who's in, who's out? evaluating the economic and social implications of participation in clean...

8/15/2019 WHO'S IN, WHO'S OUT? EVALUATING THE ECONOMIC AND SOCIAL IMPLICATIONS OF PARTICIPATION IN CLEAN VEHIC…

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WHO'S IN, WHO'S OUT? EVALUATING THE ECONOMIC AND SOCIAL1

IMPLICATIONS OF PARTICIPATION IN CLEAN VEHICLE REBATE PROGRAMS 2

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Dana Rubin5

Department of City and Regional Planning6

University of California, Berkeley7

228 Wurster Hall #18508

Berkeley, CA 94720-18509

Phone: (510) 642-3256; Fax: (510) 642-1641; Email: [email protected] 10

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Evelyne St-Louis (Corresponding author)12

Department of City and Regional Planning13

University of California, Berkeley14

228 Wurster Hall #185015Berkeley, CA 94720-185016

Phone: (510) 642-3256; Fax: (510) 642-1641; Email: [email protected] 17

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Word Count: 6,446 words + 4 Tables (250 words each) = 7,446 words22

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Submission date: July 31st, 2015, revised version on March 15th, 201624

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TRR Paper number: 16-323626

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Rubin and St-Louis 1

ABSTRACT

To motivate consumers to buy fuel-efficient vehicles, governments have established national and

state incentives to change purchasing behaviors. Using California’s Clean Vehicle Rebate Project

(CVRP) as a case study, this paper assesses the distribution of rebates across census tracts and

socio-economic divisions. Race-ethnicity, income, population density, and socioeconomic and

environmental disadvantage were used to understand variations in rebate allocation across census

tracts in California, between 2010 and 2015. Ordinary Least Squares (OLS) and negative

binomial regressions were conducted to identify the definitive effect income plays in obtaining

rebates: wealthier census tracts secure more rebates. Furthermore, our analysis determined a

significant and negative relationship between the proportion of Hispanic and African American

residents and the number of rebates received per household, even when controlling for income.

These findings suggest that the distribution of CVRP rebates is problematic across economic and

racial-ethnic lines, especially given current policies pertaining to climate change equity. This

paper aims to inform researchers and policy-makers about the barriers of rebate access, and

provide a discussion to address this de facto bias in rebate allocation by income and race-ethnicity.

Keywords: Transportation equity; Clean vehicle policies; Barriers to access; Socioeconomic

factors; California; Clean Vehicle Rebate Project.


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INTRODUCTION

To promote clean vehicle purchases, governments are introducing a variety of incentive

programs. In conjunction with national policies, many states are offering their residents

additional fiscal support. Since 2010, California has maintained its own program, the California

Clean Vehicle Project (CVRP). CVRP has distributed more than 100,000 rebates since its

inception. We use California as a case study to evaluate the socio-economic implications of clean

vehicle financing programs.

Policy Background

Ten years ago, the state of California passed the Global Warming Solutions Act of 2006 (AB

32). This was California’s first comprehensive and long-term approach to climate change. The

act requires that the state reduce its greenhouse gas (GHG) emissions to 1990 levels by 2020.

A critical component of AB 32 was the implementation of a Cap-and-Trade program.

Since its establishment in 2012, the Cap-and-Trade program has sold more than $2.27 billion of

carbon allowances. Funds collected from Cap-and-Trade are dispersed to state utility companies,which are required to spend their takings on alternative fuels. A remaining portion of Cap-and-

funding goes to the Greenhouse Gas Reduction Fund to subsidize projects that will further

California’s GHG reduction goals. CVRP is one of these programs.

