat farming, Northern China[CN, 2011] [E12] Ammonia[air] 5.52 7

[P3856] natural gas, at production[RNA, 2000-2005] [E64] Sulfur dioxide[air] 3.67 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E13] Nitrogen oxides[air] 3.62 5

[P4519] Wheat farming, Northern China[CN, 2011] [E13] Nitrogen oxides[air] 2.8 4

[P4102] Soybeans, at farm[BR, 2011] [E13] Nitrogen oxides[air] 2.86 4

[P4627] Shrimp high-level farming[GD, CN, 2011] [E12] Ammonia[air] 1.93 3

[P4401] hard coal, burned in boiler[CN, 2011] [E64] Sulfur dioxide[air] 1.52 2

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E64] Sulfur dioxide[air] 1.26 2

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E13] Nitrogen oxides[air] 1.23 2

[P4611] Diesel, burned at farm[CN, 2011] [E13] Nitrogen oxides[air] 1.12 2

[P4577] hard coal, burned at fish reduction plant[CN] [E64] Sulfur dioxide[air] 1.61 2

[P4133] Diesel, burned in fishing boats[PE, 2011] [E13] Nitrogen oxides[air] 1.09 1

[P4395] hard coal, burned in power plant[CN, 2011] [E64] Sulfur dioxide[air] 0.843 1

[P4561] Groundnut farming[CN, 2011] [E12] Ammonia[air] 0.754 1

Sum Sum 61.6 84

All All 73.7 100

Page 130

Table 65: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A10] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 1.94E+03 18

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 1.48E+03 14

[P4643] P. monodon, intensive farming[VN, 2011] [E44] Dinitrogen monoxide[air] 857 8

[P4276] Diesel, burned at farm[VN, 2011] [E11] Carbon dioxide, fossil[air] 590 6

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 666 6

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 456 4

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 366 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 363 3

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 268 3

[P4290] Diesel, burned at feed mill[VN, 2011] [E11] Carbon dioxide, fossil[air] 135 1

[P4589] natural gas, burned in industrial furnace >100kW[PE, 2011] [E11] Carbon dioxide, fossil[air] 130 1

[P848] hard coal, burned in power plant[IT, 1993-2000] [E11] Carbon dioxide, fossil[air] 121 1

[P847] hard coal, burned in power plant[FR, 1993-2000] [E11] Carbon dioxide, fossil[air] 131 1

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 127 1

[P849] hard coal, burned in power plant[NL, 1993-2000] [E11] Carbon dioxide, fossil[air] 110 1

[P2373] heavy fuel oil, burned in power plant[IT, 1985-2000] [E11] Carbon dioxide, fossil[air] 113 1

[P1369] natural gas, burned in gas turbine, for compressor station[RU, 1990-2000] [E11] Carbon dioxide, fossil[air] 111 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 121 1

Sum Sum 8.08E+03 77

All All 1.06E+04 100

Page 131

Table 66: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A11] (1 tonne of frozen, edible yield of Shrimp (L. Vannamei) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4276] Diesel, burned at farm[VN, 2011] [E11] Carbon dioxide, fossil[air] 1.60E+03 19

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 1.15E+03 14

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 971 12

[P4644] L. vannamei, intensive farming[VN, 2011] [E44] Dinitrogen monoxide[air] 604 7

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 355 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 309 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 340 4

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 285 3

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 146 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 138 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 94.2 1

[P4589] natural gas, burned in industrial furnace >100kW[PE, 2011] [E11] Carbon dioxide, fossil[air] 101 1

[P4290] Diesel, burned at feed mill[VN, 2011] [E11] Carbon dioxide, fossil[air] 105 1

[P4240] operation, lorry >16t, fleet average[VN, 2011] [E11] Carbon dioxide, fossil[air] 92.9 1

Sum Sum 6.29E+03 75

All All 8.42E+03 100

Page 132

Table 67: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A12] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in semi-intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 1.56E+03 15

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 1.28E+03 13

[P4276] Diesel, burned at farm[VN, 2011] [E11] Carbon dioxide, fossil[air] 1.14E+03 11

[P4645] P. monodon, semi-intensive farming[VN, 2011] [E44] Dinitrogen monoxide[air] 914 9

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 479 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 361 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 447 4

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 385 4

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 181 2

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 153 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 126 1

[P4240] operation, lorry >16t, fleet average[VN, 2011] [E11] Carbon dioxide, fossil[air] 110 1

[P4290] Diesel, burned at feed mill[VN, 2011] [E11] Carbon dioxide, fossil[air] 141 1

[P4589] natural gas, burned in industrial furnace >100kW[PE, 2011] [E11] Carbon dioxide, fossil[air] 136 1

Sum Sum 7.41E+03 74

All All 1.01E+04 100

Page 133

Table 68: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A13] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Guangdong, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 990 14

[P4615.1] Tilapia farming, non-integrated polyculture[GD, CN, 2011]; input of [G4706] Tilapia from non-integrated farm, at farm[GD, CN, 2011]

