How electric vehicles benefit urban air quality improvement: A study in Wuhan

Urban air quality is a global concern, and while numerous studies have examined the impact of geography, climate, and urban development on air quality, few have considered the role of electric vehicles (EVs) in predictive models. Furthermore, little attention has been paid to the spatial heterogeneity of EVs. Given the rapid growth of the EV industry, it is crucial to understand the increasing significance of EVs and electric vehicle charging stations (EVCS) on air quality. This study focuses on Wuhan, a representative polycentric city, to investigate the combined effects of EVs and EVCS on air quality, alongside other urban factors. The study employs Markov chains (MC) to process air quality data and utilizes Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR) for data modeling. The results highlight that incorporating EV and EVCS variables enhances the model's fit. Notably, EVCS demonstrates a pronounced influence on improving air quality in areas with high plot ratios and building densities along the north bank of the Yangtze River. The study identifies spatial variations in the geographic distribution of both EVs and EVCS, as well as the distribution of MGWR coefficients. Three distinct regional centers in Wuhan exhibit high concentrations of EVCS per unit area. Moreover, the projected outcomes suggest that these three regions can anticipate significant improvements in air quality, with probabilities ranging from 3.93 % to 10.06 %, 4.40 % to 11.43 %, and 2.55 % to 6.52 % in achieving an Excellent Status (S1) for future air quality, under the assumption of maintaining current EV policies. This study advances our understanding of the contribution of EVs and EVCS to air quality within polycentric cities. It introduces novel research perspectives and methodologies, enriching related fields of study. The findings can inform policymakers and urban planners in developing strategies for creating cleaner and more efficient cities.