Presentation of the IFPRI study on biofuels for the European Commission (March 2010) during a MTID, IFPRI, seminar on April the 3rd 2010. Study downloadable from http://www.ifpri.org/publication/global-trade-and-environmental-impact-study-eu-biofuels-mandate

1. Global Trade and Environmental
Impact Study of the EU Biofuels
Mandate
MTID Brown Bag Seminar, April 2nd 2010
by Perrihan Al-Riffai, Betina Dimaranan,
David Laborde Debucquet
with contributions from
Antoine Bouët and Hugo Valin

2. Overview
• Introduction and Background
• Modeling Choices
• Database
• The Baseline
• Results
• Concluding Remarks
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3. INTRODUCTION
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4. What is at stake?
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5. What is at stake?
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6. Goals of this study
• Effects of the EU mandate
• Focus on Indirect Land Use Change
• The core story
• Increase in yield
• Increase in area
Increased •Extension of crop
production land
•Reduction of other
crops
New
Reduced • Hunger?
Demand supply for
final
consumers • Substitution effects
for crops
Reduced • Feed
supply for • Other sectors
intermediate (agrifood, cosmetics)
consumers • Substitution effects
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7. Background
• Works on Biofuels with MIRAGE
• Initiated in 2007
• First study for the EC in 2008-2009
• This study initiated in Fall 2009
• More important than numbers, a pedagogical
process
• Strong interaction with EC work group on biofuels
• Intense discussions
• Clarification of several concepts
• The CGE raise issues, underline key mechanisms
and help to structure discussions
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8. MODELING
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9. Main Features
• Global CGE MIRAGE – assume perfect competition
• Improvement in demand system (food and energy) -
done in previous works
• Improved sector disaggregation
• New modeling of Ethanol sectors
• Land market and land extensions at the AEZ level
• Co-products (ethanols and vegetal oils)
• New modeling of fertilizers
• New modeling of livestocks
(extensification/intensification)
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10. Disaggregation (Sectors)
Sector Description Sector Description Sector Description
Rice Rice SoybnOil Soy Oil EthanolW Ethanol - Wheat
Wheat Wheat SunOil Sunflower Oil Biodiesel Biodiesel
Maize Maize OthFood Other Food sectors Manuf Other Manufacturing
activities
PalmFruit Palm Fruit MeatDairy Meat and Dairy WoodPaper Wood and Paper
products
Rapeseed Rapeseed Sugar Sugar Fuel Fuel
Soybeans Soybeans Forestry Forestry PetrNoFuel Petroleum products,
except fuel
Sunflower Sunflower Fishing Fishing Fertiliz Fertilizers
OthOilSds Other oilseeds Coal Coal ElecGas Electricity and Gas
VegFruits Vegetable & Oil Oil Construction Construction
Fruits
OthCrop Other crops Gas Gas PrivServ Private services
Sugar_cb Sugar beet or OthMin Other minerals RoadTrans Road Transportation
cane
Cattle Cattle Ethanol Ethanol - Main AirSeaTran Air & Sea
sector transportation
OthAnim Other animals EthanolC Ethanol - Sugar PubServ Public services
(inc. hogs and Cane
poultry)
PalmOil Palm Oil EthanolB Ethanol - Sugar Beet
RpSdOil Rapeseed Oil EthanolM Ethanol - Maize
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11. Disaggregation (Regions)
Region Description
Brazil Brazil
CAMCarib Central America and Caribbean countries
China China
CIS CIS countries (inc. Ukraine)
EU27 European Union (27 members)
IndoMalay Indonesia and Malaysia
LAC Other Latin America countries (inc. Argentina)
RoOECD Rest of OECD (inc. Canada & Australia)
RoW Rest of the World
SSA Sub Saharan Africa