[E44] Dinitrogen monoxide[air] 651 9

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 621 9

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 625 9

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 405 6

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 349 5

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 371 5

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 206 3

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 192 3

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 197 3

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 243 3

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 115 2

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 157 2

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 108 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 149 2

[P4523] Application of urea, at farm[CN, 2011] [E11] Carbon dioxide, fossil[air] 70.8 1

[P4556] Cassava farming[CN, 2011] [E44] Dinitrogen monoxide[air] 79.2 1

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 90.2 1

Sum Sum 5.62E+03 80

All All 7.03E+03 100

Page 134

Table 69: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A14] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Hainan, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4616.1] Tilapia farming, non-integrated polyculture Hainan[HI, CN, 2011]; input of [G4708] Tilapia from non-integrated farm in Hainan, at farm[HI, CN, 2011]

[E44] Dinitrogen monoxide[air] 878 11

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 761 9

[P4398] hard coal, burned in power plant, Hainan[HI, CN, 2011] [E11] Carbon dioxide, fossil[air] 727 9

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 759 9

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 454 6

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 456 6

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 404 5

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 298 4

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 253 3

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 241 3

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 235 3

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 133 2

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 192 2

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 138 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 182 2

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 110 1

[P4556] Cassava farming[CN, 2011] [E44] Dinitrogen monoxide[air] 97 1

[P4523] Application of urea, at farm[CN, 2011] [E11] Carbon dioxide, fossil[air] 86.7 1

Sum Sum 6.40E+03 79

All All 8.08E+03 100

Page 135

Table 70: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A15] (1 tonne of frozen, edible yield of Tilapia produced in polyculture reservoirs in Guangdong/Hainan, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4617.1] Tilapia farming, reservoir[CN, 2011]; input of [G4710] Tilapia from reservoir, at farm[CN, 2011] [E44] Dinitrogen monoxide[air] 775 11

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 692 10

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 698 10

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 417 6

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 433 6

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 379 5

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 350 5

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 274 4

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 221 3

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 215 3

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 176 3

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 232 3

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 125 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 167 2

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 122 2

[P4556] Cassava farming[CN, 2011] [E44] Dinitrogen monoxide[air] 89 1

[P4523] Application of urea, at farm[CN, 2011] [E11] Carbon dioxide, fossil[air] 79.6 1

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 101 1

Sum Sum 5.55E+03 79

All All 7.03E+03 100

Page 136

Table 71: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A16] (1 tonne of frozen, edible yield of Tilapia produced in ponds integrated with pigs in Guangdong, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 1.74E+03 22

[P4613.1] Tilapia farming, integrated system[GD, CN, 2011]; input of [G4703] Tilapia, from integrated farm, at farm[GD, CN, 2011]

[E44] Dinitrogen monoxide[air] 686 9

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 623 8

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 609 8

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 363 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 401 5

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 344 4

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 202 3

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 239 3

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 193 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 146 2

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 154 2

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 188 2

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 106 1

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 88.4 1

[P4402] Coal seam fires[CN, 2011] [E11] Carbon dioxide, fossil[air] 86.3 1

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 117 1

Sum Sum 6.28E+03 80

All All 7.85E+03 100

Page 137

Table 72: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A17] (1 tonne of frozen, edible yield of Tilapia produced in pond systems in Chachoengsao/Nakhon Patom/Petchburi, Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 2.02E+03 17

[P4514] Diesel, burned at farm[TH, 2011] [E11] Carbon dioxide, fossil[air] 1.30E+03 11

[P4742] Tilapia farming in ponds[TH, 2011] [E44] Dinitrogen monoxide[air] 543 5

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 489 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 414 4

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 435 4

[P4177] Hard coal, at feed mill, burned in industrial furnace 1-10MW[TH, 2011] [E11] Carbon dioxide, fossil[air] 481 4

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 391 3

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 332 3

[P3844] natural gas, burned in power plant[US, 2004] [E11] Carbon dioxide, fossil[air] 394 3

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 199 2

[P4448.1] Major rice farming[TH, 2011]; input of [G4494] Paddy rice, major, at farm[TH, 2011] [E44] Dinitrogen monoxide[air] 255 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 198 2

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 155 1

[P3870] hard coal, burned in power plant[RFC, 1998-2004] [E11] Carbon dioxide, fossil[air] 167 1

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 124 1

[P4514] Diesel, burned at farm[TH, 2011] [E44] Dinitrogen monoxide[air] 149 1

[P4448.1] Major rice farming[TH, 2011]; input of [G4494] Paddy rice, major, at farm[TH, 2011] [E41] Methane, biogenic[air] 121 1

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 118 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 123 1

[P3871] hard coal, burned in power plant[SERC, 1998-2004] [E11] Carbon dioxide, fossil[air] 157 1

Sum Sum 8.57E+03 73

All All 1.17E+04 100

Page 138

Table 73: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A18] (1 tonne of frozen, edible yield of Tilapia produced in intensive cages systems in Suphanburi, Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4743] Tilapia farming in cages[TH, 2011] [E44] Dinitrogen monoxide[air] 877 9

