Land Use Change and European Biofuel Policies

Land Use Change and European
Biofuel Policies
David Laborde Debucquet -
d.laborde@cgiar.org
April 2013
The views expressed by the author are not those of
IFPRI or the European Commission

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
References
• Presentation based on the 2011 report for the European
Commission: “Assessing the land use change consequences of
European biofuel policies”. David Laborde.
http://www.ifpri.org/publication/assessing-land-use-change-
consequences-european-biofuel-policies
• “Modeling land-use changes in a global CGE: Assessing the EU
Biofuel mandates with the MIRAGE-BioF model” David Laborde
and Hugo Valin.
http://www.worldscientific.com/doi/abs/10.1142/S2010007812500170
• Previous report, 2010, “Global trade and environmental impact
study of the EU biofuels mandate”. Al-Riffai, Perrihan; Dimaranan,
Betina;Laborde, David
http://www.ifpri.org/publication/global-trade-and-environmental-impact-
study-eu-biofuels-mandate
Page 2

1. Land use changes driven by biofuel policies are a serious concern.
2. This finding is robust as more than 99 percent of crop LUC coefficients
are positive.
3. LUC regulation and the Pandora's Box: LUC for all, LUC for none? The
real challenge is to promote better land use practices for agriculture
widely.
4. Reducing the biofuel ambition is still the most direct way to limit
additional land use emissions
5. Crop specific LUC can be difficult to implement. Increasing the minimal
requirements of direct savings can be a first step.
6. Despite all uncertainties, our findings show the hierarchy between
ethanol and biodiesel in terms of LUC.
7. Using available technologies to increase yield (e.g. biotech) and crop
conversion technology, and low carbon agricultural practices to reduce
emissions.
Page 3
Main Policy Recommendations

New Commission proposal to minimise the climate
impacts of biofuel production 17 October 2012
The Commission is therefore proposing to amend the current legislation on biofuels through
the Renewable Energy1 and the Fuel Quality2 Directives and in particular:
• To increase the minimum greenhouse gas saving threshold for new installations to
60% in order to improve the efficiency of biofuel production processes as well as
discouraging further investments in installations with low greenhouse gas performance.
• To include indirect land use change (ILUC) factors in the reporting by fuel suppliers
and Member States of greenhouse gas savings of biofuels and bioliquids;
[ Figures from the MIRAGE Biof model}
• To limit the amount of food crop-based biofuels and bioliquids that can be counted
towards the EU's 10% target for renewable energy in the transport sector by 2020, to the
current consumption level, 5% up to 2020, while keeping the overall renewable energy and
carbon intensity reduction targets;
• [ 2010 IFPRI report: 5.6% target for 1st generation]
• To provide market incentives for biofuels with no or low indirect land use change
emissions, and in particular the 2nd and 3rd generation biofuels produced from feedstock
that do not create an additional demand for land, including algae, straw, and various types
of waste, as they will contribute more towards the 10% renewable energy in transport
target of the Renewable Energy Directive.
Page 4

INTRODUCTION
Page 5

Why Biofuel Policies?
Page 6
From a pragmatic point of view: Because there are not competitive with
fossil fuels at market conditions
From a theoretical point of view: because they have a "social" value not
captured by the market. Externalities lead to market failure and justify
policies.
Why Biofuels? What is the "social" value not captured by the
market?
• ENERGY Policy (Energy security, Energy Prices, External Trade
Balance)
• FARM Policy (A new way to support agricultural prices avoiding WTO
disciplines)
• ENVIRONMENTAL Policy to reduce emissions from fossil fuels
If greenhouse gas emissions are the "market failure" aimed to be
corrected, need to do proper CO2 accounting and consider the land use
change.

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 7

The Burden of Proof?
• Should we prove that iLUC effects exist or that
does not exist?
• Working in a Farm or Working in Brussels teach
you two things: Level of agricultural production is
tied to the amount of land
• Increased demand of agricultural commodities
HAS land use effects
• The debate should be about their magnitude, not
their existence
Page 8

Preamble: iLUC or LUC
An artificial differentiation from the point of view of Carbon
Accounting.
Total Land Use Change (LUC) = Direct Land Use Change
(dLUC) + Indirect Land Use Change (iLUC)
ASSUMPTION in our study : Regulations work.
No Direct LUC. dLUC=0.
Total Land Use Change (LUC) = 0+ Indirect Land Use
Change (iLUC)
Therefore the LUC computed in our study is the iLUC
considered by policy makers.
Page 9

