Abstract
This study investigates disparities in Electric Vehicle (EV) adoption among different socio-economic groups and geographic areas within Texas. We focused on the Texas Triangle, which includes Austin, Houston, San Antonio, and the Dallas-Fort Worth metropolitan area. Using EV registration data, our study applied unsupervised machine learning techniques, specifically hierarchical clustering analysis, to identify distinct patterns of EV adoption. We conducted a longitudinal analysis to investigate changes in carmakers' market share and to understand EV adoption patterns over time. We used Anselin Local Moran's I analysis to profile the characteristics of Tesla owners. Our analysis results revealed significant market segmentation in the EV market, particularly between different sale price ranges and brands. Notably, Tesla emerged as a market leader with strong brand appeal in the mid to high price tiers. EV adoption has been spreading from the Texas Triangle. Clusters with overall high EV registration, as identified in Anselin Local Moran's I analysis, were more associated with high-income, highly educated communities, where residents own multiple vehicles and are predominantly White. This trend was also observed among Tesla owners in relative to low-tier EV adopters. The mismatch exists between the density of registered EVs and the availability of both public and Tesla charging stations, with a notable disparity between urban centers and outskirts. More importantly, we highlight the reluctance of disadvantaged groups to purchase EVs, regardless of price or brand, underscoring the need for policy interventions that address distributive fairness. Current federal incentives and local rebate programs should better serve disadvantaged communities to promote equitable EV adoption.
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