[P4177] Hard coal, at feed mill, burned in industrial furnace 1-10MW[TH, 2011] [E11] Carbon dioxide, fossil[air] 658 7

[P3844] natural gas, burned in power plant[US, 2004] [E11] Carbon dioxide, fossil[air] 539 6

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 535 6

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 591 6

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 482 5

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 455 5

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 410 4

[P4448.1] Major rice farming[TH, 2011]; input of [G4494] Paddy rice, major, at farm[TH, 2011] [E44] Dinitrogen monoxide[air] 349 4

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 262 3

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 212 2

[P3871] hard coal, burned in power plant[SERC, 1998-2004] [E11] Carbon dioxide, fossil[air] 215 2

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 170 2

[P3870] hard coal, burned in power plant[RFC, 1998-2004] [E11] Carbon dioxide, fossil[air] 229 2

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 148 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 160 2

[P4448.1] Major rice farming[TH, 2011]; input of [G4494] Paddy rice, major, at farm[TH, 2011] [E41] Methane, biogenic[air] 166 2

[P4433] Maize farming[US, 2011] [E44] Dinitrogen monoxide[air] 111 1

[P4460] Diesel, burned at feed mill[TH, 2011] [E11] Carbon dioxide, fossil[air] 111 1

[P1782] operation, lorry 3.5-20t, fleet average[CH, 2005] [E11] Carbon dioxide, fossil[air] 101 1

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 122 1

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 120 1

Sum Sum 7.02E+03 74

All All 9.46E+03 100

Page 139

Table 74: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A19] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in small-medium improved extensive systems in Bagerhat/Khulna/Satkhira, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1.53E+03 19

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 656 8

[P4767.1] Shrimp farming West[BD, 2011]; input of [G4942] Shrimp, West, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 606 8

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 542 7

[P4642.1] Aman rice farming[BD, 2011]; input of [G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 530 7

[P4691.1] Wheat farming[BD, 2011]; input of [G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 417 5

[P4642.1] Aman rice farming[BD, 2011]; input of [G4747] Aman rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 380 5

[P4641.1] Boro rice farming[BD, 2011]; input of [G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 410 5

[P4641.1] Boro rice farming[BD, 2011]; input of [G4746] Boro rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 334 4

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 278 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 219 3

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 135 2

[P4706] Diesel, burned at processing plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 166 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 123 2

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 148 2

[P2373] heavy fuel oil, burned in power plant[IT, 1985-2000] [E11] Carbon dioxide, fossil[air] 83.7 1

Sum Sum 6.56E+03 82

All All 8.04E+03 100

Page 140

Table 75: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A20] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in large improved extensive systems in Cox’s Bazar, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1.66E+03 14

[P4691.1] Wheat farming[BD, 2011]; input of [G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 1.63E+03 13

[P4642.1] Aman rice farming[BD, 2011]; input of [G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 1.60E+03 13

[P4641.1] Boro rice farming[BD, 2011]; input of [G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 1.23E+03 10

[P4642.1] Aman rice farming[BD, 2011]; input of [G4747] Aman rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 1.14E+03 9

[P4641.1] Boro rice farming[BD, 2011]; input of [G4746] Boro rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 1.00E+03 8

[P4768.1] Shrimp farming East[BD]; input of [G4944] Shrimp, East, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 489 4

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 511 4

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 370 3

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 369 3

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 285 2

[P4706] Diesel, burned at processing plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 166 1

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 152 1

[P4228] Application of urea, at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 156 1

Sum Sum 1.08E+04 89

All All 1.21E+04 100

Page 141

Table 76: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A21] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4773.1] Shrimp&Prawn farming[BD]; input of[G4954] P. monodon, from shrimp & prawn farms, at farm[BD, 2011]

[E44] Dinitrogen monoxide[air] 2.45E+03 13

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1.98E+03 11

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 1.83E+03 10

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 1.10E+03 6

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 8.55E+02 5

[P4229.1] Maize farming[BD, 2011]; input of[G4248] Maize, fresh grain[BD, 2011] [E44] Dinitrogen monoxide[air] 9.38E+02 5

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 8.56E+02 5

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 6.87E+02 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 573.9 3

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 278.5 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 405.5 2

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 434.3 2

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 311.2 2

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 360.2 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 349 2

[P4668] Natural gas, burned in boiler[BD, 2011] [E11] Carbon dioxide, fossil[air] 247 1

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 257.8 1

[P4228] Application of urea, at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 191.2 1

[P4654] Pulse farming[BD, 2011] [E44] Dinitrogen monoxide[air] 251.8 1

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 226.8 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 240.5 1

Sum Sum 1.48E+04 80

All All 1.86E+04 100

Page 142

Table 77: Contribution analysis for CML and ILCD global warming results, economic allocation, for [A22] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4773.3] Shrimp&Prawn farming[BD]; input of[G4956] Prawn, from shrimp & prawn farms, at farm[BD, 2011]

