The global shift toward electric vehicles (EVs) for climate sustainability lacks comprehensive insights into the impact of the built environment on EV ownership, especially in varying spatial contexts. This study, focusing on New York State, integrates data fusion techniques across diverse datasets to examine the influence of socioeconomic and built environmental factors on EV ownership. The utilization of spatial regression models reveals consistent coefficient values, highlighting the robustness of the results, with the Spatial Lag model better at capturing spatial autocorrelation. Results underscore the significance of charging stations within a 10-mile radius, indicative of a preference for convenient charging options influencing EV ownership decisions. Factors like higher education levels, lower rental populations, and concentrations of older population align with increased EV ownership. Utilizing publicly available data offers a more accessible avenue for understanding EV ownership across regions, complementing traditional survey approaches.

翻译：全球为应对气候可持续性而向电动汽车（EV）转型的趋势中，建成环境对EV保有量的影响尚未得到全面认识，尤其是在不同空间背景下。本研究以纽约州为案例，整合多种数据源的数据融合技术，系统分析了社会经济与建成环境因素对EV保有量的作用机制。通过空间回归模型的应用，研究揭示了一致的系数估计值，验证了结果的稳健性，其中空间滞后模型在捕捉空间自相关性方面表现更优。结果表明，10英里半径内的充电站密度对EV保有量具有显著影响，凸显了便捷充电设施对购车决策的引导作用。高等教育水平、低租赁人口比例及较高老龄化程度等社会特征，均与高EV保有量呈现正向关联。本研究利用公开数据为不同区域EV保有量的解析提供了更易获取的研究路径，有效补充了传统调查方法的不足。