Antoine Bouet (IFPRI and University of Pau)
Louise Curran (ESC Toulouse – GPC)
Betina Dimaranan (IFPRI)
Maria Priscilla Ramos (CEPII-CIREM)
Hugo Valin (CEPII-CIREM)
Center for Global Development – Discussion – Thursday, January 13th, 2009
Objective of the study: impact of biofuelsupport programs◦ Study commissioned by the EU DG Trade◦ It also benefits from the Hewlett Foundation
support
Context◦ World energy prices◦ Development of biofuels and biofuels support
programs◦ Impact on: Food prices
Environment
◦ Objective of the study
Studying the impact of a EU biofuels mandate on:
Global agricultural production
Likelihood and extent of substitution effects between different crops
Effects on trade
Environmental effects
◦ Policy context in the EU
Objectives of a biofuel policy
What are the real impact on CO2 emissions?
Methodology of research◦ Introducing energy and biofuels in the MIRAGE
model of the world economy
◦ MIRAGE is a multi-country multi-sector Computable General Equilibrium Model dedicated to trade policy analysis
◦ What are the main methodological elements to be introduced in MIRAGE?
Limited production of
OIL, COAL and GAS
Demand for energy is increasing
Increase in world prices of oil, coal
and gas
Increased demand for
biofuels
Substitution effect
Increased demand for landWhat impact on
food prices and production ?
Biofuels support programs
Increased production of
biofuels
Demand for food is rigid
What impact on carbon emission?
Negative impact on food
production
Limited production of
OIL, COAL and GAS
Demand for energy is increasing
Increase in world prices of oil, coal
and gas
Increased demand for
biofuels
Substitution effect
Increased demand for land
What impact on food prices and
production ?
Biofuels support programs
Increased production of
biofuels
Demand for food is rigid
What impact on carbon emission?
Negative impact on food
production
Demand
Energy
Land use
Three main components:
◦ Energy (Antoine Bouet)
◦ Demand (Christophe Gouel)
◦ Land use (Hugo Valin)
Data (Betina Dimaranan / Simon Mevel/ Priscilla Ramos) Bioethanol, biodiesel, maize,
rapeseeds/palm, fossil fuel and fertilizers products as new sectors
Demand◦ The demand system is a CES-LES system;
it allows for income elasticities of demand different from 1
◦ New calibration in order to get a better modeling of consumers’ demand when incomes increase: in particular, final demand for meat and meat products
increases more rapidly in rapidly growing economies
final demand for sugar and vegetable oils increases more rapidly in rapidly growing economies
final demand for cereals increases less rapidly in rapidly growing economies
Production side - Energy◦ Review of existing approaches:
The GREEN model – OECD (1992)
The MEGABARE model – ABARE (1996)
The CETM model – Rutherford, Montgomery and Bernstein (1997)
The GTAP-E model – Burniaux and Truong (2002)
Modeling Energy: main issues◦ Top-down vs. Bottom-up approaches
◦ Substitution between sources of energy
◦ Substitution between energy and primary factors
◦ Aggregate sectors
Production side - Energy◦ Energy is a composite good
◦ Within the energy composite, demand for each source of energy is substitutable, in particular gas/oil/ethanol/biodiesel/petroleum products, with different elasticities of substitution
◦ Investment in capital can reduce demand for energy
◦ In the short term capital and energy are less substitutable than in the longer term
Value
added
LandComposite
1
Composite
2
CapitalSkilled
Labor
Unskilled
Labor
V.A. &
ENERGY
UNSKILLED
LABOR
NATURAL
RESOURCESLAND
CES 1.1
SKILLED
LABOR
K & ENERGY
COMPOSITE
K ENERGY
CES 0.15
Value added
modified
Land Composite
FertilizersAnimal
feedstockLand
AEZ18AEZ…AEZ 1
Composite 1
Composite 2
Capital and
Energy
Capital Energy
Non electric
Fuel
Fossil fuelBiodieselBioethanol
Coal
Electricity
Skilled Labor
Unskilled Labor
Land use and land supply◦ In the previous version of the MIRAGE model: a single type of land,
and two types of countries: countries with/without land constraints
◦ In the new version of MIRAGE, land is split into 18 agro-ecological zones (AEZ) characterized by different types of climate
(Tropical/Temperate/Boreal)
and by humidity levels.
◦ We get data on allocation of each type of crop in each country to each AEZ
Land use and land supply◦ We get data on available surfaces and degrees of
suitability in each AEZ for cultivation.◦ Use of this information to estimate marginal
productivity of land◦ This marginal productivity of land is decreasing Today the best lands are cultivated
New lands put into cultivation have lower productivity
◦ We take into account the effect of urbanization and other phenomena on land use change by considering that land use change follows in 2004/2025 the pattern observed on2000/2004
Land use and land supply◦ When production of a crop expands, it requires
more land..