EU BIODIESEL [84% OF EU MARKET]
• EU Imports of vegetable oils have accelerated
with the biodiesel expansion
• EU has started to import rape seeds (2012:
imports > 15% EU production!) and rapeseed oil
since the beginning of the biodiesel policy
• EU rapeseed yields have not increased
2001 - 2003 = 3,06 Ton / Ha
2010 - 2012 = 2,95 Ton / Ha
Page 10

Orange line = EU Biodiesel production – different scale

MODEL - CORE
MODIFICATIONS
Since previous studies
Page 13

The MIRAGE-BIOF
• The MIRAGE model has started to be developed in 2001 in CEPII,
Paris. Focusing on EU Integration and Trade Policy analysis of the
beginning
• Now used by several institutions around the World, numerous
versions ( trade policy focused, FDI, Services, Climate Change etc.)
• Biofuels assessment started in 2008
• On land use:
• First study for the DG Trade in 2009 (limited to ethanol)
• Second study for DG Trade in 2010 (part of the public consultation)
• This new study for: DG Trade in 2011
• But other applications: mandates of other countries, comparison of
“traditional” ag policies and biofuels etc., food prices and price
stability consequences
Page 14

Modeling Biofuels in MIRAGE
• MIRAGE model
• Multi country, Multi sectoral, and global
• Recursive dynamic set-up
• Modified model and data components
• Improvement in demand system (food and energy)
• Improved sector disaggregation
• New modeling of ethanol sectors
• Co-products of ethanols and vegetable oils
• New modeling of fertilizers
• New modeling of livestocks (extensification/intensification)
• Land market and land extensions at the AEZ level

Major Efforts on Data: from Values to Quantities
• Improvement from the GTAP7 database
• 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
Page 16

Sectoral Disaggregation (43)
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 products WoodPaper Wood and Paper
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 & Fruits Oil Oil Construction Construction
OthCrop Other crops Gas Gas PrivServ Private services
Sugar_cb Sugar beet or cane OthMin Other minerals RoadTrans Road Transportation
Cattle Cattle Ethanol Ethanol - Main sector AirSeaTran Air & Sea transportation
OthAnim Other animals (inc.
hogs and poultry)
EthanolC Ethanol - Sugar Cane PubServ Public services
PalmOil Palm Oil EthanolB Ethanol - Sugar Beet
RpSdOil Rapeseed Oil EthanolM Ethanol - Maize
Page 18

Biodiesel Production
Page 19
Feedstock
Crops
Veg.Oil
sector
(+meals)
Biofuel
Biodiesel
Sunflower
oil
Sunflower
seed
Soybean
oil
Soybean
Rapeseed
oil
Rapeseed
Palm oil
Palm fruit
& Kernel

Ethanol Production
Page 20
Feedstock
Crops
Ethanol
(+ DDGS)
Biofuel
Blending
Ethanol
Ethanol
W
Wheat
Ethanol MMaize
Ethanol B
Sugar
Beet
Ethanol C
Sugar
Cane
Imported
Ethanol*
Imported
Ethanol*
* for Central America and Caribbean

Production Tree for an Ag. Sector
Page 21
Changes

Livestock Sector and Intensification
Traditional approach
• Feedstock  Intermediate
consumption
• Intermediate consumption &
Value Added (including Land)
complementary
• Increase in feedstock prices 
Increase in production cost 
Decrease in demand 
Decrease in production 
Decrease in Land Use
Intensification approach
• Like fertilizers
• Ratio price of land/price of
feedstocks  Producer choice
• Increase in price of feedstock 
Substitution effect =
Intensification + Overall price
effect = reduction in production
 Overall, potential increase in
Land use
Page 22

Land Markets – at the AEZ Level
Page 23
Managed land
Cropland
Managed
forest
Other
crops
Pasture
Wheat Corn
Livestock1 LivestockN
Unmanaged land
Natural forest - Grasslands
Land extension
CET
CET
Oilseeds
Substitutable
crops
CET
Vegetables
and fruits
CET
Agricultural
land
CET
Sugar
crops

Technical issue: Land Extension
Page 24
Total land available
for agriculture
Land
Crop
Land
price
Cropland

