Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Nature Energy | Volume 9 | August 2024 | 955–963
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nature energy
https://doi.org/10.1038/s41560-024-01546-2
Article
Evaluating community solar as a measure to
promote equitable clean energy access
Eric O’Shaughnessy
  1
, Galen Barbose
  1, Sudha Kannan
  2 &
Jenny Sumner2
Rooftop and community solar are alternative product classes for residential
solar in the United States. Community solar, where multiple households buy
solar from shared systems, could make solar more accessible by reducing
initial costs and removing adoption barriers for renters and multifamily
building occupants. Here we test whether community solar has expanded
solar access in the United States. On the basis of a sample of 11 states, we
find that community solar adopters are about 6.1 times more likely to live
in multifamily buildings than rooftop solar adopters, 4.4 times more likely
to rent and earn 23% less annual income. We do not find that community
solar expands access in terms of race. These differences are driven, roughly
evenly, by inherent differences between the two solar products and by
policies to promote low-income community solar adoption. The results
suggest that alternative solar products can expand solar access and that
policy could augment such benefits.
Nearly four million residential electricity customers had adopted
rooftop solar photovoltaics in the United States by the end of 2022
(ref. 1). Rooftop solar adopters tend to be more affluent than the
general population, are less likely to rent and are less likely to self iden-
tify as a racial minority2–5. Rooftop solar adoption inequity reflects
unequal access to solar across different demographic groups6. Vari-
ous barriers restrict solar access for low- and moderate-income (LMI)
households (for example, high up-front costs to purchase solar systems
outright), for renters (for example, split incentives) and for multifamily
building occupants (for example, shared ownership of rooftop spaces)7.
Whereas adoption inequity is common among emerging technologies8,
rooftop solar adoption inequity could pose unique challenges to clean
energy transitions and grid decarbonization9,10. A growing number of
policies seek to expand solar access11, that is, increase adoption among
demographic groups historically underserved by rooftop solar.
Previous research suggests that alternative solar products can
expand access to solar. Specifically, the development of solar leasing
models with minimal up-front costs has driven a more equitable expan-
sion of rooftop solar12,13 and the recent emergence of solar loans may
similarly address up-front cost barriers. However, rooftop solar remains
largely inaccessible to renters and families living in multifamily hous-
ing14,15. Another alternative class of solar products in the United States is
community solar, wherein multiple customers buy output from a single
solar system16. As with leasing, community solar typically entails no or
minimal up-front costs. Unlike rooftop solar, community solar poses
no specific barriers to adoption for renters or multifamily building
occupants. As a result, community solar is often theorized to promote
more equitable solar access15,17–22 and is increasingly integrated into US
solar adoption equity policies11. The federal Inflation Reduction Act
includes tax credits for projects serving LMI communities or custom-
ers, and at least 17 states have incentives or regulations that promote
LMI community solar23–25.
The impact of community solar on clean energy access has not
been empirically evaluated. In this study, we fill this research gap by
analysing the demographic profiles of rooftop and community solar
adopters to determine whether existing community solar projects
have promoted more equitable adoption. We quantitatively assess
household-level demographic data of community solar and rooftop
solar adopters, as does other research documenting the demographic
profiles of rooftop solar adopters2,4,5,26. We explore how the two cus-
tomer groups vary in terms of median income levels, housing tenure
(whether adopters own or rent their homes), housing type (single or
multifamily) and race. Our key contribution is a quantitatively rigorous
analysis of the prevailing hypothesis that community solar promotes
Received: 2 November 2023
Accepted: 2 May 2024
Published online: 3 June 2024
 Check for updates
1Lawrence Berkeley National Laboratory, Berkeley, CA, USA. 2National Renewable Energy Laboratory, Golden, CO, USA.
 e-mail: eoshaughnessy@lbl.gov

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that at least some community solar adopters would not otherwise have
adopted rooftop solar (Inference for solar access in Methods). We esti-
mate all statistics based on comparisons within states to ensure sample
independence. We focus our analyses on 11 states where we have at least
100 records for both adopter types (Table 2). The 11 states include some
of the largest community and rooftop solar markets in the United States
and comprise more than half of community solar capacity installed to
date33. Still, some caution is merited in extrapolating results from this
geographic subsample (‘Limitations’ in Methods).
We explore differences in demographic characteristics using
one-sided tests for the following hypotheses: community solar adop-
ters earn less, on average, than rooftop solar adopters; community
solar adopters are more likely to rent than rooftop solar adopters;
community solar adopters are more likely to live in multifamily build-
ings than rooftop solar adopters; and community solar adopters are
more likely to identify as people of colour or Hispanic than are rooftop
solar adopters.
Throughout this paper, solid points in figures indicate statistically
significant results according to one-sided statistical tests, whereas
empty points indicate insignificant results.
The data firmly support hypotheses 1–3 defined above (Fig. 1).
Weighting the differences by state sample sizes, the data suggest
that community solar adopters are about 6.1 times more likely to live
in multifamily buildings than rooftop solar adopters, 4.4 times more
likely to rent and earn about 23% less in annual income. At the same
time, the data suggest that community solar adopters are not demo-
graphically representative of the general population. In most states,
community solar adopters earn more than average and are less likely
to rent and live in multifamily buildings than the general population.
