WHO'S IN, WHO'S OUT? EVALUATING THE ECONOMIC AND SOCIAL IMPLICATIONS OF PARTICIPATION IN CLEAN...
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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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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 2 Descriptive statistics of the dependent and explanatory variables
TABLE 3 Correlation matrix of model variables
TABLE 4 Model regression results
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TABLE 2 Descriptive statistics of the dependent and explanatory variables
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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TABLE 3 Correlation matrix of model variables
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