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

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
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nature energy
Article
https://doi.org/10.1038/s41560-024-01603-w
Public and local policymaker preferences for
large-scale energy project characteristics
Holly Caggiano
  1
, Sara M. Constantino2, Chris Greig
  3 & Elke U. Weber
  3,4
Rapidly building utility-scale energy infrastructure requires not only
public support but also political will across levels of government. Here
we use a conjoint experiment to assess preferences for large-scale energy
projects among residents and local elected officials in Pennsylvania—a key
transition state with high solar potential where siting authority rests at
the local level. We find that residents prefer solar to other energy projects,
and job creation and cooperative community ownership are associated
with increased support. Public and elected official support decreases
when projects are owned by foreign companies. We find limited partisan
differences in preferences, suggesting a path towards bipartisan support for
such projects. Elected officials misperceive their constituents’ preferences,
underestimating support for renewable energy and the importance of job
creation. As local officials are key decision-makers regarding infrastructure
development, their preferences and perceptions of constituents’
preferences may dictate which energy projects are approved and what
community benefits they deliver.
While there are multiple pathways for the USA to reach net-zero
emissions by 2050, there is broad agreement that all rely on rapid,
large-scale deployment of renewable energy technology to meet
increased demand for electricity generation while quickly reducing
emissions1,2. This transition will require radical technological, indus-
trial, economic and social shifts: most net-zero pathways call for the
US energy and industrial system to phase out coal use by around 2030
and substantially reduce oil and natural gas use by 2050. In addition
to large-scale wind and solar energy development, transitioning to
renewable energy at scale requires major expansion and extension
of transmission grids. The viability of building out the extraordinary
levels of infrastructure development required for deep decarboni-
zation depends critically on social and community acceptance and
unprecedented political will.
Despite the fact that emissions reduction targets and renewable
energy production goals are often set at the national or state level, the
planning, siting and deployment of large-scale energy infrastructure
projects often happens locally, at the county or municipal level. Siting
authority varies widely state by state in the USA, but in the vast majority
of states, onshore wind and solar siting authority rests either solely at
the local level, or authority varies on the basis of project size, where at
least some siting happens locally rather than at the state level3,4. Thus,
local elected officials have a key role to play in the siting of large-scale
energy projects, including developing zoning ordinances, voting on
project approvals and negotiating community benefits. While some
research has explored the policy preferences of local elected offi-
cials relating to energy5, there is a notable gap in work that examines
their preferences and perceptions of their constituents’ preferences
for large-scale energy projects. This study thus seeks to understand
cross-partisan public and local elected official preferences for char-
acteristics of large-scale local energy projects, including distance
from residential areas, employment opportunities, local benefits,
ownership structures, site type and fuel type. We focus on Pennsylvania
(PA)—a critical energy transition state in the USA2 and historical home
of extractive energy industries—where in March 2021 Governor Tom
Wolf announced the largest state government solar initiative in the
USA so far6. PA grants local governments authority for both onshore
wind and solar siting3,4. Further, a recent analysis found that, across
Received: 16 February 2024
Accepted: 11 July 2024
Published online: 1 August 2024
 Check for updates
1School of Community and Regional Planning, University of British Columbia, Vancouver, British Columbia, Canada. 2Department of Psychology and
School of Public Policy, Northeastern University, Boston, MA, USA. 3Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ,
USA. 4Department of Psychology, School of Public and International Affairs, Princeton University, Princeton, NJ, USA.
 e-mail: holly.caggiano@ubc.ca

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opposition are more nuanced, depending less on physical proximity
to infrastructure and more on other characteristics of the project, like
concern that projects will decrease economic and aesthetic value of the
land16,19. In contrast, recent survey research finds that closer proximity
to residential areas or respondents’ homes is associated with reduced
support for large-scale projects, including wind turbines in Germany20
and renewable energy projects generally in the USA10. Further, concerns
about visual impacts are associated with reduced support for large-scale
solar in California21, and minimum distance requirements from residen-
tial areas increase support for CCS projects in the USA22.
In addition to proximity to residential areas, the siting of projects
on specific types of land, such as wildlife habitats and farmland, has
implications for public support. Various studies point to local concerns
about the ecological impact of large-scale energy projects. Ecological
impact was the strongest predictor of opposition for wind energy
projects in Switzerland, Estonia and Ukraine23, and respondents in
Ireland favour wind projects located away from protected habitat
areas24. This concern aligns with recurring narratives in the USA about
wind turbines as harmful to threatened bird species—recently, a wind
energy company in the USA pleaded guilty to violating the Migratory
Bird Treaty Act after their turbines killed at least 150 bald eagles25. This
concern also holds for solar, with respondents in the USA preferring
that solar farms have large buffers around wildlife habitat21. In addition
to wildlife habitat, farmland often takes a centre role in discussions
about renewable energy siting. Recent studies have explored the ten-
sions between farmers and developers, seeking to better understand
the intersection of farmland preservation and renewable energy sit-
ing26,27. Other studies have examined the potential for development of
renewables on brownfield sites or decommissioned mining sites, citing
the co-benefit of fewer land constraints and greater public support28,29.