That is, community solar expands access relative to rooftop solar
but is still inequitable relative to the general population. Differences
in race are more ambiguous. The comparative statistics are mostly
statistically insignificant according to the one-sided test that com-
munity solar adopters are more likely to identify as people of colour or
Hispanic. Across all the states in the sample, rooftop solar adopters are
about twice as likely as community solar adopters to identify as Asian/
Asian American or Black and about three times as likely to identify as
Hispanic (Fig. 2).
more equitable solar access. Whereas that hypothesis is often taken
for granted, the effects of community solar on clean energy access
thus far lacks empirical evidence27,28, and there are several reasons to
question whether community solar necessarily expands access. LMI
participation can increase community solar costs15, largely because
LMI customers can be more costly to acquire29,30. Profit-maximizing
community solar providers may thus prioritize marketing to relatively
affluent customers, consistent with evidence from rooftop solar mar-
kets31. Further, bill management and customer turnover represent
substantial costs to community solar providers32. Community solar
providers thus face economic incentives to minimize the number of
customers by maximizing energy sold per customer. Community solar
providers may thus avoid lighter energy users such as LMI households
and multifamily building occupants. Providers may also perceive that
LMI customers and renters pose higher turnover risks.
Nonetheless, we find that multifamily building occupants, renters,
and—to a lesser extent—LMI households are significantly more likely to
adopt community solar than rooftop solar. These results suggest that
community solar effectively expands access to demographic groups
underserved by rooftop solar. We do not, however, find evidence that
community solar expands access in terms of race. Further, we show that
the access benefits of community solar stem from two drivers: inherent
differences in the community solar model that reduce barriers to adop-
tion and community solar policies designed to promote LMI adoption.
Rooftop and community solar adopter
demographics
We draw on multiple sources to build rooftop and community solar
datasets with household-level estimates for income, housing type,
housing tenure and race (Table 1 and Methods; summary statistics in
Supplementary Table 1). The community solar data represent custom-
ers of community solar projects as defined by the US Department of
Energy: solar projects where financial benefits flow to multiple custom-
ers within a defined geographic area. Income and race estimates are
available for all records, whereas housing type and tenure are incom-
plete. The sample sizes associated with each variable in each analysis
are described in figure captions.
We estimate comparative statistics of adopter demographics
using Wilcoxon tests (comparison of median incomes) or Pearson
Chi-squared tests (comparisons of categorical variables) (Methods).
The comparative statistics describe differences in adoption choices
across different demographic groups. Significant differences provide
evidence that community solar expands solar access under the premise
Table 1 | Data sources
Source
Description
N
Rooftop solar
Solar demographics5
Database of rooftop solar adopters
with household-level demographic
variables
102,974
Community solar
State programme data
Data were obtained directly from
state community solar programmes in
Illinois, Maine, New York and Oregon.
The data represent customers that
were actively enroled as of 2023.
41,323
Sharing the Sun
developer data
Data obtained from community solar
developers as part of the National
Renewable Energy Laboratory’s
(NREL) Sharing the Sun project. NREL
publishes project-level data, but
customer-level data are considered
proprietary and are not publicly
available. Data reflect customers
actively enroled as of 2023.
37,391
Table 2 | Solar sample sizes and data sources by state
State
Community solar
sample size
Rooftop
solar
sample size
Community solar data
source(s)
Colorado
398
21,472
NREL
Illinois
21,180
10,774
State of Illinois
(Adjustable Block
programme, Solar For
All programme), NREL
Maine
19,907
1,322
Central Maine Power
Company, Versant
Power, NREL
Maryland
7,779
5,582
NREL
Massachusetts
1,814
10,611
NREL
Minnesota
465
4,319
NREL
New Jersey
519
16,077
NREL
New York
23,375
15,565
New York State
Energy Research
and Development
Authority, NREL
Oregon
2,033
10,185
Oregon Energy Trust,
NREL
Rhode Island
876
4,881
NREL
Washington, DC
368
2,186
NREL

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We use a conditional probability model to compare how the differ-
ent demographic factors explain household adoption decisions (Meth-
ods). The models describe the relative power of each demographic
factor in predicting whether a household is a community or rooftop
solar adopter, conditioned on correlation with the other factors (for
example, multifamily building occupants are more likely to rent than
single-family occupants; Supplementary Table 4). The model sug-
gests that the strongest predictors of adoption choices are race and
housing tenure (Fig. 3). Further, we use Akaike Information Criterion
(AIC) scores to assess the prediction accuracy of model variations
including different combinations of the demographic factors (Supple-
mentary Table 6). The AIC scores likewise suggest that race and hous-
ing tenure are the most predictive variables. As with the comparative
statistics, the conditional impacts of race are directionally opposite
to our hypothesis. In the remaining discussion, we generally focus on
the demographic differences that confirmed a priori hypotheses and
return to a discussion of race in Conclusions.
