CO2 emissions prediction based on regression, neural network and SVM

As a matter of fact, with the fast-pace development of global economics and technology, the natural environment is suffering from great amount of greenhouse gases emissions, which attract a lot of attentions from researchers. Specifically, in statistics and data science, experts believe that making accurate CO2 emissions prediction could help governments make policies accordingly. In this paper, three different machine learning models (regression, neural network and support vector machine) are analysed in terms of their construction process and performance on CO2 emissions prediction. Besides, some practical applications from these studies are shown. In general, based on the analysis, these models have made great achievement on CO2 emissions prediction and they all solve the issue in various perspectives. Therefore, this study will show the effectivity of machine learning models on CO2 emissions prediction and encourage more scientists from different majors to take part in it. Overall, these results shed light on guiding further exploration of carbon emission prediction.