Emissions redistribution and environmental justice implications of California’s clean vehicle rebate project

Jaye Mejía-Duwan; Miyuki Hino; Katharine J. Mach

doi:10.1371/journal.pclm.0000183

Abstract

Vehicle electrification is expected to reduce, in aggregate, emissions of greenhouse gases and criteria air pollutants. However, increased electricity generation to support new electric vehicles introduces possible redistribution of point-source emissions from mobile vehicles to electric generating units such that emissions may decrease in some locations and increase in others, with implications for equity. The potential for vehicle electrification to thereby shift the spatial distribution of air-pollution burdens has been previously noted, but analyses have yet to evaluate specific implemented climate policies. Here, we develop a model to analyze the implications of California’s Clean Vehicle Rebate Project (CVRP) for emissions of greenhouse gases and criteria air pollutants, both in aggregate and in their distribution. Analyzing rebates for 2010–2021, we find that the CVRP reduced aggregate statewide emissions of CO₂, NO_X, and SO₂ and increased aggregate statewide emissions of primary PM_2.5. Furthermore, changes in air pollution are not distributed equally: our results indicate that, as a result of the CVRP, net primary PM_2.5, NO_X, and SO₂ emissions reductions disproportionately occur in Least Disadvantaged Communities, as compared to Disadvantaged Communities, with community disadvantage defined according to CalEnviroScreen 4.0 per California legislation. If the current spatial distribution of electric vehicle rebates remains unchanged, we project that these inequities will continue through the state’s legislative goal of 1.5 million zero-emission vehicles on California roadways by 2025, even with increased cleanliness of the electricity sources for new vehicles. Increased uptake of electric vehicles in communities facing the highest air pollution exposure, along with accelerated clean-energy generation, could ameliorate associated environmental inequities.

Citation: Mejía-Duwan J, Hino M, Mach KJ (2023) Emissions redistribution and environmental justice implications of California’s clean vehicle rebate project. PLOS Clim 2(5): e0000183. https://doi.org/10.1371/journal.pclm.0000183

Editor: Alex Y. Lo, Victoria University of Wellington, NEW ZEALAND

Received: July 13, 2022; Accepted: February 27, 2023; Published: May 3, 2023

Copyright: © 2023 Mejía-Duwan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data needed to evaluate the conclusions in the paper are present in the paper and the supporting information. Processed datasets and the code for analyses performed can be accessed through the DRYAD Digital Repository (https://doi.org/10.6078/D1Q138).

Funding: Funding was provided through the SURGE Research Fellowship, Stanford University School of Earth, Energy & Environmental Sciences (JMD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AVERT, Avoided Emissions and Generation Tool; BEV, Battery Electric Vehicle; CES 4.0, CalEnviroScreen 4.0; CARB, California Air Resources Board; CVRP, Clean Vehicle Rebate Project; DACs, Disadvantaged Communities; EDRF, Electric Demand Reduction Factor; EGUs, Electric Generating Units; EMFAC, Emissions Factors 2021 Database; EV, Electric Vehicles; LDACs, Least Disadvantaged Communities; PHEV, Plug-in-Hybrid Electric Vehicle; PM_2.5, fine particulate matter under 2.5 μm in diameter

1. Introduction

National and subnational governments are implementing policies to reduce greenhouse gas emissions as a key mechanism for mitigating anthropogenic climate change [1]. These policies provide numerous co-benefits, including public health co-benefits from improved air quality, which can increase their political and public support [2]. Co-benefits are relevant not just in aggregate, but also in their distribution, as the costs and benefits of climate policy may be distributed unequally across different socioeconomic and demographic groups with implications for equity [3]. While many researchers and government agencies have quantified the aggregate benefits of California’s cap-and-trade program and other California climate policies in reducing greenhouse gas emissions, few have studied the distributional impacts of these policies, even though they have the potential to exacerbate the inequitable distribution of health-damaging air pollutants co-emitted with greenhouse gases across the state [3]. Given increasing attention to the environmental justice implications of climate policy, the distributional impacts of existing climate policies are important to evaluate [4].

Here, we analyze the air pollution implications of an existing climate policy in California, in aggregate and in their spatial distribution. The California state government has strong commitments to greenhouse gas reductions, air quality improvements, and environmental justice; it additionally collects and reports high-quality data relevant to the outcomes of its climate and environmental programs [5]. For these reasons, California presents an opportunity for in-depth analysis of the aggregate and distributional effects of climate policy.

In particular, the California Clean Vehicle Rebate Project (CVRP) provides an attractive case for analysis, given such detailed reporting data, statewide prioritization of vehicle electrification, and the expected rapid growth of the program [6]. For greenhouse gas and criteria air pollutant emissions, we model the aggregate and distributional consequences of the program to date and under scenarios of future deployment. For both current and future scenarios, our analysis explores (1) the distribution of CVRP rebates in relation to socioeconomic and demographic factors, (2) the aggregate benefits of the CVRP for reductions in greenhouse gas and criteria air pollutant emissions, and (3) the distributional impacts of the CVRP, with implications for changes in criteria air pollutant exposure for California’s disadvantaged and non-disadvantaged communities.

2. Background

2.1. Vehicle electrification, CO₂ emissions, and criteria air pollutants

Large-scale vehicle electrification has the potential to significantly reduce greenhouse gas emissions as well as emissions of other pollutants known to negatively affect health [7,8]. In California, on-road motor vehicles are estimated to be responsible for 32.6% of statewide anthropogenic NO_X emissions, 5.62% of statewide anthropogenic SO_X emissions, and 6.97% of statewide anthropogenic PM_2.5 (fine particulate matter under 2.5 μm in diameter) emissions for the year 2021 [9]. Differential exposure to air pollution and air toxics in Southern California results primarily from spatial patterns in transportation and other small-area sources [10].

California has been previously identified as a location where electric vehicles provide net public health co-benefits as the result of air quality improvements, largely due to the widespread use of renewable energy sources and the limited contribution of coal to the energy mix [11]. However, due to the redistribution of emissions from vehicles to electric generating units (EGUs), vehicle electrification may lead to heterogeneous local net emissions changes [12]. Local factors related to regional electricity generation, the spatial distribution of electric vehicle uptake, and the temporal distribution of vehicle charging strongly impact local net changes in ambient air quality [12,13].