The Clean Vehicle Rebate Project, SB 535 & SB 1275

The objective of the Clean Vehicle Rebate Project (CVRP) is to encourage California

residents to transition to zero-emission cars. Vehicles eligible for a rebate include: electric, plug-

in hybrid electric, and fuel cell. Qualified buyers are eligible for rebates of $5,000, $2,500, and

$1,500, respectively (1). For the 2014-2015 funding cycle, $111 million was allocated towards

CVRP. However, despite the program's popularity, preliminary evidence indicates rebatedistribution is uneven. The CVRP report from 2012-2013 states that 90% of rebates were

distributed to only three of the state’s 35 air districts: San Francisco Bay Area, Los Angeles

Metropolitan area, and San Diego Metropolitan area (2). These regions have the state’s highest

populations, and the auto industry has targeted these districts with first tier marketing campaigns.

In addition, the Plug-in Electric Vehicle Owner Survey (3) finds that 83% of CVRP recipients

had yearly incomes higher than $100,000 (3).

In parallel, a pivotal piece of legislation, Senate Bill 535, was passed in 2012 to provide

health and economic benefits to disadvantaged communities from Cap-and-Trade funds (4, 5).

Among other requirements, SB 535 demands that 10% of CVRP funds be distributed to

underserved communities. Additionally, passed in 2014, the Charge Ahead California Initiative

(SB 1275) conditions that households above an income threshold are ineligible for rebates, and

also increases rebate amounts for eligible low and moderate-income consumers (6, 7). These

program eligibility changes have yet to come in effect at the time of writing this article.

Research question and findings


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The above-mentioned policies have encouraged the adoption of cleaner vehicles, and with the

recent passing of SB 535 and SB 1275 in California, there are increased efforts to expand the

participant pool to marginalized communities. Therefore, we asked, are rebates equitably

distributed across households of varying socio-economic factors? We hypothesized that this is

not the case.

To answer this question, we studied the distribution of CVRP rebates in California by

census tract. We treated the allocation of rebates as a dependent variable and explored

geographic, socioeconomic, and environmental characteristics as explanatory variables. Using

OLS and negative binomial regressions, we discovered that census tracts with lower median

household incomes, and higher proportions of people of color, receive fewer clean vehicle

rebates. In contrast, our results also suggested that when controlling for income, tracts that are

more environmentally and socially burdened receive more rebates.

These findings contribute to the literature that investigates who benefits from clean

vehicle financing programs. This suggests that other jurisdictions could benefit from similar

analyses when looking to evaluate the outcomes of their own clean-vehicle programs.

LITERATURE REVIEW

The following literature review highlights the variety of clean vehicle financing programs and

their corresponding participant characteristics. This review emphasizes the social, economic, and

environmental implications of program access.

Clean Vehicle Policies and Participant Trends

Mock and Yang (8) estimated that “global sales of electric vehicles (EVs) have about doubled in

each of the past two years, from about 45,000 vehicles sold in 2011 to more than 200,000 in

2013” (p. ii). This is due, in part, to the establishment of policies meant to incentivize the

dispersal of clean vehicles into the private market. Li et al (9) agreed, reporting that monthly

U.S. sales of EVs increased from 345 to 11,286 between 2010 and 2014, largely due to

government support. Policies are varied across the United States. For instance, some states

support income, property, or sales tax exemptions (Georgia, New Jersey, West Virginia, and

South Carolina). Other states offer exemptions on insurance surcharges (Florida), exempt EV

owners from public parking meters and high licensing fees (Arizona), or allow exemptions from

motor inspections and free-access to HOV lanes (10).

Governments including France, Sweden, Japan, China, and the United States manage

direct subsidy programs to provide customers with up-front subsidies of $5,000-8,000 USD.

However, some authors have suggested that national tax credit programs are more effective. Li et

al. (9) claimed that without the United States’ $1.05 billion tax break program, 63,000 fewer EVs

would have been sold from 2011 to 2013. Tax breaks alleviate between 5-25% of an EV’s costs.

To assess the distribution of programs’ benefits, literature has evaluated various incentive

programs based on the characteristics of those participating.


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Chandra et al. (11) considered the cost and benefits of a tax rebate program for hybrid

electric vehicles (HEV) in various Canadian provinces. Those that benefitted from the tax rebate

were primarily consumers who would have purchased a HEV with or without a rebate. This

reveals something significant about typical program participants. As shared by Liu et al. (12),

“earlier adopters of clean vehicles are likely to be higher-income consumers, willing to accept

the difficulties of adopting alternative fuel vehicles” (p.381).

