Relação entre emissão de dióxido de carbono e variáveis macroeconomicas na agropecuária brasileira

• Ano de defesa: 2021
• Autor(a) principal: Lopes, Julianna Alves Spall
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/26339/001300000f9d0
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia de Produção; UFSM; Programa de Pós-Graduação em Engenharia de Produção; Centro de Tecnologia
• Palavras-chave em Português: Análise de cluster; Modelos autorregressivos vetoriais; Agropecuária brasileira; Gases do efeito estufa; Cluster analysis; Vector autoregressive models; Brazilian agriculture; Greenhouse gases; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/21124

The objective of this research was to investigate, through Cluster analysis, which variables resulting from the production of beef cattle and fertilizers that have similar behaviors and determine a Vector Auto-regressive Model (VAR) in order to analyze the interrelationship between the variables, through the analysis of the impulse-response function. Amid sources of GHG emissions in agriculture, beef cattle and fertilizer use, the variables of the Brazilian agriculture and livestock sector will be studied (enteric fermentation, management of animal waste, agricultural soils, slaughtered animals, fertilizer consumption, direct emissions, atmospheric deposition and leaching); economic variables (Free On Board value of imports and exports, Gross Domestic Product and Broad National Consumer Price Index). The period of analysis is from January 1997 to August 2006 while the variables used were extracted from the page of the Greenhouse Gas Emissions Estimation System (SEEG), from the database of the Institute of Applied Economic Research and from the portal of the Brazilian Institute of Geography and Statistics. Cluster analysis has proven to be a skillful tool for classifying sector variables and economic variables in terms of exogeneity, as well as the AVM model for identifying the interrelationship between variables. After understanding the dynamics and the effects that the variables cause on each other, sources of GHG emission reduction measures were proposed.