Land Extension Allocation
from Winrock Intl. – EPA report
Forest
Primary
Other Savannah &
Grassland
Argentina 0.0% 24.7% 23.3%
Brazil 16.3% 11.2% 48.5%
CAMCarib 30.4% 10.7% 42.9%
Canada 7.8% 42.5% 16.1%
China 2.2% 27.3% 26.0%
CIS 5.6% 33.3% 26.7%
EU27 0.4% 23.5% 30.9%
IndoMalay 51.7% 7.0% 31.0%
LAC 10.8% 14.3% 33.8%
Oceania 0.0% 32.6% 22.5%
RoOECD 0.0% 18.8% 45.8%
RoW 3.7% 36.9% 16.7%
SEasia 20.4% 21.5% 33.8%
SouthAfrica 5.1% 28.4% 22.2%
SouthAsia 0.0% 32.4% 23.9%
SSA 13.0% 16.7% 41.7%
USA 2.5% 21.1% 23.7%
Methodology
• Amount of land extension:
“isoelastic” land supply
based on cropland price
• Evolution of the elasticity
• Where the land is taken:
• Ad Hoc coefficients: Winrock
• Limitations
• Done at the AEZ level
• RAS procedure to consider
land availability constraint at
the AEZ level
Page 25

Technical issues: land substitution (1)
• CET function: concavity
• Aimed to capture imperfect transformation
• Adjustment costs / stylised facts
• Changes in productivity
• Nested CET: consequences of
concavity
• Sum (i, Land_i) ≠ CET (Land_I)
• In the model / out of the model rescaling
• In MIRAGE: in the model
• CET:
• Good in static
• More problematic in dynamics:
• Adjustment costs based on calibration year.
• Recalibration issue
Page 26
wheat
corn

Modifications in Modelling for this new report
• Dynamic baseline and food demand
• Dynamic recalibration to maintain price elasticity in the CES LES.
Standard of living evolution
• Co-products substitution: one level
• Two type of effects:
• Displacement of other crops
• Intensification
• Modification of central values for elasticities
• Marginal shock simulations
• From a marginal 1 to 60 Mios GJ
• But still, concept to manipulation with precaution:
• Substitution effects vs expansion effects
• Peat emissions and Indonesia land availability
Page 27

SIMULATION DESIGN
Page 28

Baseline
• Sugar reform (still a source of numerical problems)
• End of the Land Set Aside
• EU trade measures vs US Biodiesel
• No change in trade policy for Ethanol
• Some restrictions on Brazilian exports to the US in the baseline:
• Partially capture the change in the real exchange rate real/USD
• Avoid too much confusion between corn and sugar cane ethanol for the central
scenario
• Stronger Brazilian domestic consumption: but still large export
supply response
• Modification of initial profitability in Argentina
• New yield changes: Aglink Cosimo
• VERY IMPORTANT EFFECTS but no SENSITIVITY ANALYSIS on this
assumption
• ISSUE ON EU WHEAT  New Members catch up
Page 29

Scenarios
• Biofuel mandate:
• Member states Action Plan
• Trade policy options:
• Status Quo
• Full Liberalization in the EU of Ethanol and Biodiesel
Page 30

Sensitivity Analysis
• On linearity/non linearity issue
• Estimation of crop LUC at a “half mandate”, at a full mandate
• But still weak on Ethanol: no saturation effects
• On food consumption
• Endogenous vs Fixed to Baseline level
• On Co-products: with or without
• Monte Carlo simulations on selected parameters
• But in reality, much more uncertainties (see Box 2, 25 items related
to LUC, but even more regarding net emissions…)
• About the land (amount, location, carbon values)
• About future technologies
• Both behavioral and technical uncertainties
Page 31

Monte Carlo Simulations
• 1000 runs = 240 days of computations time
• Some restrictions for the model version
• Iterative process during the earlier stage
• Any modification of the model or data has to be done
of the core version and for the sensitivity analysis
• Log Uniform distribution
• Wide uncertainty
• We focus mainly on elasticities
• We do not have distribution estimates
Page 32

RESULTS
Page 33

Scenario Design
(Table 3) EU consumption pattern by feedstock. Percent
Palm Oil Rapeseed Soya Sun-
flower
All
Biodiesel
Maize Sugar
Beet
Sugar
Cane
Wheat All
Ethanol
Structure of consumption in 2008 – Total =11.7 MTOE
Baseline 4 57 20 2 83 3 3 5 5 17
Structure of consumption in 2020 – Total =27.2 MTOE
Baseline 11 60 10 3 83 3 3 7 4 17
No Trade
liberalization
17 41 11 4 72 4 5 13 6 28
Full Trade
Liberalization
17 41 11 4 72 1 1 25 1 28
Additional Mandate Composition +10 Mtoe +5Mtoe
No Trade
liberalization
22 26 12 5 65 4 6 18 7 35
Full Trade
Liberalization
22 26 12 5 65 -1 -1 38 -
1
35
Page 34