[E44] Dinitrogen monoxide[air] 3.50E+03 13

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 2573 10

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 2603 10

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 1569 6

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 1196 5

[P4229.1] Maize farming[BD, 2011]; input of[G4248] Maize, fresh grain[BD, 2011] [E44] Dinitrogen monoxide[air] 1338 5

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 1221 5

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 980.7 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 758.5 3

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 397.3 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 578.5 2

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 619.6 2

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 444 2

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 513.9 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 497.9 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 342.2 1

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 3.68E+02 1

[P4668] Natural gas, burned in boiler[BD, 2011] [E11] Carbon dioxide, fossil[air] 3.52E+02 1

[P4654] Pulse farming[BD, 2011] [E44] Dinitrogen monoxide[air] 359.3 1

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 323.5 1

[P4228] Application of urea, at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 272.7 1

Sum Sum 2.08E+04 80

All All 2.60E+04 100

Page 143

Table 78: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A1] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Khulna, Bangladesh for consumption in the EU (reference period 2010-2011) [P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 1.31E+03

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1.21E+03

[P4697.1] Prawn farming Khulna[BD, 2011]; input of[G4821] Prawn, Khulna, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 1.19E+03

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 8.65E+02

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 7.38E+02

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 4.43E+02

[P4654] Pulse farming[BD, 2011] [E44] Dinitrogen monoxide[air] 5.12E+02

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 3.60E+02

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 3.32E+02

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 336.9

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 2.37E+02

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 235.5

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 131.3

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 178.1

[P4229.1] Maize farming[BD, 2011]; input of[G4248] Maize, fresh grain[BD, 2011] [E44] Dinitrogen monoxide[air] 1.67E+02

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 171

[P236] wheat grains IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 1.52E+02

[P298] nitric acid, 50% in H2O, at plant[RER, 1990-01-2001-12] [E44] Dinitrogen monoxide[air] 174.4

[P217] protein peas, IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 1.38E+02

[P220] rape seed IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 1.45E+02

Sum Sum 9.03E+03

All All 1.26E+04

Page 144

Table 79: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A2] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in improved extensive systems in Bagerhat, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1.14E+03 11

[P4702.1] Prawn farming Bagerat[BD, 2011]; input of[G4823] Prawn, Bagerat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 9.80E+02 10

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 9.27E+02 9

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 8.81E+02 9

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 7.23E+02 7

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 4.69E+02 5

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 4.19E+02 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 290.1 3

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 158.4 2

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 223 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 159.2 2

[P298] nitric acid, 50% in H2O, at plant[RER, 1990-01-2001-12] [E44] Dinitrogen monoxide[air] 117.4 1

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 123 1

[P4706] Diesel, burned at processing plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 103.9 1

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 138.2 1

[P236] wheat grains IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 102.5 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 135.7 1

Sum Sum 7085 72

All All 9895 100

Page 145

Table 80: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A3] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 3587 25

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 2.17E+03 15

[P4514] Diesel, burned at farm[TH, 2011] [E11] Carbon dioxide, fossil[air] 8.76E+02 6

[P4517] Shrimp farming in Eastern Thailand[TH, 2011] [E44] Dinitrogen monoxide[air] 5.30E+02 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 526.1 4

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 272.1 2

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 343.1 2

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 329.8 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 213.5 2

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 332.4 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 246.6 2

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 237.8 2

[P1493] light fuel oil, burned in boiler 100kW, non-modulating[CH, 1991-2000] [E11] Carbon dioxide, fossil[air] 315.2 2

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 194.2 1

Sum Sum 1.02E+04 72

All All 1.41E+04 100

Page 146

Table 81: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A4] (1 tonne of frozen, edible yield of Shrimp (L. vannamei) produced in intensive systems in the south of Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 3626 29

[P4187] LPG, burned in gas motor[TH] [E11] Carbon dioxide, fossil[air] 1.58E+03 12

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 9.42E+02 7

[P4518] Shrimp farming in Southern Thailand[TH, 2011] [E44] Dinitrogen monoxide[air] 5.36E+02 4

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 336 3

[P1493] light fuel oil, burned in boiler 100kW, non-modulating[CH, 1991-2000] [E11] Carbon dioxide, fossil[air] 318.7 3

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 346.8 3

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 275.1 2

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 205.9 2

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 247.2 2

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 299.9 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 249.3 2

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 317.7 2

[P1490] heavy fuel oil, burned in refinery furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 138.7 1

Sum Sum 9418 74

All All 1.27E+04 100

Page 147

Table 82: Contribution analysis for CML and ILCD global warming results, mass allocation, for, mass allocation, for [A5] (1 tonne of frozen, edible yield of Pangasius produced in small systems in the Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 2570 33

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 608.1 8

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 484.2 6

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 417.3 5

[P4486] Pangasius farming, small scale[VN, 2011] [E44] Dinitrogen monoxide[air] 347.7 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 221.1 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 255 3

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 269.6 3

[P4492] Hard coal, at feed mill, burned in industrial furnace 1-10MW[VN] [E11] Carbon dioxide, fossil[air] 194.3 2