California’s vehicle buy-back program – known as “cash-for-clunkers” – distributed

$400-$1,000 USD for vehicles older than thirty years. Alberini et al. (13) suggested that a

person’s “willingness to accept” a scraping price is subjective: if a consumer disagrees with an

offering price, there is no incentive to replace their current vehicle. Dill (14) confirmed similar

hesitations upon her own analysis of the 1996-2000 “cash-for-clunkers” program. Dill found that

low-income households, although they partially took advantage of the program, were not

“participating as much as expected, and were unlikely to replace the vehicle with the least

polluting, newest vehicle” (p. 27).

Lachapelle (15) examined the characteristics of those partaking in an early vehicleretirement program in Quebec, Canada. This program encouraged residents to scrap old vehicles

in exchange for cash, a vehicle rebate, or a subsidy for alternative modes of transportation.

Lachapelle determined that residing in a metropolitan area was one of the strongest variables for

explaining participation rates. Surprisingly, income was not a variable of significance - though

within metropolitan areas, participation rates did increase with poverty levels. Low

unemployment rates and high proportions of young residents were positively associated with

participation rates. Lower-income participants were more likely to choose a direct cash incentive,

even though this option represents a smaller dollar amount compared to the other transportation

options.

Issues of Access and Economic Opportunity

Why is non-participation in subsidized clean-vehicle programs problematic? As defined by

Levinson (16), equity can be measured on the basis of “winners and losers.” Non-participation

by certain groups points towards the existence of barriers to access and highlights the missed

opportunities that could have been gained if program participation was possible. Although one

could argue that a low-income family could equally benefit from purchasing a cheaper vehicle,

we offer that poorer residents deserve the opportunity to access the long-term economic benefits

provided by a program.

There is an overarching agreement that social-exclusion, as stated in Levitas et al. (17), is

“...the lack or denial of resources, rights, goods and services, and the inability to participate in

the normal relationships and activities, available to the majority of people in a society” (p.86).

The inability to afford a vehicle can become a form of economic and social exclusion: without a

vehicle, opportunities are unmet. Lucas (18) linked policy and poverty, suggesting that many

policies do not benefit burdened populations. Insufficient public transportation services,

deteriorating infrastructure, and in this case, the inability to purchase a clean vehicle, is due to


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inadequate policies. Cervero (19) found that in the U.S., low-income and non-car owning

households, have more difficulty accessing job centers and opportunities. Beyond employment,

Frei et al. (20) argued that car ownership has a positive effect on one’s social network.

Disadvantaged communities that do not live near transit, and do not have access to a vehicle, lose

out on valuable social capital. According to Clifton and Lucas (21), minority populations

including Asians, Hispanics, and Pacific Islanders are less likely to own cars when compared to

White Americans. Those living without a vehicle face additional access and opportunity

challenges.

Issues of Environmental Inequities

Research consistently finds negative health effects when exposed to high concentrations of

vehicle-related pollutants (22). Living in heavy-traffic corridors often marginalizes low-income

residents. Gunier et al. (23) found that in California, “block groups in the lowest quartile of

median family income are three times more likely to have high traffic density than block groups

in the highest income quartile” (p. 240). Also, low-income and minority children are moreexposed to traffic-related pollution (24). Owning a clean vehicle or living in a community with a

higher number of clean vehicles could mitigate some of these effects.

Assessment of alternative clean vehicle program designs

Research has begun to explore reforms to clean vehicle programs to address income-related

barriers to participation. One recent study by DeShazo et al. (25) looks at California’s rebate

program and simulates the program’s performance under three alternative designs. De Shazo et

al. concluded that the most equitable and cost-effective alternative is setting an income

progressive rebate, in conjunction with an income cap. DeShazo et al. found that this alternative

improves the overall cost-effectiveness of rebate programming, and clean vehicle allocationacross income groups. This also leads to fewer rebates being received by people who would have

purchased a clean vehicle, regardless of a rebate. In this alternative, the total number of clean

vehicles purchased was predicted to remain unchanged. DeShazo et al. demonstrated the

feasibility of tailoring clean vehicle policies to address income-related barriers, while remaining

effective from an environmental standpoint.