Economic risks and opportunities associated with renewable and
low-carbon energy sources, specifically about employment, are par-
ticularly salient in political arguments about energy transition dynam-
ics. Studies have found that previous federal legislation in the USA,
including the 2009 American Recovery and Reinvestment Act, have
proven successful in creating energy sector jobs30. Tackling climate
change while creating jobs was key to US President Biden’s agenda,
aiming to dispel narratives that a renewable energy transition will result
in widespread job loss, particularly in states that are economically tied
to the production of coal and gas. However, while net employment is
expected to increase as renewable energy production ramps up, this
is true at a regional or national scale. At the local level, coal communi-
ties will probably see job losses at the individual and household level,
resulting in loss of retail and commercial employment and decreases
of local tax revenue bases14. Furthermore, workers justifiably fear lower
wages and precarious job security in renewables work compared with
traditional energy industry jobs that are often unionized31.
There is potential for other local benefits, in addition to job crea-
tion, to increase local support, including direct financial benefits,
reduced local air pollution and environmental justice considerations.
In Germany, both individual and collective financial benefits, including
discounted electricity rates, payments to communities and payments
to municipalities, were found to improve acceptance of local wind
projects32. In the case of both wind turbines and electricity transmis-
sion pylons, perceptions of health risks were also found to be crucial
for public support: hypothetical projects that cited a high frequency
of health complaints were strongly rejected and projects that cited no
occurrence of health complaints were accepted, and health-related
attributes were more important than location and compensation
payments33. Beyond just predicting support or opposition to projects,
understanding preferences for local benefits also has implications
for equitable distribution of energy transition costs and benefits; it is
critical that marginalized groups already subject to historical environ-
mental inequities do not bear the additional brunt of potential negative
transition impacts, including rising energy prices14.
more than 2,500 municipalities in the Commonwealth, 87% of zoning
codes provide no guidance on siting utility-scale solar7.
We find that direct benefits to communities, including the creation
of permanent, union-wage jobs and cooperative community owner-
ship, increase support for energy projects. Pennsylvanians prefer
solar projects over wind, nuclear and natural gas power plants with
carbon capture and storage (CCS). Local elected officials, however,
misperceive the preferences of their constituents, underestimating
support for renewable energy and the importance of job loss and
creation. The public and local elected officials have similar opinions of
foreign-owned products, which is associated with the greatest reduc-
tions in support. Importantly, we find limited partisan differences
in preferences for large-scale renewable energy project characteris-
tics, suggesting a promising path towards building bipartisan sup-
port for such projects. Given the role of local elected officials as key
decision-makers regarding energy infrastructure development, their
preferences and how they perceive their constituents’ preferences may
be important predictors of which projects come to fruition and what
benefits they provide to local communities, offering opportunities to
realize just energy transitions.
Results
Understanding preferences for large-scale energy projects
Polls consistently show that Americans overwhelmingly support the
development of renewables, including wind and solar8, and are more
likely to support wind or solar than nuclear energy9. People who already
live near wind turbines tend to prefer additional wind to other types of
centralized power plant10. Sharpton et al.10 also found positive percep-
tions of natural gas as an energy supply, as respondents associated it
with positive economic impact and local employment. In this study, in
addition to measuring support for large-scale onshore wind and solar
projects, we also examine support for nuclear energy and natural gas
to enable comparisons across technologies.
Beyond support or opposition based on type of technology or
energy infrastructure, there are a wide variety of project characteristics
that may influence public preferences. In public discourse, large-scale
energy projects are routinely framed in ways that emphasize specific
risks or benefits to local communities, and support or opposition to
projects is often tied to narratives about these risks and benefits11,12. At
the individual level, narratives help individuals make sense of uncer-
tainty, influence cognition and support or challenge existing power
relations and policy outcomes13. Narratives, in this sense, also help
to draw attention to and evaluate justice and equity implications sur-
rounding project development14. Thus, we can consider a package of
characteristics as one narrative, and bundling different combinations
of characteristics may help us understand which narratives influence
support or opposition across which groups. In this work, we consider
characteristics across six dimensions: energy source, distance from
residential areas, type of development site, job opportunities, local
benefits and project ownership. We selected these dimensions on the
basis of an extensive review of existing literature, as well as conversa-
tions with experts in the field, and informed by narratives prevalent in
recent news stories about energy project development.
A rich body of literature examines preferences for siting energy
infrastructure, with a focus on renewable energy. Early research focused
largely on visual aesthetics and distance from residential areas in
response to ‘not in my back yard’ (NIMBY) concerns from local resi-
dents15–17. NIMBY looked to be a promising explanation for the ‘social
gap’ in renewable energy siting decisions—why, when there is consistent
broad public support for renewable energy, are specific projects heavily
opposed18? However, across geographical, cultural and project-specific
contexts, there is mixed evidence for the importance of proximity to res-
idential areas as a predictor of opposition to large-scale energy projects.
