Understanding the Correlation of Demographic Features with BEV Uptake at the Local Level in the United States

Battery Electric Vehicles (BEVs) have seen a substantial growth in the recent past, and this trend is expected to continue. This growth has been far from uniform geographically, with large differences in BEV uptake between countries, states, and cities. This non-uniform growth can be attributed to the demographic and non-demographic factors that characterize a geographical location. In this paper, the demographic factors that affect BEV uptake at the Zone Improvement Plan (ZIP) code level are studied extensively across several states in the United States to understand BEV readiness at its most granular form. Demographic statistics at the ZIP code level more accurately describe the local population than national-, state-, or city-level demographics. This study compiled and preprocessed 242 demographic features to study the impact on BEV uptake in 7155 ZIP codes in 11 states. These demographic features are categorized based on the type of information they convey. The initial demographic features are subjected to feature engineering using various formed hypotheses to extract the optimal level of information. The hypotheses are tested and a total of 82 statistically significant features are selected. This study used correlation analysis to validate the feature engineering and understand the degree of correlation of these features to BEV uptake, both within individual states and at the national level. Results from this study indicate that higher BEV adoption in a state results in a stronger correlation between demographic factors and BEV uptake. Features related to the number of individuals in a ZIP code with an annual income greater than USD 75 thousand are strongly correlated with BEV uptake, followed by the number of owner-occupied housing units, individuals driving alone, and working from home. Features containing compounded information from distinct categories are often better correlated than features containing information from a single category. In-depth knowledge of local BEV uptake is important for applications related to the accommodation of BEVs, and understanding what causes differences in local uptake can allow for both the prediction of future growth and the stimulation of it.