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

• URL: http://arxiv.org/abs/2511.14999v1
• Date: Wed, 19 Nov 2025 00:45:20 GMT
• Title: A County-Level Similarity Network of Electric Vehicle Adoption: Integrating Predictive Modeling and Graph Theory
• Abstract summary: This study introduces a graph-theoretic framework that complements predictive modeling to better capture how county-level characteristics relate to EV adoption.<n>Findings reveal consistent global trends, such as declining median income, educational attainment, and charging-station availability.<n>Some low-adoption groups are rural but not economically disadvantaged, whereas others are urbanized yet experience high poverty rates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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