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