Overall, the results show that multifamily building occupants,
renters, and—to a lesser extent—LMI households are significantly more
likely to adopt community solar than rooftop solar. These results
a
0
90
180
CO
DC
IL
MA
MD
ME
MN
NJ
NY
OR
RI
b
0
20
40
60
CO
DC
IL
MA
MD
ME
MN
NJ
NY
OR
RI
c
0
20
40
60
80
DC
IL
MA
MD
ME
MN
NJ
NY
OR
RI
d
0
35
70
CO
DC
IL
MA
MD
ME
MN
NJ
NY
OR
CO
RI
Community solar
Rooftop solar
Statewide
State
State
State
State
People of colour or Hispanic (%)
Renters (%)
Median income (× US$1,000)
In multifamily buildings (%)
Fig. 1 | Comparisons of demographic characteristics of community and
rooftop solar adopters. a, Median income levels (N = 181,688). b, Percentage
of renters (N = 147,881). c, Percentage of multifamily building occupants
(N = 181,672). d, Percentage of people of colour or Hispanic (N = 181,688). Solid
diamonds indicate statistically significant (P < 0.05) results based on one-sided
Wilcoxon tests (income) or Pearson Chi-squared tests (all other variables).
Statewide estimates for race are omitted for reasons explained in Methods.
Numerical results in Supplementary Table 2. CO, Colorado; DC, Washington, DC;
IL, Illinois; MA, Massachusetts; MD, Maryland; ME, Maine; MN, Minnesota; NJ,
New Jersey; NY, New York; OR, Oregon; RI, Rhode Island.
a
0
7
14
CO
DC
IL
MA
MD
ME
MN
NJ
NY
OR
RI
Asian/Asian
American (%)
Black (%)
Hispanic (%)
b
0
30
60
CO
DC
IL
MA
MD
ME
MN
NJ
NY
OR
RI
c
0
15
30
CO
DC
IL
MA
MD
ME
MN
NJ
NY
OR
RI
Community solar
State
State
State
Rooftop solar
Fig. 2 | Comparisons of race of rooftop and community solar adopters. a, Percentage of Asian or Asian American people. b, Percentage of Black people.
c, Percentage of Hispanic people. Solid diamonds indicate statistically significant (P < 0.05) results based on one-sided Pearson Chi-squared tests. N = 181,688.
Numerical results in Supplementary Table 3.

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suggest that community solar expands solar access under the reason-
able assumption that at least some of those adopters would not or could
not have adopted rooftop solar. That assumption probably holds for
most multifamily building occupants and renters, groups that face
substantial barriers to rooftop solar adoption7. Many LMI households
face those same barriers, given that LMI households are more likely
to rent or live in multifamily buildings than are non-LMI households
(Supplementary Table 4). In contrast, the emergence of rooftop solar
leasing partly addressed adoption cost barriers for LMI homeowners.
These differences in adoption barriers may partly explain the observed
differences in the demographic factors. To further explore this hypoth-
esis, we separately identified rooftop solar system owners and lessees
in the five states where leasing is allowed, and the data allowed us to
identify lessees. In three of the five states, rooftop solar lessee incomes
more closely resemble the typical incomes of community solar adop-
ters than rooftop solar system owners (Fig. 4). However, in those same
three states, rooftop solar lessees are not substantially more likely to
rent or live in multifamily housing. The data suggest that rooftop solar
leasing addresses barriers to adoption for LMI homeowners but does
not effectively address housing barriers. The renter and multifamily
housing market is thus the clearest market niche for community solar
to address. Further, the data suggest that rooftop solar lessees are
consistently more likely to identify as people of colour or Hispanic.
That result suggests that rooftop solar leasing addresses adoption
barriers for people of colour and Hispanic households in ways that are
not replicated by community solar.
Inherent and policy impacts
The preceding analyses demonstrate that community solar adopters
differ demographically from rooftop solar adopters. These differ-
ences could reflect some combination of differences between the
two solar products—what we refer to as ‘inherent impacts’—and dif-
ferences between rooftop and community solar policies—what we
refer to as ‘policy impacts’. LMI solar policies typically take the form of
customer-level incentives (for example, reduced enrolment costs for
LMI adopters), project-level incentives (for example, financial incen-
tives provided to project managers for enroling LMI customers) or
carve outs that reserve shares of capacity or output for LMI customers25.
Supplementary Table 8 provides a summary of LMI community solar
programmes for the 11 states in our study sample. Isolating inherent
from policy impacts would be useful for understanding how effectively
community solar promotes access without additional policy support
and how impactful community solar policies have been in expanding
solar access.
We analyse inherent and policy impacts by comparing the demo-
graphic profiles of adopters that participated in LMI community
solar programmes (programme participants) and those who did not
(non-participants). We can only precisely distinguish participants
from non-participants in three states: Illinois, New York and Oregon
(Methods). As a robustness check, we obtain similar results based on
an analysis of inferred LMI programme participation in three other
states (Methods and Supplementary Fig. 1). Demographic differences
between non-participant community solar and rooftop solar adopters
provide evidence of inherent impacts, given that LMI policies did not
directly affect non-participants. Demographic differences between
participant and non-participant community solar adopters provide
evidence of policy impacts. The accuracy of that evidence depends
on reasonable assumptions around the share of participants who
would have otherwise adopted community solar, also known as free
riding (Methods).