Aggregate increases in PM_2.5 emissions as the result of vehicle electrification are possible because vehicle electrification only minimally reduces vehicular PM_2.5 emissions, while at the same time EGUs meeting increased electric demand may increase their production of PM_2.5 emissions. A literature review found that electric vehicles (EVs) provide vehicular PM_2.5 emissions reductions of only 1–3% in comparison to internal combustion engine vehicles despite their lack of exhaust emissions, largely due to the heavier weight of electric vehicles which increases vehicular PM_2.5 emissions stemming from non-exhaust sources such as brake wear, tire wear, road wear, and particulate resuspension [14]. Heavier EVs, equipped with large battery packs to support a driving range in excess of 300 miles, actually have total vehicular PM_2.5 emissions that are 3–8% higher than internal combustion engine vehicles [15]. A study of vehicle electrification in Barcelona and Madrid found that 40% vehicle electrification yielded only a 5–7% reduction in PM_2.5 emissions [16]. In contrast, a study of 34 Chinese cities found that vehicle electrification resulted in an average increase of PM_2.5 emissions by a factor of 19, largely due to the high contribution of coal to the Chinese electric grid [17]. Furthermore, vehicle electrification has the potential to shift PM_2.5 emissions burdens from higher-income to lower-income communities, as lower-income communities are disproportionately impacted by EGU emissions while higher-income communities are disproportionately more likely to experience local air quality improvements resulting from higher EV uptake [18].

2.2. California’s clean vehicle rebate project

Vehicle electrification is central to California’s efforts to reduce greenhouse gas emissions: California dedicated 17.2%, or $238 million, of the state’s 2019–2020 Greenhouse Gas Reduction Fund discretionary spending to the CVRP [19]. Established in 2010, the CVRP has provided over 400,000 rebates for the purchase or lease of battery electric vehicles, plug-in-hybrid electric vehicles, fuel cell electric vehicles, and other EVs such as electric motorcycles [20]. Rebates range in value up to $7000, depending on the vehicle category and the applicant’s income level. The program is expected to continue growing rapidly: in June 2021, the CVRP expected to deploy $967 million worth of funding to provide 323,000 additional vehicle rebates over the next three years through June 2024 [6].

Rebates through the CVRP are expected to improve local air quality in and surrounding the census tracts in which the rebates are issued. However, as illustrated in existing literature, significant disparities exist in the distribution of electric vehicle rebates, largely related to demographic and socioeconomic factors [21,22].

2.3. Environmental Justice in California

California state law requires that environmental policies explicitly consider, address, and prevent the exacerbation of the inequitable distribution of environmental pollution burdens [5]. California government policy employs the California Communities Environmental Health Screening Tool to define Disadvantaged Communities. The most recent edition of the California Communities Environmental Health Screening Tool is the CalEnviroScreen 4.0 (CES 4.0) [23]. CES 4.0 accounts for 20 indicators related to environmental exposures and socioeconomic factors to rank California census tracts by their exposure and vulnerability to environmental pollution [24]. Higher CES 4.0 scores indicate increasing levels of environmental disadvantage, with the top 25% highest-scoring census tracts being considered "Disadvantaged Communities." Our model takes census tracts as the unit of analysis in order to investigate the relationship between community-level indicators of environmental disadvantage and estimated changes in net local emissions.

In California, as in many other locations, low-income people, people of color, and members of other marginalized groups disproportionately suffer from exposure to air pollution [25,26]. Members of these groups disproportionately live in Disadvantaged Communities, which account for only one quarter of the California state population but contain 50% of California natural gas power plants [27]. Low-income and minority residents in Southern California are additionally exposed to over twice the average traffic density for the region, significantly increasing exposure to criteria air pollutants from vehicular emissions [28]. Not only are these communities exposed to higher average levels of criteria air pollutant emissions, but they are additionally more vulnerable to the detrimental health impacts of air pollutant emissions due to greater prevalence of underlying chronic health issues, lower access to quality healthcare, and political marginalization [29]. The public health impacts of increased exposure to criteria air pollutants range from elevated cancer risks to diminished school performance for affected children [30,31]. Long-term exposure to PM_2.5 has been repeatedly found to increase the incidences of cancer, cardiovascular disease, diabetes, respiratory illness, and premature death [32].

3. Methods

Drawing from government databases and publicly available modeling platforms, we designed a model to estimate the aggregate effects of the CVRP on greenhouse gas and criteria air pollutant emissions, as well as the expected net change in local criteria air pollutant emissions for each of California’s 8057 census tracts. Fig 1 illustrates the analytical model designed for this study, which compares the expected emissions reductions from replacing conventional vehicles with EVs to the modeled increase in emissions generated by EGUs required to supply the increased electricity demand resulting from these new EVs (Fig 1). This model was run for 4 scenarios, as summarized in Table 1, including 1 current scenario and 3 future scenarios (Table 1).

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Fig 1. The air pollution implications of electric vehicle rebates were modeled according to this schematic.

Blue boxes represent data inputs and model parameters, including the current Clean Vehicle Rebate Project rebate distribution, the EMFAC (Emission Factors) model, and the Clean Vehicle Rebate Project consumer survey [20,33,34]. Yellow boxes represent calculated values, with the calculations described correspondingly in the methods section. Italicized text summarizes the calculations performed to convert blue inputs into yellow calculated values. The model was used to evaluate emissions changes under 4 scenarios (Table 1).

https://doi.org/10.1371/journal.pclm.0000183.g001

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Table 1. The clean vehicle rebate project was evaluated under four scenarios, including one current and three possible future scenarios.

https://doi.org/10.1371/journal.pclm.0000183.t001

For the 3 future scenarios, it is expected that electric vehicles will decreasingly rely on current fossil-fuel-based EGUs, either through an increase in off-grid renewable (such as solar) charging or through an increase in the portion of the grid composed of renewable energy sources. This expected trend was accounted for with the Electric Demand Reduction Factor (EDRF). Higher EDRF values correspond to a greater expected increase in the cleanliness of the electric supply for new electric vehicles. The EDRF is further explained in 3.2.1 Change in Electrical Load, below.

