An Explainable Machine Learning Method for Neighborhood-Level Traffic Emissions Prediction: Insights from Ningbo, China

Road transport is a major source of urban carbon emissions. Numerous studies have investigated the factors influencing road traffic emissions. However, the nonlinear relationships between carbon emissions and their determinants have yet to be fully quantified and validated. In this study, an interpretable machine learning model is developed to empirically investigate the nonlinear effect of the built environment on neighborhood-level road traffic emissions. Field-measured CO2 concentrations are further collected to validate the model results. It is found that the effect of built-environment characteristics varies across different regions. The SHAP (SHapley Additive exPlanations) dependency plots indicate that road length, land use mix, and transportation infrastructure are positively associated with emissions in densely populated commercial and older inner-city districts. In contrast, in high-tech zones, more homogeneous land use and sparse leisure/dining provision are associated with lower growth in traffic-related CO2 emissions. These findings provide valuable guidance for urban policymakers and planners in designing targeted emission reduction strategies and optimizing spatial planning to achieve sustainable road transport.