Introducing Biodiversity issues in a Global Computable General Equilibrium Model: the MIRAGE example

DR. DAVID LABORDE D.LABORDE@CGIAR.ORG, IFPRIBASED ON JOINT WORKS WITH :

Anouch Missirian, IFPRI-Columbia University for biodiversity focusDr. Lauren Deason, IFPRI for nutrition focusProf. Antoine Bouet, IFPRI-University of Bordeaux for Household modeling focus

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Introducing biodiversity in a global CGE

Multi dimensional issues (additivity, separability)

Different scales

Direct and indirect effects

Conceptually: still some challenges◦ Biodiversity as a Stock and factor of production◦ Ecosystem services as a flow◦ Role of time frame◦ Role of scale: average vs concentration, average vs marginal◦ Non linearity, irreversibility◦ Moving across scale: No Bijection!◦ Dynamic issues and discount factor!

Quantification: huge challenges, especially when tackling uncertainties properly

Conceptual Framework: A simplified view on [Ag.] biodiversity from a CGE modeler

Crop genetics, evolution, traits

Long term yield growth, adaptation capacity

Total Factor Productivity (TFP)

Crop varieties, diversification

Higher resilience, stress responseLower yield/TFP volatility, joint production pattern, constraint

e.g. on mechanization

Biocontrol / Pollination

Reduced input uses, “higher” yieldTFP, land productivity and input uses, constraint e.g. on human

capital

Biodiversity in Food

Better nutrition Nutrition metrics, Labor productivity

Biodiversity Ecosystem services beyond agriculture

Efficiency of the water system, Productivity of tourism, Health

Land

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Loop effects

MIRAGE-CGE FrameworkKEY ELEMENTS

Global dynamic CGE, multi sectoral. Baseyear 2012

Main data sources:◦ GTAP but a lot of modifications, fixes and updates◦ Reconstruction of agricultural accounts using FAO, EUROSTAT, USDA,

Bloomberg (consistency between physical and monetary flows)◦ Trade policy instruments (specific sources)◦ Foreign Direct Investments◦ Household surveys◦ ….

Up to 87 sectors/products and 130 regions. However, most simulations focus on a 35 x 40 configuration.

Different versions◦ MIRAGE-CC: Long term, 2050, climate change focus◦ MIRAGE-HH: Medium term, 2030, Focus on household heterogeneity.

Full bottom up HH modelling.◦ MIRAGE-Biof: Medium term, 2030, land use focus, different biomass-

to-energy pathways, higher crop disagregation (e.g. 5 oilseed crops)

FRAMEWORK

Remark: CGE are bottom-up models. Should not confuse the level of aggregation and how entities interact in a model.

Structural model, no reduced forms:◦ Production function with a inputs and factors◦ Utility function driven demand function: true welfare

analysis

Prices clear markets for each product, from each origin Product differentiation

Factors of production (2 to 10 types of labor, capital, natural resources, land)

Private and Public income (government finance)

Recursive dynamic

Model focus: Land Markets – at the AEZ LevelIn average 200-300 production unit by sector

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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

Infra-country modelling to capture land heterogeneity

Illustration MIRAGE-CC:Global Land Use Change by 2050: Cropland expansion. +2.4 Mio Km2 (+20.3%). Pasture will be reduced by 2.07 Mio Km2 (still deforestation)

AfricaAmericaAsiaCISEU27LACRow

Illustration. MIRAGE-CC by 2050. Central Scenario. Laborde 2014

Illustration MIRAGE-Biof:Differentiated Cropland increase of an increase of the EU biofuel demand, by region, by trade policy regime

Laborde 2012

Illustration MIRAGE-Biof:More cropland or More Carbon (Ha by TJ and Tons CO2 eq by Ha of cropland), by feedstock

Note : The bars (left y-axis) show the amount of additional net cropland by TJ of biofuel produced for one feedstock. The line (right x-axis) shows the average tons of CO2 equivalent by net Ha of cropland.

Laborde 2012

Illustration MIRAGE-HH: Assessment of policy reforms or external shocks at the country and HH level. Illustration: global trade liberalization

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Initial level of Income of the household

% In

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Tanzania Brazil

One bubble = one household categoryBubble size = Number of people in this household category

0 1 2 3 4 5 6 7 8 9 10

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Initial level of Income of the household%

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MIRAGE Model outputsAgricultural production

land use

input uses (chemicals, manure)

agricultural and non agricultural prices

Factor prices (wages, different skills, urban/rural, formal/informal, male/female)

Capital accumulation

income effects, including

Employment

Food consumption

At this stage, no feedback effect from biodiversit

Land Use Biodiversity. Top down approach 1. A generic approach to link land use and biodiversity measured as Species richness

Different habitats (model outputs), at an AEZ level

2. A site specific approach: linking CGE output with a local land use model.

Spatial econometric model estimated on detailed grid-information :◦ for land use

◦ for bird population (STOC data, biodiversity metrics)

Focus: The role of a detailed bilateral trade database on nutrition contents. 800 products, 220 trading partners

Focus: Food Diversity and concentrationLooking at imports for human consumption only.

Average Number of Products Herfindahl-Hirshman Index Product Space

# Products # Exporters Proteins Calories

1998-2000 2011-2013 1998-2000 2011-2013 1998-2000 2011-2013 1998-2000 2011-2013

Afghanistan 97 397 1.5 3.7 0.731 0.428 0.516 0.263

Argentina 514 429 5.3 4.8 0.402 0.064 0.201 0.067

Australia 548 546 10.4 15.7 0.129 0.041 0.054 0.021

Brazil 540 502 6.4 7.4 0.338 0.309 0.341 0.262

China 575 558 9.4 14.5 0.371 0.733 0.132 0.270

Ghana 310 491 3.0 7.1 0.234 0.088 0.187 0.093

Guatemala 491 495 3.9 4.7 0.173 0.139 0.134 0.132

Malawi 221 359 1.7 2.2 0.207 0.268 0.160 0.180

Mali 250 309 3.0 3.7 0.137 0.244 0.146 0.187

Paraguay 379 369 3.0 3.7 0.209 0.158 0.135 0.056

United States of America 601 585 20.9 24.9 0.035 0.030 0.026 0.024

Uzbekistan 230 299 2.5 3.2 0.498 0.364 0.370 0.263