USA United States of America
But: Land markets at the AEZ level
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12. Production Tree for an Ag. Sector
Changes
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13. Yield Changes in the Model
• Exogenous technology: TFP in agriculture
• Endogenous effects:
• Factor accumulation:
• More capital and labor by unit of land
• Fertilizers
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14. Fertilizers
• Price elasticities calibrated from the IMPACT
model
• Logistic approach for yield effects
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15. Livestock Sector and Intensification
Traditional approach Intensification approach
• Feedstock  Intermediate • Like fertilizers
consumption • Ratio price of land/price of
• Intermediate consumption & feedstocks  Producer choice
Value Added (including Land) • Increase in price of feedstock 
complementary Substitution effect =
• Increase in feedstock prices  Intensification + Overall price
Increase in production cost  effect = reduction in production
Decrease in demand   Overall, potential increase in
Decrease in production  Land use
Decrease in Land Use
+ Limitations in the interactions between
pasture land and crop lands (P0/P1/P2)
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16. Biodiesel Production
Oil
Crops sector Biofuel
(+meals)
Sunflower Sunflower
seed oil
Soybean
Soybean
oil
Biodiesel
Rapeseed
Rapeseed
oil
Palm fruit
Palm oil
& Kernel
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17. Ethanol Production
Biofuels
Crops Blending
(+ DDG)
Ethanol
Wheat
W
Maize Ethanol M
Sugar
Ethanol B Ethanol
Beet
Sugar
Ethanol C
Cane
Imported Imported
Ethanol* Ethanol*
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18. Land Markets – at the AEZ Level
Wheat Corn Oilseeds
CET
Sugar Substitutable Vegetables Other
Livestock1 LivestockN
crops crops and fruits crops
CET CET
Cropland Pasture
CET
Agricultural Managed
land forest
CET
Land extension
Unmanaged land
Managed land
Natural forest - Grasslands
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19. Land Extension Coefficients (from Winrock Intl. – EPA
report)
ForestManaged ForestPrimary Other Pasture Savannah & Grassland
Argentina 16.4% 0.0% 24.7% 35.6% 23.3%
Brazil 0.5% 16.3% 11.2% 23.5% 48.5%
CAMCarib 0.0% 30.4% 10.7% 16.1% 42.9%
Canada 1.4% 7.8% 42.5% 32.2% 16.1%
China 5.6% 2.2% 27.3% 39.0% 26.0%
CIS 3.7% 5.6% 33.3% 30.7% 26.7%
EU27 8.4% 0.4% 23.5% 36.8% 30.9%
IndoMalay 3.2% 51.7% 7.0% 7.0% 31.0%
LAC 17.8% 10.8% 14.3% 23.4% 33.8%
Oceania 9.0% 0.0% 32.6% 36.0% 22.5%
RoOECD 14.6% 0.0% 18.8% 20.8% 45.8%
RoW 3.4% 3.7% 36.9% 39.3% 16.7%
SEasia 1.1% 20.4% 21.5% 23.1% 33.8%
SouthAfrica 1.1% 5.1% 28.4% 43.2% 22.2%
SouthAsia 12.7% 0.0% 32.4% 31.0% 23.9%
SSA 0.1% 13.0% 16.7% 28.6% 41.7%
USA 5.4% 2.5% 21.1% 47.4% 23.7%
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20. Computing Marginal ILUC by Crop
EU Biofuel demand: x% mandate in 2020 Foreign
+ 106 GJ extra demand in feedstock 2 consumption
Exports
Domestic biofuel Foreign biofuel
production production
Feedstock Feedstock Feedstock Feedstock Feedstock Feedstock
1 2 3 1 2 3
Domestic land Foreign land
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21. Emissions
• Direct savings: Feedstock Set 1
• 1 MJ of fossile fuel: 25gCo2 eq Wheat (EU) -45%
• What about biofuels?
Wheat (Other) -32%
• Life cycle analysis (LCA)
Maize (EU) -56%
• Consumption vs Production
place Maize (USA)* -46%
• Kyoto vs RED? Maize (Other)** -29%
• Indirect emissions: Sugar Beet -61%
• Land use effects: Sugar Cane -71%
• Forest (Primary + Managed)
Soya -40%
• Other biotopes (Grassland…
Peatlands effects) Rapeseed -45%
• Direct savings (reductions) + Palm Oil -62%
ILUC effects/20years Sunflower -58%
(emissions) = Net effects
Role of certification ?
