Technical Session Model coupling within the GoViLa project

Transcription

1 GoViLa Modelling Workshop Darmstadt Technical Session Model coupling within the GoViLa project Rüdiger Schaldach 1, David Laborde 2, Florian Wimmer 1 1 Center for Environmental Systems Research (CESR), Universität Kassel 2 International Food Policy Research Institute (IFPRI), Washington D.C.

2 Overview Research questions and objectives Methodology Modelling framework MIRAGE-BIOF model LANDSHIFT model Model coupling Scenario analysis Summary

3 Research questions and objectives How can countries produce or import the raw materials for biofuel production without triggering adverse land use changes, leading to a release of CO 2 that would worsen the footprint of biofuels in terms of climate change. How can alternative governance scenarios lead to better or worse outcomes, and how can policy makers in the EU act to improve the environment in which the biofuel target will take place? Assess (direct and indirect) land use change in the most critical regions, namely Brazil, Indonesia and Ukraine. Provide information that help to identify the room for maneuver through several scenarios for the mitigation of LUC effects assuming an increasing demand for biofuels in the EU

4 Methodology Model-based assessment of land-use change globally and within the focus countries under the GoViLa governance scenarios. Combination of a global economic model (MIRAGE-BIOF) with a spatially explicit land-use model (LANDSHIFT) Linkage of global trade and markets with regional land-use decisions and spatial details of the biophysical environment. Spatial information of land suitability and land-use constraints provide a more detailed picture of land availability. Incorporation of spatially explicit crop yield data into economic analysis. The generated land-use maps will allow more detailed assessment of CO 2 emissions from LUC.

5 Modelling framework MIRAGE-BIOF Socio-economy module Scenarios Population Agricultural production and trade State variables Land-use change module Land-use activities Settlement LANDSHIFT GAEZ Biophysical module Biomass productivity Grazing Climate scenarios Grassland NPP Crop yields Hydrology Water availability Water stress State variables Crop cultivation + Irrigation Time series of maps and statistics (Schaldach und Koch, 2009)

6 MIRAGE-BIOF The MIRAGE model has started to be developed in 2001 in CEPII, Paris. Focusing on EU Integration and Trade Policy analysis of the beginning Now used by several institutions around the World, numerous versions ( trade policy focused, FDI, Services, Climate Change etc.) Biofuels assessment started in 2008 On land use: First study for the DG Trade in 2009 (limited to ethanol) Second study for DG Trade in 2010 (part of the public consultation) study for the EC: Impact Assessment and draft legislation But other applications: mandates of other countries, comparison of traditional ag policies and biofuels etc., food prices and price stability consequences

7 MIRAGE-BIOF: Special features MIRAGE model Multi country, Multi sectoral, and global Recursive dynamic set-up Modified model and data components Improvement in demand system (food and energy) Improved sector disaggregation New modeling of ethanol sectors Co-products of ethanols and vegetable oils Modeling of fertilizers Modeling of livestocks (extensification/intensification) Land market and land extensions at the AEZ level

8 MIRAGE-BIOF: New developments New data Higher level of crop disaggregation Higher level of regional disaggregation Double cropping Carbon markets (all sectors, including LULUCF) Explicit FQD and RED modelling

9 Products Sector Description Sector Description Sector Description Rice Rice Permcrops Permanents crops EthanolB Ethanol - Sugar Beet Wheat Wheat Fodder Fodder crops EthanolM Ethanol - Maize Maize Maize SoybnOil Soy Oil EthanolW Ethanol - Wheat PalmFruit Palm Fruit SunOil Sunflower Oil Biodiesel Biodiesel Rapeseed Rapeseed OthFood Other Food sectors Manuf Other Manufacturing activities Soybeans Soybeans MeatDairy Meat and Dairy WoodPape Wood and Paper products r Sunflower Sunflower Sugar Sugar Fuel Fuel OthOilSds Other oilseeds Forestry Forestry PetrNoFuel Petroleum products, except fuel Vegetable Vegetable Fishing Fishing Fertiliz Fertilizers OthCrop Other crops Coal Coal ElecGas Electricity and Gas Sugar_cb Sugar beet or cane Oil Oil Constructi Construction on Cattle Cattle Gas Gas PrivServ Private services OthAnim Other animals (inc. OthMin Other minerals RoadTrans Road Transportation hogs and poultry) PalmOil Palm Oil Ethanol Ethanol - Main sector AirSeaTran Air & Sea transportation RpSdOil Rapeseed Oil PubServ Public services

10 Illustration Biodiesel sectors Feedstock Crops Sunflower seed Soybean Rapeseed Palm fruit & Kernel Veg.Oil sector (+meals) Sunflower oil Soybean oil Rapeseed oil Palm oil Biofuel Biodiesel

11 Agricultural Production (1 sector)

12 Land Markets at the AEZ Level Wheat Corn Oilseeds CET Sugar crops Substitutable crops Vegetables and fruits Other crops Livestock1 LivestockN CET CET Cropland Pasture CET Agricultural land Managed forest Unmanaged land Natural forest - Grasslands Land extension CET Managed land

