A Graph Theoretic Approach for Exploring the Relationship between EV Adoption and Charging Infrastructure Growth
Fahad S. Alrasheedi, Hesham H. Ali
TL;DR
This work investigates the bidirectional relationship between electric vehicle (EV) adoption and charging infrastructure (CI) growth using a graph-theoretic correlation network across 137 counties in six U.S. states, comparing Early Adoption (EV-driven CI) and Late Adoption (CI-driven EV) under multiple time granularities and Granger-causality tests. It constructs county-based correlation graphs, detects communities with the Louvain method, and employs edge-betweenness refinement to reveal robust clustering patterns, while also applying Granger causality to identify directional effects across several lags. The study finds consistently weakly positive EV–CI correlations, with Early Adoption networks denser than Late Adoption, and shows that about 84% of counties exhibit significant causal relationships in at least one configuration, generally with CI growth more quickly triggering adoption than the reverse. The results highlight state-level clustering in EV adoption, heterogeneous CI patterns, and the importance of policy-aware, multivariate analyses to disentangle external drivers and inform targeted infrastructure and incentives.
Abstract
The increasing global demand for conventional energy has led to significant challenges, particularly due to rising CO2 emissions and the depletion of natural resources. In the U.S., light-duty vehicles contribute significantly to transportation sector emissions, prompting a global shift toward electrified vehicles (EVs). Among the challenges that thwart the widespread adoption of EVs is the insufficient charging infrastructure (CI). This study focuses on exploring the complex relationship between EV adoption and CI growth. Employing a graph theoretic approach, we propose a graph model to analyze correlations between EV adoption and CI growth across 137 counties in six states. We examine how different time granularities impact these correlations in two distinct scenarios: Early Adoption and Late Adoption. Further, we conduct causality tests to assess the directional relationship between EV adoption and CI growth in both scenarios. Our main findings reveal that analysis using lower levels of time granularity result in more homogeneous clusters, with notable differences between clusters in EV adoption and those in CI growth. Additionally, we identify causal relationships between EV adoption and CI growth in 137 counties, and show that causality is observed more frequently in Early Adoption scenarios than in Late Adoption ones. However, the causal effects in Early Adoption are slower than those in Late Adoption.