Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques

Abstract

Machine Learning (ML) models and the massive quantity of data accessible provide useful tools for analyzing the advancement of climate change trends and identifying major contributors. Random Forest (RF), Gradient Boosting Regression (GBR), XGBoost (XGB), Support Vector Machines (SVC), Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), ensemble methods, and Genetic Algorithms (GA) are used in this study to predict CO2 emissions in Thailand. A variety of evaluation criteria are used to determine how well these models work, including R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and correctness. The results show that the RF and XGB algorithms function exceptionally well, with high R-squared values and low error rates. KNN, PCA, ensemble methods, and GA, on the other hand, outperform the top-performing models. Their lower R-squared values and higher error scores indicate that they are unable to accurately anticipate CO2 emissions. This paper contributes to the field of environmental modeling by comparing the effectiveness of various machine learning approaches in forecasting CO2 emissions. The findings can assist Thailand in promoting sustainable development and developing policies that are consistent with worldwide efforts to combat climate change.