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2. APPROCHE MÉTHODOLOGIQUE EN CGE. Different approaches Agronomic et ecological models Sound physical ground Focused on production side Detailed resolution.

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Presentation on theme: "2. APPROCHE MÉTHODOLOGIQUE EN CGE. Different approaches Agronomic et ecological models Sound physical ground Focused on production side Detailed resolution."— Presentation transcript:


2 Different approaches Agronomic et ecological models Sound physical ground Focused on production side Detailed resolution level Suitable for potential of production assessment, environmental impacts, carbon accounting No information on prices Economic models Bottom-up approach: partial equilibrium Top-down approach: computable general equilibrium Representation of production, demand and trade Economic behaviours: income and substitution effects Price and quantities Linking models > Many initiatives underway High level of detail and use of refined models BUT Risk of theoretical flaws - Technical difficulties Developing an integrated approach > our choice Very flexible and fully consistent tool BUT More simplistic representation

3 Using a CGE approach Background model: MIRAGE model (CEPIIs Trade Policy CGE) – GTAP7 based – Dynamic recursive – Used with fine tariff description Adaptations for biofuel policy – Improvement of the database for explicit representation of biofuels – Agriculture production functions: role of fertilisers – Energy markets: – Energy demand (non homothetic) – Capital-energy substitution – Oil, gas, coal, electricity, fuels and biofuels – Land use decomposition Questions studied: – Impact on trade of different policy scenarios – Impact on land use with direct and indirect effects and carbon emissions Support from DG Trade and DG Research, EC Introduction

4 BIOFUELS SECTORAGRICULTURAL SECTORENVIRONMENT European Biofuel Consumption BIOFUELS SECTOR Rest of the worldRest of the world - Substitution Effect MANDATE EUROPEAN UNIONEUROPEAN UNION Trade Policy European Biofuel Production European Production of Crops for Biofuels European Production of Crops for Food Foreign Biofuel Production Foreign Production of Crops for Biofuels Foreign Production of Crops for Food Substitution Effect Land Set Aside Marginal Land Production Cost Effect Demand for Land Production Cost Effect Deforestatio n Marginal Land + + Net CO 2 Emissions from Cultivated Soil Net CO 2 Emissions from Deforestation CO 2 Emissions ? + ? ENVIRONMENTAGRICULTURAL SECTOR + Mechanisms at stake Demand for Land 4

5 Fossil fuel (fixed shares of gasoline and diesel) An explicit implementation of biofuels in GTAP7 (2004) Other transportation sector (OTP) Final consumer BiodieselEthanol Other petroleum and coke products P_C sector Fuel composition in biofuels (mandate driven – exogenous shares) CornWheat Sugar crops Oilseeds + Other intermediate products and traditional factors New sectors GTAP7 sectors 1 2 Vegeta ble oil Split with 4 oil types


7 Land use: our modelling framework Description of regions with several 18 AEZ (GTAP-AEZ) Land rents // Physical land values Substitution tree using multinested CET Module for land expansion with an exogenous and an endogenous component Marginal productivity factor Crop yield: – Exogenous technology factor – Explicit use of fertiliser for modelling land productivity increase 7 1 – Introduction

8 Land use representation in GTAP CGE GTAP-like production function Value added is decomposed into labor and capital Capital payments are decomposed into natural resources payments, land rents and capital payments Volume of payments vary according to price fluctuations Elasticities of land drive the representation of behavior: - low elasticity = low reaction to prices - high elasticity = neoclassical behavior of an efficient land use market Linkage with physical hectares of land

9 Using data on land heterogeneity The SAGE database has been adapted by Ramankutty and Seth for working in GTAP framework Cropland is classified by 175 crops * 18 AEZ for 226 countries Provides land rents at the GTAP level for 18 AEZ zones by country Agro-Environmental Zone (AEZ) characterised by: – 6 Lengths of cultivation period: 0-60 days/ days/ days/… etc (related to humidity and precipitation regime) – 3 Climatic zones: Boreal/Temperate/Tropical Allows to distinguish between specificities of each cultivation zone within a country Substitution mainly occurs within a zone Substitution from one zone to another is conditioned by the presence of crops on the two zone by indirect effect

10 Correspondance between AEZ and local patterns: Brazil AEZ zoning in 6 Lengths of Growing Period Regional deforestation model Source: Monfreda et al (2007) Source: Nepstad et al. (2006)

