Abstract
Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies.
Highlights
The choice to focus this investigation on CO2 emissions is justified, since it is the main greenhouse gas emitted in the state of Sao Paulo [26,34]
We investigated the relation among CO2, gross domestic product (GDP), and energy consumption (EN) by using three different approaches: multivariate linear regression, penalized regression and multilayer perceptron artificial neural network
Three different approaches were proposed: multivariate linear regression, elastic-net regression, and a multilayer perceptron artificial neural network. This investigation is of great relevance because, for the first time, an application of neural network models for predicting CO2 emissions in Sao Paulo state, Brazil, was proposed
Summary
Despite the increasing concern about climate change and the need of equity for sharing associated economic impacts and the urgency of developing low-carbon economy, fossil fuels continue to prevail in the global energy consumption [1,2]. In this regard, economic advancement and urbanization are the major processes that contribute to the high levels of consumption of fossil fuels. There has been an increased interest in modeling approaches to explore the relation among carbon emissions (CO2 )—the main greenhouse gas (GHG) emitted worldwide [7]—and economic indicators, such as gross national product (GDP) and energy consumption (EN)
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