Land Use analysis of Biofuel Mandates: A CGE perspective with MIRAGE-Biof

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Land Use analysis of Biofuel Mandates: A CGE perspective with MIRAGE-Biof

Presented by David Laborde at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis

June 6-7, 2011 • Dakar, Senegal

For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop

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Land Use analysis of Biofuel Mandates: A CGE perspective with MIRAGE-Biof

  1. 1. Land Use analysisof Biofuel Mandates:A CGE perspectivewith MIRAGE-Biof Presented by: David Laborde Debucquet www.agrodep.org IFPRI AGRODEP Workshop on Analytical Tools for Climate Change Analysis June 6-7, 2011 • Dakar, Senegal Please check the latest version of this presentation on: http://agrodep.cgxchange.org/first-annual-workshop
  2. 2. Land Use analysis of BiofuelMandates: A CGE perspective with MIRAGE-Biof by David Laborde Debucquet (d.laborde@cgiar.org)
  3. 3. MIRAGE• Computable General Equilibrium• Development started in 2001 in CEPII, Paris• Initial focus on trade issues• Multi country, Multi sector: Global model• Dynamic recursive• Main source of data: GTAP database• Numerous extensions: Exist version that allows trade modeling at the product level, FDI etc.• GAMS basedINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 2
  4. 4. Why Land Use is important?• Starting point: Trade liberalization • Agriculture is the most protected sector • Large tariff heterogeneity • Land is a production factor ! Mobility & Supply • Trade liberalization = Efficiency gains = Reallocation of production among sectors • Role of Land Mobility: perfect (e.g. most of LINKAGE simulations WB) or imperfect (MIRAGE): x2 gains for developing countries• Beyond trade liberalization: role in the baseline  relative prices between agriculture and the rest of the economy• First implementation in MIRAGE (at the „country‟ level): • Imperfect land substitution: CET • Isoelastic land supply• And then Land Use related emissionsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 3
  5. 5. One challenge: data• Different databases • FAO, IIASA, M3 • Not harmonized (within datasets / between datasets) • Poor quality of time series • Multi cropping • Problems on croplands, pasture, suitable land • Information on protected lands• Consequences on yields, land availability, estimationINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 4
  6. 6. MIRAGE-BIOFINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 5
  7. 7. ILLUSTRATIVE RESULTSINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 6
  8. 8. EU Biodiesel ImportsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 7
  9. 9. Feedstocks ImportsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 8
  10. 10. LUC (decomposition)INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 9
  11. 11. Trade Policies EffectINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 10
  12. 12. LUC vs EmissionsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 11
  13. 13. LUC – Constant Food vs Endogenous Food …Alternative approach for measuring the disappearing foodINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 12
  14. 14. Assessing Non LinearityINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 13
  15. 15. Production patternINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 14
  16. 16. Land Use pattern: Ha by 1E3GJINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 15
  17. 17. Feed pricesINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 16
  18. 18. Extension vs IntensificationINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 17
  19. 19. Monte Carlo Analysis: LUC, grCO2/MJINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 18
  20. 20. Distribution LUC grCO2/MJINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 19
  21. 21. Cropland HaINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 20
  22. 22. Modeling issues BIOFUELSINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 21
  23. 23. 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)INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 22
  24. 24. 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 - MaizeINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 23
  25. 25. 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 levelINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 24
  26. 26. 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 BrazilINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 25
  27. 27. 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 YINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 26
  28. 28. Production Tree for an Ag. Sector ChangesINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 27
  29. 29. Yield Changes in the Model• Exogenous technology: TFP in agriculture• Endogenous effects: • Factor accumulation: • More capital and labor by unit of land • FertilizersINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 28
  30. 30. Fertilizers• Price elasticities calibrated from the IMPACT model• Logistic approach for yield effectsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 29
  31. 31. 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 policiesINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 30
  32. 32. Biodiesel Production Oil Crops sector Biofuel (+meals) Sunflower Sunflower seed oil Soybean Soybean oil Biodiesel Rapeseed Rapeseed oil Palm fruit Palm oil & KernelINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 31
  33. 33. 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*INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 32
  34. 34. Modeling issues DEMAND OF LANDINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 33
  35. 35. In MIRAGE• Two components • Endogenous (what is important in our model) • Exogenous (urbanization trend, non economic program related to forestry)INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 34
  36. 36. Production Tree for an Ag. Sector ChangesINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 35
  37. 37. Livestock Sector and IntensificationTraditional 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)INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 36
  38. 38. Modeling issues SUPPLY OF LANDINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 37
  39. 39. 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 - GrasslandsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 38
  40. 40. Land Extension Allocationfrom Winrock Intl. – EPA report Methodology Forest Other Savannah & Primary GrasslandArgentina 0.0% 24.7% 23.3% • Amount of land extension:Brazil 16.3% 11.2% 48.5%CAMCarib 30.4% 10.7% 42.9% land supply based onCanada 7.8% 42.5% 16.1% cropland priceChina 2.2% 27.3% 26.0%CIS 5.6% 33.3% 26.7% • Evolution of the elasticityEU27IndoMalay 0.4% 51.7% 23.5% 7.0% 30.9% 31.0% • Where the land is taken:LAC 10.8% 14.3% 33.8% • Ad Hoc coefficients: WinrockOceania 0.0% 32.6% 22.5% • LimitationsRoOECD 0.0% 18.8% 45.8%RoW 3.7% 36.9% 16.7% • Done at the AEZ levelSEasia 20.4% 21.5% 33.8% • RAS procedure to considerSouthAfrica 5.1% 28.4% 22.2% land availability constraint atSouthAsia 0.0% 32.4% 23.9%SSA 13.0% 16.7% 41.7% the AEZ levelUSA 2.5% 21.1%INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 23.7% Page 39
  41. 41. Technical issue: Land Extension Total land available for agricultureCropLandprice Cropland Land INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 40
  42. 42. Technical issues: land substitution (1)• CET function: concavity corn • Aimed to capture imperfect transformation • Adjustment costs / stylised facts • Changes in productivity• Nested CET: consequences ofconcavity • Sum (i, Land_i) ≠ CET (Land_I) wheat • 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 issueINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 41
  43. 43. Technical issues: land substitution (2)• CET: Which elasticities? • Poor estimates in the literature • In many cases, indirect estimates • Past estimates / future behaviour: the role of policy reform• Ongoing researches: • Crop rotation and crop complementarities • Alternative functional form: from nested CET to CRESH (or better, more flexible functional form). • Conversion costs: problem of expectation and fixed costsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 42
  44. 44. Technical issue: Productivity of new land• Initially:based on IMAGE model. Interesting, complex, but seems not very robust (very complex issue: short term/long term)• Now: fixed marginal productivity in terms of average productivity : 0.5 for everyone, 0.75 for BrazilINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 43
  45. 45. Sensitivity Analysis• Numerous parameters with uncertainties • Different values for elasticities • Marginal productivity • Etc• Test different closure of the model• Monte Carlo simulationsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 44

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