Prediction of CO2 Emissions Using Machine Learning - Kanyarat Bussaban; Kunyanuth Kularbphettong; C. Boonseng (CONECT International Scientific Conference of Environmental and Climate Technologies, 2023)
Carbon dioxide (CO2) is one of the important issues concerning human evolution that drives global climate change. It is emitted from the combustion of fuels causing global warming. The global community has gradually turned to pay more attention to environmental issues. This paper implements four prediction models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN, or ConvNet) to predict CO2 trapping efficiency among CO2 emissions, energy use, and GDP. The Machine Learning (ML) approaches used in this study have shown good performance with SVM and CNN models with MAPE. The result can be a significant model for the decision support system to improve a suitable policy for global CO2 emission reduction.
Keywords: Atmospheric and Environmental Gas Dynamics; Air Quality Monitoring and Forecasting; Energy, Environment, and Transportation Policies