Land already in cultivation can be reallocated to maize; this process is described by a CET function with 4 levels of land mobility
Total land can be allocated to forest or non forest
Non forest can be allocated to pasture or crops
Cropland can be allocated to rice or other cereals
Land allocated to a one cereal, say wheat, can be reallocated to, say maize.
The degree of mobility increases.
Environmental impact Assessment of the impact of a mandate on CO2
emission
Direct effect:
Reduction of CO2 emissions related to the production and consumption of biofuels instead of fossil fuels;
We apply coefficients of the EC Renewable Energy Directive available for different feedstocks and different production pathways.
Environmental impact Assessment of the impact of a mandate on CO2
emission
Indirect land use effect
New CO2 emission related to new land put into cultivation in order to produce crops for food in replacement of feedstock allocated to production of biofuels
The model evaluates the Indirect Land Use Changes (ILUC)
We rely on IPCC guidelines for National GreenHouse Gas Inventories for estimates of carbon stocks in natural forests/managed forests/+ different practices: non cultivation of land/rice cultivation/ land set aside
• 6 GTAP sectors to split?– Biofuel processing
• Other food (ofd) Bioethanol + Other food nec
• Vegetable oil and fat (vol) Diesel oil + Other vol
– Biofuels inputs
• Other grains (gro) Maize + Other grains nec
• Oilseeds (osd) Rapeseeds/Palm + Oilseeds nec
– Fertilizers
– Chemical, Rubber and Plastics (crp) Fertilizers +crp nec
– Regular fuel
• Petroleum and coal products (p_c) Fossil fuel products + Petroleum and coal products nec
GTAP Feedstock
Sector
GTAP Processing
Sector
Top GTAP
Producing Regions
Ethanol from
Coarse grains (GRO)
Other Food Products
(OFD)
USA, China, France,
Germany, Canada,
Russia, Spain
Ethanol from
sugarcane and
sugarbeets (C_B)
Chemicals, rubber,
and plastics (CRP)
Brazil
Ethanol from
sugarcane and
sugarbeets (C_B)
Sugar (SGR) India, Central America
(XCA), Pakistan
Biodiesel from
oilseeds (OSD)
Vegetable Oils and
Fats (VOL)
Germany, France,
Italy, USA
GTAP 6.2 Database
(57 sectors)
OFD(food
products)
GETH(grain
ethanol)
SOFD (subset of OFD)
SGR(sugar)
SETH (sugar
ethanol)
SSGR(subset of SGR)
CRP(chemicals)
SETH(sugar
ethanol)
SCRP(subset of CRP)
Relevant for Brazil only
VOL (vegetable
oils)
BIOD(biodiesel)
SVOL(subset of VOL)
Splitting the GTAP Processing Sectors
• The relevant GTAP processing sectors will be split using the program SplitCom developed by Horridge (2005).
• SplitCom requires specification of user weights such as trade shares, row shares, column shares, and cross shares:
•External data, expressed in US dollars, will be used to split the relevant production and trade values (in US$ 2001) in the GTAP data base
•Bilateral trade data for 2006 will be used to introduce trade in ethanol (esp. USA and Brazil exports)
• After splitting the GTAP data base to introduce 3 ethanol sectors and 1 biodiesel sector, the 3 ethanol sectors will be aggregated into 1 ethanol sector
Scenarios
• Scenario 1: European obligation to incorporate biofuels into fossil fuels for transport = 6% in 2010 and 10% in 2020
• Scenario 2: Liberalization of trade in biofuels by removal of European import tariffs on ethanol and biodiesel.
