Evaluating spatial effect of transportation planning factors on taxi CO2 emissions.

Abstract

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.