Income and racial disparity in household publicly available electric vehicle infrastructure accessibility

The United States aims for 50% of new vehicle sales to be zero-emission by 2030, supported by a national network of EV chargers. Meeting projected EV adoption levels will require rapid growth in public charging, with annual additions far exceeding historical rates. Public EV infrastructure is essential to stimulate demand, shape preferences, reduce range anxiety—especially for long-distance travel—and close homeowner–renter gaps in EV ownership. Yet, rapid deployment risks exacerbating inequities, as prior trends indicate lower-income and underrepresented communities often have poorer access to EV charging. This study leverages a highly granular, national household-level dataset to assess equity in public EV infrastructure, focusing specifically on proximity (travel distance) to charging as a critical dimension of accessibility. The objectives are to quantify disparities by income and race/ethnicity, map geographic heterogeneity at county and state levels, and explore determinants of observed accessibility gaps to inform equitable, locally tailored policy solutions.

Prior research shows EV adoption is constrained by limited charging infrastructure, high costs, siting and planning challenges, and grid capacity. Evidence indicates declining accessibility to public charging among low-income groups, people of color in specific regions, and residents of multi-family dwellings, with most prior analyses conducted at aggregated geographies (zip code/CBG) and specific locales (e.g., California, New York City), potentially masking heterogeneity. Recent federal initiatives (e.g., Bipartisan Infrastructure Law, NEVI) elevate equity considerations, and analyses suggest sustained investment (≈30% of chargers) in lower-income communities is needed to ensure equitable access. However, a comprehensive, micro-level, national analysis of income- and race-based disparities in access to public EV infrastructure has been lacking. This study addresses that gap by using household address-level data to assess disparities and their geographic variation.

Data and scope: The study uses a 2021 national micro-level dataset from Data Axle covering 120,962,661 U.S. households (≈97.5% of households). Household records include residential location, estimated income, race/ethnicity, and dwelling type. For some analyses, focus is on Black and White households (94,878,711 observations). Public EV infrastructure data come from the U.S. DOE Alternative Fuels Data Center: 56,940 stations with 128,964 EVSE ports (88% electric). Urban/rural status follows the 2010 Census Urbanized Areas and Urban Clusters; thresholds for low- and moderate-income (LMI) are drawn from the U.S. Treasury tool (moderate-income for a 3-person household by county). Distances to the nearest freeway/highway are computed using Python Geopandas. County-level variables (population density, median income, housing characteristics, etc.) come from the 2021 ACS 5-year estimates; MUD rate is total MUD units divided by total housing units. Accessibility measures: For each household, compute the shortest road distance to the nearest public charging station (DP1) and to the nearest 2–5 stations (DP2–DP5). Robustness includes consideration up to 10 nearest stations and weighting DC fast versus Level 2 stations, and an inhabitant-to-station ratio measure. Nationwide association: Employ LOESS (nonparametric) regressions to depict the relationship between income and accessibility across racial/ethnic groups (Black, White, Hispanic, Asian) separately for urban and rural geographies. Conduct OLS regressions conditioning on income to compare accessibility across racial groups. County- and state-level accessibility gaps: Define two core gaps as differences in average distance to EV infrastructure between (a) LMI vs non-LMI households and (b) Black vs White households, for urban and rural areas. Map gaps at county (and state) levels and compute summary shares of counties with positive gaps and average extra distance for disadvantaged groups where gaps exist. Predictive importance (LASSO): Use least absolute shrinkage and selection operator models at the state level to compare predictive power of income versus race for EV accessibility, stratified by dwelling type (single-family dwelling, SFD; multi-unit dwelling, MUD) and location (urban/rural). Residualize county fixed effects, distance to highway, number of children, and duration of residence, then assess which variable (income or race) persists longer under increasing penalties and the sign of its coefficient. Extend to DP2–DP5 outcomes. Mechanism analysis (county-level OLS): Regress county-level accessibility gaps (income-based and race-based, separately for urban/rural) on normalized gap variables: highway distance gap, income gap, poverty gap (for racial models), Black percent gap (for income models), population density gap, MUD gap, and SFD gap, including state fixed effects. Interpret signs: positive coefficients imply larger gaps associated with greater disparities in the predictor variable. Ethics and validation: Data Axle income estimates are adjusted to Census distributions; validation via correlation with ACS at ZIP code level. IRB exemption (#2176750-1). Code available on GitHub; sharable data provided via repository and upon request.