Further, for the future scenarios, the current CVRP rebate distribution from program inception to the most recent data available (application dates ranging from 3/18/2010 to 1/31/2021) was scaled upwards to achieve 1,500,000 total rebates to approximate fulfillment of the mandated state policy milestone to achieve 1.5 million zero-emission vehicles on California roadways by 2025 [35]. This goal exists in advance of an additional goal for 5 million zero-emission vehicles on California roadways by 2030 [36]. We analyze the 2025 goal, given the higher reliability of modeled changes in emissions rates over this time frame. While the number of rebates issued through the CVRP does not correspond exactly to the number of new electric vehicles in the state, as not all electric vehicles are eligible for rebates and only approximately 74% of eligible vehicle owners apply to and are approved by the program, we focused on the number of CVRP rebates issued to specifically evaluate the impact of one existing government policy [37].

[20,33,34]

The current scenario used the present-day 2020 Clean Vehicle Rebate Project rebate distribution, with application dates ranging from 3/18/2010 to 1/31/2021, while future Scenarios 1–3 used this same rebate distribution scaled proportionally by census tract to achieve 1.5 million total rebates [20]. The EMFAC (Emissions Factors) model projects vehicle emissions factors for 2020 (current scenario) and 2025 (future scenarios) [33]. For the current scenario, calculated electric vehicle electric demand is offset by currently curtailed solar and wind energy production as reported by CAISO (California Independent System Operator) based on hourly 2020 data [38]. For future scenarios, it is expected that electric vehicles will decreasingly rely on current fossil-fuel-based electric generating units, either through an increase in off-grid renewable (such as solar) charging, or through an increase in the portion of the grid composed of renewable energy sources. This expected trend was accounted for with the Electric Demand Reduction Factor (EDRF). Higher EDRF values correspond to a greater expected increase in the cleanliness of the electric supply for new electric vehicles.

The model was run in RStudio (version 2021.09.0+351). All code files used in the analysis are included in the DRYAD Digital Repository (https://doi.org/10.6078/D1Q138) [39]. Missing data was excluded from calculations and figures.

3.1. Calculating vehicle emissions avoided due to rebates

3.1.1. Modified rebate distribution.

The CVRP reports the spatial distribution, designated by census tract, of all rebates with application dates ranging from 3/18/2010 to 1/31/2021 [20]. Because vehicles cross census tract boundaries, the level of local pollution emitted from vehicles is affected by the number of rebates issued within each tract as well as the number of rebates issued to nearby tracts. We estimated a modified rebate distribution to account for these spatial spillovers. Our analysis includes CVRP rebates for battery electric vehicles (BEVs) and plug-in-hybrid electric vehicles (PHEVs), for which more detailed emissions data are available; these two categories account for 97.8% of all CVRP rebates [20].

The number of redistributed rebates for each vehicle type (v) apportioned to a census tract (t) was calculated by averaging the number of rebates allocated to tracts within a variable radius (r) of the tract. Radii of 3 kilometers, 8 kilometers, or 15 kilometers were used, with 15 kilometers representing the largest typical commute distance among large metro areas in the state of California [40]. Census tracts vary in shape and size, with a few extremely large outliers (median: 1.9 km²; range: 0–18,106.9 km²; 90^th percentile: 28.3 km²). In comparison, a radius of 8 km corresponds to a circle of area 201.1 km². The number of allocated rebates is based on the current CVRP rebate distribution or the scaled CVRP rebate distribution depending on model scenario (k) as described in Table 1.

Eq 1

The number of scaled redistributed rebates apportioned to a census tract was calculated by normalizing the number of redistributed rebates to the total rebates initially distributed across the state: Eq 2

The resulting modified rebate distribution now accounts for vehicle movement between census tracts while maintaining the original total number of rebates under the corresponding scenario.

3.1.2. Change in local vehicle populations.

Although each rebate results from the purchase or lease of one new EV, recipients may use rebates to replace an existing electric or fossil-fuel vehicle, or they may use rebates to acquire an additional vehicle while maintaining the use of other vehicles. The distribution of rebates therefore corresponds in different ways to the changes in four local vehicle populations: BEVs, PHEVs, diesel vehicles, and gasoline vehicles. The CVRP consumer survey, which all approved applicants are invited to complete, reports the rates at which BEV and PHEV applicants replace existing vehicles, and the proportion of replaced vehicles that were BEVs, PHEVs, diesel vehicles, and gasoline vehicles, with gasoline vehicles including conventional hybrids [34]. Results from the most recent edition of this survey, for rebates issued in 2016 and 2017, were used to convert the modified distribution of BEV and PHEV rebates into the corresponding changes in these four vehicle populations, with conversion factors provided in the supporting information (S1 Table). The household addition of a new vehicle is assumed not to impact usage of existing vehicles.

3.1.3. Change in local vehicular emissions.

The California Environmental Protection Agency’s EMFAC (Emissions Factors) 2021 database provides yearly emissions estimates for BEV, PHEV, diesel, and gasoline vehicles and has been widely utilized as a source of California emission standards in the quantification of benefits from vehicle electrification programs [11,33]. EMFAC projections for 2020 and 2025 were employed, with annual statewide emissions rates retrieved for LDA (passenger) vehicles, corresponding to the type of vehicle eligible for the CVRP, with aggregated speeds, seasons, and model years. Unlike the EMFAC 2017 database, the EMFAC 2021 database considers regenerative braking for electric vehicles. EMFAC emissions factors for SO_X were assumed to equal emissions factors for SO₂ for comparison with emissions from EGUs, based on an understanding that SO₂ emissions constitute roughly 98% of anthropogenic SO_X emissions [41].

These EMFAC emissions factors were then multiplied by the local change in the vehicle populations of BEVs, PHEVs, diesel vehicles, and gasoline vehicles (see Change in Local Vehicle Populations, above). The change in emissions from each of these four vehicle categories was summed to calculate the total modeled change in local vehicular emissions for each census tract for each model scenario. Our model averages vehicle usage over nearby census tracts and does not account for the type and density of roads or implications for transit patterns.

3.2. Calculating emissions from power plants

The change in emissions from existing power plants depends on their emissions intensity, additional demand from new electric vehicles, and the contribution of new electricity generation sources, such as off-grid solar or new renewable sources. First, we estimated the total additional demand created by new EVs using EMFAC values. Then, the additional demand was modified to isolate only the share of the electric load that would be sourced from current fossil fuel power plants, using data on currently curtailed wind and solar data for the current scenario and a variable Electric Demand Reduction Factor (EDRF) for future scenarios. Finally, this modified demand was input into the Avoided Emissions and Generation Tool (AVERT), a United States Environmental Protection Agency policy-analysis model that projects the changes in annual CO₂, primary PM_2.5, NO_X, and SO₂ emissions at each EGU that would result from a given change in electricity demand [42,43]. AVERT registers a warning when inputted electric demand changes exceed 15% of the original value; among model runs, a maximum of 2.1% of modeled hours exceeded this threshold, with the highest inputted change equalling 24.5%.

