Evaluating the Economic Impacts of Climate Change on the Brazilian Agriculture Juliana Speranza Manaus, November, 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 2 nd 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.

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 (3 th 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 Y it :land price (Ricardian approach); land profitability (fixed-effects model) X it : observable variables W it : climate variables estimated θ: response to climate changes

Agricultural model Second stage: simulation Climate change scenarios GCM-projected climate (A1B and A2 scenario) timeslices: ; ;

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 ( ) 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 s 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 = % inclination PERO2 = % 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 GCM-projected climate A2 scenario (IPCC 3 rd AR)-3.7%-26% B2 scenario (IPCC 3 rd AR)-0.8%-9.4%

Results: variation in agricultural profitability – B2 scenario Region North-34.8%-65.7% Northeast-14.3%-27.8% Southeast8.5%6.4% South9.2%12.8% Center-West-23.2%-73.2%

Results: variation in agricultural profitability – A2 scenario Region North-50.0%-124.6% Northeast-20.4%-51.8% Southeast8.5%-0.5% South13.3%17.3% Center-West-46%-161.8%

Results: variation of Temperature and Precipitation

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