METHODS

The purpose of this paper is to quantify the relationship between the allocation of CVRP rebates

and the socioeconomic and environmental characteristics of California census tracts. To our

knowledge, no one has demonstrated the statistical relationship between rebate distribution and

income, while also controlling for other factors. Thus, this analysis builds on literature that

explores which population groups are more effectively able to access clean vehicle programs.

Measuring the distribution of rebates across California


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CVRP rebate data was obtained from the Center for Sustainable Energy (CSE), the official

administrator of CVRP. Since 2010, CSE has compiled monthly data to include: census tracts

where rebates were distributed, date of allocation, vehicle category (Battery Electric Vehicle

(BEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel-cell electric vehicle, or other), and owner

type (individual, business, non-profit, local /state/federal government). The data used for this

analysis looks at all clean vehicle rebates allocated from 2010 to March 2015, for all vehicles

and owners. At the time of this study, a total of 98,901 rebates were distributed, of which 97%

were received by individuals. 56% were BEVs and 44% were PHEVs. We assume that benefits

were accrued to a census tract, regardless of whether a rebate was received by an individual or an

organization.

Our sample consists of all census tracts in California. The dependent variable of interest

is ‘the rate of CVRP rebates received per one thousand households per census tract’ (i.e., herein,

referred as rebates/thousand households). We normalized the total number of CVRP rebates per

census tract per 1,000 households because this simplifies the interpretation of regression

coefficients. The number of households was obtained from 2010 U.S. Census data. In the case ofthe count-model, we simply use the number of CVRP rebates per census tract.

For both versions of our dependent variable, we removed outliers that were above the

99th percentile (equal to 54.51 rebates normalized by thousand households, and 95 rebates for

the non-normalized measure). This effectively removed 80 tracts from the sample. In the case of

the normalized measure, the original dataset was highly skewed, with several very high values

due to tracts with near-null populations.

Explaining the distribution of rebates

The independent variables included in this analysis were chosen based on a review of the

literature as well as research of the current political landscape in California. The independentvariables are listed as follows:

The Pollution Burden and Socioeconomic Burden score

The Disadvantaged Community (DAC) score quantifies levels of community burden at the

census tract level. Updated in 2014, it was developed for the CalEnviroScreen Tool by the

California Environmental Protection Agency (CalEPA). It was created to assist the Office of

Environmental Health Hazard Assessment in the distribution of funding. Since the passing of SB

535, it has also been identified as a tool to meet equity objectives. CalEPA defines a census tract

as disadvantaged if it falls in the top 25th percentile of the DAC score (26, 27).

The geographic specificity of this measure is considered sufficient to capture the nuances

of neighborhood disadvantages. The DAC score aggregates a total of nineteen measures. Scored

out of ten points, pollution burden (PB) is determined from twelve pollution indices and socio-

economic burden (SB) is determined from seven population indices (see Table 1). Rather than

include the DAC score, we include PB and SB scores separately in our models to parse out


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variations in environmental exposure from socioeconomic conditions. These scores aggregate

several relevant measures such as poverty level, unemployment rate, and exposure to traffic;

therefore, we do not to include these variables individually in the regressions.

Of note, in 2015, the National Environmental Protection Agency released a similar score

called the Environmental Justice mapping and screening tool (EJSCREEN) for the national scale.

In cases where state-specific environmental justice measures are unavailable, EJSCREEN could

be used to replicate this analysis.

TABLE 1 Measures included in the CalEnviro Screen Disadvantaged Communities Score

Race and Ethnicity

Although the pollution and socioeconomic burden scores are useful predictor variables given

their availability and relevance to policy makers, crucial measures missing are race and ethnicity.

