Modeling of Brazilian Carbon Dioxide Emissions: a Review

Abstract

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.

The Prediction of Medium- and Long-Term Trends in Urban Carbon Emissions Based on an ARIMA-BPNN Combination Model

“…MAE P represents the average magnitude of the model's prediction error, which is not sensitive to outliers. The calculation formula is shown in Equation (7). MAPE is the average relative error between the true value and the predicted value, expressed as a percentage, as shown in Equation (8).…”

“…The current research mostly considers factors such as population size, economic scale, energy structure, energy intensity, industrial structure, and urbanization level to establish carbon emission prediction models, including carbon emission assessments globally across six continents (Europe, North America, South America, Asia, Africa, and Oceania) [4] 2 of 19 and in the Beijing-Tianjin-Hebei region [5], as well as Guangdong Province [6]. Meanwhile, the land use change rate, forest protection situation, biomass energy utilization efficiency, transportation mode, and renewable energy utilization rate were taken into account for Brazil's carbon emissions by Pedreira [7]. Chen et al [8] added the proportion of R&D investment as a variable affecting the carbon emissions in Beijing, while Mitchell et al [9] considered the impact of automobile usage intensity, public transportation usage intensity, and climate factors such as season, temperature, and wind speed on the carbon emissions in 12 cities in the United States.…”

Mathematical modeling of vehicle carbon dioxide emissions

“…The transportation sector is a significant contributor to emissions due to the combustion of fossil fuels, which contributes to the concentration of atmospheric [8] , [9] , [10] , [11] . Urban transport alone accounted for 23% of total energy-related emissions, and the global transportation sector contributes approximately 25% of total emissions from fossil fuel combustion [10] , [12] .…”