Scenarios for Amazon future Eustáquio J. Reis IPEA 18th LBA-SSC Meeting São Paulo, November 2005

Modeling issues: environmental consequences (Bosello and Zang 2005) Predicted changes in climate variables: GHG and CO 2 emissions, temperature and precipitation Temperature affects production decisions directly or through changes in water availability and in biodiversity (insects variety, plant diseases, weed infestation) Evapotranspiration and precipitation affects soil moisture and erosion Biophysical reactions of crops to climate changes: C0 2 fertilization (smaller in tropical crops) Feedback of agriculture on GHG emissions small (except for NO x CH 4 in rice and cattle raising and CO 2 emission from deforestation)

Modeling issues: Socio-economic adaptations Microeconomic adaptation Choice of techniques: factor intensity (land/labor), cultivation timing, mix and location, irrigation Technologies (R&D): development of cultivar adapted to new climate Macroeconomic adjustment Price, income and wealth (land price) effects Changes in production, consumption and trade Migration and capital flows: regional, national and international Role of policies

Modeling approaches: Structural, experimental- simulation or bottom-up Experimental model of crop response (plant physiology and vegetation distribution) Extrapolation to the relevant universe Model of human or socio-economic adaptations and responses appended

Modeling approaches: Spatial analogue, top-down or Ricardian Statistical inferences based upon geographical cross-section of climate conditions Adaptations and responses, both natural and socioeconomic, are automatically incorporated Criticisms (Schneider 1997): equivalence of time and space sufficient information on climate data as well as other variables across space (edaphic, technology, infrastructure, etc.) extreme events and abrupt changes unicity of steady states

Empirical results: crucial issues and assumptions Dumb farmer hypothesis x socioeconomic adaptation (Rosenweig et al. 1993, Reilly 1994, Mendelsohn et al. 1994, 1999, 2005) CO 2 fertilization accounted General/global x partial/regional equilibrium models: substitution and different agents Time of evaluation (higher GDP in the future) Sustainability, vulnerability and uncertainty: effects of extreme events and abrupt changes in climate (Reilly 1999, Schimmelpfenning et al. 1996)

Empirical results: main findings (Bosello and Zang 2005, Mendelshon 2005) Small impact of 2 x CO 2 on world agriculture Food production (-2.5% to –0.07%) Welfare (-0.047% to 0.01%) Higher value for specific regions: Welfare (-5.48% to %) Crucial role of adaptation: hill shaped damage functions (CO2 fertilization) Crucial role of adaptation: hill shaped damage functions (CO2 fertilization) Equity: vulnerability of low latitude developing countries: geography and lower capacity to adapt Extreme events: effects become negative above Δ3% C.

Damage functions are hill shaped: increases in temperature have positive benefits at first (Mendelsohn, 2005)

Empirical results: the Brazilian case Region Crops/Sector Effects of a doubling of CO 2 (+Δ 2.5ºC and +7% precip. ) on: Reference Δ% land value Brazil ( ) Cerrado GO, MT, TO, RO MG RGS, SC -2.6% to –7.4% -3.67% to – 18.44% -2,99% to –16.58% +0.80% to +4.66% Sanghi et al. 1997

Modeling deforestation: basic assumptions Exogenous drivers (or structural causes) of deforestation Population Roads Agricultural land uses are sources (or proximate cause) of deforestation Logging caused or induced by deforestation and thus plays a subsidiary role in the model (very questionable assumption)

Modeling deforestation: crucial issues Demographic transition and urbanization  smaller long run rates of population growth Population density  higher price of land  intensification of land use  saturation effects in deforestation Roads  lower transport cost  higher price of land  intensification of land use Feedback of climate on land yield, uses of land and settlement in AML Broader geographical perspective of models

EXOGENOUS VARIABLES ================ POPULATION Urbanization ___________________ TECHNOLOGY Agric. productivity ___________________ INFRASTRUTURE Roads Ports Health Education Energy ================== AGROCLIMATIC CONDITIONS Vegetation Soil quality Climate ENDOGENOUS VARIABLES ============== FACTOR PRICES Wages Land prices Transport costs ________________ FACTOR USES Labor employment Land use: Crop area Pasture area Fallow lands Logging POPULATION GROWTH AND INFRASTRUCTE INVESTMENT DEFORESTATION Biomass content carbon stocks in soil and vegetation CO2 EMISSIONS

Production fuction: 2nd law of thermodynamics applied to economics: c onservation of economic value Output = F(Land, Labor, Roads, Temperature, Precipitation, etc.)  Land = G(Output, Labor, Roads, Temperature, Precipitation, etc.) Land Yield = G(Output, Labor, Roads, Temperature, Precipitation, etc.)

Scenarios Labor  Population scenario Land  agro-ecological zoning Roads  infrastructure policies Precipitation and temperature from climate models

Methodological strategy Lack of historical data on relationship between climate and economic activity Spatial analogue  spatial cross-section at municipal or Census tract Panel data 260 municipios in AML and 3660 in Brazil from 1960 to 2000 Census tract 1995 and 1985 (non- georeferenced)

Modeling deforestation: building blocks Investment in municipal roads (proxy for infrastructure) is a policy decision Dynamics of municipal (rural and urban) population is determined by agro-ecological and socio-economic conditions in previous periods Agricultural land use and yields are determined by profit maximization in hirearchical model Logging is a function of deforestation Distribution of deforestation according to vegetation types Dynamics of land use (includeing fallow areas) and carbon stocks

Infrastructure investment and population dynamics Period t Population Infrastructure Roads Investments in infrastructure (policy decision) Infrastructure Roads Population growth Socio-economic conditions Land availability (zoning policy) Infrastructure conditions Period t+1

Socio-economic Geo-ecologic: climate Agro-pastoral area Pristine forest area Productive area Fallow area Crop area Pasture area Annual crops Perennial crops Hierarchical land use model

Carbon stock in Vegetation (ton / ha) decomposition + use secondary recovery Time abandon 1st burn initial carbon stock Above-ground carbon cycle in-slash-and-burn agriculture

Demand for agricultural land in period t Supply of agricultural land in period t-1 Clearing is necessary ? ( period t ) Yes No Recovered fallow areas available? Fallow areas = Recovered vegetation. Yes No Clear fallow areas Emission of CO 2 Deforestation of pristine forest Absorption of CO 2 Economic Socio demographic Geo-ecologic conditions Dynamics of carbon stocks

Source: Author´s simulation Simulation Legal Amazonia

Source: Author´s simulation Simulation Legal Amazonia

Source: Author´s simulation Obs: The values of CO2 in 1985 are estimated Simulation Legal Amazonia