Uncovering the Differences in Environmental Justice of Passenger and Freight Transportation Emissions Through Multi-Task Interpretable Deep Learning

Transportation emissions raise critical environmental justice concerns, yet most studies overlook the distinct inequity patterns between passenger and freight systems. This study aims to compare the spatial disparities and driving mechanisms of exposure injustice from passenger and freight emissions at the U.S. county level. Using 2020 county-level cross-sectional data, we construct an environmental injustice index (EII) and apply spatial autocorrelation analysis, a two-stage multi-task TabNet model, and SHAP interpretation to identify spatial divergence, key determinants, and heterogeneous effects of urban compactness. Results show that passenger EII features continuous regional clustering, while freight EII concentrates along corridors and nodes with limited spatial overlap. Passenger injustice is driven by population density, auto dependence, and public transit, whereas freight injustice is dominated by truck intensity, freight network location, and logistics employment. Urban compactness has dual impacts on passenger injustice but consistently exacerbates freight injustice. These findings highlight the necessity of differentiated governance and provide empirical support for equitable low-carbon transport policies.