11 Correspondance between AEZ and local patterns: USA AEZ zoning in 6 Length of Growing Periods Corn cultivation in 2007 Source: Monfreda et al (2007)

12 Correspondance between AEZ and local patterns: Europe AEZ zoning in 6 Length of Growing Periods Crop density in Europe in 1992 Source: Monfreda et al (2007) Source: Ramankutty et al (2002)

13 Source: Ramankutty et al. (2008) Complementarities between cropland and pasture: importance of AEZ

14 Approach for land substitution for each AEZ Managed land Cropland Managed forest Other crops Pasture Wheat Corn Livestock1 LivestockN Unmanaged land Natural forest - Grasslands Land extension CET Oilseeds Substitutable crops CET Vegetables and fruits CET Agricultural land CET Approach chosen by many models: OECD-PEM, GTAP, GOAL, LEITAP Sugar crops 14

15 CET and elasticities Use of CET is one the most popular approach for this type of issue Several designs have been tested (GTAP-BIO, OECD-PEM) Nests and differentiated elasticities can represent: – Regional specificities – Crops substitution possibilities Behavioral parameters can be derived from elasticities data from econometric studies Land substitution elasticities used in literature ModelForest/ Crops Pasture/ Crops Crops/ Crops GTAP-BIO model (Golub et al, 2007) GOAL model (Gohin, 2006) OECD-PEM model (OECD, 2003) From 0.05 to 0.1 From 0.1 to 0.2 From 0.2 to – Land substitution

16 Variability among elasticity estimates: EU 16 Source: Salhofer (2000) 2 – Land substitution

17 Land elasticities chosen per region 17 2 – Land substitution σ TEZ σ TEZH σ TEZM σ TEZL Source Oceania OECD China Set similar to RoOECD (inc. Korea) RoOECD OECD (Japan) RoAsia Set similar to RoOECD (inc. Korea) Indonesia Set similar to Mexico SouthAsia Set similar to Mexico Canada OECD USA OECD Mexico OECD EU OECD (EU15) LACExp Set similar to Mexico LACImp Set similar to Mexico Brazil Set similar to Mexico EEurCIS Set similar to EU27 MENA OECD (Turkey) RoAfrica Set similar to MENA SAF Set similar to MENA

18 CARB LUC Results – Sugarcane Ethanol 2 – Land substitution Source: CARB, 2009

19 Approach for land expansion Land supply: Several questions – What is the land available? – What is the associated productivity ? – How much can land expand? – Where do land expand ? Land expansion of managed land: – elasticity – asymptotic position are the two important parameters Marginal yield determines the land rent and production possibility increase 19 yield Mean yield Initial land Maximum land 3 – Land expansion

20 Marginal productivity First solution: External source (spatially explicit approach) So far, potential for rainfed cultivation from IMAGE But does not take into account the fact that some land is not accessible although productive Second solution: Corrected from direct calculations from production time series, average yield and land area ? 20 Source: IMAGE model, MNP acknowledged 3 – Land expansion

21 Data for available land Based on IIASA data: several criteria. We consider land very suitable + suitable + moderately suitable. We consider land productive under mixed input level. Mio ha 21 Source: IIASA, AEZ database (2000) 3 – Land expansion

22 Land available – High level of input 22 Source: IIASA, AEZ database (2000) 3 – Land expansion

23 23 Land available – Medium level of input Source: IIASA, AEZ database (2000) 3 – Land expansion

24 24 Land available – Low level of input Source: IIASA, AEZ database (2000) 3 – Land expansion

25 Managed land use expansion Land use within managed land is endogenous Unmanaged land – Baseline is exogenous – Land expansion marginal endogenous component: we distribute between unmanaged land following historical land use change – Conversion source is allocated proportionnaly to past conversion intensity of different land use. Cropland expansion comes from: – Substitution between economic uses – Expansion from grassland, primary forest and other land 25 4 – Allocation within unmanaged land

26 Historical land use Based on FAO estimates on the 5 or 10 last years – How marginal ? – How accurate are the data ? – FAO has limited number of land use Computing expansion at the national level or at the national * AEZ level ? – > need of historical changes at this level to be really effective 26 Approach chosen by EPA: – building a precise historical database – Relying on remote- sensing data 4 – Allocation within unmanaged land