Participants earn significantly less and are more likely to rent and
live in multifamily housing than non-participants (Fig. 5). Participants
are also significantly more likely to identify as people of colour or
Hispanic than non-participants. These results imply that community
solar policies have effectively expanded access in all four demo-
graphic dimensions. The results suggest that various approaches
can effectively expand access, as evidenced by the distinct LMI pro-
gramme structures of the three states in the analysis: Illinois subsi-
dizes LMI customer participation, whereas New York incentivizes LMI
customer enrolment at the project level and Oregon administers an
LMI carve out (Supplementary Table 8). The analysis likewise pro-
vides evidence of the inherent impacts of community solar on adop-
ter demographics in most cases. Non-participant community solar
adopters earn significantly less than rooftop solar adopters in Illinois
and New York. However, non-participant community solar adopters
earn slightly more in Oregon, suggesting that income differences in
Oregon are fully explained by policy. Differences in housing type and
tenure remain significant for non-participants in Illinois and Oregon
(consistent with inherent impacts) but are rendered insignificant in
New York. However, a robustness check to account for exceptional
circumstances in New York (Methods) suggests that demographic
differences in that state are more similar to other states when account-
ing for geographic differences in rooftop and community solar siting
(Supplementary Table 10).
To generate rough estimates of the contributions of inherent and
policy impacts, we calculate the effects of removing participants on
overall demographic differences. For instance, in Illinois, the median
income difference between all community and rooftop solar adop-
ters is US$13,000, whereas the median income difference between
non-participants and rooftop adopters is US$8,000, such that policy
accounts for about US$5,000 or 38% of the difference (assuming mini-
mal free riding). Under that approach, if free riding is trivial, the results
suggest that policy explains around 67% of income differences between
community and rooftop solar adopters, 43% of the differences in hous-
ing tenure and 23% of the differences in housing type on average across
the three states. The fact that policy appears to contribute more to
income differences is not surprising given that the policies evaluated
here target income levels. Thus, broadly speaking, the data suggest that
policy impacts are the primary driver of income differences, whereas
inherent impacts are the primary driver of differences in housing
type, and both impacts contribute roughly evenly to differences in
housing tenure.
ß = 1.49
SE = (0.03)
0.75
(0.02)
0.64
(0.02)
−1.67
(0.02)
−40
−20
0
20
40
Renter
Multifamily
LMI
People of colour
or Hispanic
Demographic factor
Adjusted logit coeficient
(~percentage points)
Fig. 3 | Conditional associations between demographic factors and solar
adoption choices. Results based on coefficients (ß) and standard errors (SE)
from model defined in equation (1) in Methods. The dependent variable is a
binary variable taking on a value of 1 for community solar adopters and 0 for
rooftop solar adopters. LMI for the purposes of this figure refers to households
earning less than the state’s median income. For simplicity, we convert the
coefficients to percentage point terms using the approximation of multiplying
the coefficients (ß) by 100 then dividing by four. The blue columns represent
the adjusted regression coefficients; the grey bars represent 95% confidence
intervals based on regression standard errors. Full numerical results are provided
in Supplementary Table 5. N = 147,874. SE, standard error.

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Conclusions
Our results suggest that certain demographic groups are significantly
more likely to adopt community solar than rooftop solar. Specifically,
we find that community solar adopters in 11 states are about 6.1 times
more likely to live in multifamily buildings, 4.4 times more likely to rent,
and earn 23% less annual income than rooftop solar adopters, on aver-
age. These results suggest that community solar has expanded solar
access by removing adoption barriers for renters and multifamily build-
ing occupants, and to a lesser extent for LMI households. We cautiously
infer from these results that other alternative clean energy adoption
models that remove such adoption barriers could similarly expand
clean energy access. For instance, community wind programmes—
though less common—could provide similar clean energy access ben-
efits as community solar.
Though community solar expands access relative to rooftop solar,
community solar adopters tend to earn more than the broader popu-
lation and are less likely to rent and live in multifamily housing. This
outcome is not surprising given the economic incentives that commu-
nity solar providers face (see Introduction). It is likely that community
solar will become more equitable over time, both because of the broad
tendency of emerging technologies to diffuse to underserved markets
over time8 and because of increasingly ambitious community solar
policies to expand access34.
We do not find evidence that community solar has expanded
solar access in terms of race. Indeed, we find that people of colour and
Hispanic households have been less likely to adopt community solar
than rooftop solar. The reason for these racial differences is unclear.
One hypothesis is that some households may be suspicious of market-
ing for relatively unknown products such as community solar29. Such
suspicions may be particularly prevalent among people of colour and
Hispanic households that are more likely to be victimized by fraudulent
marketing than White households35. At the same time, that hypothesis
would need to be squared with the apparent acceptance of rooftop
solar leasing by people of colour and Hispanic adopters (see Fig. 4).
