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.