Carbon Emission Intensity Modeling in Freight Operations Using eXplainable AI in Real-World Conditions

The present study evaluates the effect of real-world operational factors and driving behaviors that significantly contribute to CO₂ emissions and total energy consumption of the port-based heavy-duty vehicles (HDVs). Interpretable machine learning techniques are applied within an eXplainable Artificial Intelligence (XAI) framework to assess the impact of input variables on prediction accuracy. The inherent simplifications in these approaches often limit their ability to capture the complex, nonlinear characteristics of vehicular emission determinants, particularly under dynamic, micro-operational conditions associated with real-world settings. XGBoost showed higher predictive accuracy over conventional regression and other ensemble methods, with up to 46% improvement in <i>R</i> ² and over 80% reduction in estimation errors. To address the black-box nature associated with the model, this study adopts XAI techniques, with SHapley Additive exPlanations (SHAP) employed to quantify feature contributions and enhance the interpretability. The results show that real-world CO₂ emission levels remain substantially high under dynamic operational conditions, emphasizing the need for improved transit and freight management strategies to mitigate vehicular emissions. This further reinforces the importance of regulatory frameworks that incorporate CO₂ emission and fuel-efficiency standards alongside conventional pollutant limits. Such progressive targets are intended to curb the climate impact, stimulate technological innovation, and support long-term low-carbon transition goals.