Explainable Machine Learning Framework for Automotive Fuel Efficiency and CO2 Emission Estimation: A Comparative Study Toward Environmental Sustainability

The transportation sector is the primary consumer of vehicle fuel worldwide and is thus a major contributor to climate change via carbon dioxide (CO2) emissions. In addition to severe environmental impacts, such as global warming, droughts, floods, and rising sea levels, these emissions have a negative effect on public health by increasing the prevalence of respiratory disease. Achieving environmental sustainability through regulatory oversight requires a strong understanding of vehicular fuel consumption and CO2 emissions. However, accurate modeling of these remains challenging due to the complex non-linear relationships between various vehicular characteristics and the lack of interpretability of many predictive models. Traditional linear models often fail to capture high-dimensional data complexities, while black-box methods provide few actionable insights for policymaking. To address these gaps, we developed a robust and data-driven two-stage machine-learning (ML) framework designed to enhance model performance and reliability. First, we implemented standard data preprocessing, enhanced feature engineering, and hyperparameter tuning for 14 cutting-edge ML algorithms and three advanced modeling techniques to explore their predictive performance. Second, we introduced three interpretable explainable AI (XAI) approaches. These were evaluated on a publicly available Kaggle static dataset of 550 vehicles, dominated by gasoline-powered vehicles, with only two diesels and two electric vehicles. The tuned CatBoost model demonstrated strong predictive performance, achieving an impressive R2 of 0. 9260, a root mean square error (RMSE) of 1. 1759, and a mean absolute error (MAE) of 0. 8147. In parallel, we deterministically estimated CO2 emissions from fuel consumption, which provide direct estimates of tailpipe emissions. To ensure transparency and model interpretability, we employed Shapley additive explanations, local interpretable model-agnostic explanations, and permutation importance to identify the key factors contributing to the model predictions. Across the explainability analyses, cylinder count, front-wheel drive (drivefwd), and the displacement–year interaction were the primary contributors to the predicted combined miles per gallon; in other words, they strongly affected fuel consumption. Collectively, these findings demonstrate the ability of the proposed model to capture complex feature relationships; thus, it offers a valuable tool for researchers and policymakers in sustainability planning and emission control. Future research should focus on real-time driving or dynamic measurements data and enhancing practical applications to further reduce emissions and promote environmental sustainability.