Who owns Electric Vehicles (EVs)? The relationship between EV adoption and socio-demographic characteristics across different price segments and brands in the Texas triangle

a) Spatial distribution of registered electric vehicles in the State of Texas

b) Spatial distribution of the number of registered electric vehicles per eligible driver’s license holder in Texas (age 18 and over).

(a) Variance explained by PCA; (b) Identification and removal of outlier; (c) Dendrogram from hierarchical clustering analysis.

Silhouette score calculation and optimization of hierarchical clustering.

a) EVs owned in the State of Texas by merchandise; b) EVs owned in the State of Texas by the 6 major manufacturers in different sale price ranges; c) Elasticity of EVs between price and quantity by major 6 manufacturers and all samples.

a) Scatter plot based on Anselin Local Moran’s I analysis result between race and income factor

b) Scatter plot based on Anselin Local Moran’s I analysis result between education and vehicle ownership factor.

Abstract

This study explores disparities in Electric Vehicle (EV) adoption across different socio-economic groups and geographic areas in Texas, focusing on the Texas Triangle (Austin, Houston, San Antonio, and Dallas-Fort Worth). Using EV registration data and hierarchical clustering, the research identified distinct adoption patterns. Tesla emerged as the dominant brand, particularly in mid-to-high price ranges, with EV adoption concentrated in affluent, highly educated, predominantly White communities. A significant disparity was found between EV density and the availability of charging stations, especially in urban versus rural areas. The study highlights the reluctance of disadvantaged groups to adopt EVs and calls for policy interventions to promote equitable adoption.

Team

Seung Jun Choi , Junfeng Jiao , Tigris Mendez

Funding sources

This research was supported by the NSF Grants (2043060, 2133302, 1952193, 2125858, 2236305) U.S. DOT Consortium of Cooperative Mobility for Competitive Megaregions, Good System at the University of Texas at Austin and The MITRE Corporation.

Data availability

Data will be made available on request.

Acknowledgments

N/A

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