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

Abstract

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.