(Table 5) EU biofuel production in 2020 by
feedstock. Energy content. Percent.
Baseline No Trade
Liberalization
Trade Liberalization
Biodiesel 79.29 69.25 92.54
PalmFruit 7.55 12.87 16.96
Rapeseed 62.04 44.37 59.58
Soybeans 6.52 7.45 9.90
Sunflower 3.17 4.56 6.09
Ethanol 20.71 30.75 7.46
Maize 5.83 7.59 2.16
Sugar_cb 6.53 10.96 2.17
Wheat 8.35 12.20 3.12
Page 35

(Table 6) Commodity balance sheet - World -
Full mandate - No trade liberalization. 1000 tons
Biofuel
demand
Additional
Supply
Total Demand
displacement
Livestock
demand
displace
ment
Ratio Additional
Supply / Biofuel
demand
Share of livestock
demand
displacement in
total demand
displacement
Wheat 5,366.6 -1,595.9 -6,962.5 -6,326.6 -30 90.9
Maize 4,353.0 -2,986.3 -7,339.3 -6,471.7 -69 88.2
Sugar Cane
& Beet
76,616 69,574 -7,042 -6.6 91 0.1
Soybeans 4,677.6 4,677.6* -1,889.9 -40.4
Sunflower 2,676.0 2,676.0* -344.2 -12.9
Rapeseed 7,135.4 7,135.4* -544.2 -7.6
PalmFruit 22,207.0 22,207.0* -208 -0.9
Rice -101.9 -101.9 418.1 -410.4
OthCrop -765.9 -765.9 -363.4 47.5
OthOilSds -395.4 -395.4 -322.4 81.5
VegFruits -3,372.2 -3,372.2 25.6 -0.8
OilPalm 3,850.6 5,342.0 1,491.4 139 0.0
OilRape 4,456.9 2,474.4 -1,982.5 56 0.0
OilSoyb 2,063.5 1,270.8 -792.8 62 0.0
OilSunf 933.3 1,172.4 239.1 126 0.0
Page 36
back

(Figure 5) Land use changes for main
crops, 1000 Ha
Page 38

(Figure 6) Location of cropland extension.
Changes compared to the baseline. Km2
Page 39

(Figure 7) Distribution of source of
cropland (world)
Page 40
43.8%
41.8%
36.2%
38.6%
3.7%
3.0%
16.3%
16.5%
FullTradeLiberalization
No TradeLiberalization
Pasture(1) Managed Forest(1) Primary Forest(2) Savannah and Grassland (includes Cerrado in Brazil) (2)

(Figure 8) Cropland extension vs.
Exploited land extension. Km2
Page 41

LUC coefficient (grCO2/MJ)
Page 42
495 MtCo2 for
15.5 MToe

(Figure 10) Source of emissions
Page 43
with a value of peatland emissions of 55gTCO2 HA/an

(Figure 11) Intensification and Extensification drivers. Normalized
effects: Additional Mandate, Trade Policy Status Quo
Page 44

(Figure 14) Crop specific LUC. Source of
emissions
Page 45
5 4 2
6 8
18 19 177
5
3
7
13
22
23
22
2
1
2
0
33
16 10 15
Wheat Maize Sugar Beet Sugar Cane Palmfruit Soybean Sunflower Rapesed
Annual carbon release from palm
extension on peat lands (gCO2Eq/MJ)
Annual carbon release from forest
biomass (gCO2Eq/MJ)
Annual carbon release from carbon
mineral soil (gCO2Eq/MJ)