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 131.5 2

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E44] Dinitrogen monoxide[air] 193.1 2

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 113.9 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 87.44 1

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 90.27 1

Sum Sum 5983 76

All All 7824 100

Page 148

Table 83: Contribution analysis for CML and ILCD global warming results, mass allocation, for, mass allocation, for [A6] (1 tonne of frozen, edible yield of Pangasius produced in medium systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 2138 28

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 597.5 8

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 596.2 8

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 359.1 5

[P4489] Pangasius farming, medium scale[VN, 2011] [E44] Dinitrogen monoxide[air] 353.9 5

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 348.1 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 276.6 4

[P4492] Hard coal, at feed mill, burned in industrial furnace 1-10MW[VN] [E11] Carbon dioxide, fossil[air] 208.3 3

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 218.8 3

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E44] Dinitrogen monoxide[air] 161 2

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 117.6 2

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 151.7 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 104.6 1

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 89.49 1

Sum Sum 5721 74

All All 7712 100

Page 149

Table 84: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A7] (1 tonne of frozen, edible yield of Pangasius produced in large systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 1823 27

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 537.8 8

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 515.9 8

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 326.7 5

[P4490] Pangasius farming, large scale[VN, 2011] [E44] Dinitrogen monoxide[air] 298.9 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 264.5 4

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 302.5 4

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 189.7 3

[P4492] Hard coal, at feed mill, burned in industrial furnace 1-10MW[VN] [E11] Carbon dioxide, fossil[air] 184.7 3

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 138 2

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 101.5 2

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E44] Dinitrogen monoxide[air] 140 2

[P4361] operation, lorry >32t, EURO4[US, 2011] [E11] Carbon dioxide, fossil[air] 68.36 1

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 77.7 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 94.2 1

[P4240] operation, lorry >16t, fleet average[VN, 2011] [E11] Carbon dioxide, fossil[air] 69.73 1

Sum Sum 5133 76

All All 6751 100

Page 150

Table 85: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A8] (1 tonne of frozen, edible yield of Shrimp produced in low-level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 4104 49

[P4626] Shrimp low-level farming[GD, CN, 2011] [E44] Dinitrogen monoxide[air] 460.8 5

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 368.2 4

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 327.1 4

[P4589] natural gas, burned in industrial furnace >100kW[PE, 2011] [E11] Carbon dioxide, fossil[air] 240.3 3

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 242.2 3

[P4611] Diesel, burned at farm[CN, 2011] [E11] Carbon dioxide, fossil[air] 250.5 3

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 177.2 2

[P4402] Coal seam fires[CN, 2011] [E11] Carbon dioxide, fossil[air] 139.3 2

[P4401] hard coal, burned in boiler[CN, 2011] [E11] Carbon dioxide, fossil[air] 133.3 2

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 147.3 2

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 193.8 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 136.7 2

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 100.9 1

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 122.6 1

[P4409] hard coal, burned in coal mine power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 106.8 1

[P4133] Diesel, burned in fishing boats[PE, 2011] [E11] Carbon dioxide, fossil[air] 111.7 1

Sum Sum 7363 87

All All 8458 100

Page 151

Table 86: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A9] (1 tonne of frozen, edible yield of Shrimp produced in high level pond systems in Guangdong, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 4587 52

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 343.8 4

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 377.9 4

[P4589] natural gas, burned in industrial furnace >100kW[PE, 2011] [E11] Carbon dioxide, fossil[air] 252.6 3

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 254.5 3

[P4627] Shrimp high-level farming[GD, CN, 2011] [E44] Dinitrogen monoxide[air] 298.3 3

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 200.7 2

[P4401] hard coal, burned in boiler[CN, 2011] [E11] Carbon dioxide, fossil[air] 140.1 2

[P4611] Diesel, burned at farm[CN, 2011] [E11] Carbon dioxide, fossil[air] 137 2

[P4402] Coal seam fires[CN, 2011] [E11] Carbon dioxide, fossil[air] 154.5 2

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 189.8 2

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 154.8 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 143.6 2

[P4133] Diesel, burned in fishing boats[PE, 2011] [E11] Carbon dioxide, fossil[air] 117.4 1

[P4409] hard coal, burned in coal mine power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 118.4 1

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 106 1

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 128.8 1

Sum Sum 7706 87

All All 8905 100

Page 152

Table 87: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A10] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 5555 35

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 1733 11

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 958.9 6

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 615.6 4

[P4643] P. monodon, intensive farming[VN, 2011] [E44] Dinitrogen monoxide[air] 559.5 3

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 390.5 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 249.6 2

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 318.5 2

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 282.5 2

[P4276] Diesel, burned at farm[VN, 2011] [E11] Carbon dioxide, fossil[air] 384.9 2

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 224.3 1

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 188.1 1

Sum Sum 1.15E+04 72

All All 1.60E+04 100

Page 153

Table 88: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A11] (1 tonne of frozen, edible yield of Shrimp (L. Vannamei) produced in intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 4440 34