27 Yield representation Production structure tree An exogenous technology component An endogenous factor distribution effect Calibration on elasticities of yield to fertiliser prices (provided by IFPRI partial equilibrium models) Still research topic Crop production H L K E Land and fertilisers Ferti- lisers Capital (K) + Energy (E) Unskilled Labour (L) H K E Land Skilled labour (H) 1 2 +TFP 27

28 Calibrating yield behaviour Idea: modelling input/land optimisation under a physical response? Difficult calibration At the moment, more ad hoc approach with an isoelastic reaction to prices under physical constraint Three parameters for the physical function: – response of yield to fertiliser at the initial point (a) – level of saturation (b) – response of fertiliser consumption to price 28 (b) (a)


30 Impact of a few biofuel policies Scenario presented – EU + US Ethanol mandate – EU + US Ethanol mandate + liberalisation EU: 10% mandate with 4% ethanol in 2020 US: 36 bn gallon by 2022 decreased to 30 bn gallon Modelling of oilseeds market is delicate

31 Our baseline 18 regions and 35 sectors Assumptions are important for: – Oil prices (demand for biofuel) $65 in 2020 (IEA 2007) $110 in 2020 (IEA 2008) – Evolution of crop production (productivity and cropland expansion) Productivity increase (technology component): +0.5/+1% per year Higher productivity for cattle and animals in developing countries – Exogenous land use change: FAO 5 year variation extrapolated – Crop prices (demand for new crops) highly dependant on regions and elasticitiy of substitution between fossil fuel and biofuel: – Wheat: +38% in 2020 – Maize: +23% in 2020 – Oilseeds: +42% in 2020 – Sugar crops: +16% in 2020 – Biofuel production level: 38 Mtoe in 2007, 64 Mtoe in 2020 (biodiesel)

32 Impact of an EU mandate Production Imports 2020 Mtoe RefDM FTM Lev VarLevVar EthanolUSA % % EthanolEU % % EthanolBrazil % % BiodieselUSA % % BiodieselEU % %

33 Feedstock production 2020 RefDM FTM Lev VarLevVar WheatSouthAsia % % WheatEU % % WheatMENA % % WheatChina % % MaizeUSA % % MaizeChina % % MaizeRoAfrica % % MaizeEU % % MaizeMexico % % Sugar cropsSouthAsia % % Sugar cropsEU % % Sugar cropsBrazil % % Sugar cropsLACImp % %

34 Feedstocks markets 2020 RefDM FTM Lev VarLevVar WheatEEurCISEU % % WheatCanadaUSA %1210.8% WheatCanadaEU %1093.4% WheatBrazilEU %882.0% WheatMENAEU %698.5% MaizeBrazilEU % % MaizeCanadaUSA % % MaizeLACExpEU %1960.3% MaizeUSAEU % % MaizeLACImpUSA % % MaizeEEurCISEU %866.1% MaizeLACImpEU %712.3% MaizeRoOECDEU %46-2.8% OthCropLACImpUSA % % OthCropRoAfricaEU % % OthCropLACImpEU % % OthCropBrazilEU % % OthCropEU27USA % % OthCropBrazilUSA % % VegFruitsLACImpEU % % VegFruitsUSAEU % % VegFruitsMexicoUSA % % VegFruitsLACImpUSA % % VegFruitsMENAEU % % VegFruitsRoOECDEU % % OilseedBioBrazilEU % % OilseedBioUSAEU % % OilseedBioLACExpEU % % OilseedBioEEurCISEU %5443.1% OilseedBioCanadaEU % % OilseedBioCanadaUSA %2551.6% OilseedBioRoOECDEU %1630.0% OilseedBioBrazilUSA %1603.7%

35 Feedstocks markets 2020 Mio USDFromTo RefDM FTM Lev VarLevVar WheatEEurCISEU % % WheatCanadaUSA %1210.8% WheatCanadaEU %1093.4% WheatBrazilEU %882.0% WheatMENAEU %698.5% MaizeBrazilEU % % MaizeCanadaUSA % % MaizeLACExpEU %1960.3% MaizeUSAEU % % MaizeLACImpUSA % % MaizeEEurCISEU %866.1% MaizeLACImpEU %712.3% MaizeRoOECDEU %46-2.8% OthCropLACImpUSA % % OthCropRoAfricaEU % % OthCropLACImpEU % % OthCropBrazilEU % % OthCropEU27USA % % OthCropBrazilUSA % %

