Spatiotemporal characteristics and driving factors of CO₂ emissions from road freight transportation

Urban freight emissions play a vital role in the overall CO₂ emissions of the transportation sector. However, previous studies have mainly focused on the CO₂ emissions of the entire transportation sector. This study addresses this gap by employing a bottom-up emission estimation model and constructing a high spatiotemporal resolution road freight emission inventory using GPS trajectory data from over 17,000 heavy-duty trucks (HDTs) in Shenzhen. Spatiotemporal analysis reveals distinct patterns and spatial non-stationarity in CO₂ emissions from road freight transportation. A multiscale geographically weighted regression model is applied to analyze the potential factors that may influence CO₂ emissions at varying spatial scales. The findings demonstrate that land use distribution exhibits the highest relative explanatory power at 35.7%, followed by accessibility to freight hubs at 23.9%. Based on these findings, this study proposes four policy recommendations to reduce CO₂ emissions and enhance urban sustainability.