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 1024 8

[P4276] Diesel, burned at farm[VN, 2011] [E11] Carbon dioxide, fossil[air] 1069 8

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 766.4 6

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 372.7 3

[P4644] L. vannamei, intensive farming[VN, 2011] [E44] Dinitrogen monoxide[air] 405.1 3

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 257.6 2

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 312.1 2

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 280.1 2

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 152.2 1

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 179.3 1

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 150.4 1

Sum Sum 9408 73

All All 1.30E+04 100

Page 154

Table 89: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A12] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in semi-intensive systems in Mekong Delta, Vietnam for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4259] Diesel, burned in fishing boat[VN, 2011] [E11] Carbon dioxide, fossil[air] 5838 36

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 1329 8

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 1008 6

[P4276] Diesel, burned at farm[VN, 2011] [E11] Carbon dioxide, fossil[air] 743.5 5

[P4645] P. monodon, semi-intensive farming[VN, 2011] [E44] Dinitrogen monoxide[air] 596.6 4

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 410.4 3

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 481.7 3

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 309 2

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 318.6 2

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 235.7 1

[P4252.1] Rice farming[VN, 2011]; input of[G4267] Paddy rice, at farm[VN, 2011] [E41] Methane, biogenic[air] 197.7 1

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 196.5 1

Sum Sum 1.17E+04 72

All All 1.62E+04 100

Page 155

Table 90: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A13] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Guangdong, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 592.2 13

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 335.1 8

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 350.1 8

[P4615.1] Tilapia farming, non-integrated polyculture[GD, CN, 2011]; input of[G4706] Tilapia from non-integrated farm, at farm[GD, CN, 2011]

[E44] Dinitrogen monoxide[air] 303.7 7

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 307.6 7

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 225.2 5

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 214.8 5

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 163 4

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 181.9 4

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 144.7 3

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 73.85 2

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 108 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 74.72 2

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 107.9 2

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 57.46 1

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 65.73 1

Sum Sum 3306 75

All All 4408 100

Page 156

Table 91: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A14] (1 tonne of frozen, edible yield of Tilapia produced in polyculture farms in Hainan, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 405.7 8

[P4616.1] Tilapia farming, non-integrated polyculture Hainan[HI, CN, 2011]; input of[G4708] Tilapia from non-integrated farm in Hainan, at farm[HI, CN, 2011]

[E44] Dinitrogen monoxide[air] 399.2 8

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 426.7 8

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 335.5 7

[P4398] hard coal, burned in power plant, Hainan[HI, CN, 2011] [E11] Carbon dioxide, fossil[air] 331.2 6

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 274.5 5

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 244.7 5

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 221.7 4

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 188.5 4

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 198.6 4

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 131.6 3

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 131.5 3

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 176.4 3

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 91.07 2

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 78.92 2

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 90.01 2

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 70.03 1

[P4523] Application of urea, at farm[CN, 2011] [E11] Carbon dioxide, fossil[air] 51.28 1

Sum Sum 3847 75

All All 5095 100

Page 157

Table 92: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A15] (1 tonne of frozen, edible yield of Tilapia produced in polyculture reservoirs in Guangdong/Hainan, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 392.4 9

[P4617.1] Tilapia farming, reservoir[CN, 2011]; input of[G4710] Tilapia from reservoir, at farm[CN, 2011] [E44] Dinitrogen monoxide[air] 349.7 8

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 371 8

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 319.6 7

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 322.9 7

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 252.5 6

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 231.2 5

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 203.9 5

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 182.7 4

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 162.2 4

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 121 3

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 120.9 3

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 71.75 2

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 83.75 2

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 82.78 2

[P4566] Diesel, burned in fishing boats[GLO, 2011] [E11] Carbon dioxide, fossil[air] 46.65 1

[P248] ammonia, steam reforming, liquid, at plant[RER, 2000-12] [E11] Carbon dioxide, fossil[air] 45.76 1

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 64.41 1

[P4523] Application of urea, at farm[CN, 2011] [E11] Carbon dioxide, fossil[air] 47.16 1

Sum Sum 3472 77

All All 4524 100

Page 158

Table 93: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A16] (1 tonne of frozen, edible yield of Tilapia produced in ponds integrated with pigs in Guangdong, China for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4397] hard coal, burned in power plant, Guangdong[GD, CN, 2011] [E11] Carbon dioxide, fossil[air] 928.4 20

[P4395] hard coal, burned in power plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 332.8 7

[P4613.1] Tilapia farming, integrated system[GD, CN, 2011]; input of[G4703] Tilapia, from integrated farm, at farm[GD, CN, 2011]

[E44] Dinitrogen monoxide[air] 310.2 7

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 342.8 7

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 305 6

[P3032] rape seed, at farm[US, 2001-2006] [E44] Dinitrogen monoxide[air] 220.5 5

[P4220] Operation, lorry 3.5-20t, fleet average[CN, 2011] [E11] Carbon dioxide, fossil[air] 212.1 4

[P4519] Wheat farming, Northern China[CN, 2011] [E44] Dinitrogen monoxide[air] 178.1 4