3.2.1. Change in electrical load.

We estimate the total annual demand from CVRP-subsidized EVs by summing the change in local vehicle populations of BEVs and PHEVs across all census tracts to determine the expected statewide change in BEV and PHEV populations and then multiplying by the average annual electricity consumption of BEVs and PHEVs as reported by the EMFAC 2021 database.

Eq 3

However, not all of this increased demand will be met by current fossil fuel power plants. Two adjustments were implemented to account for greater contribution from on- and off-grid renewables:

Eq 4

For the current scenario, calculated EV electric demand is offset by solar and wind generation that is currently curtailed. The California Independent Service Operator (CAISO) provides hourly solar and wind curtailment data for 2020 [38]. For the current scenario, hourly reports of curtailed wind and solar supply were subtracted from the hourly electric demand incurred by new electric vehicles.

For future scenarios, it is expected that electric vehicles will decreasingly rely on current fossil-fuel-based EGUs, either through an increase in off-grid renewable (such as rooftop solar) charging, or through an increase in the portion of the grid composed of renewable energy sources. This expected trend was accounted for with the Electric Demand Reduction Factor (EDRF). A factor of 0.7, for example, represents a projected future scenario where only 30% of the calculated electric demand added from 1.5 million vehicle rebates is sourced from the 231 California EGUs currently catalogued by the 2020 AVERT model. In this scenario, the remaining 70% of the calculated electric demand would come from currently curtailed renewable sources, increased renewable energy supply to the California electric grid, or additional off-grid renewable charging infrastructure such as rooftop solar. Higher EDRFs correspond to an expected increase in the cleanliness of the electric supply for new EVs. Our model employs EDRFs of 0.5, 0.7, and 0.9, with 0.5 corresponding to the legislative requirement of California’s 2018 Senate Bill 100 (De León) that California achieve 50% renewable energy generation by 2026, and higher factors incorporating additional off-grid renewable energy sources [44]. Increased cleanliness of the electric grid is possible both through the addition of on-grid and off-grid renewable energy sources as well as through declining emissions intensities of existing power plants. While future changes in emissions intensities will vary at the level of individual EGUs, we use the EDRF as a simplifying factor to capture average overall changes to the future cleanliness of the California electricity grid.

3.2.2. Emissions changes from electricity generating units.

The modified demand, representing only the demand to be sourced from existing fossil fuel power plants, was input into the AVERT model to estimate EGU emissions. AVERT is an electricity dispatch model created by the EPA. The AVERT California sub-model is based on historical data for the year 2020 from 231 EGUs in California. It projects changes in annual CO₂, primary PM_2.5, NO_X, and SO₂ emissions at each EGU that would result from a given change in requested hourly electricity demand [42,43]. Power plants may be composed of several EGUs, which are each catalogued separately in the AVERT model. The additional load calculated above was spread evenly over three charging time frames: over 24 hours each day, from 7am to 7pm to reflect daytime charging, or from 7pm to 7am to reflect nighttime charging.

3.2.3. Distribution of power plant emissions.

Previous empirical work on the public health impacts of California oil and gas plants has determined residents living within 5 kilometers of a plant to be highly exposed to plant emissions and their potential impacts, those living between 5 and 10 kilometers from a plant to be less exposed, and those living between 10 and 20 kilometers from a plant to be minimally exposed [45]. We follow this model to identify census tracts that intersect a circle with radius of 10 kilometers centered on an EGU as being exposed, with values of 5 kilometers and 20 kilometers used for sensitivity analysis. For each EGU, the modeled change in criteria air pollutant emissions was assigned to all exposed census tracts, weighted by each tract’s total land area, with land area sourced from 2010 Census Tiger shapefiles [46]. The annual emissions from each EGU (e) are distributed to all census tracts (t) that intersect a circle with radius (r) centered on the EGU (e), weighted by land area: Eq 5

The total emissions for each census tract are then determined by summing the emissions from all EGUs to which the tract is exposed: Eq 6

3.3. Estimating changes in secondary PM_2.5 emissions

Our model investigates net changes in primary PM_2.5 emissions as well as secondary PM_2.5 precursor emissions, including NO_X and SO₂. In 2008, the EPA proposed a set of "offset ratios" that enable the conversion of NO_X and SO₂ emissions into secondary PM_2.5 emissions, based on an analysis of broad regions of the United States and nine urban areas [47]. For the Western United States, these ratios are 40:1 (SO₂:PM_2.5) and 100:1 (NO_X:PM_2.5) [47]. In 2011, the EPA revised this methodology, indicating that the original modeling analysis remained valid but that these ratios must be accompanied by a technical demonstration of their applicability to the relevant geographic region [48]. For example, a 2012 analysis of Plant Washington in Washington County, Georgia calculated local near-source ratios of 40:1 (SO₂:PM_2.5) and 85:1 (NO_X:PM_2.5), which are largely consistent with the ratios initially proposed by the EPA [49]. The offset ratio method maintains advantages in its simplicity and applicability to policy analysis but does not fully account for variations across time and space [50].

4. Results

We refer to two groupings of census tracts based on their CES 4.0 and sub-indicator percentile scores. In line with California legislative policy, "Disadvantaged Communities" refers to census tracts with CES 4.0 and sub-indicator scores in the top quartile of statewide census tract scores. We additionally refer to census tracts scoring in the bottom quartile as "Least Disadvantaged Communities." These designations refer simply to each census tract’s percentile ranking of CES 4.0 and sub-indicator scores, and they do not imply any other judgment of community privilege. The indicator CalEnviroScreen 4.0 Percentile refers to the percentile ranking of a census tract’s CES 4.0 score. The indicator Hispanic Population Percentile refers to the percentile ranking of a census tract’s Hispanic or Latino population percentage as measured in the 2019 American Community Survey. The indicator Less Than High School Education Percentile refers to the percentile ranking of the proportion of a census tract’s population over age 25 with less than a high school education. The indicator Poverty Percentile refers to the percentile ranking of a census tract’s population who have incomes falling below 200% of the federal poverty level. Across all four indicators, values above 75 indicate "Disadvantaged Communities." Census tracts without reported values for CES 4.0 or sub-indicator scores were excluded from the relevant analysis.

