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Evaluating the Economic Impacts of Climate Change on the Brazilian Agriculture
Juliana Speranza
Manaus, November, 2008.Manaus, November, 2008
José Feres, Juliana Speranza, Eustáquio Reis
Motivation Benchmark global warming is projected
to increase global mean surface temperature by 1.1 – 6.4ºC over the period 1990 to 2100 (IPCC 2007). Many questions remain regarding how the costs and benefits of warming are likely to be distributed across the globe and how a change in climate will affect various greenhouse effect mitigation projects, such as avoided deforestation and carbon-trading, over their lifetime.
Motivation One of the most significant ways
that global climate change is predicted to affect economic activity is through its effects in agriculture.
Motivation The damages are particularly
critical in tropical countries, like Brazil. Indeed, Brazilian agricultural and forestry sectors are particularly vulnerable to global warming since considerable production is currently undertaken under high-temperature conditions.
Motivation Among the several
consequences, falling farming incomes may have an expressive negative impact on economic development, may increase poverty and reduce the ability of households to invest in a better future.
Brazilian particularity The Amazon rainforest Since deforestation is the 2nd largest
global source of carbon dioxide emissions, global warming will depend in part on future land use in the Amazon and the ability of the area’s vegetation to sequester carbon, thus creating a feedback within the
climate change mechanism.
Change in Land
Use
CO2
Climate Change
Policy concerns Adaptative and mitigation policies
(global warming) Population socioeconomic
reproduction (poverty) Deforestation and agricultural border
expansion Migratory flows Agricultural versus non-agricultural
activities
Objective
What are the impacts of climate change in terms of agricultural profitability/productivity, land values and area used in agro-pastoral activities in the distinct Brazilian regions?
Agricultural model Basic aim: measuring the impact of
climate change on the agricultural Cross-Sectional Panel Model with Census
Data Input to GCM (3th AR data from IPCC 2001) Georeferenced database
Methodology: Ricardian approach Fixed-effects approach
Climate profits land conversion
Literature review Production function approach:
assumptions Takes an underlying production function and
varies the relevant environmental input variables to estimate the impact of these inputs on production.
Agroeconomic approach (specific crops). Caveat: estimates do not account for the full
range of compensatory responses to changes in weather made by profit-maximizing farmers (biased downward – “dumb-farmer”).
Literature review Ricardian approach: assumptions
Land prices reflect the present discounted value of land rents into the infinite future.
Land prices are able to capture the impact of climate variables.
Captures all of the ways that farmers have adapted their climate, so long as the land is still classified as farmland (crop switching included).
Caveat: ommited variable bias.
Literature review: Ricardian Model
Reproduced from Mendelsohn et al. (1994).
Literature review Fixed effects approach: assumptions
Exploit the year-to-year random variation in temperature and precipitation to estimate whether agricultural profits/yields vary with climate.
Advantage: an alternative to Ricardian model.
Caveat: adopted temporary shocks.