Future research could explore how differences in marketing, customer
perceptions, or other factors could explain racial differences across
the two solar products.
Our data suggest that inherent differences across the two solar
products largely explain historical differences in housing type and
tenure between community and rooftop solar adopters. Evidence of
inherent impacts on housing type and tenure is generally encouraging
from a policy perspective. Inherent impacts suggest that policymak-
ers could expand solar access by creating a basic infrastructure for
community solar, such as virtual net metering, even without specific
measures to promote equity, such as LMI incentives or carve outs.
Nonetheless, we find evidence that LMI community solar programmes
increase LMI adoption by subsidizing enrolment for LMI customers,
incentivizing project managers to acquire LMI customers, or reserving
shares of project capacity for LMI customers. Our results also suggest
that LMI community solar programmes expand access to people of
colour and Hispanic households. Overall, the results suggest that
targeted LMI community solar policy can augment the access benefits
of community solar.
We conclude with suggestions for further research. Here, we have
identified the impacts of community solar on adopter demographics
a
0
95
190
DC
IL
MA
NJ
NY
Median income
(× US$1,000)
Renters (%)
Multifamily (%)
People of colour or
Hispanic (%)
b
0
19
38
DC
IL
MA
NJ
NY
c
0
40
80
DC
IL
MA
NJ
NY
d
0
40
80
DC
IL
MA
NJ
NY
Community solar
State
State
State
State
Rooftop solar owners
Rooftop solar lessees
Fig. 4 | Comparisons of demographic characteristics across three solar
products. a, Median income levels (N = 47,256 community, 23,133 rooftop
owners, 18,174 rooftop lessees). b, Percentage of renters (N = 36,391 community,
19,058 rooftop owners, 15,558 rooftop lessees). c, Percentage of multifamily
building occupants (N = 47,242 community, 23,133 rooftop owners, 18,174
rooftop lessees). d, Percentage of people of colour or Hispanic (N = 47,256
community, 23,133 rooftop owners, 18,174 rooftop lessees). Solid diamonds
indicate statistically significant (P < 0.05) differences between community
solar adopters and rooftop solar lessees based on one-sided Wilcoxon tests
(income) or Pearson Chi-squared tests (all other variables). Numerical results in
Supplementary Table 7.
a
0
90
180
IL
NY
OR
Median income (× US$1,000)
In multifamily buildings (%)
Renters (%)
People of colour or
Hispanic (%)
b
0
15
30
c
0
25
50
IL
NY
OR
State
d
0
30
60
IL
NY
OR
State
State
State
Community solar:
LMI programme participants
Community solar:
non-participants
Rooftop solar
IL
NY
OR
Fig. 5 | Demographic characteristics in community solar and rooftop solar
subsamples. a, Median income levels (N = 2,999 participants, 19,191 non-
participants, 36,110 rooftop). b, Percentage of renters (N = 1,817 participants,
13,317 non-participants, 29,816 rooftop). c, Percentage of multifamily building
occupants (N = 2,991 participants, 19,185 non-participants, 36,110 rooftop).
d, Percentage of people of colour or Hispanic (N = 2,999 participants, 19,191
non-participants, 36,110 rooftop). Solid points indicate statistically significant
(P < 0.05) differences based on one-sided Wilcoxon tests (income) or Pearson Chi-
squared tests (all other variables). Numerical results in Supplementary Table 9.

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stemming from policies and broad differences between the prod-
ucts. Future research could explore which specific aspects of different
solar products most effectively promote solar access. For instance,
do community solar features such as cancellation terms and transfer-
ability (whether customers can keep community solar subscriptions
when changing addresses) affect adopter demographics? Similarly,
future research could analyse how different community solar LMI
policies affect adopter demographics. Our results suggest that vari-
ous approaches can be effective—such as customer-level incentives,
project-level incentives, and LMI carve outs—but future research could
explore whether certain approaches are more cost-effective than
others. Further, future research could explore how LMI community
solar programmes could optimize the benefits that accrue to LMI
customers, such as through guaranteed bill savings and customer
protections. Finally, our results raise many questions about access
to solar across race. Future research could explore why rooftop and
community solar appear to be reaching distinct racial communities
and why policy appears to be particularly critical for expanding solar
access to racial minorities.
Methods
Data
Our data sources are defined in Table 1. We used home addresses to
match adopter records at the address level to household-level vari-
ables for income, housing type and housing tenure purchased from
Experian. Household-level incomes are estimated from individual- and
household-level variables and a proprietary model developed by Expe-
rian, an empirically driven algorithm built to effectively predict sample
statistics (for example, means, medians) of solar adopter incomes. The
income estimates have been empirically validated in previous research
on solar adopters13. The housing type variable is based on US Postal
Service data. The housing tenure variable is based primarily on tax
assessment and deed data. We predicted household-level racial char-
acteristics using the ‘wru’ package in R36. The wru package estimates
continuous probabilities for household race/ethnicity in five catego-
ries (Asian or Asian American, Black, Hispanic, White, other) based on
the surname of the household’s Census tract and the surname of the
head of household. In cases where the surname was unavailable (12%
of records), wru predicts race based only on the Census tract. Remov-
ing these tract-only predictions from the data does not substantially
affect the results (Supplementary Fig. 2). The wru algorithm has been
empirically validated to predict household race with around or above
80% accuracy5,37. We converted the continuous probabilities to a binary
people of colour or Hispanic variable score based on whether some race
other than White was assigned the greatest probability. We compare
solar adopter demographics to the general population using state-level
demographic statistics from the US Census American Community
Survey. However, we omit the statewide comparison for race because
the continuous race probabilities estimated by wru cannot be mean-
ingfully compared to the self-identified races reported in Census data.
