Introducing Biodiversity issues in a Global Computable General Equilibrium Model: the MIRAGE example DR. DAVID LABORDE BASED ON JOINT WORKS WITH : Anouch Missirian, IFPRI-Columbia University for biodiversity focus Dr. Lauren Deason, IFPRI for nutrition focus Prof. 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 response Lower yield/TFP volatility, joint production pattern, constraint e.g. on mechanization Biocontrol / Pollination Reduced input uses, “higher” yield TFP, 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 Use changes - Production Pattern Inpiut Uses - (flows, accumulation) Loop effects

MIRAGE-CGE Framework KEY 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 Level In average production unit by sector PAGE 6 Infra-country modelling to capture land heterogeneity

Illustration MIRAGE-CC: Global Land Use Change by 2050: Cropland expansion Mio Km2 (+20.3%). Pasture will be reduced by 2.07 Mio Km2 (still deforestation) Illustration. MIRAGE-CC by 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 TanzaniaBrazil One bubble = one household category Bubble size = Number of people in this household category

MIRAGE Model outputs  Agricultural 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 concentration Looking at imports for human consumption only. Average Number of ProductsHerfindahl-Hirshman Index Product Space # Products# ExportersProteinsCalories Afghanistan Argentina Australia Brazil China Ghana Guatemala Malawi Mali Paraguay United States of America Uzbekistan