[P4521] ammonia, steam reforming from coal, liquid, at plant[CN, 2011] [E11] Carbon dioxide, fossil[air] 141.7 3

[P4561] Groundnut farming[CN, 2011] [E44] Dinitrogen monoxide[air] 159.6 3

[P4577] hard coal, burned at fish reduction plant[CN] [E11] Carbon dioxide, fossil[air] 73.16 2

[P4110] operation, lorry >16t, fleet average[BR, 2011] [E11] Carbon dioxide, fossil[air] 105.7 2

[P4541] Maize farming[CN, 2011] [E44] Dinitrogen monoxide[air] 105.6 2

[P4606] hard coal, burned at feed mill[CN, 2011] [E11] Carbon dioxide, fossil[air] 72.31 2

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 66.29 1

[P4474] Diesel, burned in fishing boats[CN] [E11] Carbon dioxide, fossil[air] 56.26 1

Sum Sum 3610 76

All All 4754 100

Page 159

Table 94: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A17] (1 tonne of frozen, edible yield of Tilapia produced in pond systems in Chachoengsao/Nakhon Patom/Petchburi, Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 1923 19

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 981.3 10

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 632.1 6

[P4514] Diesel, burned at farm[TH, 2011] [E11] Carbon dioxide, fossil[air] 608.5 6

[P3844] natural gas, burned in power plant[US, 2004] [E11] Carbon dioxide, fossil[air] 276.1 3

[P851] hard coal, burned in power plant[DE, 1993-2000] [E11] Carbon dioxide, fossil[air] 252.6 3

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 305.3 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 296.7 3

[P4742] Tilapia farming in ponds[TH, 2011] [E44] Dinitrogen monoxide[air] 248.1 3

[P4177] Hard coal, at feed mill, burned in industrial furnace 1-10MW[TH, 2011] [E11] Carbon dioxide, fossil[air] 219.9 2

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 201.7 2

[P4448.1] Major rice farming[TH, 2011]; input of[G4494] Paddy rice, major, at farm[TH, 2011] [E44] Dinitrogen monoxide[air] 128.8 1

[P3871] hard coal, burned in power plant[SERC, 1998-2004] [E11] Carbon dioxide, fossil[air] 110.8 1

[P3870] hard coal, burned in power plant[RFC, 1998-2004] [E11] Carbon dioxide, fossil[air] 117.8 1

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 115.2 1

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 140.4 1

[P846] hard coal, burned in power plant[ES, 1993-2000] [E11] Carbon dioxide, fossil[air] 103.8 1

Sum Sum 6662 67

All All 9903 100

Page 160

Table 95: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A18] (1 tonne of frozen, edible yield of Tilapia produced in intensive cages systems in Suphanburi, Thailand for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4136] Diesel, burned in fishing boats[TH, 2011] [E11] Carbon dioxide, fossil[air] 2634 25

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 865.7 8

[P3844] natural gas, burned in power plant[US, 2004] [E11] Carbon dioxide, fossil[air] 378 4

[P4743] Tilapia farming in cages[TH, 2011] [E44] Dinitrogen monoxide[air] 400.5 4

[P823] hard coal, burned in industrial furnace 1-10MW[RER, 1988-1992] [E11] Carbon dioxide, fossil[air] 415.5 4

[P4177] Hard coal, at feed mill, burned in industrial furnace 1-10MW[TH, 2011] [E11] Carbon dioxide, fossil[air] 301.1 3

[P2439] natural gas, burned in power plant[UCTE, 1990-2000] [E11] Carbon dioxide, fossil[air] 266 3

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 276.2 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 336 3

[P4448.1] Major rice farming[TH, 2011]; input of[G4494] Paddy rice, major, at farm[TH, 2011] [E44] Dinitrogen monoxide[air] 176.4 2

[P3870] hard coal, burned in power plant[RFC, 1998-2004] [E11] Carbon dioxide, fossil[air] 161.3 2

[P4129] operation, lorry >16t, fleet average[TH] [E11] Carbon dioxide, fossil[air] 147.5 1

[P4192] Wheat farming[AU, 2011] [E44] Dinitrogen monoxide[air] 130.3 1

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 129.8 1

[P298] nitric acid, 50% in H2O, at plant[RER, 1990-01-2001-12] [E44] Dinitrogen monoxide[air] 114.8 1

[P1504] refinery gas, burned in furnace[RER, 1980-2000] [E11] Carbon dioxide, fossil[air] 155.4 1

[P3871] hard coal, burned in power plant[SERC, 1998-2004] [E11] Carbon dioxide, fossil[air] 151.6 1

Sum Sum 7040 67

All All 1.05E+04 100

Page 161

Table 96: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A19] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in small-medium improved extensive systems in Bagerhat/Khulna/Satkhira, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1008 18

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 751.9 14

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 351.5 6

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 251.6 5

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 269.9 5

[P4767.1] Shrimp farming West[BD, 2011]; input of[G4942] Shrimp, West, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 261.3 5

