Uncovering the spatiotemporal patterns of traffic-related CO2 emission and carbon neutrality based on car-hailing trajectory data

Abstract

Traffic-related carbon dioxide (CO2) emissions constitute a significant portion of greenhouse gas emissions in urban areas, necessitating a comprehensive understanding of their spatiotemporal patterns and their implications for carbon neutrality. While previous studies have focused on exploring CO2 emission patterns, they often overlook the current carbon neutrality associated with these emissions. This study addresses this gap by investigating spatiotemporal patterns and carbon neutrality using car-hailing trajectory data from Xi'an, China. Initially, a link-based CO2 emission model is formulated by considering various driving conditions. Subsequently, the spatiotemporal patterns of traffic-related CO2 emissions are examined by kernel density estimation (KDE) and Getis-Ord models. Finally, carbon neutrality is assessed using the coupling coordination degree (CCD) model, which explores the relationship between traffic-related CO2 emissions and urban vegetation coverage. Empirical experiments found that hotspots of traffic-related CO2 emissions are concentrated at main roads or intersections, while coldspots are dispersed on residential roads. The findings highlight the significant influence of land use type, public services, and road levels on CO2 emission patterns, as well as the critical role of vegetation coverage in improving carbon neutrality levels. Based on these findings, several strategies are recommended to foster low-carbon city development, including expanding public transport services, integrating subway stations along main roads, optimizing traffic lights, increasing urban green spaces, and diversifying commercial facilities in traffic-related areas. These strategies emphasize the importance of incorporating carbon neutrality into urban planning and policy-making to achieve sustainable urban development.