A Stochastic Analysis of Biofuel Policies

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A Stochastic Analysis of Biofuel Policies

Presented by Michael Obersteiner 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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A Stochastic Analysis of Biofuel Policies

  1. 1. A StochasticAnalysis ofBiofuel Policies Presented by: Michael Obersteiner, IIASA www.agrodep.org 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. A Stochastic Analysis of Biofuel PoliciesSabine Fuss , Petr Havlik, Jana Szolgayová, Michael Obersteiner, Erwin Schmid (BOKU) International Institute for Applied Systems Analysis Ecosystems Services & Management Program Dakar, June 2011
  3. 3. Background Volatility of crop yields  Food security concerns  Impact on prices Analysis so far largely deterministic  Uncertainty taken into account through scenario analysis  Scenarios appropriate to explore ranges of outcomes  Decisions taken under uncertainty different from those formed on the basis of complete information  Need for a fully stochastic framework
  4. 4. Relative Difference in Variances (2050/2100) in Wheat Yields [Data: Tyndall, Afi Scenario]
  5. 5. Research Questions Promotion of biofuels  Climate change mitigation (e.g. in the European Union)  Consolidation of energy security (e.g. in the US) BUT: additional pressure on land  Competition with efforts to store more carbon by decreasing deforestation rates  Diversion of food crops into the production of bio-fuels as a reason for increased food price volatility  Wright (2010): US/EU bio-fuel mandates contributing to food price spikes Two channels to dampen this:  Storage  “Option agreements with domestic biofuel producers” to ensure diversion of grain to human consumption during food shortages
  6. 6. Overview Brief overview of the Global Biosphere Management Model (GLOBIOM) Stochastic version of GLOBIOM Scenarios Results
  7. 7. Global Biosphere Management ModelCoverage: the EarthBasic resolution: 28 regions
  8. 8. Three Land-based SectorsForestry: traditional forests for sawnwood, and pulp andpaper productionAgriculture: major agricultural crops and livestockproductsBioenergy: conventional crops and dedicated forestplantations
  9. 9. Supply Chains Forest products:Unmanaged Forest Sawnwood Wood Processing Woodpulp Energy products:Managed Forest Ethanol (1st gen.) Biodiesel (1st gen.)Short Rotation Ethanol (2nd gen)Tree Plantations Bioenergy Processing Methanol Heat …Cropland Crops: Barley Corn Cotton …Grassland Livestock Production Livestock: Cattle meat & milk Sheep & Goat meat & milkOther Natural Pork meatVegetation Poultry meat & egg
  10. 10. Cropland - EPICProcesses• Weather• Hydrology EPIC• Erosion Evaporation• Carbon sequestration and• Crop growth Rain, Snow, Transpiration• Crop rotations Chemicals• Fertilization• Tillage Subsurface• Irrigation Flow Surface• Drainage Flow• Pesticide• Grazing Below Root• Manure Zone Major outputs:  Crop yields, environmental effects (e.g. soil carbon, )  20 crops (>75% of harvested area)  4 management systems: High input, Low input, Irrigated, Subsistence
  11. 11. Optimization Model (FASOM structure) Recursive dynamic spatial equilibrium model Partial equilibrium model: endogenous prices Maximization of the social welfare (PS + CS)
  12. 12. Drivers and OutputMain exogenous drivers: Population (IIASA SRES projections) Diets (FAO, 2006) Bio-energy demand (POLES team, JRC Seville, WEO) (GDP, technological change,…) Output: production Q  land use, water use, GHG, environment consumption Q trade flows prices
  13. 13. GLOBIOM-S 1.0 Optimize under uncertainty  Realize trade flows etc upon realization of a state (of yield) in the future. Stochasticity  crop yield variability estimated from historical yields (FAO 1961-2006). Means and co-variance matrix → yield distributions (100 per crop/region) Changes in the deterministic model  State-dependent primal variables in the model are – supply, “final food demand”, trade flows (adjusting to realization of a state). Objective function  State-dependent variables’ expected value  Safety-first constraint
  14. 14. ScenariosGradually more ambitious Bioenergy Mandate (BM)Strict Food Security (FS) constraintStrict versus flexible BM enforcement
  15. 15. Price Volatility
  16. 16. Environmental Implications: Deforestation
  17. 17. Conclusions Inflexible bioenergy mandates  food price volatility  food security under fluctuating yields  deforestation Long-term analysis  more sources of uncertainty: oil price, climate change, cost of adaptation options, ...  storage capacity
  18. 18. Thank you. 17
  19. 19. Bioenergy Mandates

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