Profiling user segments and spatial clusters of EV uptake through multi-method modeling - Bailing Zhang; Jing Kang (Sustainable Environment)
Understanding how electric vehicle (EV) uptake varies across spatial and demographic segments is essential for informing spatially targeted planning and supporting low-carbon transitions in urban systems. However, most existing approaches treat EV adoption as a homogeneous process or rely on fixed-rule models that overlook spatial clustering. This study implements a multi-method approach that combines probabilistic modeling and geospatial analysis to classify and profile EV and conventional vehicle (CV) users. Using 84,834 anonymized vehicle user records from Beijing and Shenzhen, we analyze demographic and locational features—including age, gender, education, income, life stage, occupation, and residence—to identify distinct user groups. Results reveal that EV adoption is concentrated among middle-aged, well-educated, and higher-income individuals, with spatial clustering in innovation-intensive and rapidly urbanizing districts. In contrast, CV users exhibit more diffuse demographic and geographic distributions, often associated with traditional industrial or peripheral zones. This study highlights the potential of multi-method profiling to reveal underlying patterns in EV adoption and support targeted planning and deployment.