Emissions grCO2/MJ
Page 46
If
considerin
g oil
leakage
effects

Differences? DEMAND effect
Page 47
Table 10 Ratio
Additional
Supply/Biofuel
demand. Percent.
Sugar Beet 94.40
Sugar Cane 98.30
Maize 56.69
Wheat 51.38
Palm Fruit 96.6
Rapeseed 78.2
Soybeans 40.3
Sunflower 71.0
mandate
Ethanol
Maize
Ethanol
Wheat
Biodiesel
Rapeseed
Biodiesel
Soybean
Maize -2693 -213 -2000 -4431
Wheat -333 -2799 -2228 -1740
Palm Fruit -1 -2 -81 -110
Rapeseed -4 -5 -465 -126
Soybeans 11 12 -810 -2747
Sunflower -6 -6 -126 -131
DDGS 3485 2419 1 -32
Meal-Palm 1 1 23 29
Meal-Rape -37 -78 3841 813
Meal-Soyb -187 -105 2431 8954
Meal-Sunf -1 -5 253 272
Other
Crops
174 411 -154 -834
Table 10 Evolution of Livestock consumption of feedstocks. Selected Crop
specific scenarios (60GJ incremental demand of biofuel in the EU). Tons.

Differences? Supply effect
• Yield reactivity
• Land displacement
Page 48
Table 14 Land Use
displacement Summary.
Value by feedstock. Ha by Tj
Scenario
Feedstock
Net Energy
Crops
Net
Cropland
Pasture Net Exploited
Land
Biodiesel_Rapeseed
EU27 4.42 2.94 0.51 -0.10 0.14
World 10.91 11.72 3.90 -1.39 0.64
Biodiesel_Soybean
EU27 0.14 0.77 0.10 -0.02 0.03
World 11.61 11.41 3.86 -1.50 0.76
Ethanol_Maize
EU27 2.40 1.13 0.08 -0.02 0.01
World 6.52 3.69 0.88 -0.40 0.00
Ethanol_Wheat
EU27 3.27 1.77 0.17 -0.04 0.03
World 7.64 4.99 1.39 -0.54 0.10

Differences: Carbon stocks
Page 49
Note : The bars (left y-axis) show the amount of additional net cropland by TJ of biofuel
produced for one feedstock. The line (right x-axis) shows the average tons of CO2
equivalent by net Ha of cropland.
(Figure 16) More cropland or More Carbon (Ha by TJ and Tons
CO2 eq by Ha of cropland)

Sensitivity Analysis
Page 50

(Figure 21) Correlation matrix of LUC factor,
grCO2eg/MJ. Trade policy Status quo.
Page 51

(Figure 22) Consequences of alternative
closures on LUC (grCO2e/MJ)
Page 52
Worst case (all demand fixed, no
coproducts) can x3 total LUC

POLICY RECOMMENDATIONS
From model results to the policy space
Page 53

1. Land use changes driven by biofuel policies are a serious concern.
2. This finding is robust as more than 99 percent of crop LUC coefficients
are positive.
3. LUC regulation and the Pandora's Box: LUC for all, LUC for none? The
real challenge is to promote better land use practices for agriculture
widely.
4. Reducing the biofuel ambition is still the most direct way to limit
additional land use emissions
5. Crop specific LUC can be difficult to implement. Increasing the minimal
requirements of direct savings can be a first step.
6. Despite all uncertainties, our findings show the hierarchy between
ethanol and biodiesel in terms of LUC.
7. Using available technologies to increase yield (e.g. biotech) and crop
conversion technology, and low carbon agricultural practices to reduce
emissions.
Page 54
Main Policy Recommendations

New Commission proposal to minimise the climate
impacts of biofuel production 17 Octobre 2012
The Commission is therefore proposing to amend the current legislation on biofuels through
the Renewable Energy1 and the Fuel Quality2 Directives and in particular:
• To increase the minimum greenhouse gas saving threshold for new installations to
60% in order to improve the efficiency of biofuel production processes as well as
discouraging further investments in installations with low greenhouse gas performance.
• To include indirect land use change (ILUC) factors in the reporting by fuel suppliers
and Member States of greenhouse gas savings of biofuels and bioliquids;
[ Figures from the MIRAGE Biof model}
• To limit the amount of food crop-based biofuels and bioliquids that can be counted
towards the EU's 10% target for renewable energy in the transport sector by 2020, to the
current consumption level, 5% up to 2020, while keeping the overall renewable energy and
carbon intensity reduction targets;
• [ 2010 IFPRI report: 5.6% target for 1st generation]
• To provide market incentives for biofuels with no or low indirect land use change
emissions, and in particular the 2nd and 3rd generation biofuels produced from feedstock
that do not create an additional demand for land, including algae, straw, and various types
of waste, as they will contribute more towards the 10% renewable energy in transport
target of the Renewable Energy Directive.
Page 55

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