Evaluating spatial effect of transportation planning factors on taxi CO2 emissions

In recent years, the impact of transportation activities on carbon (CO2) emissions has gained global attention. In China, the severity of CO2 emissions from transportation is a pressing issue, necessitating the development of effective emission reduction strategies. This study uses taxi GPS data from Xi'an, China, to explore the spatial patterns and influencing factors of CO2 emissions. Initially, the research area was segmented into spatial grids of 500 m∗500 m to examine the spatial distribution of CO2 emissions. Subsequently, the trip patterns were extracted using the Latent Dirichlet Allocation (LDA) model, and considering road network density, land use, and social demographic characteristics, the factors influencing CO2 emissions were identified. A Geographically Weighted Regression (GWR) model was constructed to analyze how various factors impact CO2 emissions in spatial areas. The results indicated that: (1) Trip patterns significantly impact CO2 emissions; (2) Various factors have diverse effects on taxi emissions, with some exerting only positive (e.g., primary road network density, etc.) or negative impacts (e.g., trip pattern 9, etc.) on CO2 emissions. Most factors, however, exhibit both positive and negative impacts (e.g., various POI densities, etc.) on CO2 emissions; (3) The spatial impacts of different factors on CO2 emissions vary significantly across regions. The findings of this study will help formulate more targeted and refined management measures to reduce emissions in urban areas. [Display omitted] •The spatiotemporal distribution characteristics of taxi CO2 emissions and their potential influencing factors in urban areas were analyzed.•Taxi trip patterns were identified using the LDA model based on GPS data.•Significant factors affecting taxi CO2 emissions, particularly trip patterns, were determined.•The causes of high CO2 emissions in specific areas were analyzed based on the spatial distribution of significant factors.