Impactos das queimadas e políticas ambientais: uma análise utilizando jogos agregativos

Detalhes bibliográficos
Ano de defesa: 2021
Autor(a) principal: Barbosa, Jessica de Abreu lattes
Orientador(a): Maldonado, Wilfredo Fernando Leiva lattes
Banca de defesa: Maldonado, Wilfredo Fernando Leiva, Holanda, Francisco Bruno de Lima, Ribeiro, Jussara
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Goiás
Programa de Pós-Graduação: Programa de Pós-graduação em Economia (FACE)
Departamento: Faculdade de Administração, Ciências Contábeis e Ciências Econômicas - FACE (RG)
País: Brasil
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/11364
Resumo: Burning and deforestation are relevant problems that affect the environment and the economic growth. The way in which governments deal with the impacts and externalities caused by fire and deforestation decisions can have consequences that affect the country's reputation abroad, causing an environment of uncertainty, affecting investment decisions, in addition to affecting the health of people, the air and water qualities, with consequences on the production itself. Using an aggregative game framework, we provide a model that allows us to find the equilibrium in the producers' burning and deforesting decisions. The equilibrium also allows us to analyze the effect of fines over those decisions seen as costs of the producers. In the Nash equilibrium, fines negatively affect farmers' optimal decisions. To empirically verify the results, we run a Cross-section data regression model using information of the municipalities of the Legal Amazon for the year 2017 and a Panel data regression for the States of the Legal Amazon from 2009 to 2018. The results found indicate that the fines have a negative impact on the deforestation over time, but factors such as poor oversight enforcement hamper the implementation of the assessment policy, making it not as efficient as what previewed in the theoretical model.