Uncovering driving factors and spatiotemporal patterns of urban passenger car CO2 emissions: A case study in Hangzhou, China

Abstract

While passenger cars offer the convenience of door-to-door travel, they are responsible for a substantial share of urban traffic-related carbon emissions, accounting for 44% of the global transport sector's emissions in 2022. Existing studies have not adequately identified the driving factors and spatiotemporal patterns of Passenger car CO2 Emissions (PCEs). To fill this gap, a comprehensive analytical framework for analysing PCE, encompassing several key components, has been developed. First, the PCE is estimated at a fine-grained level by utilising million-scale GPS trajectory data. Secondly, road network and urban functional features are extracted based on multi-source data, examining their nonlinear impacts on PCE. The driving factors of PCE during peak and off-peak periods are identified based on interpretable factor analysis. Finally, latent space clustering techniques are applied to reveal the spatiotemporal patterns of PCE, verified by significance tests. The effectiveness of this framework is demonstrated via a case study in Hangzhou, China. Notably, during peak hours, the driving factors of PCE are the road network's closeness centrality, service-related Points of Interest (POI) density, and trunk road density. In addition, five distinctive PCE patterns are identified. These findings facilitate the development of targeted and differentiated passenger car traffic management strategies, promoting carbon emission reduction in the transport sector.