In the main results presented here, the model employed the 8 kilometer parameter for modifying the rebate distribution, the 10 kilometer parameter for determining exposure to emissions from power plants, and the 24-hour temporal charging distribution. The results of our sensitivity analysis with respect to these parameters are shown in the supporting information (S1 Fig) and are discussed in Sensitivity Analysis, below.

4.1. Distribution of rebates

Least Disadvantaged Communities received substantially more CVRP rebates per census tract than Disadvantaged Communities from program inception to the most recently reported data (application dates ranging from 3/18/2010 to 1/31/2021) (Fig 2). In relation to CES 4.0 percentile, Least Disadvantaged Communities received 74 (median, range: 0–1199) rebates per census tract while Disadvantaged Communities received 8 (median, range: 0–245) rebates per census tract over this time frame; 46.4% of rebates were received by Least Disadvantaged Communities while only 7.27% were received by Disadvantaged Communities. This finding holds across demographic and environmental health indicators: census tracts with a larger Hispanic population, higher population over 25 with less than a high school education, and higher poverty levels all received fewer rebates over this time frame. Census tracts in the lowest quartile of percent Hispanic population received 10.3 times as many rebates as tracts in the highest quartile (medians 72 and 7, respectively); census tracts in the lowest quartile of percent of the population over age 25 with less than a high school education received 11.6 times as many rebates as tracts in the highest quartile (medians 81 and 7, respectively); and census tracts in the lowest quartile of percent population in poverty received 14.8 times as many rebates as tracts in the highest quartile (medians 89 and 6, respectively).

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Fig 2. More disadvantaged communities have received fewer clean vehicle rebates.

Based on (a) CalEnviroScreen 4.0, (b) Hispanic population, (c) less than high school education, and (d) poverty indicators, communities of greater disadvantage have received fewer Clean Vehicle Rebate Project rebates from 3/18/2010 to 1/31/2021. Boxes show the median and extend to the 25^th and 75th percentiles; whiskers span 1.5 Interquartile Range (IQR). Notches approximate a 95% confidence interval for the median, extending to ± 1.58 * IQR / sqrt(n). Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 or sub-indicator percentile.

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The California Air Resources Board (CARB) has implemented numerous initiatives aimed at improving the proportion of EV rebates granted to marginalized communities. On March 29, 2016, new eligibility criteria were established for the CVRP that both instituted an income cap for the rebate program and increased the rebate value for low-income applicants; these criteria were further modified on November 1, 2016 [20]. However, these new criteria did not substantially alter the heterogeneous distribution of rebates. From 3/18/2010-10/31/2016, 5.91% of rebates and 52.2% of rebates were awarded to Disadvantaged Communities and Least Disadvantaged Communities, respectively. From 11/1/2016-1/31/2021, 8.25% of rebates and 42.3% of rebates were awarded to Disadvantaged Communities and Least Disadvantaged Communities, respectively. As a result, the relationship between CES 4.0 Percentile and Net Change in Local Primary PM_2.5 Emissions is minimally impacted by changes in CVRP eligibility requirements (S2 Fig).

In addition, EGUs are disproportionately located in Disadvantaged Communities (Fig 3). Of the 231 California EGUs catalogued by the 2020 AVERT model, 202 lie within census tracts for which a CES 4.0 score is reported. Of these 202 EGUs, 79 (39.1%) lie within Disadvantaged Communities, while only 4 (1.98%) lie within Least Disadvantaged Communities.

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Fig 3. In California, electricity generating units (EGUs) are disproportionately located in disadvantaged communities.

Panel (a) presents statewide data and panel (b) presents a regional example, centered on the city of Los Angeles in Southern California. Black circles represent the 231 EGUs in the state of California catalogued by the 2020 AVERT (Avoided Emissions and Generation Tool) model. Census tracts are divided into 4 quartiles based on CalEnviroScreen 4.0 (CES 4.0) percentile score, with higher CES 4.0 percentile scores corresponding to a darker background. Disadvantaged Communities (DACs) are defined as having CES 4.0 scores in the highest quartile for the state, while we define Least Disadvantaged Communities (LDACs) as having CES 4.0 scores in the lowest quartile. This map was produced in R using United States Census Bureau 2010 TIGER/Line Shapefiles [46], available from https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2010&layergroup=Census+Tracts.

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4.2. Aggregate and heterogeneous emissions impacts

Our model finds that the CVRP has yielded net statewide per-year emissions reductions of 280,000 tonnes CO₂, 4.44 tonnes SO₂, and 292 tonnes NO_X, and a net statewide per-year emissions increase of 8.89 tonnes primary PM_2.5 under the present-day scenario. These values reflect the difference between increased emissions from EGUs and avoided emissions from the replacement of conventional vehicles with EVs: for example, the CVRP has increased statewide per-year primary PM_2.5 emissions from EGUs by 13.4 tonnes and has reduced statewide per-year primary PM_2.5 vehicle emissions by 4.51 tonnes. Increasing CES 4.0 Percentile is correlated with lower reductions in local PM_2.5 emissions from vehicles as well as with greater PM_2.5 emissions from EGUs. Our analysis suggests that the disproportionate distribution of vehicle rebates has a substantially larger impact on the distribution of net changes in local PM_2.5 emissions in comparison to distance to EGUs (S3 Fig). We employ the EPA offset ratios described in 3.3: Estimating Changes in Secondary PM_2.5 Emissions to estimate the magnitude of changes in secondary PM_2.5 emissions as a result of the CVRP, with the understanding that these ratios are generalized approximations. Future research could use air chemistry and transport models to more precisely analyze changes in secondary PM_2.5 emissions across California.

Our model finds that the CVRP has yielded net statewide per-year emissions reductions of 4.44 tonnes SO₂ and 292 tonnes NO_X, and a net statewide per-year emissions increase of 8.89 tonnes primary PM_2.5 under the present-day scenario. Following the EPA offset ratios method [47], these SO₂ and NO_X emissions correspond to a net statewide per-year emissions decrease of 3.03 tonnes secondary PM_2.5 under the present-day scenario, based on the ratios of 40:1 (SO₂: PM_2.5) and 100:1 (NO_X: PM_2.5). This estimated decrease of 3.03 tonnes secondary PM_2.5 emissions is not large enough to offset the increase of 8.89 tonnes primary PM_2.5 emissions calculated by our model. As a result, it is likely that total net statewide per-year PM_2.5 emissions increase as a result of the CVRP.

