A County-Level Similarity Network of Electric Vehicle Adoption: Integrating Predictive Modeling and Graph Theory

TL;DR

This paper develops a graph-theoretic framework combining predictive modeling and network analysis to identify county clusters with similar EV adoption patterns, revealing both global trends and local variations for targeted policy insights.

Contribution

It introduces a novel county similarity network based on integrated predictive feature importance and graph theory, capturing regional heterogeneity in EV adoption.

Findings

Identified 27 county clusters with distinct EV adoption profiles.

Revealed that low adoption can occur in both rural and urban areas with different socioeconomic factors.

Global trends include declining income and charging stations in lower adoption groups.

Abstract

Electric vehicle (EV) adoption is essential for reducing carbon dioxide (CO2) emissions from internal combustion engine vehicles (ICEVs), which account for nearly half of transportation-related emissions in the United States. Yet regional EV adoption varies widely, and prior studies often overlook county-level heterogeneity by relying on broad state-level analyses or limited city samples. Such approaches risk masking local patterns and may lead to inaccurate or non-transferable policy recommendations. This study introduces a graph-theoretic framework that complements predictive modeling to better capture how county-level characteristics relate to EV adoption. Feature importances from multiple predictive models are averaged and used as weights within a weighted Gower similarity metric to construct a county similarity network. A mutual k-nearest-neighbors procedure and modularity-based community detection identify 27 clusters of counties with similar weighted feature profiles. EV adoption rates are then analyzed across clusters, and standardized effect sizes (Cohens d) highlight the most distinguishing features for each cluster. Findings reveal consistent global trends, such as declining median income, educational attainment, and charging-station availability across lower adoption tiers; while also uncovering important local variations that general trend or prediction analyses fail to capture. In particular, some low-adoption groups are rural but not economically disadvantaged, whereas others are urbanized yet experience high poverty rates, demonstrating that different mechanisms can lead to the same adoption outcome. By exposing both global structural patterns and localized deviations, this framework provides policymakers with actionable, cluster-specific insights for designing more effective and context-sensitive EV adoption strategies.