Ensuing from the race-based access and environmental justice issues mentioned earlier, it is

essential to consider the variation of benefits along racial groups. We use data from the 2010U.S. Census to obtain percent Hispanic or Latino, and percent non-Hispanic Black-African

American, by census tract, as our two measures. We focus on these populations because they

represent the largest minority groups in California.

Median household income

As documented in previous studies, median household income is often related to the allocation of

clean vehicle benefits. Therefore, it must be included as a variable to ensure that intervening

effects of income are controlled for. It is also relevant to do so in order to measure the impact

income has in explaining number of rebates/household. Data on income was acquired from the

2009-2013 ACS 5-year estimates.

Vehicle ownership

We use vehicle ownership as a measure to control for the overall market or demand for

automobile purchases and for a tract’s general ability to purchase private vehicles. We use

‘number of vehicles per census tract’ (ACS 2009-2013 5-year estimates) divided by the total

number of households to obtain the estimated number of vehicles per household.

Availability of alternative fuel charging stations

Another variable to control for is the availability of alternative fuel charging stations by census

tract. This data is accessible from the U.S. Department of Energy (28). The measure used is the

number of in-service stations per census tract. We count both public and private stations,

assuming that either could facilitate or incentivize clean vehicle purchase.

Urbanized areas


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To control for the dynamics between urban and rural census tracts, we include a binomial

variable to determine whether a tract is located in an urbanized area. To do this, we define a tract

as “urban” if its centroid falls within California’s designated urban areas, as defined by the U.S.

Census. We considered a different measure, in which a tract was deemed urban if it fell entirely

within the urban area. However, we opted for the former because it allowed for a more lax

definition of what is “urban.”

Population density

Although we already normalized the number of rebates by the number of households, population

density could influence the likelihood of applying for a rebate, and it acts as a proxy for other

characteristics such as the availability of transit (15, 29–31). Certain tracts might be receiving

more rebates not because of their residents’ socio-economic status or income but because of their

type of environment and land-use.

Number of householdsOur final variable is number of households per census tract (from the 2010 U.S. Census), which

we include only for the count-model regression.

Statistical analysis

We developed two regression models to explore the statistical relationship between the above

variables and the rate of rebates received by census tract.

Model 1 is an Ordinary Least Square (OLS) regression. The assumptions of linearity

required for conducting multiple linear regressions were met in our variables. The natural log of

median income was used in order to have a normally distributed income variable. In addition,

checks for multicollinearity between independent variables were conducted. A variance forinflation factor (VIF) test confirmed that all the predictors displayed individual scores at or

below 4.20, with a mean VIF score equal to 2.20 for the model, which is considered below the

acceptable threshold (32).

A sizeable number of census tracts within our sample (1,174 tracts) received zero rebates

between 2010 and 2015. For this reason, we run a negative binomial count model, Model 2, to

test the robustness of the OLS results. Table 2 provides summary statistics for all the variables

considered in these models.

TABLE 2 Descriptive statistics of the dependent and explanatory variables

RESULTS

For the benefit of the reader, Table 3 provides a correlation matrix of all variables. We interpret

these relationships through regression analyses, for which results are summarized in Table 4.

TABLE 3 Correlation matrix of model variables


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The number of alternative fuel stations per census tract is significant with a sizeable

positive coefficient; for every additional charging station, a tract is likely to have 0.29 additional

rebates. This is supported by research that has shown that this type of infrastructure is key for

market penetration of clean vehicles (33, 34). It is probable that for households living in better-

equipped census tracts, purchasing clean vehicles and/or applying for rebates is more feasible.

An urban census tract is likely to receive 1.14 more rebates per thousand households than

a non-urban tract. One plausible explanation is that urban areas are more likely to have the

infrastructure to support clean vehicles (e.g. charging stations). However, since we control for

this in our model, we consider other options: this may be due to more demand for clean vehicles

in urban regions or differences in advertising and norms around the ownership of clean vehicles.