In considering the distribution of emissions changes, Disadvantaged Communities, on average, have experienced smaller net reductions in primary PM_2.5 emissions as a result of the CVRP in comparison to Least Disadvantaged Communities (Fig 4). This trend holds across all four indicators analyzed: census tracts with higher CES 4.0 scores, larger Hispanic populations, higher populations over 25 with less than a high school education, and higher poverty levels all experience smaller net reductions in primary PM_2.5 emissions, on average. With respect to CES 4.0 score, Least Disadvantaged Communities experience 4.00 times greater median net reductions in primary PM_2.5 emissions (medians of -0.649 kg/year/tract and -0.162 kg/year/tract, respectively) compared to Disadvantaged Communities. 17.2% of Disadvantaged Communities experience net annual primary PM_2.5 emissions increases compared to only 8.27% of Least Disadvantaged Communities. While 85.8% of census tracts experience net reductions in primary PM_2.5 emissions, some census tracts experience such large increases in primary PM_2.5 emissions that the aggregate statewide change in net primary PM_2.5 emissions is positive. Census tracts that have net primary PM_2.5 emissions increases in excess of 1 kg/year/tract are not visible in Fig 4 below. Of these tracts, 29.7% are Disadvantaged Communities while 16.3% are Least Disadvantaged Communities.

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Fig 4. As a result of the clean vehicle rebate project, net local primary PM_2.5 emissions decrease more for least disadvantaged communities than for disadvantaged communities, as modeled under the present-day scenario.

Based on (a) CalEnviroScreen 4.0, (b) Hispanic population, (c) less than high school education, and (d) poverty indicators, as the level of community disadvantage increases moving to the right, the modeled change in annual primary PM_2.5 emissions per census tract increases, moving from more negative to less negative median values. Boxes show the median and extend ^to the 25^th and 75th percentiles; whiskers span 1.5 Interquartile Range (IQR). Notches approximate a 95% confidence interval for the median, extending to ± 1.58 * IQR / sqrt(n). The red horizontal line marks a net local emissions change of zero. Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 or sub-indicator percentile.

https://doi.org/10.1371/journal.pclm.0000183.g004

Disadvantaged Communities, on average, have experienced smaller net reductions in SO₂ and NO_X emissions as a result of the CVRP in comparison to Least Disadvantaged Communities (Fig 5). With respect to CES 4.0 score, Least Disadvantaged Communities experience 3.05 times greater median net reductions in SO₂ emissions (medians -0.881 kg/year/tract and -0.289 kg/year/tract, respectively) and 2.89 times greater median net reductions in NO_X emissions (medians -50.7 kg/year/tract and -17.5 kg/year/tract, respectively) compared to Disadvantaged Communities. 4.44% of Disadvantaged Communities experience net annual SO₂ emissions increases compared to only 1.41% of Least Disadvantaged Communities; 1.92% of Disadvantaged Communities experience net annual NO_X emissions increases compared to only 0.50% of Least Disadvantaged Communities.

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Fig 5. As a result of the clean vehicle rebate project, net local SO₂ and NO_X emissions decrease more for least disadvantaged communities than for disadvantaged communities, as modeled under the present-day scenario.

Based on the CalEnviroScreen 4.0 indicator, as the level of community disadvantage increases moving to the right, the modeled change in annual (a) SO₂ or (b) NO_X emissions per census tract increases, moving from more negative to less negative median values. Boxes show the median and extend to the 25^th and 75^th percentiles; whiskers span 1.5 Interquartile Range (IQR). Notches approximate a 95% confidence interval for the median, extending to ± 1.58 * IQR / sqrt(n). The red horizontal line marks a net local emissions change of zero. Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 percentile.

https://doi.org/10.1371/journal.pclm.0000183.g005

If the current rebate distribution is scaled proportionally per census tract to achieve 1.5 million total CVRP rebates by 2025, Disadvantaged Communities, on average, are expected to continue experiencing smaller reductions in net primary PM_2.5 emissions in comparison to Least Disadvantaged Communities as a result of the CVRP, as modeled under 3 future scenarios (Fig 6). This trend holds even with moderate to substantial increases in the utilization of zero-emissions sources to supply electricity for new electric vehicles (Electric Demand Reduction Factors of 50%, 70%, and 90%). However, the net change in primary PM_2.5 emissions is impacted by various Electric Demand Reduction Factors: under an EDRF of 0.5 (Future Scenario 1) the statewide increase in net primary PM_2.5 emissions is 113 tonnes, under an EDRF of 0.7 (Future Scenario 2) the statewide increase in net primary PM_2.5 emissions is 61.6 tonnes, and under an EDRF of 0.9 (Future Scenario 3) the statewide increase in net primary PM_2.5 emissions is 9.99 tonnes.

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Fig 6. Future scenarios with an unchanged rebate distribution maintain distributional inequities.

Under future model scenarios, net local primary PM_2.5 emissions reductions are disproportionately concentrated among Least Disadvantaged Communities, but increased utilization of renewable sources to supply electricity for new electric vehicles lowers the total net change in primary PM_2.5 emissions resulting from the Clean Vehicle Rebate Project. Based on the CalEnviroScreen 4.0 indicator, as the level of community disadvantage increases moving to the right, the modeled change in annual primary PM_2.5 emissions moves from more negative to less negative median values. Increasing the Electric Demand Reduction Factor from 0.5 (black, future scenario 1) to 0.7 (dark green, future scenario 2) to 0.9 (light green, future scenario 3) lowers total primary PM_2.5 emissions but leaves the distributional trend largely unchanged. The red horizontal line marks a net local emissions change of zero. Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 percentile, with points representing the median for each quantile group.

https://doi.org/10.1371/journal.pclm.0000183.g006

4.3. Charging time

Vehicle charging time had a significant impact on the net change in local primary PM_2.5 emissions. Charging spread evenly over a 24-hour period resulted in a net annual increase of 8.89 tonnes primary PM_2.5 as a result of the CVRP. Shifting charging times to fall evenly between the daytime hours of 7am and 7pm lowered the net increase in aggregate statewide annual primary PM_2.5 emissions to 0.76 tonnes, while shifting charging times to fall evenly between the nighttime hours of 7pm and 7am raised the net increase in aggregate statewide annual primary PM_2.5 emissions to 18.1 tonnes. Daytime charging resulted in a larger utilization of currently-curtailed wind and solar electricity generation to offset the increased demand required by new EVs. However, various temporal charging scenarios do not substantially alter the heterogenous distribution of net emissions changes (Fig 7).

