Achieving Brazil's Deforestation Target Will Reduce Fire and Deliver Air Quality and Public Health Benefits
In one sentence. Machine learning on climate and deforestation data attributes Amazon fire counts to deforestation, estimating a 42% rise in 2020 and 32% fewer fires under the Brazilian target.
Abstract
Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry-climate model and exposure-response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2,300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon.
Labels
- Topic
- Extremes & hazards, Climate analysis & attribution
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1029/2022ef003048 OpenAlex:W4310059367
- Added
- 2026-10-22
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