Agricultural model Two-stage method First: econometric estimation
Equation specification
Yit:land price (Ricardian approach); land profitability (fixed-effects model)
Xit: observable variables Wit: climate variables estimated θ: response to climate changes
iijj
jii WfXy )(´
Agricultural model Second stage: simulation
Climate change scenarios GCM-projected climate (A1B and A2
scenario) timeslices:2010-2039; 2040-2069; 2070-
2099
Agricultural model:Census Data Agricultural Census: 1970, 75, 80, 85, 95
Municipality level data - approx. 3,200 obs by year Land acreage, crop prices and quantities
Agricultural model:climate data
Base climatology: Climate Research Unit (CRU)
10 minute (~20km) interpolated grids intersected with AMC boundaries
30-year averages (1961-1990) temperature (Celsius) precipitation (mm/month)
Seasonal specification December, January, February March, April, May June, July, August September, October, November
Agricultural model:climate change data
General Circulation Model (GCM) projections Wagner Soares, INPE/CPTEC Projected timeslices
1961-1990 2020s 2050s 2080s
Intersected grids with MCAs
Projected climate change = observed (CRU) base + intra-modeled anomaly
Agricultural model:Geographic data (soil)
1:5,000,000 digital maps of Brazilian soils (Embrapa)
Erosion potential PERO1 = 7.5 - 15% inclination PERO2 = 30 - 45% inclination
Proportion of município in each of 12 categories of soil type
Proportion in 5 categories of soil quality
Soil type – 1:5,000,000
Results: variation in agricultural profitability
GCM-projected climate
2040-2069
GCM-projected climate
2070 - 2099
A2 scenario(IPCC 3rd AR) -3.7% -26%
B2 scenario(IPCC 3rd AR) -0.8% -9.4%
Results: variation in agricultural profitability – B2 scenario
Region 2040-2069 2070-2099
North -34.8% -65.7%
Northeast -14.3% -27.8%
Southeast 8.5% 6.4%
South 9.2% 12.8%
Center-West -23.2% -73.2%
Results: variation in agricultural profitability – A2 scenario
Region 2040-2069 2070-2099
North -50.0% -124.6%
Northeast -20.4% -51.8%
Southeast 8.5% -0.5%
South 13.3% 17.3%
Center-West -46% -161.8%
Table 1: Variation of Temperature Yearlong Average in (ºC)
Region Base A2 2050s A2 2080s B2 2050s B2 2080s North 26,4 2,2 4,0 1,8 2,8
Northeast 25,1 1,8 3,4 1,6 2,4 Southeast 21,3 2,1 3,8 1,7 2,6
South 19,3 2,2 3,7 2,0 2,6 Central-West 24,1 2,4 4,3 1,9 3,0
Brazil 22,7 2,0 3,7 1,7 2,5 Table 2: Percentage variation of Precipitation Yearlong Average in (mm/month)
Yearlong Average
in of Precipitation (mm/month) Yearlong Average
Region Base A2 2050s A2 2080s B2 2050s B2 2080s North 189,0 -4,1 -6,7 -1,8 -4,5
Northeast 83,8 -2,0 -10,0 -0,4 -0,2 Southeast 114,9 -1,7 -5,0 0,2 0,6
South 131,5 1,7 5,3 0,5 3,3 Central-West 130,8 1,1 -0,6 4,3 0,7
Brazil 110,4 -1,1 -4,2 0,3 0,5
Results: variation of Temperature and Precipitation
Table 3: Simulation of Percent Change in Converted Land per Hectare of MCA Land
the IPCC Scenarios A2 and B2 for the Timeslices 2050s and 2080s - C Weighted Region Model Error A2 2050s A2 2080s B2 2050s B2 2080s North -31,8 -13 -27 -21 -41
Northeast 16,2 11 27 1 9 Southeast 8,2 11 15 8 11
South -0,9 24 32 18 19 Central-West -5,6 12 2 9 -1
Brazil 0,3 12 14 6 5 Table 4: Simulation of Percent Change in Land Value per Hectare of MCA Land for
the IPCC Scenarios A2 and B2 for the Timeslices 2050s and 2080s – C Weighted Region Model Erro A2 2050s A2 2080s B2 2050s B2 2080s North -21,3 -32 -63 -29 -53
Northeast -1,8 6 -5 2 1 Southeast -23,3 28 22 18 24
South 4,4 202 602 134 194 Central-West -15,5 84 134 42 56
Brazil -12,4 90 221 57 79
Results: variation in converted land* and land value
* Converted land: total area used in agro-pastoral activities including six land use categories (temporary and perennial crops, planted and natural pasture, planted forest and short fallow).
Agricultural model:Preliminary Conclusions Overall impact of climate change will be
quite modest in the medium term, but effects are significantly more severe in the long term
Consequences of climate change will vary across Brazilian regions North and Center-West may be significantly
harmed South may benefit mildly