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Fig 7. Despite impacting modeled aggregate primary PM_2.5 emissions changes as a result of the current clean vehicle rebate project scenario, different temporal charging distributions do not substantially alter the heterogenous distribution of net emissions changes.

Models were run according to (a) 24-hour charging distribution, (b) daytime charging from 7am to 7pm, and (c) nighttime charging from 7pm to 7am. Boxes show the median and extend to the 25^th and 75^th percentiles; whiskers span 1.5 Interquartile Range (IQR). Notches approximate a 95% confidence interval for the median, extending to ± 1.58 * IQR / sqrt(n). The red horizontal line marks a net local emissions change of zero. Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 percentile.

https://doi.org/10.1371/journal.pclm.0000183.g007

4.4. Sensitivity analysis

A sensitivity analysis reveals that the main findings are not sensitive to the selected input parameters of the radius over which rebates are averaged and the radius for determining exposure to emissions from power plants and their potential impacts (S1 Fig). The general distribution of local net primary PM_2.5 emissions changes remains largely consistent across all tested model parameters.

5. Discussion

Our study finds that the Clean Vehicle Rebate Project has reduced statewide emissions of CO₂, NO_X and SO₂. However, our results indicate that the CVRP has displaced emissions from vehicle tailpipes to electric generating units, leading to a net increase in primary PM_2.5 emissions across the state of California. We estimate that the reduction in secondary PM_2.5 emissions resulting from decreases in NO_X and SO₂ emissions is not large enough to offset the increase in primary PM_2.5 emissions. Of particular concern for environmental justice and public health is the finding that Disadvantaged Communities, as defined according to CalEnviroScreen 4.0, are disproportionately more likely to experience either larger net increases or smaller net reductions in primary PM_2.5, NO_X, and SO₂ emissions as a result of the CVRP.

This trend holds across multiple indicators of community disadvantage. While the emissions increases reported here remain relatively small compared to overall statewide PM_2.5 emissions, they may increase the cumulative burden experienced by communities already facing inequitable air pollution exposure and disproportionate vulnerability to adverse health impacts [29]. Shifting vehicle charging time to daytime hours and increasing the cleanliness of the electricity sources for new vehicles may reduce aggregate statewide PM_2.5 emissions as a result of the program yet leave the inequitable distribution of emissions changes largely unaffected.

In addition to the disproportionate proximity of Disadvantaged Communities to power plants, a primary driver of heterogeneous emissions redistribution as a result of the CVRP is the overwhelming allocation of rebates to Least Disadvantaged Communities, despite attempts by CARB to target rebates to residents of Disadvantaged Communities. Determinants of this trend could include the requirement that rebate applicants purchase or lease the car new rather than second-hand and the requirement that consumers pay full price initially before receiving the rebate after filing an application [51]. Additionally, CVRP applicants residing in Disadvantaged Communities report lower access to charging stations at work and at home, higher dissatisfaction with dealership experiences, and less exposure to marketing materials and personal experiences pertaining to the purchase of EVs [52]. Similar disparities in the uptake of other emissions-reducing technologies, such as rooftop solar panels, have been identified among low-income communities and communities of color [53]. In order to avoid increasing the pollution burden in marginalized communities, as mandated by California environmental justice legislation, future vehicle electrification policies could incentivize further penetration into underserved markets, especially communities that are disproportionately affected by power plant pollution. This could be achieved by eliminating systemic barriers to rebates—for example, restructuring payment schemes, installing public charging stations, or relaxing eligibility requirements—or by explicitly targeting rebates to residents of Disadvantaged Communities through a quota system or alternative distribution scheme. The National Electric Vehicle Infrastructure (NEVI) Formula Program, for example, coordinates federal, state, and local governments to deploy electric vehicle charging infrastructure that considers, among other factors, the infrastructure needs of disadvantaged communities [54]. Similar programs may target the structural factors that produce the disproportionate allocation of electric vehicle rebates.

In relation to existing literature, our results are consistent with estimates that vehicle electrification programs may disproportionately redistribute air pollution impacts towards disadvantaged communities, even in markets like California that are often considered prime candidates for electrification. A previous study on vehicle electrification in China, for instance, revealed that increased electric vehicle adoption shifted PM_2.5 emissions from high-income urban areas to low-income rural areas, reflecting a shift from urban vehicular emissions to emissions at rural EGUs [18]. Although these redistributive effects in China stemmed largely from the use of coal-powered EGUs, our study reveals that a similar redistribution may occur in California despite the existence of a much cleaner energy mix. Our findings demonstrate that additional effort is required to ameliorate the inequitable distribution of costs and benefits of California’s current climate policy. Previous studies on the heterogenous public health impacts of vehicle electrification policies may have been unable to identify such distributional inequities due to their use of county-level, rather than census tract, data [4].

The CVRP constitutes just one part of California’s wide-ranging efforts to reduce greenhouse gas and criteria air pollutant emissions, especially in Disadvantaged Communities. CARB, for example, announced $205 million in funding towards clean freight transportation in 2018, with freight traffic contributing to diesel emissions predominantly in Disadvantaged Communities [55]. This study focuses solely on the CVRP and does not attempt to evaluate the overall contributions of California climate policy in reducing greenhouse gas and criteria air pollutant emissions.

We create an open-access, policy-relevant simplified model that prioritizes ease of use and census tract-level analysis. Limitations of this study include the use of a variable radius to identify census tracts as being exposed to emissions from power plants rather than an air dispersion model. Pollution exposure will vary based on atmospheric chemistry and transport, which are not accounted for in this model. While exposure is expected to decrease with increasing distance from power plants, non-linear relationships between distance and exposure may exist due to the impacts of local topography, meteorology, stack height, and population distribution throughout a census tract. In parallel, a variable radius is used to average vehicle rebates from nearby census tracts, which does not account for complex statewide transit patterns among census tracts. Additionally, the AVERT model uses historical data to extrapolate sourcing for modeled electric demand and may not account for current off-grid energy production or planned changes to the California electric grid, which were approximated in this model by the use of various EDRFs. Future research could employ more complex models surrounding emissions intensity, air transport, atmospheric chemistry, and traffic data in order to more precisely quantify changes in local emissions exposures.