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22. DATA
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23. Major Efforts on Data: from Values to Quantities
• Improvement from GTAP7
• Split for fertilizers and fossil fuels
• Disaggregation with specific procedure for Maize, Soybeans, Sunflower
seed, Palm fruit, Rapeseed + relevant Oils + Co-products
• Production targeting (FAO) for all relevant crops
• Creation of a “harmonized” price database for calibration
• Case of co-products
• Creation of Ethanol and Biodiesel (2008 trade and production structure).
• Correction of some I-O data (e.g. China)
• Land use (AEZ GTAP database 2001  2004, + consistency with
FAO and M3)
• Correction for Sugar cane AEZ in Brazil
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24. General Remarks
• CGE:
• A world of value and prices
• But rarely “real” prices
• Calibration issue X
• Here, physical linkages dX pY
are crucial
• Substitution effects
dY pX
• Transformation effects
• Limits of CES and CET
• Constraints on the choice of
elasticities
Y
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25. BASELINE
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26. Defining a Relevant Baseline
• Macroeconomic targets
• Growth
• Oil prices
• EU fuel consumption for Road transportation
• Technology and yields
• Ludena and al.
• EU specific case
• Policies
• Trade policies
• Ag policies
• Biofuel policies
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27. EU AD/CD on US Biodiesel
2.00
1.80
1.60
EU imports, Mtoe
1.40
USA
1.20 RoOECD
1.00 LAC
0.80 IndoMalay
0.60 China
Brazil
0.40
0.20
0.00
2008 2020
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28. Other Policies in the Baseline
• Agricultural policies
• Sugar reform in the EU
• End of the land set-aside
• Biofuel policies
• Status quo in the EU (3.3%=10.13 Mtoe in 2020)
• Mandate of 5% in OECD countries, China, Indonesia
and Malaysia
• US mandate (51.64 Mtoe by 2020)
• Brazilian policy (14.05 Mtoe by 2020)
• World market: 88.79 Mtoe in 2020 (80% ethanol)
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29. RESULTS
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30. Defining a Central Scenario
• Policy uncertainties
• Trade policies
• Status quo
• Full liberalization of biofuel (not feedstocks) in the EU
• Degree of ambition
• 5.6% ?
• 316 Mtoe in 2020: Total EU consumption for Road Transportation
• 31.6 Mtoe of renewable energies: 10% target
• 17.8 Mtoe of biofuels first generation by 2020
• Compared to the current situation: 9.23 Mtoe in baseline 2008 (3.3%)
• Ethanol/Biodiesel mix: 45%/55%
• Parameter uncertainties
• Weak estimations on many parameters
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31. Biofuels Production
REF Mandate 5.6% Mandate 5.6%+ Trade liberalization
Lev Lev Var Lev Var
Biodiesel Brazil 0.36 0.37 1.81% 0.37 2.92%
Biodiesel China 0.23 0.23 -0.72% 0.23 -0.76%
Biodiesel EU27 8.15 9.04 10.92% 9.07 11.27%
Biodiesel IndoMalay 3.58 3.65 2.06% 3.65 2.07%
Biodiesel LAC 0.45 0.48 5.91% 0.48 6.10%
Biodiesel RoOECD 3.24 3.24 -0.01% 3.24 0.12%
Biodiesel USA 3.46 3.45 -0.18% 3.46 -0.03%
Biodiesel World 19.46 20.45 5.08% 20.49 5.30%
Ethanol Brazil 28.51 32.78 14.97% 34.36 20.50%
Ethanol CAMCarib 7.25 7.45 2.64% 7.19 -0.89%
Ethanol China 10.81 10.83 0.18% 10.83 0.16%
Ethanol EU27 0.84 2.17 156.89% 0.44 -48.23%
Ethanol LAC 0.69 0.69 0.95% 0.70 2.21%
Ethanol RoOECD 5.66 5.78 2.03% 5.84 3.03%
Ethanol RoW 1.51 1.50 -0.54% 1.50 -0.49%
Ethanol USA 29.10 29.57 1.64% 29.72 2.14%
Ethanol World 84.38 90.77 7.58% 90.57 7.34%
