Determinantes socioeconômicas da distribuição espacial de painéis fotovoltáicos residenciais no Brasil

• Ano de defesa: 2019
• Autor(a) principal: Sabadini, Felipe
• Orientador(a): Moralles, Herick Fernando
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção - PPGEP
• Palavras-chave em Português: Painel fotovoltáico; Distribuição regional; Econometria espacial
• Palavras-chave em Inglês: Peer effects; Regional distribution; Spatial econometrics; Photovoltaic panel
• Área do conhecimento CNPq: CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::ADMINISTRACAO PUBLICA::PLANEJAMENTO EM CIENCIA E TECNOLOGIA; CIENCIAS SOCIAIS APLICADAS::DEMOGRAFIA::DISTRIBUICAO ESPACIAL::DISTRIBUICAO ESPACIAL GERAL; CIENCIAS SOCIAIS APLICADAS::ECONOMIA::ECONOMIA INDUSTRIAL::MUDANCA TECNOLOGICA; CIENCIAS SOCIAIS APLICADAS::ECONOMIA::METODOS QUANTITATIVOS EM ECONOMIA; ENGENHARIAS::ENGENHARIA DE PRODUCAO::ENGENHARIA ECONOMICA; ENGENHARIAS::ENGENHARIA DE PRODUCAO::ENGENHARIA ECONOMICA::ESTUDO DE MERCADO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/12108

The environmental situation demands new forms of energy generation; the current energy matrix is dependent on fossil fuels that besides its harmful potential is also a non-renewable natural resource. Sensitive to this change, the Brazilian government published ANEEL's normative resolution 482/2012, which established the conditions for the exchange of energy between micro-generators. The current scenario shows that besides the environmental benefits, other motivations and barriers are also raised by the literature as motivations that lead to the adoption of photovoltaic panels. This work tries to fill part of this gap, being the first to examine the influence of socioeconomic factors on the spatial distribution of residential photovoltaic panels in Brazilian municipalities taking into account the spatial factor as a determinant. A cross-section model was elaborated, and spatial models of data in SAR and SEM panels were also used. The results found in this work demonstrate influences of the spatial variable as well as population; GDP of agribusiness and salary. Demonstrating convergence with previous studies, the best model was the spatial error for presenting better indexes, the spatial variable and peer effects showed a significant influence on the adoption of panels.