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Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas

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  • Seung Jun Choi

    (The University of Texas at Austin)

  • Junfeng Jiao

    (The University of Texas at Austin)

Abstract

Transportation electrification is promoted for its environmental and energy efficiency benefits. However, recent studies examining electric vehicle (EV) adoption have revealed complex patterns influenced by race and income disparities. These studies, primarily based on surveys, often overlook regional ownership variations and built environment measures linked to urban form. Our study addresses this gap by analyzing actual EV registration data with spatial details using hot spot and K-means clustering analyses. The analysis results revealed a pronounced East–West divide in EV adoption. In West Austin, clusters indicate a higher number of EVs, greater energy consumption, and residents who are predominantly White, with higher income and education levels. They mainly live in single-family housing units. Conversely, in East Austin, clusters show a lower number of EVs. They are predominantly home to African-American and Hispanic populations with lower income and education levels, often residing in mobile homes. Land use conditions, such as the availability of green open spaces, play a significant role in this divide. Density, diversity, and design measures of the built environment are lower in East Austin compared to West Austin. We argue that survey-reported preferences for EVs do not always align with actual market behavior. While the 30–45 age group may show a higher willingness to purchase EVs, this interest is not consistently reflected in the actual ownership patterns. Factors like residential choice and the built environment may influence EV adoption rates. A broader set of studies is needed to link urban forms with equity.

Suggested Citation

  • Seung Jun Choi & Junfeng Jiao, 2024. "Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas," Journal of Computational Social Science, Springer, vol. 7(3), pages 2403-2456, December.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:3:d:10.1007_s42001-024-00310-6
    DOI: 10.1007/s42001-024-00310-6
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