Finally, we also look at population density and vehicle ownership as control variables.

The number of vehicles per household, per tract, is significant with a negative coefficient. The

model states that with an addition of a vehicle per household, there is a decrease in the number of

rebates received by 2.9 per thousand households. We find that population density is not

significant in explaining the number of rebates/thousand households. Acknowledging that otherauthors have found that lower-density areas within metropolitan areas have displayed higher

program participation rates in the past (15), we recommend exploring this relationship in more

depth by examining the availability of transit. We also suggest that density is not significant in

our model because we included a measure of urban tracts.

Model 2: Negative binomial regression

To confirm the robustness of our OLS results, we ran a negative binomial regression. We find

that Model 2 is significant with a pseudo R-square (McFadden’s R-square) of 0.165. We

interpret this pseudo R-square with caution as it isn’t comparable to an OLS R-square and the

magnitude of McFadden’s R-square tends to be lower (35).

More importantly, we find that the negative binomial regression displays very similar

results to the OLS regression, thus supporting the validity of the relationships outlined in the

previous section. The large majority of results from the OLS regression still stand in terms of the

direction and significance of the relationships.

For Model 2, we interpret variables’ incidence rate ratios (IRRs) to explain the “rate of

receiving a rebate” per tract. We are mainly concerned with whether the IRR is above or below

1, and compare the direction of the relationship to the coefficients’ signs, as a barometer against

Model 1.

Starting with income, for every 10% unit increase in income, the rate of rebates received

per tract, is expected to increase by a factor of 0.556. This verifies our OLS model, which

suggests that an increase in income increases a tract’s likelihood to receive rebates. For race-

ethnicity, for every percent increase in Hispanics/African Americans per tract, the rate of rebates

received is expected to decrease by a factor of 0.987 and 0.99, respectively. These values are

close to 1, but the direction of the relationship still holds, and follows suit with Model 1.


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The only variable that changes direction in Model 2 is the SB score. In Model 2, we find

that for a 1-point increase in the SB score, the rate of rebates received per tract is expected to

decrease by a factor of 0.93. This discrepancy suggests that future research needs to tease out the

separate components of the SB score to conclude whether these social and economic

characteristics within census tracts influence the diffusion of rebates.

The remaining variables offer no changes between the OLS model and the negative

binomial regression. These complimentary results verify our earlier conclusions and support our

policy recommendations.

Limitations

For future investigations, a more comprehensive evaluation of the program would allow for a

more nuanced analysis of participation rates. Currently, CVRP recipients are not mandated to fill

out participation surveys, and household-level disaggregated data on CVRP rebates are

unavailable to the public due to privacy concerns. Disaggregated data would reduce

generalizations found at the census tract level and would support more detailed spatial analysis.We recognize that our sample displays some spatial autocorrelation (Global Moran’s I

varied from 0.2 to 0.4 depending on the various threshold distance used, p-value = 0.000). As

suggested by Chatman, Tulach and Kim (36), omitted spatial variables are often the basis for this

issue. Thus, we re-ran our models with additional spatial variables – census tract county and

centroid latitude/longitude – in an effort to address spatial autocorrelation (also suggested in

Laraira et al. (37)). Although these variables increased the OLS adjusted R-square by a few

decimal points, they did not dramatically improve the model, substantively nor conceptually.

Furthermore, our variables of interest (SB score, PB score, income and race-ethnicity) remain

significant and of similar sign/magnitude with or without them.

DISCUSSION AND RELEVANCE TO POLICY AND RESEARCH

Data analysis of CVRP confirmed that program participation is dependent on household income.

This echoes other research: those confined by economic obstacles are less likely to apply for and

benefit from rebates. Due to the influence income has on rebate allocation, we encourage setting

income caps to shift the distribution towards lower-income brackets, as exemplified by SB 1275.

In the case of CVRP, DeShazo et al. (25) suggests that setting an income cap can redistribute

financial support to those with greater economic burden while maintaining the cost-effectiveness

of the program – this has yet to be seen in practice in California.

