Brazil is a signatory to the Paris Agreement and aims to reduce 43% of CO2 emissions by 2030, compared to 2005. However, changes in energy policies are needed to achieve this goal, evaluating the produced effects on emissions. One way to predict these effects is through mathematical modeling. In this paper, we carried out a literature review to identify the most used model types and independent variables to forecasting Brazilian CO2 emissions. The review showed that gray models and artificial neural networks are the most used ones. Furthermore, we also identified that economic growth and energy consumption are the main independent variables.
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Univ Fed Espirito Santo, Energy Grad Program, Sao Mateus, BrazilUniv Fed Espirito Santo, Energy Grad Program, Sao Mateus, Brazil
Diniz Chaves, Gisele de Lorena
Boldrini, Olivia Nascimento
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Univ Fed Espirito Santo, Energy Grad Program, Sao Mateus, BrazilUniv Fed Espirito Santo, Energy Grad Program, Sao Mateus, Brazil
Boldrini, Olivia Nascimento
Rosa, Rodrigo de Alvarenga
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Univ Fed Espirito Santo, Civil Engn Grad Program, Vitoria, ES, BrazilUniv Fed Espirito Santo, Energy Grad Program, Sao Mateus, Brazil
Rosa, Rodrigo de Alvarenga
Ghisolfi, Veronica
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Univ Fed Rio de Janeiro, Alberto Luiz Coimbra Inst Grad Studies & Res Engn, Transport Engn Program, Rio De Janeiro, BrazilUniv Fed Espirito Santo, Energy Grad Program, Sao Mateus, Brazil
Ghisolfi, Veronica
Ribeiro, Glaydston Mattos
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Univ Fed Rio de Janeiro, Alberto Luiz Coimbra Inst Grad Studies & Res Engn, Transport Engn Program, Rio De Janeiro, BrazilUniv Fed Espirito Santo, Energy Grad Program, Sao Mateus, Brazil