Population Density–Informed Framework for Optimal EV Charging Station Deployment: Evidence from Chengdu, China

The increasing prevalence of electric vehicles (EVs) has driven the expansion of electric vehicle charging station (EVCS) networks, which play a vital role in reducing harmful emissions and mitigating climate-related risks. However, the suboptimal deployment and uneven allocation of resources for EVCS adversely impact user experience and the widespread adoption of EVs. As such, this study proposes a novel population density–informed framework for electric vehicle charging station siting and capacity allocation. By integrating cluster analysis, linear regression, and genetic algorithms, the model aims to minimize operational costs while addressing spatial resource distribution imbalances. Unlike conventional approaches, the proposed method emphasizes adaptability to urban population density and infrastructure efficiency. The framework’s effectiveness is validated through empirical analysis conducted in the main urban areas of Chengdu, China, where it significantly improves station layout, enhances user accessibility, and reduces deployment costs. The findings offer actionable guidance for strengthening green energy infrastructure while enhancing urban resilience to environmental and energy challenges.