Vehicle registrations, socioeconomic and political data to understand electric vehicle adoption in Alabama

Understanding the socioeconomic, demographic, and political drivers of battery electric vehicle (BEV) adoption requires fine-grained data that most existing datasets do not provide. We present a curated dataset linking 2025 vehicle registration records from the Alabama Department of Revenue to American Community Survey (ACS) 5-year estimates and 2024 general election results at three geographic levels: zip codes and counties in Alabama, and U.S. states. Starting from 907 ACS variables across 53 table groups, we generate cumulative and proportional derivatives to expand the feature set to 3,254 variables in 702 groups. To address dimensionality and multicollinearity, we apply a group-wise forward stepwise regression procedure using BEV counts, proportions, and per capita measures as dependent variables, retaining 642 variables in 97 groups that collectively explain over 95% of variance in BEV adoption patterns. The resulting dataset supports a wide range of applications including demand forecasting, infrastructure siting, market segmentation, and equity analysis of EV access across communities with varying socioeconomic and political characteristics.