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Amazon deforestation and CO2 emissions: A macroeconomic approach using the GVAR

Luccas Assis Attílio

Journal of Regional Science, 2025, vol. 65, issue 1, 189-220

Abstract: We analyzed the relationship between economic variables, Amazon deforestation, and CO2 emissions in Brazil. We used a macroeconomic approach of a system of open economies (global vector autoregressive [GVAR]). We constructed the international economy using 34 countries, representing 77% of the world GDP and 80% of world CO2 emissions. GVAR allows us to simulate the world economy, capture spillover effects, incorporate the externality of gas emissions, treating Brazil as a small open economy, and including trade integration in the analysis. We found that domestic and external shocks affect Amazon deforestation and CO2 emissions; the principal external shock is the Chinese one, followed by the European and the US shocks. China, directly and indirectly, affects CO2 emissions and deforestation in Brazil through two channels (exchange rates and policy rates). The estimates showed that Brazilian currency and commodity prices are relevant for deforestation, while industrial production is for gas emissions. Other results are that (i) the Brazilian economy affects CO2 emissions in Latin America (and the principal influence of China is on Asia), and (ii) the Chinese shock loses importance when using bilateral trade in 1999–2001, when China was not a fundamental trade partner of Brazil. Alternative model configurations demonstrate that soybean prices and Argentina influence Brazilian carbon emissions and Amazon deforestation. Specifically, soybean prices emerge as a major driver of carbon emissions. These results suggest that geography and trade integration matter to understanding Amazon deforestation and CO2 emissions. Our estimates highlight the importance of government policies and international cooperation in curbing Amazon deforestation.

Date: 2025
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https://doi.org/10.1111/jors.12738

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Journal of Regional Science is currently edited by Marlon G. Boarnet, Matthew Kahn and Mark D. Partridge

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