Based on our literature review, many clean vehicle-financing programs only support the

distribution of rebates. Yet often, the greatest challenge for low-income families is finding the

upfront capital. We advise that future iterations of CVRP, and like programs, offer vouchers to

those unable to source upfront costs. In Southern California, a voucher program is being trialed

by the South Coast Air Quality Management District. The program is directed towards low and

moderate-income consumers and excludes residents living in high-income zip codes (38, 39).


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In addition to modifying the internal structure of programs, various types of incentives

can be combined. This has been exemplified by a San Joaquin County pilot program, the

Enhanced Fleet Modernization Program. Referred to as “bundling”, incentives are layered to

increase consumer benefit. For example, a voucher nearly doubles when a participant retires an

old vehicle in exchange for a plug-in hybrid or battery electric. This has a dual advantage: (1) It

increases the odds of a household replacing their vehicle with a clean vehicle and (2) It makes

the purchase of a clean vehicle more feasible for lower-income consumers. As an additional

option, for those uninterested in claiming a new vehicle, households could opt for a $2,500-

$4,000 transit mobility voucher (38).

Beyond income, our research underscores the unevenness of rebate distribution across

race-ethnicity groups. As we move forward with future iterations of rebate programming, it is

important to acknowledge this and aim to resolve this incongruence by targeting groups most

affected by transportation poverty, and thereby, social exclusion. Levinson (16) suggests the

inclusion of equity impact statements when assessing the success of a particular policy or

program. Interviews and focus groups can help improve the impact of rebate programs withindisadvantaged tracts to increase application rates in these areas.

Another way to increase access for disadvantaged communities is to implement a similar

rebate program but for reused clean vehicles. This is a novel idea that might generate more

appealing incentives for lower-income households. AB 904, introduced in California in February

2015, seeks to accomplish this (40). This type of program could complement the current suite of

clean car incentives.

CONCLUSION

Using CVRP as a case study, we find that the distribution of clean vehicle rebates across

different socioeconomic groups is uneven; those of higher income are more likely to receiverebates. Our analysis goes further to suggest that census tracts where the majority of the

population is Hispanic or African American, are less likely to receive rebates, even when income

is accounted for.

Knowing the above, it is crucial to amend current programming to address the lack of

socioeconomic diversity in rebate participation – particularly in light of current policies

pertaining to climate change equity, such as SB 535 and SB 1275. Corburn (41) suggests that

through local knowledge ( focus groups, interviews) more consequential conversations can occur

between practitioners, policy makers, and communities of concern.

In the long term, it will be important to reassess the trade-offs of clean-vehicle, and by

default, vehicle ownership over other cleaner forms of transportation (e.g. public transportation

or car sharing). There has been a recent initiative in California to implement pilot projects for car

sharing programs, specifically in disadvantaged communities. Car-sharing programs have the

potential to reduce localized pollution (42) and can be an inexpensive transportation alternative.

Placing more car-sharing programs in low-income communities, where GHG emissions are

notoriously higher, could be an effective alternative to promoting ownership of clean vehicles.


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Notwithstanding the debate of whether vehicles should be promoted over other

alternative transportation options, it is important that governments sponsor transportation

systems that are affordable and accessible across socioeconomic lines. All programs need to be

evaluated not just on the basis of their economic value but also on the basis of equity.

ACKNOWLEDGMENTS

We thank Karen Trapenberg Frick for her support during this investigation, as well as Carolina

Reid and Dan Chatman for giving us the methods and conviction to question the status quo. To

Jesus Barajas, for donating his time and knowledge without hesitation. Lastly, we thank our

friends and family for their encouragement.