The accuracy of sample statistics for the income and race estimates
is subject to modelling error. Nonetheless, potential modelling error
is not a material concern to the validity of the comparative statistics.
Modelling bias may yield random erroneous results in certain states,
but the directional consistency of comparative statistics across states
suggests that those results are robust to any potential modelling bias.
Unconditional demographic differences
We test observed (unconditional) differences for the hypotheses
described in the main text. We use Wilcoxon rank-sum tests to test
hypotheses for household incomes and Pearson χ2 tests for the cat-
egorical variables (housing tenure, housing type, race). We compare
medians because household income levels are not normally distrib-
uted. We make two adjustments to ensure independence between the
comparison groups. First, we estimate comparative statistics within
states to ensure that the community and rooftop solar data are pulled
from the same geographic subsamples. Second, we restrict the rooftop
solar adopter data to systems installed in 2022 to account for the fact
that our community solar data reflect samples of customers enroled in
community solar in 2022 or 2023. That temporal misalignment matters
because rooftop solar adoption has become more demographically
equitable over time5. Both restrictions are reflected in the sample sizes
reported in Table 1.
Conditional probability model
To isolate the relative effects of household demographics on adoption
choices we use the following logit model:
p (CS) = α + Dβ + S + ε
(1)
Where p(CS) is the probability that a household is a community solar
adopter (as opposed to a rooftop adopter), D is a vector of dummy
variables for the four demographic dimensions and S is a state random
effect. We convert the income variable into a dummy value by bifurcat-
ing the records into households that earn more or less than the state
median income. The coefficient of interest is β, which represents the
statistical association between household demographics and the
household’s adoption choice. Note that the model is not designed for
causal inference. Household adoption choices are probably driven by
numerous idiosyncratic factors that could correlate with the demo-
graphic factors. The purpose of this model is to compare the rela-
tive weights of the β coefficients to understand which demographic
dimensions are most strongly associated with household adoption
choices. We use state random effects to account for the possibility that
community solar has distinct impacts on adopter demographics in
different states with distinct policy contexts. In addition to comparing
the coefficients, we also implement variations of the model in equa-
tion (1) with different combinations of the demographic factors. We
then compare Akaike Information Criterion (AIC) values across those
models (Supplementary Table 6). The AIC is a metric that simultane-
ously measures prediction accuracy while penalizing models with more
variables. The AIC comparisons provide another way of comparing
the relative contributions of demographic differences to household
adoption choices.
Inference about solar access
The comparative statistics and conditional model describe differences
in household adoption choices. Those differences provide evidence
that community solar expands access (that is, promotes adoption
among demographic groups that are underserved by rooftop solar)
under the premise that at least some community solar adopters would
not or could not have adopted rooftop solar. That is, the premise is that
at least some community solar adopters face constraints that make
community solar the only viable option for solar adoption. Under
that premise, systematic differences in adopter choices partly reflect
systematic differences in adoption constraints, not just heterogene-
ous preferences, and community solar expands access by mitigating
those constraints. That premise is supported by evidence of barri-
ers to rooftop solar adoption for specific demographic groups such
as multifamily building occupants, renters, LMI households4,7 and
racial minorities2.
Analysis of LMI programme participants and non-participants
State programmes in Illinois, New York and Oregon provided identifiers
for LMI programme participants. Participants may have received finan-
cial incentives to participate or were otherwise prioritized for adoption
to comply with state LMI carve outs. We analyse evidence of inherent and
policy impacts by distinguishing participants from non-participants
in each state: Illinois (N = 918 participants; 11,143 non-participants),
New York (1,363 participants; 6,733 non-participants) and Oregon

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(718 participants; 1,315 non-participants). For each state, we likewise
create subsets of rooftop solar adopters who did not receive any LMI
incentives for rooftop solar adoption. The accuracy of that analysis
depends on how many participants would have adopted community
solar without LMI programme benefits, a concept known as free riding.
At one extreme, inherent and policy impacts can be precisely identified
if free riding is non-existent. However, if free riding is common, then
the analysis would tend to understate inherent impacts and overstate
policy impacts. There are at least three reasons to assume that free
riding is not common in LMI community solar programmes. First,
evidence that LMI programme participants are relatively difficult
and costly to acquire29,30 suggests that participants would not other-
wise have adopted. Second, LMI community solar programmes use
similar eligibility criteria as LMI rooftop solar incentives, and available
evidence suggests that free riding in LMI rooftop solar programmes
is infrequent38. Third, the data suggest that participants vary signifi-
cantly from non-participants in terms of race, indicating that LMI
programme benefits are reaching a distinct population of adopters.
