Analysis of Carbon Emissions in Brazil Using Artificial Neural networks: Na Investigation into Industrial Processes - Miceno Frigo, Murilo; Fernando (Latin American Journal of Energy Research, 2025)
Given the need for more effective public policies for mitigating greenhouse gas emissions and for decision making aimed at strategic planning in key economic sectors, such as energy, industry, and agriculture, among others, with a view to environmental sustainability, this investigation aimed to evaluate how the process of computational modeling, forecasting, and simulation based on artificial neural networks can allow the assessment of the impact of changes in the energy matrix on carbon dioxide emissions from the Brazilian industrial sector. The results observed indicate that the process of computational modeling, forecasting, and simulation based on artificial neural networks has as its primary outcome the possibility of objectively evaluating the impact of changes in the energy matrix on the CO2 emissions of the Brazilian industrial sector. As a secondary outcome, resulting from the simulation process itself, the results observed in this scientific study made it possible to identify those energy sources whose replacement with alternative sources considered clean and renewable, even at small percentages up to 1 percent, would allow a more than proportional reduction up to 10.30 percent in CO2 emissions from the Brazilian industrial segment as a whole. Additionally, the results observed indicated that fuel substitutions in the energy matrix should be carried out in a targeted manner, or in other words, specifically and individually by fuel type, since simultaneous reductions in all fuels that compose the energy matrix were not as significant as the reductions observed in scenarios in which percentage reductions were proposed for only one source.