Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2024
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.
Keywords
Electric vehicle, Ownership, Transportation equity, K-means clustering, Built environment
Discipline
Transportation
Research Areas
Integrative Research Areas
Publication
Journal of Computational Social Science
Volume
7
Issue
3
First Page
2403
Last Page
2456
ISSN
2432-2717
Identifier
10.1007/s42001-024-00310-6
Publisher
Springer
Citation
CHOI, Seung Jun and JIAO, Junfeng.
Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas. (2024). Journal of Computational Social Science. 7, (3), 2403-2456.
Available at: https://ink.library.smu.edu.sg/cis_research/474
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s42001-024-00310-6