Limiting the data (N = 7,492) to adopters earning less than 80% of their
states’ median income (a common threshold for identifying LMI house-
holds), participants are about 1.8 times more likely to identify as people
of colour or Hispanic than non-participants (t = 14.0, two-sided). These
differences suggest that LMI programmes are reaching a distinct popu-
lation of LMI households than those adopting without programme
benefits. Still, some degree of free riding probably exists, meaning
that policy impacts are imprecisely identified. Further, at least some
community solar adopters that were eligible for LMI programmes may
have adopted without participating in LMI programmes. Such adopters
would have been, by definition, free riders if they had participated in
the LMI programmes, such that their classification as non-participants
would not affect the results.
Robustness check of New York data sample
About 75% of rooftop solar adopters in New York in the data reside
in the relatively densely populated ‘downstate’ region of the greater
New York City area, whereas about 95% of community solar capac-
ity has been deployed in the less densely populated ‘upstate’ region
outside the New York City area39. That geographic skew partly drives
the exceptional results for demographics differences in New York. For
instance, as shown in Fig. 1, the geographic skew partly explains why
New York rooftop solar adopters are about as likely to live in multifamily
buildings as community solar adopters. That geographic skew is a valid
component of the comparison: the fact that community solar projects
are predominantly deployed in less densely populated areas affects
the degree to which community solar can expand solar access to mul-
tifamily building occupants and renters. However, the valid impacts of
that geographic skew are exacerbated by the fact that community solar
data for downstate projects (about 5% of capacity) were not available
for this study, creating a small source of data bias. To test the robust-
ness of the New York results to this geographic skew and data bias, we
implement the analyses behind Figs. 1 and 5 while limiting the New York
data to rooftop and community solar adopters living in upstate New
York (specifically, all counties north of Rockland and Westchester).
The results of those analyses are provided in Supplementary Table 10.
Imputed carve outs
In addition to the three states depicted in the analysis in Fig. 5, two
states in our data (Colorado and Maryland) have specified percentage
point minimum carve outs for LMI customers. We impute an effective
LMI carve out for Massachusetts using data compiled in the Low Income
Financing and Transactions (LIFT) Solar Toolkit40, downloaded on 14
April 2023. The LIFT Solar Toolkit identifies project capacity reserved
for LMI customers. We divide the reserved LMI capacity by the total,
cumulative community solar capacity deployed in the state based on
data from Connelly30. As a point of reference, the same method yielded
an imputed carve out of 5.9% in Colorado, close to the state’s mandated
carve out of 5%. For all three states, we isolate the bottom end of the
community solar adopter income distribution in proportion to the
carve out. For instance, Colorado requires that LMI households account
for at least 5% of community solar customers, such that in that state we
isolate the 5% of community solar adopters with the lowest incomes
as ‘below’ the carve out. In effect, this represents the most optimistic
assumption for the efficacy of the LMI carve out and would thus tend
to minimize any residual inherent impact. The results of this analysis
are provided in Supplementary Fig. 1, where points ‘below carve out’
represent adopters below the implied carve out on the income distri-
bution and ‘above carve out’ points represent adopters above those
implied carve outs.
Limitations
Two limitations noted in the main text are worth expanding upon. First,
we analyse a geographically restricted sample of 11 states. Those 11
states comprise some of the country’s largest community solar mar-
kets, accounting for around 57% of cumulatively deployed community
solar capacity by the end of 2022 (ref. 33). The 11 states are geographi-
cally diverse, with at least one state in each region of the United States
except the Southeast. The 11 states also reflect a diversity of commu-
nity solar policy contexts25. The policy and geographic diversity of our
sample and the consistency of results across these states suggest that
our results can be generally extrapolated to community solar markets
in other states. The external validity of the results is also supported
by the similarities in community solar definitions and policies across
states34,41. Nonetheless, some caution is required in extrapolating
our analysis to states with distinct demographic, market and policy
contexts. We also recognize that our analysis of policy impacts is based
on a further restricted sample of three states, though again these three
states reflect a diversity of LMI programme contexts (Supplementary
Table 8). Whereas our results suggest that LMI community solar poli-
cies have been impactful, our retrospective analysis cannot necessarily
be extrapolated to future LMI community solar policies, which are
increasingly ambitious in scope and scale34. Second, the community
solar data are cross-sections of households that were actively enroled
at the time the data were generated. With those cross-sectional data,
we lack insights into trends in community solar adoption over time.
This limitation shaped our analysis of inherent and policy impacts.
We have used the practical method of comparing cross-sections of
community solar adopter demographics between policy programme
participants and non-participants. As noted, our method only pre-
cisely identifies policy impacts under the strict assumption of no
free riding in community solar LMI programmes. Whereas available
evidence suggests that free riding is infrequent, free riding is prob-
ably non-zero and thus our policy impacts are imprecisely identified.