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32. EU Imports (2020)
Mandate 5.6%+ Trade
REF Mandate 5.6% liberalization
Lev Lev Var Lev Var
Biodiesel Brazil 0.00 0.00 6.21% 0.00 5.49%
Biodiesel China 0.00 0.00 14.45% 0.00 14.59%
Biodiesel IndoMalay 0.44 0.51 15.29% 0.51 15.46%
Biodiesel LAC 0.19 0.22 15.69% 0.22 16.04%
Biodiesel RoOECD 0.00 0.00 12.92% 0.00 82.07%
Biodiesel USA 0.00 0.00 11.78% 0.00 12.10%
Biodiesel World 0.64 0.74 15.40% 0.74 15.79%
Ethanol Brazil 0.92 5.53 502.82% 7.56 724.32%
Ethanol CAMCarib 0.04 0.27 517.35% 0.01 -83.48%
Ethanol USA 0.00 0.01 546.96% 0.00 111.89%
Ethanol World 0.96 5.82 503.58% 7.57 685.98%
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33. EU Production by Feedstock – Biodiesel
2020
Biodiesel
100%
0.38 0.45 0.45
90%
Share of EU production (MToe) by feedstock
80% 2.58 2.96 2.98
70%
60%
Sunflower
50% Soybeans
Rapeseed
40%
4.33 4.58 4.60 PalmFruit
30%
20%
10%
0.86 1.04 1.04
0%
REF Mandate 5.6% Mandate 5.6%+ Trade liberalization
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34. EU Production by Feedstocks - Ethanol
2020
Ethanol
100%
90%
Share of EU production (Mtoe) by feestock
80%
0.99
0.43
0.24
70%
60%
Wheat
50%
Sugar_cb
40% Maize
30% 0.98
0.35 0.16
20%
10%
0.07 0.20 0.04
0%
REF Mandate 5.6% Mandate 5.6%+ Trade liberalization
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35. Agricultural Production (2020)
Crops Region REF Mandate 5.6% Mandate 5.6%+ Trade
liberalization
Lev Lev Var Lev Var
Sugar_cb Brazil 913385 1001556.15 9.65% 1045492.08 14.46%
Rapeseed CIS 571 583.00 2.06% 583.42 2.13%
PalmFruit Brazil 3117 3196.06 2.53% 3181.86 2.07%
Rapeseed Brazil 151 153.15 1.59% 152.85 1.39%
Rapeseed SSA 108 108.87 1.10% 108.89 1.12%
Sunflower Brazil 153 155.23 1.24% 154.91 1.03%
Rapeseed RoOECD 13848 13969.92 0.88% 13975.74 0.92%
Soybeans RoOECD 3999 4020.98 0.54% 4025.62 0.66%
Sunflower USA 2142 2155.86 0.64% 2156.20 0.65%
Soybeans CIS 1129 1134.41 0.46% 1135.71 0.58%
Soybeans LAC 77981 78349.47 0.47% 78428.70 0.57%
Sunflower LAC 5883 5916.54 0.57% 5916.34 0.57%
Rapeseed LAC 141 142.09 0.52% 142.10 0.53%
OthCrop Brazil 9090 9034.08 -0.61% 9002.90 -0.96%
Wheat IndoMalay 1 0.55 -5.92% 0.55 -6.81%
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36. Agricultural Value-Added (2020)
1.20%
Mandate 5.6%
1.00%
Mandate 5.6%+
Trade liberalization
0.80%
0.60%
0.40%
0.20%
0.00%
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37. Real Income Impacts (2020)
Mandate 5.6%+ Trade
REF Mandate 5.6% liberalization
Lev Lev Var Lev Var
Brazil 856 857 0.06% 857 0.08%
CAMCarib 444 444 -0.01% 444 -0.02%
China 4593 4592 0.00% 4592 -0.01%
CIS 1093 1091 -0.18% 1091 -0.17%
EU27 15182 15184 0.01% 15182 0.00%
IndoMalay 564 564 -0.02% 564 -0.03%
LAC 1605 1604 -0.05% 1604 -0.06%
RoOECD 8590 8589 -0.01% 8588 -0.01%
RoW 5639 5633 -0.11% 5633 -0.11%
SSA 912 911 -0.12% 911 -0.12%
USA 15219 15218 0.00% 15218 -0.01%
World 54697 54687 -0.02% 54684 -0.02%
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38. Livestock Value-Added (2020) – Scenario
without trade liberalization
0.10%
0.05%
0.00%
Brazil USA EU27 World Cattle
Other Animals
-0.05%
-0.10%
-0.15%
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39. Cropland Extension by 2020
Brazil CAMCarib China CIS EU27 IndoMalay LAC RoOECD RoW SSA USA
Mandate with trade liberalization
EU27: 460
Brazil 6,866
Mandate without trade
liberalization EU27: 780
Brazil: 4813
0 2,000 4,000 6,000 8,000 10,000 12,000
Km2
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40. Where the Land Extension Occurs?