- National associations: Across all racial/ethnic groups and in both urban and rural areas, higher income is associated with better accessibility (shorter distance) to public charging. Rural households, on average, have worse accessibility than urban households. - Racial patterns nationally: Conditioning on income, Black households have the least accessibility in rural areas; in urban areas, Black households are the second least accessible group, slightly better than White households on national averages. - Additional stations: In urban areas, the lowest-income households show greater percentage increases in distance from the (N−1)th to Nth nearest station than the highest-income households across all racial/ethnic groups. Similar trends hold for Asian and Hispanic households in rural areas, indicating compounding disadvantage in accessing multiple nearby stations. - County-level income gaps: 53% of rural counties show LMI households traveling farther than non-LMI to reach charging; when gaps exist, rural LMI households travel on average 2.92 km more. In urban areas, 35% of counties show LMI gaps; in gap counties, LMI households travel 1.73 km more on average, while in counties without gaps LMI travel less by 1.55 km, consistent with lower-income residents being closer to city centers. - County-level racial gaps: In 49% of rural counties, Black households must travel 2.30 km more on average than White households. In urban areas, about 34% of counties show Black–White gaps; when gaps occur, Black households travel 1.20 km more on average, versus 1.10 km more when White households are disadvantaged, indicating slightly larger disadvantage magnitudes for Black households. - Predictive importance (LASSO): In rural areas, income is generally the stronger predictor of accessibility than race—e.g., for rural SFD residents, income dominates in 48 of 50 states. For rural MUD residents, race becomes more influential than income in 7 of 50 states. In urban areas, race grows in importance, especially for MUD residents, with 12 of 51 states showing race (being Black) as a stronger positive predictor of longer distances (worse access). These results persist across DP2–DP5. - Mechanisms (county OLS): The highway gap is the primary contributor to both income- and race-based accessibility gaps, followed by income gap, with poverty gap also important for racial gaps. Poverty gap effects differ by geography: larger poverty gaps increase racial accessibility gaps in rural settings but decrease them in urban settings. MUD and SFD gaps have opposite signs between rural and urban contexts, likely reflecting siting patterns of public chargers near MUDs in urban areas. - State vs county aggregation: State-level summaries can obscure within-state heterogeneity, understating or overstating gaps compared to county-level analyses, which reveal more nuanced patterns requiring tailored interventions.

Public charging is crucial for households without reliable home charging, notably renters and residents of multi-unit dwellings (MUDs), and for enabling long-distance travel. Disparities in home charging access are pronounced: 68% of Black populations live in SFD units versus 81% of White populations; only 71% of low-income populations own homes versus 90% of high-income; 63% of Black populations are renters versus 30% of White populations. These structural differences compound public charging needs for disadvantaged groups. The findings show that income and race both shape access, with income more dominant in rural and single-family dwelling contexts and race more influential in urban and MUD contexts. County-level heterogeneity indicates that national and state averages may mask local inequities; thus, equity policies should be locally tailored. Mechanism analysis highlights proximity to highways, income disparities, poverty, and housing type distribution as key correlates of accessibility gaps, suggesting levers for siting and investment decisions. Policy reviews reveal limited explicit equity-focused regulations (26 across 16 states), while incentives are more common; current frameworks often emphasize deployment volume over equitable distribution. Aligning with the Equitable and Transformative Investment Framework, targeted siting in disadvantaged communities, especially urban MUD areas and rural locales with large highway and income gaps, is essential. Given projections that 42% of new charging by 2030 will be in MUDs, strategic allocation can help close observed gaps, but additional measures are needed to ensure that low-income and underrepresented communities benefit equitably.

This study provides a national, micro-level assessment of equity in access to public EV infrastructure, revealing that lower-income households consistently face worse accessibility in both urban and rural areas, while racial disparities vary by geography and dwelling type—race being more influential in urban/MUD contexts and income in rural/SFD contexts. County-level analyses uncover substantial heterogeneity that state-level summaries can obscure. Mechanism analyses point to highway proximity disparities, income and poverty gaps, and housing-type distributions as key correlates of accessibility gaps. These insights support targeted, locally tailored policies—such as prioritizing charger deployment near disadvantaged MUD neighborhoods and rural areas with large highway and income gaps—to advance equitable access and support 2030 EV transition goals. Future work should incorporate additional accessibility dimensions beyond distance (e.g., congestion, reliability, availability, safety, pricing), and further disentangle the interplay between race and income to better inform equitable infrastructure planning.

- Accessibility measured primarily as travel distance from residence to nearest public charging stations; other dimensions (e.g., congestion, reliability, availability, safety, pricing, workplace access) are not captured and may affect true accessibility. - Potential confounding between race and income: these factors are correlated. County-level regression indicates income gap significantly influences racial accessibility gaps, suggesting caution in causal interpretation. - Urban/rural classification uses 2010 Census definitions mapped to ZIP codes; spatial classification nuances may affect local categorizations. - Public station data and household datasets, while comprehensive and validated, may contain measurement error; the raw household microdata are proprietary (Data Axle) and cannot be shared, limiting external replication at the micro level (though code and sharable aggregates are provided).