• If the consumption target is not reached, tax exemptions are increased in order to reach the same target
• Results• - Impact on ethanol and biodiesel prod’n
• - Impact on agricultural production
• - Impact on agricultural added value
• - Impact on land use
• - Impact on CO2 emissions
• Sensitivity analysis
2025 2025 2025
Ref
Man
dat
eEU
2_
Tra
deL
ib_
Lev Var Var
USA 41.3 0.8% 2.5%
EU27 2.5 358.8% 175.3%
LACImp 3.7 5.6% 1.8%
Brazil 20.1 1.6% 22.8%
EEurCIS 8.1 -0.9% 5.5%
2025 2025 2025
Ref MandateEU2_ TradeLib_
Lev Var Var
SouthAsia 0.00 1.0% 1.2%
EU27 11.0 32.6% 32.9%
LACExp 0.00 2.2% 2.2%
LACImp 0.00 3.9% 5.0%
Brazil 0.00 3.1% 2.8%
EEurCIS 0.00 1.5% 1.5%
2025 2025 2025
Ref MandateEU2_ TradeLib_
Lev Var Var
Wheat USA 26884 0.85% 0.37%
Wheat EU27 34069 3.79% 2.10%
Wheat Brazil 1274 0.52% -0.79%
Maize USA 39986 0.17% 0.61%
Maize EU27 14837 1.95% 1.01%
Maize Brazil 6047 -0.22% -0.39%
OilseedBio USA 47909 2.30% 2.31%
OilseedBio EU27 29180 8.42% 8.82%
OilseedBio Brazil 29244 3.28% 2.99%
Sugar_cb USA 2873 0.07% 0.08%
Sugar_cb EU27 10904 20.88% 10.83%
Sugar_cb Brazil 7614.80 0.38% 6.52%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
2015 2015 2020 2020 2025 2025
MandateEU2_ TradeLib_ MandateEU2_ TradeLib_ MandateEU2_ TradeLib_
USA
EU27
Brazil
2025 2025 2025
Ref MandateEU2_ TradeLib_
Lev Var Var
Rice 435 -0.35% -0.31%
Wheat 25301 1.72% 1.02%
Maize 8380 0.48% 0.34%
OthCrop 50910 -0.59% -0.49%
VegFruits 11995 -0.75% -0.59%
OilseedBio 9414 4.62% 5.02%
Sugar_cb 2235 8.79% 4.31%
2025 2025 2025
Ref MandateEU2_ TradeLib_
Lev Var Var
Wheat USA 36612 0.1% -0.1%
Wheat Brazil 3045 -0.3% -1.3%
Maize USA 37994 -0.3% 0.0%
Maize Brazil 17818 -0.7% -0.9%
OilseedBio Canada 8804 1.1% 1.2%
OilseedBio USA 52988 1.0% 1.0%
OilseedBio LACExp 17322 0.6% 0.6%
OilseedBio LACImp 4115 2.9% 3.0%
OilseedBio Brazil 28197 1.9% 1.6%
OilseedBio RoAfrica 9584 1.1% 1.1%
Sugar_cb Brazil 9424 0.0% 3.0%
2025 2025 2025
Ref MandateEU2_ TradeLib_
Lev Lev Lev
Ethanol - Wheat -7.4 -11.8 -9.7
Ethanol - Maize -16.8 -17.6 -17.6
Ethanol - Sugar Beet -30.1 -37.3 -33.9
Ethanol - Sugar Cane -41.3 -42.0 -50.5
Biodiesel - Soya -3.9 -5.5 -5.5
Biodiesel - Rapeseed -10.1 -12.8 -12.9
Total -109.6 -127.0 -130.0
Difference -17.4 -20.4
2025 2025
MandateEU2_ TradeLib_
Net CO2 Emission from forest SouthAsia 0.01 0.01
Net CO2 Emission from forest USA 3.12 3.18
Net CO2 Emission from forest EU27 5.19 3.87
Net CO2 Emission from forest LACExp 0.89 0.84
Net CO2 Emission from forest LACImp 0.88 0.77
Net CO2 Emission from forest Brazil 4.34 5.08
Net CO2 Emission from forest RoAfrica 0.92 0.78
Net CO2 Emission from forest World 16.25 15.47
Net CO2 Emission from cultivated soil SouthAsia 0.01 0.01
Net CO2 Emission from cultivated soil USA 1.13 1.16
Net CO2 Emission from cultivated soil EU27 3.12 2.33
Net CO2 Emission from cultivated soil LACExp 0.28 0.27
Net CO2 Emission from cultivated soil LACImp 0.30 0.26
Net CO2 Emission from cultivated soil Brazil 1.48 1.80
Net CO2 Emission from cultivated soil RoAfrica 0.30 0.26
Net CO2 Emission from cultivated soil World 7.79 7.50
Conclusions◦ Preliminary report ◦ Needs improvement in the development of the database◦ Other issues to be tackled: co – products (DDGS)…
Preliminary policy conclusions◦ Mandate has a huge impact on ethanol and sugar beet
prod’n in Europe◦ Trade lib’n has a significant impact on prod’n of sugar
cane in Brazil and oilseeds for biodiesel in Brazil and in Europe
◦ Substantial variations of land use and land supply in Europe and in Brazil
◦ Negative impact on CO2 emissions◦ From the environmental point of view, trade lib’n is the
best option.