Forest
managed
Brazil EU27
1% Grassland
Forest Pasture
0% 0%
primary
15%
Forest
managed
34%
Other
12%
Savanah
Grassland
58%
Forest
primary
Other
Pasture 66%
0%
14%
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41. Extension vs Intensification
Mandate 5.6% Mandate 5.6%+ Trade
liberalization
Yield Yield Land use Total Yield Yield Land use Total
Factors Fertilizer Change Production Factors Fertilizer Change Production
increase increase increase Increase
Rapeseed 0.32% 0.04% 0.54% 0.90% 0.34% 0.02% 0.61% 0.97%
PalmFruit 0.10% 0.21% 0.31% 0.10% 0.20% 0.30%
Maize 0.04% 0.03% 0.01% 0.08% 0.03% 0.03% -0.01% 0.05%
OthCrop 0.01% 0.00% 0.00% 0.01% 0.02% 0.02% -0.01% 0.03%
OthOilSds 0.01% 0.01% -0.03% -0.01% 0.01% 0.02% -0.03% 0.00%
Rice 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Soybeans 0.04% 0.06% 0.12% 0.22% 0.05% 0.07% 0.15% 0.27%
Sugar_cb 0.66% 0.54% 2.67% 3.87% 0.62% 0.37% 3.98% 4.97%
Sunflower 0.11% -0.10% 0.37% 0.38% 0.11% -0.10% 0.39% 0.40%
VegFruits 0.00% 0.05% -0.06% -0.01% 0.00% 0.05% -0.06% -0.01%
Wheat 0.06% 0.05% 0.00% 0.11% 0.00% 0.04% -0.09% -0.05%
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42. Land Use Emissions – Mios tCO2eq – by 2020
5.6% EU Mandate 5.6% EU Mandate + Full trade
liberalization on biofuels
Forest Biomass Organic Carbon in Total land use Forest Biomass Organic Carbon in Total land use
change Mineral Soil emissions change Mineral Soil emissions
Brazil 23.97 33.33 57.30 28.50 46.02 74.52
CAMCarib 0.52 0.52 0.22 0.22
China 1.57 0.65 2.22 1.43 0.60 2.03
CIS 3.18 5.08 8.26 2.91 4.52 7.43
EU27 3.03 7.60 10.63 1.80 4.50 6.30
IndoMalay 3.39 1.53 4.92 3.38 1.53 4.90
LAC 2.63 3.58 6.21 2.71 3.70 6.41
RoOECD 1.08 2.47 3.55 0.87 2.34 3.22
RoW 1.20 0.94 2.14 0.88 0.71 1.59
SSA 1.49 4.50 5.99 1.36 4.04 5.41
USA 1.88 2.89 4.76 2.24 3.47 5.71
World 43.41 63.09 107.50 46.07 71.66 117.74
Additional MtCo2 emissions from peatlands IPCC method 0.17
Values are identical in both scenarios at 0.01 MtCO2eq Couwenberg(2009): 1.38
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43. Carbon Balance Sheet (2020)
Mandate 5.6%+
Trade
REF Mandate 5.6% liberalization
Total carbon release from forest biomass (MtCO2eq) 43.41 46.07
Total carbon release from organic carbon in mineral
soil (MtCO2eq) 63.09 71.66
EU Consumption of biofuel in 2020 (million GJ) 443 743 746
Annual carbon release from forest biomass
(gCO2eq/MJ) 7.23 7.61
Annual carbon release from organic carbon in mineral
soil (gCO2eq/MJ) 10.50 11.84
Annual direct savings (gCO2/MJ) -60.55 -66.38
Total emission balance on a 20 years period
(gCO2/MJ) -42.82 -46.93
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44. Marginal – Crop-Specific ILUC, in 2020
Mandate 5.6% Mandate 5.6%+ Trade liberalization
Without With Peatland Without Peatland With Peatland
Peatland effects effect effect effect
Ethanol 17.74 17.74 19.16 19.18
Ethanol SugarBeet 16.07 16.08 65.48 65.47
Ethanol SugarCane 17.78 17.78 18.86 18.86
Ethanol Maize 54.11 54.12 79.10 79.15
Ethanol Wheat 37.26 37.27 16.04 16.12
Biodiesel 58.67 59.78 54.69 55.76
Palm Oil 46.40 50.13 44.63 48.31
Rapeseed Oil 53.01 53.68 50.60 51.24
Soybean Oil 74.51 75.40 67.01 67.86
Sunflower Oil 59.87 60.53 56.27 56.89
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45. Net Emissions – Marginal, by Crop, in 2020
