Abstract
BACKGROUND: The continuous increase in carbon dioxide (CO(2)) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle CO(2) emissions is crucial in promoting environmental sustainability and reducing greenhouse gas emissions in the atmosphere. METHODS: This study performed a comparative regression analysis using 18 different regression algorithms based on machine learning, ensemble learning, and deep learning paradigms to evaluate and predict CO(2) emissions from fuel vehicles. The performance of each algorithm was evaluated using metrics including R(2), Adjusted R(2), root mean square error (RMSE), and runtime. RESULTS: The findings revealed that ensemble learning methods have higher prediction accuracy and lower error rates. Ensemble learning algorithms that included Extreme Gradient Boosting (XGB), Random Forest, and Light Gradient-Boosting Machine (LGBM) demonstrated high R(2) and low RMSE values. As a result, these ensemble learning-based algorithms were discovered to be the most effective methods of predicting CO(2) emissions. Although deep learning models with complex structures, such as the convolutional neural network (CNN), deep neural network (DNN) and gated recurrent unit (GRU), achieved high R(2) values, it was discovered that they take longer to train and require more computational resources. The methodology and findings of our research provide a number of important implications for the different stakeholders striving for environmental sustainability and an ecological world.