Finally, our model does not account for the increased generation of criteria air pollutant emissions from two coal-powered EGUs in Utah and two gas-powered EGUs in Nevada currently catalogued in the AVERT model, which supply energy to meet California electric grid demand and may negatively impact air quality in Utah and Nevada.

Future research could explore broader consequences of vehicle electrification programs for public health. Vehicle electrification may provide additional benefits through a decrease in emissions from refineries and from other sources besides EGUs involved in energy production and vehicle gasoline supply. Our model does not account for the air quality and other health impacts of constructing, transporting, and deploying EVs and their corresponding charging infrastructure. Vehicle electrification programs, even at the state level, may therefore impact public health on a much broader scale.

6. Conclusions

California’s CVRP provides aggregate statewide emissions reductions of greenhouse gases and criteria air pollutants, with the exception of primary PM_2.5, but exacerbates distributional inequities relating to pollution exposure. Decreases in net primary PM_2.5, NO_X, and SO₂ emissions as a result of the program disproportionately occur in Least Disadvantaged Communities, as compared to Disadvantaged Communities. These inequities are projected to continue through the state’s legislative goal of 1.5 million rebates by 2025, even with increased cleanliness of the electric source for new vehicles. The disproportionately low allocation of rebates to Disadvantaged Communities is largely responsible for the heterogeneous local impacts of the CVRP.

This paper develops and employs a model based on public government data and modeling tools to evaluate the environmental justice impacts of a currently ongoing policy. Such analyses can help policymakers avoid exacerbating the inequitable distribution of environmental burdens in the pursuit of greenhouse gas emissions reductions.

Supporting information

S1 Fig. Across different model parameter choices, distributional inequities remain.

Based on the CalEnviroScreen 4.0 indicator, as the level of community disadvantage increases moving to the right, the modeled change in annual primary PM_2.5 emissions per census tract increases, moving from lower to higher median values. Points represent the median value for each of 9 model runs for the current year formed with all combinations of the possible parameters for the radius used to average rebates and the radius for determining exposure to emissions from power plants and their potential impacts. The red horizontal line marks a net local emissions change of zero. Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 percentile.

https://doi.org/10.1371/journal.pclm.0000183.s001

(TIF)

S2 Fig. Changes in eligibility requirements for the clean vehicle rebate project have minimal impacts on the distribution of net local primary PM_2.5 emissions.

Panel (a) includes rebates with an application date before the change in eligibility criteria on March 29, 2016 and Panel (b) includes rebates with an application date after the change in eligibility criteria on March 29, 2016. Based on the CalEnviroScreen 4.0 indicator, as the level of community disadvantage increases moving to the right, the modeled change in annual primary PM_2.5 emissions per census tract increases, moving from more negative to less negative median values. Boxes show the median and extend to the 25th and 75th percentiles; whiskers span 1.5 Interquartile Range (IQR). Notches approximate a 95% confidence interval for the median, extending to ± 1.58 * IQR / sqrt(n). The red horizontal line marks a net local emissions change of zero. Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 or sub-indicator percentile.

https://doi.org/10.1371/journal.pclm.0000183.s002

(TIF)

S3 Fig. Changes in net primary PM_2.5 emissions are impacted substantially more by changes in vehicle emissions in comparison to changes in emissions from electric generating units.

In Panel (a), based on the CalEnviroScreen 4.0 indicator, as the level of community disadvantage increases moving to the right, the modeled change in annual vehicular primary PM_2.5 emissions per census tract increases, moving from more negative to less negative median values. In Panel (b), based on the CalEnviroScreen 4.0 indicator, as the level of community disadvantage increases moving to the right, the modeled change in annual primary PM_2.5 emissions from electric generating units increases. Boxes show the median and extend to the 25th and 75th percentiles; whiskers span 1.5 Interquartile Range (IQR). Notches approximate a 95% confidence interval for the median, extending to ± 1.58 * IQR / sqrt(n). The red horizontal line marks a net local emissions change of zero. Disadvantaged Communities are highlighted in red. Census tracts are grouped into 20 quantile groups based on CalEnviroScreen 4.0 or sub-indicator percentile.

https://doi.org/10.1371/journal.pclm.0000183.s003

(TIF)

S1 Table. Vehicle replacement parameters.

Values represent the reported responses from the most recent edition of the Clean Vehicle Rebate Project consumer survey, for rebates issued in 2016 and 2017.

https://doi.org/10.1371/journal.pclm.0000183.s004

(TIF)

Acknowledgments

We thank Chris Field for providing research feedback and E.J. Baik, S. Heft-Neal, and C.M. Anderson for providing feedback on a draft version of the manuscript. We thank Elisabeth (Lyssa) Freese for conversations on emissions dispersion.

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Figures

Abstract

1. Introduction

2. Background

2.1. Vehicle electrification, CO2 emissions, and criteria air pollutants

2.2. California’s clean vehicle rebate project

2.3. Environmental Justice in California

3. Methods

3.1. Calculating vehicle emissions avoided due to rebates

3.1.1. Modified rebate distribution.

3.1.2. Change in local vehicle populations.

3.1.3. Change in local vehicular emissions.

3.2. Calculating emissions from power plants

3.2.1. Change in electrical load.

3.2.2. Emissions changes from electricity generating units.

3.2.3. Distribution of power plant emissions.

3.3. Estimating changes in secondary PM2.5 emissions

4. Results

4.1. Distribution of rebates

4.2. Aggregate and heterogeneous emissions impacts

4.3. Charging time

4.4. Sensitivity analysis

5. Discussion

6. Conclusions

Supporting information

S1 Fig. Across different model parameter choices, distributional inequities remain.

S2 Fig. Changes in eligibility requirements for the clean vehicle rebate project have minimal impacts on the distribution of net local primary PM2.5 emissions.

S3 Fig. Changes in net primary PM2.5 emissions are impacted substantially more by changes in vehicle emissions in comparison to changes in emissions from electric generating units.

S1 Table. Vehicle replacement parameters.

Acknowledgments

References

2.1. Vehicle electrification, CO₂ emissions, and criteria air pollutants

3.3. Estimating changes in secondary PM_2.5 emissions

S2 Fig. Changes in eligibility requirements for the clean vehicle rebate project have minimal impacts on the distribution of net local primary PM_2.5 emissions.

S3 Fig. Changes in net primary PM_2.5 emissions are impacted substantially more by changes in vehicle emissions in comparison to changes in emissions from electric generating units.