Mandate 5.6% Mandate 5.6%+ Trade liberalization
Without Peatland With Peatland Without Peatland With Peatland
effects effect effect effect
Ethanol -49.69 -49.68 -53.55 -53.53
Ethanol Sugar Beet -35.86 -35.85 21.84 21.83
Ethanol SugarCane -53.95 -53.95 -55.53 -55.53
Ethanol Maize 3.64 3.65 62.82 62.87
Ethanol Wheat -7.00 -6.99 -5.02 -4.95
Biodiesel 5.95 7.06 3.63 4.70
Palm Oil -21.98 -18.25 -22.43 -18.76
Rapeseed Oil 8.76 9.42 7.42 8.06
Soybean Oil 24.07 24.96 18.95 19.80
Sunflower Oil 8.73 9.38 7.74 8.37
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46. Non-linearity? (No Trade Liberalization)
4.60% 5.60% 6.60% 7.60% 8.60%
40.00
20.00
0.00
-20.00
-40.00
-60.00
Annual carbon release (gCO2eq/MJ)
Annual direct savings (gCO2/MJ)
-80.00
Total emission balance on a 20 years period (gCO2/MJ)
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47. Why Non-linearity?
• Biofuel mix
• Share of biodiesel vs ethanol in the marginal increase
• Modeling framework
• CES & CET
• Land use
• Productivity of new land: small or equal to existing
average productivity
• To which extent the model reflects reality?
• Marginal productivity and utility
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48. FINAL REMARKS
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49. Conclusions
• Mechanisms more important than numbers
• A lot of research is needed to improve the quality of the
parameters and modeling can still evolve too
• Limited amount of biofuels = positive effects
• But all biofuels are not equal
• We simulate a mandate with a strong ethanol component
• In our simulations, clear superiority of sugarcane ethanol from
Brazil
• Limited environmental impacts
• Energy efficiency
• Cost efficiency
• No world wide effects on food prices
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50. Answering Comments
• Already a lot echoes in the news and more pedagogy is
needed
• “UNICA welcomed the Commission's efforts to engage independent experts in its assessments but
called for improvements in the current analysis. "The report currently contains a certain number of
inaccuracies, so once these are corrected, we anticipate even higher benefits resulting from the
use of Brazilian sugarcane ethanol. For example, the type of land for sugarcane expansion
highlighted in the report does not take into consideration the agro-ecological zoning for sugarcane
in Brazil, which prevents cane from expanding into any type of native vegetation," Desplechin
added.”
“A 2008 study published by The Netherlands' Wageningen University forecast that about 62% of
the expansion of sugarcane in South-Central Brazil, the heart of the country's sugarcane
harvesting region, would take place primarily on pasture land, while 37,8% would happen in lands
previously occupied by other crops. The projection covered the period from 2008 to 2018.
• We do model land extension at the AEZ level and it plays a strong role in
the reduction of emissions in our simulations.
• The fact that sugar cane will displace “pasture lands and other crops” is not
a good argument. This is exactly the problem of ILUC and potentially the
difficulty to implement good certification.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 50