Ensaios sobre economia ambiental na América Latina

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Bento, José Alex do Nascimento
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/67461
Resumo: This thesis encompasses three related essays in climate changes. The first one assesses the the vulnerability of Latin American economies, measured by GDP per capita, to precipitation and temperature fluctuations, in order to estimate how these countries are affected by climatic conditions. The Differentiated Generalized Moments Method (DIF-GMM) was used to verify the influence of climatic variables, together with economic indicators, on the level of economic activity. The results indicated that precipitation extremes, as measured by the Standardized Precipitation Index (SPI), are the dominant climatic influences on economic growth and that the effects are significant and negative. The Drought dummy was associated with a positive influence on GDP per capita, while the flood dummy Rain was associated with a negative influence for Latin American countries. It was found that temperature has a significantly lower effect than precipitation. In turn, the second essay evaluates the conditioning factors of hospitalizations for respiratory diseases for the Legal Amazon, at the municipal level, in the period 2000-2019, using models addressing spatial dependence. The results of the Exploratory Spatial Data Analysis suggest the existence of “clusters” in a High-High pattern in the Arc of Deforestation region and in the northeastern part of that region. The econometric results indicated the presence of unobserved effects, being more adequate the estimation by the Spatial Autocorrelation Model (SAC). The variables deforestation, population density, Greenhouse Gases (GHG) emissions and rural credit had a positive impact, while the capital-labor ratio and precipitation had a negative impact. Finally, the third essay proposes to identify the predictors of hospitalizations for respiratory diseases for the Legal Amazon, at the municipal level, in the period 2000-2019, using the Geographically Weighted Panel Regression (GWPR) model dealing with spatial dependence. The results of the estimation of the GWPR model resulted in parameters calculated for each municipality, making it possible to represent on a map the different relationships found, concentrated in the central and northeast portions of the Amazon, which revealed the heterogeneity in the study area. Finally, it is believed that studies and policies for the Brazilian Amazon should consider, above all, these existing intra-regional differences.