Technical Session

Model coupling within the GoViLa project

Rüdiger Schaldach1, David Laborde2, Florian Wimmer1

1Center for Environmental Systems Research (CESR), Universität Kassel 2International Food Policy Research Institute (IFPRI), Washington D.C.

GoViLa Modelling Workshop 23.09.2014 Darmstadt

Overview

• Research questions and objectives

• Methodology

− Modelling framework

− MIRAGE-BIOF model

− LANDSHIFT model

− Model coupling

• Scenario analysis

• Summary

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 CO2 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

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 CO2 emissions from LUC.

Land-use changemodule

Population

Agriculturalproductionand trade

Socio-economy module

Clim

ate

scen

ario

s

GrasslandNPP

Crop yields

Biomassproductivity

Hydrology

Wateravailability

Water stress

Biophysical moduleSc

enar

ios

Time seriesof maps and

statistics

Cropcultivation

+Irrigation

Grazing

Settlement

Land-useactivities

Stat

e va

riabl

esSt

ate

varia

bles

Modelling framework

(Schaldach und Koch, 2009)

MIRAGE-BIOF

GAEZ

LANDSHIFT

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) • 2011-2012 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

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

MIRAGE-BIOF: Special features

• 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

MIRAGE-BIOF: New developments

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 products

WoodPaper

Wood and Paper

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

on Construction

Cattle Cattle Gas Gas PrivServ Private services

OthAnim Other animals (inc. hogs and poultry)

OthMin Other minerals RoadTrans Road Transportation

PalmOil Palm Oil Ethanol Ethanol - Main sector AirSeaTran Air & Sea transportation

RpSdOil Rapeseed Oil PubServ Public services

Products

Feedstock Crops

Veg.Oil sector

(+meals) Biofuel

Biodiesel

Sunflower oil

Sunflower seed

Soybean oil Soybean

Rapeseed oil Rapeseed

Palm oil Palm fruit & Kernel

Illustration Biodiesel sectors

Agricultural Production (1 sector)

Managed land

Cropland

Managedforest

Othercrops

Pasture

Wheat Corn

Livestock1 LivestockN

Unmanaged landNatural forest - Grasslands

Land extension

CET

CET

Oilseeds

Substitutablecrops

CET

Vegetablesand fruits

CET

Agricultural land

CET

Sugarcrops

Land Markets – at the AEZ Level

Total land available for agriculture

Land

Crop Land price

Cropland

Technical issue: Land Extension

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

Land Extension Allocation: Old Method

• 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

Yield dynamics in MIRAGE-Biof

LANDSHIFT

• 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)

Land Simulation to Harmonize and Integrate Freshwater availability and the Terrestrial environment

Macro level (Countries / regions)

t t+1

Socio-economic drivers (Population, agricultural production, governance)

Spatial simulation with LANDSHIFT

Potential crop yields Environmental data

Micro level (5‘ Raster = 9 x 9 km)

Land-use change

MIRAGE-BIOF

LANDSHIFT

GAEZ

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)

Land allocation „Multi-Objective Land Allocation“ Heuristics

Spatial distribution of crop types Land-use map (t)

Crop yields (t) (AEZ)

Feedback to suitability assessment (t+1)

[Fischer et al. 2002]

Suitability assessment

Suitability assessment Multi-criteria Analysis (MCA)

( ) ( )

∏∑ ×

m

jkjj

ikiiik cgpfwsuit

1=,

n

1=,=

∑i iw 1 =

Factor weights

Evaluation functions

( ) [ ]1,0∈ii pf

Evaluation factors

Crop yields Terrain slope …

Constraints

Constraining factors

LU-transitions Conservation areas …

( ) [ ]1,0∈jj cg

Suitability factors

Constraints

Model coupling

• 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)

LANDSHIFT area (A*), production (P*), yield change (biophysical)

MIRAGE-BIOF area (A), production (P), yield change (econ. input)

Common data (2012) production, area,

available land

Model initialization MIRAGE-BIOF / LANDSHIFT

Land use map Carbon storage

MIRAGE-BIOF update

assumptions on biophysical yield

change

stop

Test: A==A* P==P*

Yes

No 3

2

1

4

• 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

Model initialization 1. Regions and agricultural products in MIRAGE-BIOF

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

Model initialization

• 300x300m aggregated to 5 arc-minutes cells

• Map only shows land-cover, not land-use − no spatial distribution of crops

− no grazing areas

2. Global remote sensing data: MODIS – land cover

Model initialization

3. Merging of remote sensing data and census data

− Spatial distribution of 19 crop types

− Rangeland and stocking density

− Result is a land-use map

MODIS

(GAEZ)

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

Data exchange during a simulation

GoViLa scenarios − International climate policy − Regional governance − European biofuel policy

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

Translation of scenario assumptions

GIS Analysis and Evaluation − CO2-Emissions: IPCC Tier 1 approach − Effectiveness of governance − Guidelines, room to maneuver

Design of the scenario analysis