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LIST OF TABLES

TABLE 1 Measures included in the CalEnviro Screen Disadvantaged Communities Score



TABLE 4 Model regression results


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Mean MedianStandard

deviationMin Max

Sample size: n = 7,978

Dependent variables

CVRP rebates per census tract 11.07 5.00 14.96 0.0 95

CVRP rebates per thousand household per census tract 6.88 3.59 8.73 0.0 54.51

Independent variables

Median household income of census tract (in 2013 dollars) 65,988 59,858 31,305 6,189 233,125

Ln of income of census tract 10.99 10.99 0.467 8.731 12.359

Percent Hispanic or Latino in census tract (%) 36.3 28.5 26.3 0.0 100.0

Percent non-Hispanic African American in census tract (%) 5.9 2.5 9.4 0.0 89.8

Pollution Burden Score of census tract (out of 10) 4.9 4.9 1.6 0.43 10.0

Socioeconomic Burden Score of census tract (out of 10) 5.2 5.1 1.9 0.49 10.0

Number of vehicles per household per census tract 1.86 1.88 0.42 0.15 6.67

Number of alternative fuel stations per census tract 0.50 0.0 1.45 0.0 29.0

Urban census tract (1=yes, 0=no) 0.857 n/a n/a n/a n/a

Population density of census tract (persons/square mile) 8,343 6,145 9,457 0 161,545

Number of households 1,558 1,472 684 0 8,382


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Rebates

per 1,000

HH

Ln

income

Percent

Hispanic

Percent

African

American

SB

score

PB

score

Vehicles

per HH

Nb ofalternative

fuel

stations

Urban

tract

Population

density

Rebates per 1,000

HH

1.00

Ln income 0.685 1.00

Percent

Hispanic-0.485 -0.557 1.00

PercentAfrican

American

-0.181 -0.250 0.040 1.00

SB score -0.579 -0.791 0.712 0.302 1.00

PB score -0.152 -0.251 0.452 0.067 0.320 1.00

Vehicles

per HH 0.234 0.534 -0.035 -0.211 -0.266 -0.059 1.00

Nb of

alternative

fuel

stations

0.073 -0.012 -0.079 -0.020 -0.064 0.081 -0.141 1.00

Urban tract 0.024 -0.039 0.139 0.135 0.078 0.146 -0.195 -0.021 1.00

Population

density-0.176 -0.299 0.264 0.136 0.233 0.110 0.234 0.073 0.024 1.00


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TABLE 4 Model regression results

Regression Model 1

Ordinary Least Squares

Regression Model 2

Negative binomial

N 7,790 7,790

Adjusted R-square / Pseudo R-

square0.508 0.165

Model p-value 0.0000 0.0000

Coefficient Std error t-statistic IRR Std error z-statistic

Dependent variable Rebates received per thousand

households per census tractRebates received per census tract

Ln of median household income

of census tract (in 2013 dollars)13.938*** 0.30769 45.30 5.5683*** 0.21777 43.90

Percent Hispanic or Latino in

census tract (%)-0.05448*** 0.00450 -12.11 0.9872*** 0.000610 -20.84

Percent non-Hispanic African

American in census tract (%)-0.04841*** 0.00824 -5.88 0.9929*** 0.00113 -6.32

Pollution Burden Score of census

tract (out of 10)0.37992*** 0.05103 7.44 1.1038*** 0.00709 15.37

Socioeconomic Score of census

tract (out of 10)0.33420*** 0.07439 4.49 0.9310*** 0.00867 -7.68

Number of vehicles per

household per census tract-2.89409*** 0.25235 -11.47 0.7991*** 0.02658 -6.74

Number of alternative fuel

stations per census tract0.2964*** 0.05112 5.80 1.0603*** 0.00675 9.19

Urban census tract (1=yes, 0=no) 1.1385*** 0.22062 5.16 1.2796*** 0.03517 8.97

Population density of census tract

(persons/square mile)-0.00001 9.89e-06 -1.26 1.000004* 1.50e-06 2.49

Number of households ---- ---- ---- 1.0005*** 0.00002 31.60

Constant -143.181 3.39231 -42.21 3.30e-08 1.43e-08 -39.84

---- Not included in model

***Significant at 99.9% confidence level

**Significant at 99% confidence level

*Significant at 95% confidence level

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