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California's zero-emission vehicle adoption brings air quality benefits yet equity gaps persist

Environmental Studies and Forestry

California's zero-emission vehicle adoption brings air quality benefits yet equity gaps persist

Q. Yu, B. Y. He, et al.

This study uncovers significant disparities in zero-emission vehicle (ZEV) ownership in California, particularly between disadvantaged communities and their counterparts. Conducted by Qiao Yu, Brian Yueshuai He, Jiaqi Ma, and Yifang Zhu, the research highlights the urgent need for targeted policies to alleviate pollution burdens, especially for racial and ethnic minorities, paving the way for a cleaner future.

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Playback language: English
Introduction
California's transportation sector contributes significantly to greenhouse gas (GHG) emissions and air pollution, particularly diesel particulate matter (PM). Zero-emission vehicles (ZEVs), including battery electric, plug-in hybrid, and hydrogen fuel cell vehicles, offer a key climate mitigation strategy and promise health co-benefits by reducing traffic-related air pollution (TRAP). Executive Order N-79-20 mandates that all new passenger vehicles sold in California be ZEVs by 2035, a goal aligned with similar initiatives globally. Despite past air quality improvements, many metropolitan areas and disadvantaged communities (DACs) in California still experience poor air quality. DACs, as defined by Senate Bill 535 and based on CalEnviroScreen, are communities disproportionately affected by pollution, often with high percentages of racial and ethnic minorities. These communities often reside near transportation infrastructure, leading to higher exposure to TRAP from a higher proportion of medium- and heavy-duty trucks and older vehicles. This study addresses the critical need to assess the equitable distribution of ZEV benefits and associated air quality improvements through an environmental justice lens, aiming to inform future ZEV policies and prevent exacerbation of health disparities among DAC residents.
Literature Review
Prior research has quantified the ambient air quality and health benefits of ZEV adoption at broader scales, showing significant potential for reduced premature deaths and substantial health cost savings from widespread electrification. Studies focused on California have demonstrated PM2.5 reductions from vehicle electrification, but these analyses often lacked a community-level, environmental justice focus. The existing literature highlighting disparities in EV ownership among racial and ethnic groups despite income, and the unequal distribution of ZEV incentives across communities, necessitates a granular analysis of near-roadway air quality benefits to understand the distributive equity of ZEV adoption and its impact on DAC residents.
Methodology
This study analyzed historical ZEV adoption trends in California DACs and non-DACs from 2015-2020, focusing on the Greater Los Angeles area due to its large ZEV population. A detailed near-roadway air quality analysis was conducted using ZEV adoption data for 2020 and projections for 2035. The methodology involved creating an integrated transportation model incorporating ZEV ownership data, household travel demand, and transportation supply data to predict ZEV trips (electric vehicle miles traveled, or eVMT) at the roadway link level. The model also considers the spatial distribution of different racial and ethnic groups. Emission changes associated with modeled eVMT were then calculated and a dispersion model (R-LINE V1.2) was used to project PM2.5 and NOx concentrations at the census tract level. The SB 535 DAC designation, based on CalEnviroScreen 4.0, was used to identify disadvantaged communities, considering pollution burden, socioeconomic factors, and population characteristics. The study used data from the CARB Fleet Database, EMFAC2021, the Southern California Association of Governments (SCAG) 2020 and 2035 regional travel models, and the Multi-Agent Transport Simulation (MATSim) for traffic simulation. Logistic growth models were used to project ZEV adoption in 2035. Spatial analysis was conducted using ArcGIS and QGIS.
Key Findings
The study revealed persistent disparities in ZEV ownership between 2015 and 2020. Lorenz curves illustrated that while overall ZEV ownership increased, the most disadvantaged communities consistently held a smaller share of ZEVs compared to their population share, particularly for battery electric vehicles (BEVs). Spatial analysis showed that most ZEV ownership increases occurred in non-DACs, primarily in coastal Southern California and the Bay Area. In Los Angeles County, the white population held a disproportionately large share of ZEV ownership (45%) and eVMT (31%) compared to their population share (26%), with similar disparities observed among other racial and ethnic groups, regardless of DAC designation. While DAC residents received 40% more pollutant reduction than non-DACs in 2020 due to inter-community ZEV trips, they remained disproportionately exposed to higher levels of TRAP. The analysis projected that by 2035, with increased ZEV adoption, the exposure disparity would narrow, but not eliminate. In 2020, DACs owned only 18% of ZEVs but accounted for 43% of eVMT, showing cross-community ZEV travel. The simulated reductions in traffic-emitted PM2.5 and NOx were greater in DACs in both 2020 and 2035 scenarios, but the relative reductions (compared to baseline levels) remained smaller in DACs due to higher baseline pollutant concentrations. Spatial maps illustrated higher NOx and PM2.5 reduction percentages in areas with higher ZEV traffic volumes.
Discussion
The findings demonstrate that while ZEV adoption brings overall air quality benefits, significant equity gaps persist. Disparities in ZEV ownership and continued higher exposure to TRAP in DACs highlight the need for targeted interventions beyond universal incentive programs. The observed cross-community travel of ZEVs suggests that ZEV adoption in wealthier areas can indirectly benefit DACs, but this effect is insufficient to address the existing inequalities. The lower relative air quality benefits in DACs are likely due to factors such as vehicle start emissions, contributions of medium- and heavy-duty vehicles to TRAP in DACs, and the current predominance of light-duty ZEVs. The study's bottom-up approach, focusing on ZEV trip routes, offers a novel perspective on the spatial distribution of air quality benefits, contrasting with previous top-down analyses. The persistent disparities, despite existing ZEV incentive programs, point to the need for more targeted programs to overcome barriers to ZEV adoption in DACs and among racial and ethnic minorities. Addressing historical injustices in land-use and transportation policies is crucial for achieving a just transition.
Conclusion
This study demonstrates that while California's ZEV adoption brings significant air quality benefits, it exacerbates existing environmental injustices. Disparities in ZEV ownership and continued high TRAP exposure in DACs remain concerning. Targeted policies focusing on equitable ZEV distribution, particularly for trucks, and addressing non-tailpipe emissions are essential for achieving environmental justice in the transition to clean transportation. Future research should refine ZEV trip prediction models, incorporating individual-level data to improve accuracy. Further investigation into the cumulative impacts of socioeconomic, environmental, and health factors within DACs is also needed.
Limitations
The study has limitations. Precise prediction of individual ZEV trips was challenging due to data gaps, leading to random selection of ZEV trips from each census tract. The assumption of equal vehicle miles traveled (VMT) for ICEVs and ZEVs may not fully capture the reality of driving patterns. The near-roadway air quality benefits are initially small due to the low ZEV penetration rate in 2020, although they increase significantly by 2035. Upstream emissions from electricity generation were not fully considered, but these are anticipated to have a minimal impact on near-roadway TRAP. Future studies can overcome these limitations through more comprehensive data collection, enhanced model calibration, and more detailed analysis of individual driving behavior.
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