Scenarios for Amazon future

Eustáquio J. Reis IPEA

18th LBA-SSC MeetingSão Paulo, 14-15 November 2005

Modeling issues:environmental consequences (Bosello and Zang 2005)

Predicted changes in climate variables: GHG and CO2 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: C02 fertilization (smaller in tropical crops)

Feedback of agriculture on GHG emissions small (except for NOx CH4 in rice and cattle raising and CO2 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)

CO2 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 CO2 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 + 2.73%)

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

RegionCrops/Sector

Effects of a doubling of CO2 (+Δ 2.5ºC and +7%

precip. ) on: ReferenceΔ% land value

Brazil (1970-85)Cerrado GO, MT, TO, RO MGRGS, 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

EXOGENOUSVARIABLES================

POPULATIONUrbanization___________________

TECHNOLOGYAgric. productivity___________________

INFRASTRUTURERoadsPortsHealthEducationEnergy==================

AGROCLIMATICCONDITIONSVegetationSoil qualityClimate

ENDOGENOUS VARIABLES==============

FACTOR PRICESWagesLand pricesTransport costs________________ FACTOR USESLabor employment

Land use:Crop areaPasture areaFallow landsLogging

POPULATION GROWTH AND

INFRASTRUCTEINVESTMENT

DEFORESTATIONBiomass contentcarbon stocks in

soil and vegetation

CO2 EMISSIONS

Production fuction: 2nd law of thermodynamics applied to

economics: conservation 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

Population

InfrastructureRoads

Investments in infrastructure

(policy decision)

InfrastructureRoads

Population growth

•Socio-economic conditions•Land availability (zoning policy)•Infrastructure conditions

Period t+1

Demand for agricultural

land in period t

Supply of agricultural

land in period t-1

Clearing is necessary ?( period t )

Yes No

Recovered fallowareas available?

Fallow areas =Recoveredvegetation. Yes

No

Clear fallow areas

Emission of CO2

Deforestation of pristine forest

Absorption of CO2

Economic Socio demographic

Geo-ecologic conditions

Dynamics of carbon stocks

Source: Author´s simulation

Simulation 1985-2010 - Legal Amazonia

Source: Author´s simulation

Simulation 1985-2010 - Legal Amazonia

Source: Author´s simulationObs: The values of CO2 in 1985 are estimated

Simulation 1985-2010 - Legal Amazonia