13 Technical issue: Land Extension Crop Land price Cropland Total land available for agriculture Land

14 Land Extension Allocation: Old Method Forest Primary Other Savannah & Grassland Argentina 0.0% 24.7% 23.3% Brazil 16.3% 11.2% 48.5% CAMCarib 30.4% 10.7% 42.9% Canada 7.8% 42.5% 16.1% China 2.2% 27.3% 26.0% CIS 5.6% 33.3% 26.7% EU27 0.4% 23.5% 30.9% IndoMalay 51.7% 7.0% 31.0% LAC 10.8% 14.3% 33.8% Oceania 0.0% 32.6% 22.5% RoOECD 0.0% 18.8% 45.8% RoW 3.7% 36.9% 16.7% SEasia 20.4% 21.5% 33.8% SouthAfrica 5.1% 28.4% 22.2% SouthAsia 0.0% 32.4% 23.9% SSA 13.0% 16.7% 41.7% USA 2.5% 21.1% 23.7% Methodology Amount of land extension: isoelastic land supply based on cropland price Evolution of the elasticity Where the land is taken: Ad Hoc coefficients: Winrock Limitations Done at the AEZ level RAS procedure to consider land availability constraint at the AEZ level Pag

15 Yield dynamics in MIRAGE-Biof An exogenous factor that accounts for technical change (defined in the baseline(s) and scenario(s)); Economic drivers Factors of production (capital, labor) used by unit of land; Fertilizer use (amount of fertilizer by ha); Intrinsic quality of the land by crop Landshift

16 LANDSHIFT Land Simulation to Harmonize and Integrate Freshwater availability and the Terrestrial environment Spatially explicit approach Multiple spatial scales Integration of socio-economic and environmental aspects Land-use change on the global scale Land-use intensity and competition between activities Spatial resolution of 5 arc minutes (9 km x 9 km at the Equator)

17 Spatial simulation with LANDSHIFT MIRAGE-BIOF Macro level (Countries / regions) Socio-economic drivers (Population, agricultural production, governance) t t+1 GAEZ Land-use change Micro level (5 Raster = 9 x 9 km) LANDSHIFT Potential crop yields Environmental data

18 Crop cultivation activity Driving factors for quantitative land-use change: - Crop production (t) - Yield increases (t) Driving factors for location of land-use change: - Topography - Road infrastructure - Conservation area Suitability map (t) Suitability assessment Land allocation Multi-Objective Land Allocation Heuristics Crop yields (t) (AEZ) Spatial distribution of crop types Land-use map (t) [Fischer et al. 2002] Feedback to suitability assessment (t+1)

19 Suitability assessment Multi-criteria Analysis (MCA) suit k Factor weights = n w ( ) ( ) i fi pi, k g j c j, k i=1 Suitability factors m j=1 Constraints i wi = 1 Evaluation functions ( ) [ 0,1] f i p i Evaluation factors Crop yields Terrain slope Constraints ( ) [ 0,1] g j c j Constraining factors LU-transitions Conservation areas

20 Model coupling Common data (2012) production, area, available land 1 2 Model initialization MIRAGE-BIOF / LANDSHIFT MIRAGE-BIOF area (A), production (P), yield change (econ. input) MIRAGE-BIOF update assumptions on biophysical yield change Land use map Carbon storage 3 LANDSHIFT area (A*), production (P*), yield change (biophysical) 4 Test: A==A* P==P* No Yes stop Initialization of both models with common land-use data set (1) Simulation of scenarios Agricultural production from MIRAGE-BIOF on regional level (2) Calculation of land-use change on raster level (3) Iteration until model results converge (4)

21 Model initialization 1. Regions and agricultural products in MIRAGE-BIOF Ukraine Brazil 8 sub-regions Indonesia, 6 sub-regions Other countries and regions (100+) 24 products 19 crop types Rangeland Forest area Settlement Primary forest & savannah (Sub-)national statistical data Modelled crops fruits and nuts palm tree olive tree other permanent crops rice corn wheat corn-soybean cotton-soybean other cereals soybeans sunflower rapeseed other oilseeds sugar beet sugar cane fiber vegetables fodder

22 Model initialization 2. Global remote sensing data: MODIS land cover 300x300m aggregated to 5 arc-minutes cells Map only shows land-cover, not land-use no spatial distribution of crops no grazing areas

23 Model initialization 3. Merging of remote sensing data and census data MODIS Spatial distribution of 19 crop types Rangeland and stocking density Result is a land-use map (GAEZ)

24 Base year land-use map Harmonized initial conditions for simulation with MIRAGE-BIOF/LANDSHIFT Spatial distribution of crop types and rangeland Agricultural production (and implicitly mean crop yields) Potentially available area for cropland and rangeland

25 Data exchange during a simulation

26 Design of the scenario analysis GoViLa scenarios International climate policy Regional governance European biofuel policy Translation of scenario assumptions MIRAGE-BIOF Change of crop production Technological change /crop yield increases Livestock numbers LANDSHIFT Direct and indirect land-use change Maps for Brazil, Indonesia, Ukraine GIS Analysis and Evaluation CO 2 -Emissions: IPCC Tier 1 approach Effectiveness of governance Guidelines, room to maneuver