36 Economic impact

37 Quantifying biofuel direct effects

38 Carbon savings (Mtoe) 2020 DM FTM LevShareLevShare WorldEthanol - Wheat-3,742,1468.6%-918,6741.8% WorldEthanol - Maize-7,222, %-5,507, % WorldEthanol - Sugar Beet-5,403, %-1,573,1083.1% WorldEthanol - Sugar Cane-27,255, %-42,292, % WorldEthanol - Other Crops-57,9400.1%-123,2530.2% WorldEthanol - All crops-43,681, %-50,415, %

39 Global land use effect 2020 RefDM FTM Lev VarLevVar PastureEU % % CroplandEU % % OtherEU % % Forest_managedEU % % Forest_primaryEU Forest_totalEU % % Total exploited landEU % % PastureUSA % % CroplandUSA % % OtherUSA % % Forest_managedUSA % % Forest_totalUSA % % Total exploited landUSA % % PastureBrazil % % CroplandBrazil % % OtherBrazil % % Forest_managedBrazil % % Forest_primaryBrazil % % Forest_totalBrazil % % Total exploited landBrazil % %

40 Quantifying biofuel indirect effects Land use indirect effects – Emissions from deforestation Based on IPCC values Natural forest vs plantation Distinction per AEZ Integration of below ground values – Emissions from mineral carbon in soil Release due to land use (IPCC values) Agricultural use on new land generates emissions Other indirect effect: related to price of energy and crops for other sectors

41 Indirect land use emissions Deforestation emissionsNew land cultivation emissions 2020 DMFTMDMFTM Oceania220,187147,552325,594234,395 China172,90361,826192,276139,459 RoOECD339,948238,736218,874155,093 RoAsia198,612166,806134,936117,737 Indonesia372,087321,848100,19387,928 SouthAsia38,77233,82162,35032,461 Canada624,051452,104705,587523,202 USA1,979,8671,583,7286,714,3035,309,671 Mexico801,583649,672241,330199,499 EU271,465,003873,4252,843,7121,072,558 LACExp580,587554,376715,341559,374 LACImp3,803,8262,332,5191,375,410814,762 Brazil12,391,46625,150,3763,364,5356,783,709 EEurCIS-286, ,0881,888,6931,340,669 MENA-184, ,173292,257191,597 RoAfrica4,145,4153,362,179908,297730,871 SAF-28,701-74,165234,462580,962 World26,634,98335,589,54320,318,15018,873,946

42 Total environmental effect The indirect effect induced by first generation biofuel could degrade significantly their benefits.

43 Data issues and critical parameters Issue of the link between SAMS data on land use and real land use data Elasticities are the most critical parameters and especially sensitive for the results – Biofuel vs Fuel: has important implications on subsidy effects and incentive due to high oil prices. Hard to evaluate because of the role of policy effect against market effect. – Land substitution elasticities: studies made for OECD PEM illustrate the degree of uncertainty. – Land expansion: very debated link: the progress of research must concentrate here – Land yield elasticities – Role of Armington: more effect on domestic markets Non market effects play important role Carbon debt (years in 2020)DMFTM F+ (fertilisers x4) F- (fertilisers /4) L+ (x4 in South, x2 in North) L- (/4 in South, /2 in North)4.42.7


45 Evolutions et perspectives Développements nouveau en cours: – Données Huiles végétales détaillées DDGS Tourteaux doléagineux – Modélisation Reflexion sur le lien livestock / land use Simplification des hypothèses pour analyse de sensibilité massive

46 Initiatives dans le cadre de la directive européenne sur lusage des énergies renouvelables dans les transports Policy: Commission européenne: – DG Trade: Commande de résultats pour fin septembre/début octobre: effets marginaux des ILUC et politique commerciale – Initiative conjointe du JRC et de lOCDE pour faire une comparaison des modèles et de leurs résultats Recherche: – FP7 AgFoodTrade – poursuite des travaux dans le cadre IFPRI (impacts et opportunités PVD)

47 Conclusions Un problématique complexe qui se heurte aux limites actuelles du savoir et des outils Une forte demande des décideurs face à la pression des acteurs et au manque dinformation Un décalage de temporalité délicat à gérer face à lagenda politique Le contexte des négociations climatiques rajoutent un besoin dexpertise Des pistes de recherche nombreuses promettant encore des années de mobilisation

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