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 218.3 4

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 233.7 4

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 219.8 4

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 169.9 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 180.1 3

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 116.7 2

[P4706] Diesel, burned at processing plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 107.9 2

[P2373] heavy fuel oil, burned in power plant[IT, 1985-2000] [E11] Carbon dioxide, fossil[air] 55.74 1

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 82.57 1

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 81.04 1

Sum Sum 4359 79

All All 5529 100

Page 162

Table 97: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A20] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in large improved extensive systems in Cox’s Bazar, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1101 16

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 849.1 12

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 694.8 10

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 607.8 9

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 652.2 9

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E41] Methane, biogenic[air] 531 8

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 327.3 5

[P4768.1] Shrimp farming East[BD]; input of[G4944] Shrimp, East, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 313.4 5

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 260.3 4

[P4203] Diesel, burned in agricultural machinery[BD, 2011] [E11] Carbon dioxide, fossil[air] 178.1 3

[P4706] Diesel, burned at processing plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 107.9 2

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 147.4 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 138.9 2

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 91.07 1

[P4228] Application of urea, at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 76.1 1

Sum Sum 6077 88

All All 6911 100

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Table 98: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A21] (1 tonne of frozen, edible yield of Shrimp (P. monodon) produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 1193 11

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1183 11

[P4773.1] Shrimp&Prawn farming[BD]; input of[G4954] P. monodon, from shrimp & prawn farms, at farm[BD, 2011]

[E44] Dinitrogen monoxide[air] 998.6 9

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 743.8 7

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 447.8 4

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 477.4 4

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 302.2 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 340.9 3

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 377.4 3

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 215.7 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 165.6 2

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 214.7 2

[P4229.1] Maize farming[BD, 2011]; input of[G4248] Maize, fresh grain[BD, 2011] [E44] Dinitrogen monoxide[air] 192.9 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 120.6 1

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 151.6 1

[P220] rape seed IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 132.4 1

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 116.4 1

[P217] protein peas, IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 125.5 1

[P236] wheat grains IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 138.9 1

[P298] nitric acid, 50% in H2O, at plant[RER, 1990-01-2001-12] [E44] Dinitrogen monoxide[air] 159 1

Sum Sum 7798 71

All All 1.10E+04 100

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Table 99: Contribution analysis for CML and ILCD global warming results, mass allocation, for [A22] (1 tonne of frozen, head-less shell-on Macrobrachium Prawns produced in shrimp and prawn polyculture systems, Bangladesh for consumption in the EU (reference period 2010-2011) Process Extension kg CO2 eq. Contribution (%)

[P4382] Pig manure, storage[RER, 2011] [E41] Methane, biogenic[air] 1149 11

[P4209] natural gas, burned in power plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 1139 11

[P4773.3] Shrimp&Prawn farming[BD]; input of[G4956] Prawn, from shrimp & prawn farms, at farm[BD, 2011]

[E44] Dinitrogen monoxide[air] 961.7 9

[P4685] Mustard seed farming[BD, 2011] [E44] Dinitrogen monoxide[air] 716.3 7

[P4684] Diesel, burned at farm[BD, 2011] [E11] Carbon dioxide, fossil[air] 431.3 4

[P4691.1] Wheat farming[BD, 2011]; input of[G4809] Wheat, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 459.8 4

[P4343] Wheat farming, annual average[US, 2011] [E44] Dinitrogen monoxide[air] 291.1 3

[P1819] operation, transoceanic freight ship[OCE, 1980-2000] [E11] Carbon dioxide, fossil[air] 333.5 3

[P4205] Diesel, burned in lorry[BD] [E11] Carbon dioxide, fossil[air] 365.1 3

[P4102] Soybeans, at farm[BR, 2011] [E44] Dinitrogen monoxide[air] 207.7 2

[P1329] natural gas, burned in industrial furnace >100kW[RER, 2000] [E11] Carbon dioxide, fossil[air] 159.5 2

[P4363] Soybean farming, USA average[US, 2011] [E44] Dinitrogen monoxide[air] 206.8 2

[P4229.1] Maize farming[BD, 2011]; input of[G4248] Maize, fresh grain[BD, 2011] [E44] Dinitrogen monoxide[air] 185.7 2

[P4213] ammonia, steam reforming, liquid, at plant[BD, 2011] [E11] Carbon dioxide, fossil[air] 116.1 1

[P4642.1] Aman rice farming[BD, 2011]; input of[G4747] Aman rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 146 1

[P220] rape seed IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 127.5 1

[P4641.1] Boro rice farming[BD, 2011]; input of[G4746] Boro rice, at farm[BD, 2011] [E44] Dinitrogen monoxide[air] 112.1 1

[P217] protein peas, IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 120.9 1

[P236] wheat grains IP, at farm[CH, 1996-2003] [E44] Dinitrogen monoxide[air] 133.8 1

[P298] nitric acid, 50% in H2O, at plant[RER, 1990-01-2001-12] [E44] Dinitrogen monoxide[air] 153.1 1

Sum Sum 7516 71

All All 1.06E+04 100

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