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Investigating Electric Vehicle Adoption Using Correlation and Prediction Analyses

Authors:
Fahad Alrasheedi
Hesham Ali

Keywords: electric vehicle; charging stations; electric vehicle adoption; graph modeling, correlation networks.

Abstract:
The transportation sector, dominated by gas-powered vehicles, is a major contributor to carbon dioxide emissions that pose significant threats to both environmental and public health. To address this issue, Electric Vehicles (EVs) have emerged as a promising alternative aimed at achieving zero-carbon emissions. However, EV adoption faces several challenges, including high costs, insufficient charging infrastructure, range anxiety, and other barriers. To promote EV adoption, authorities responsible for the management of EVs have implemented various incentives, such as tax reductions, credits, and support for charging infrastructure programs. Despite these targeted management efforts, the adoption of EVs remains a complex issue that requires extensive analysis to understand the factors driving increases or decreases in adoption rates. In this study, we employ a two-pronged approach to examine EV adoption growth rates across counties in six U.S. states. Our methodology integrates correlation network analysis and statistical prediction-based analysis. The primary finding of these analyzes highlights the critical role of geographical features and practices of local management of EVs in influencing similar patterns of EV adoption among counties. Additionally, we identify two clusters exhibiting declines in EV adoption, underscoring the need for further investigation into the management strategies and underlying causes of these decreases.

Pages: 6 to 10

Copyright: Copyright (c) IARIA, 2025

Publication date: March 9, 2025

Published in: conference

ISSN: 2327-2058

ISBN: 978-1-68558-233-3

Location: Lisbon, Portugal

Dates: from March 9, 2025 to March 13, 2025