An ideal approach—a suggestion for future research—would be to
more precisely identify policy impacts through econometric analy-
sis of changes in community solar adoption trends before and after
policy implementation.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
This work was performed using proprietary, household-level data that
cannot be shared. However, elements of the rooftop solar data are
publicly available from the Lawrence Berkeley National Laboratory at
https://emp.lbl.gov/projects/solar-demographics-trends-and-analysis.
Aggregated data are available in via Github at https://github.com/
eoshaugh2/community_solar_access. The LIFT Solar Toolkit used in the
imputed carve outs analysis (Methods) is publicly available at https://
lift.groundswell.org/solar-projects/.

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Code availability
Code used in this analysis is available via Github at https://github.com/
eoshaugh2/community_solar_access.
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Acknowledgements
This material is based upon work supported by the US Department
of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy
(EERE) under the Solar Energy Technologies Office award number
38444 and contract number DE-AC02-05CH11231 (E.O. and G.B.).
This work was authored in part by the National Renewable Energy

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Laboratory, operated by Alliance for Sustainable Energy, LLC, for DOE
under contract number DE-AC36-08GO28308 (S.K. and J.S.). Funding
was provided by the DOE Office of Energy Efficiency and Renewable
Energy Solar Energy Technologies Office. The views expressed in
the article do not necessarily represent the views of the DOE or the
US Government. The US Government retains and the publisher, by
accepting the article for publication, acknowledges that the US
Government retains a non-exclusive, paid-up, irrevocable, worldwide
license to publish or reproduce the published form of this work, or
allow others to do so, for US Government purposes.
Author contributions
E.O. wrote the paper and led the analysis; G.B. conceived of the study,
supported the analysis and co-led data acquisition; and S.K. and J.S.
led data acquisition and supported the analysis.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41560-024-01546-2.
Correspondence and requests for materials should be addressed to
Eric O’Shaughnessy.
Peer review information Nature Energy thanks Michael Garvey,
Christine Lasco Crago and the other, anonymous, reviewer(s) for their
contribution to the peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to
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applicable law.
© The Author(s), under exclusive licence to Springer Nature Limited
2024, corrected publication 2024

    Policy Brief:

Answer:
**Community solar reaches adopters underserved by rooftop solar**

**Policy Problem**Rooftop solar has enabled millions of households to reduce electricity bills and participate in the renewable energy transition. However, rooftop solar adoption remains largely out of reach for households in certain communities, especially multifamily building occupants, renters, and low‑income households. In addition to reducing the deployment potential of rooftop solar, inequitable solar access generates political opposition to rooftop solar policies. Policymakers are responding through measures to provide more equitable solar access. One such approach is to enable and support community solar, a business model wherein multiple households buy solar output from a single shared system. Proponents argue that community solar does not carry the same adoption challenges that have resulted in inequitable rooftop solar access. However, the impacts of community solar on solar access have not yet been empirically evaluated.

**Scientific Research Findings**Using household‑level data from 11 US states, we find that community solar adopters in 2023 were about 6.1 times more likely to live in multifamily buildings than rooftop solar adopters, were about 4.4 times more likely to rent, and earned about 23% less annual income. These results suggest that community solar has expanded solar access to households that would or could not otherwise have adopted solar. We also find that policies have been effective in further expanding solar access, such as programmes that reserve community solar shares for low‑income customers. We estimate that income‑targeted community solar policies explain around 70% of differences in income levels between community and rooftop solar adopters in our sample, around 40% of differences in renter rates, and around 20% of differences in housing type. The impacts of community solar on solar access may vary from the results of this study in other jurisdictions with distinct policy and socioeconomic contexts.

**Scientific Research Study Methods**Household‑level adopter data were compiled from the Lawrence Berkeley National Laboratory’s Tracking the Sun data set (rooftop solar), the National Renewable Energy Laboratory’s Sharing the Sun research project (community solar), and directly from state community solar programmes. Household‑level solar adopter data were matched to household‑level demographic data for income, housing type (for example, single versus multifamily), housing tenure (for example, own versus rent), and race/ethnicity. We then selected a subset of data from 11 US states with at least 100 rooftop and community solar adopters with available data for key variables. We analysed the demographic characteristics of rooftop and community solar adopters to identify statistically significant differences between the two adopter groups. Demographic differences were relatively consistent and generally significant across all states in terms of income, housing type, and housing tenure, but not in terms of race/ethnicity. We used additional statistical modelling to estimate the impacts of community solar policies on community solar adoption trends.

**Policy Implications**• Community solar expands solar access by eliminating barriers associated with rooftop solar adoption, especially for multifamily housing occupants and renters.
• Policy can augment the access benefits of community solar, such as by reserving community solar shares or providing adoption incentives for low‑income households.
• Community solar does not yet achieve equitable access relative to the general population. Policymakers could identify and aim to resolve remaining barriers to community solar adoption in underserved communities.
• Alternative business models that enable shared adoption of clean energy technologies could support equitable clean energy access in other domains.