Prediction of Co2 Brazilian Emissions with Scenario Analysis Based on Energy and Environmental Indicators

Abstract

Brazil is a developing country that emits high amounts of CO2 per year. Therefore, controlling these emissions is essential to achieving sustainable development. In this paper, we modeled an Artificial Neural Network capable of quantitatively relating CO2 emissions, energy matrix, and burning in Brazilian biomes, such as the Amazon Forest. The literature still does not have studies that quanti- tatively demonstrate the impact that changes in the Brazilian energy matrix have on CO2 emissions in the country. In addition, there are also no studies that use fires in Brazilian biomes as input in predictive models for emissions. Our results showed that Brazilian CO2 emissions will increase in the coming years. However, partially replacing fossil energy resources with renewables associated with reducing fires in Brazilian biomes could significantly reduce these emissions. In our first scenario, with a partial replacement of 30% of fossil resources by renewable resources and a 70% reduction in the burning of Brazilian biomes, CO2 emissions decreased by 13.58% for the year 2030. In the second scenario analyzed, we replaced fossil fuels by 90% with renewable ones, while the burning in Brazilian biomes was reduced by 90%. We observed a 28.45% reduction in Brazilian CO2 emissions in this situation. Thus, the model developed here can help Brazil to predict and control its CO2 emissions from changes in its energy and environmental indicators to find a balance between development and sustainability. Other developing countries can also use our model. For this, the indicators must be adapted to the reality of the country studied.