Early research suggests that, while proximity can have strong effects on
public support for proposed projects, the ‘selfish’ components of NIMBY

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Project ownership is another facet of energy infrastructure devel-
opment that may impact public support. More than 80% of US energy
infrastructure is owned by the private sector, including national and
international companies34. Studies have found preferences for local
ownership, though this is not the norm in practice23,35. Other studies
find opposition to foreign ownership36, which is supported in the USA by
‘America First’ rhetoric and the prioritization of national industry over
foreign competitors. While it may currently seem unlikely for large-scale
energy projects to be community owned, recent work has explored pos-
sibilities for scaling up or mainstreaming community-owned energy37,38.
While the trends discussed above are supported by various sam-
ples and studies, there is also heterogeneity by individual characteris-
tics, including partisanship. While members of both parties in the USA
largely support renewable energy, Republican support has dropped
in recent years39. Reasons for renewable support also differ by party:
while Republicans express support when they anticipate economic
opportunities associated with renewable energy development, Demo-
crats tend to focus on renewables as a climate change solution40. At the
national level, Republicans typically have a more favourable opinion
of natural gas than Democrats41 and are also more likely to support
nuclear energy9. We draw attention to partisanship, here, as climate
and energy are often viewed as highly polarized issues and political
identities have been found to exert powerful influences preferences42.
Finding opportunities for bipartisan cooperation is one avenue towards
advancing climate action, and these may be more likely to occur at the
state and local levels than in national agenda-setting43.
In addition to its predilection for local, decentralized decision-
making, PA is an ideal study site as the state has a rich and diverse energy
history. Princeton’s Net-Zero America modelling identified PA as a
top five state for solar capacity in the decade leading up to 2050, with
one model calling for 95 GW of new installed capacity2. In addition to
being a key state for renewables development in the coming decades,
for almost two centuries PA has been central to US energy resources.
The state has historically been a site of extractive energy practices,
including coal mining and hydraulic fracturing (fracking) of shale
gas, which has been linked to adverse public health impacts44. Nuclear
energy is another component of PA’s energy production portfolio,
and indeed PA is home to Three Mile Island, the site of a partial reactor
meltdown and worst nuclear incident in US history. While the accident
has not been linked to adverse physical health impacts, scholars have
documented long-term mental health consequences for locals45. In the
face of this history, the documented health co-benefits of renewable
energy deployment in PA may be particularly salient46.
Public preferences for large-scale energy projects
In this study, we ask: (1) what attributes of large-scale energy projects
impact public and political support for their development among PA
residents? And (2) how do local elected officials perceive public sup-
port for projects? Figure 1 shows the average marginal component
effects (AMCEs) from experiment 1, with 95% confidence intervals
(see Supplementary Table 1 for model estimates). When reporting
AMCEs, one level per attribute is displayed as a baseline category and
Distance
Jobs
Benefits
Ownership
Site
Type
−0.2
−0.1
0
0.1
0.2
50 miles (not likely to be visable from your home, school or workplace)
10 miles (potentially visable during drives or commutes in and out of the area)
5 miles (potentially visable from your home, school or workplace)
2 miles (probably visable from your home, school or workplace)
No changes to number of local energy jobs
50 jobs lost due to retirement of an old plant
Creation of 50 permanent jobs with union-scale wages
Creation of 50 temporary hourly-wage jobs during construction
Reduced energy costs for all local residents
Increased access to afordable clean energy in marginalized communities
Increased overall economic activity in the community
Reduced local air pollution
Owned by an American private company
Owned by a foreign private company
Owned by state government
Owned cooperatively by community
Low visual impact on scenic resources
Agricultural land
Decommissioned mining or industrial site
Low impact on wildlife habitats
Natural gas power plant with CCS
Large-scale onshore wind turbines
Large-scale solar energy farm
Nuclear power plant
Estimated AMCE
Fig. 1 | AMCEs of attribute levels on public support for project development.
This plot reports average effects of each project element on support for the
project in an online sample of PA residents (n = 894). Point estimates are AMCEs
with 95% confidence intervals (mean values ± s.e.m.) for each level. Each AMCE
estimates how inclusion of the listed project feature affects support for energy
projects. Each element is compared against a baseline category, represented by
points without lines.

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is set at zero. AMCEs should be interpreted as the average change in
probability associated with a one-unit change in the predictor variable.
When reporting in text, we convert changes in probability to changes
in percentage points (p.p.) for ease of interpretation.
Considering distance from residential areas, PA residents are
less likely to support projects close to home, compared with projects
located far from residential areas (50 miles). Support decreases by
7 p.p. when projects are located 2 miles away from residential areas,
compared with when they are 50 miles away. Considering job oppor-
tunities, changes to local employment opportunities are also salient
to respondents. Using no changes to jobs as a reference category, job
loss reduces overall support for projects by 12 p.p. and the creation
of permanent jobs increases support by 12 p.p. Creating temporary
jobs increases support by 4 p.p. compared with no job changes. With
regard to local project benefits, respondents find personal monetary
benefits (for example, reduced costs) preferable to community or
health benefits. They prefer the reference category, reduced energy
costs for all local residents, to increased overall economic activity in
the community, which is associated with a reduction of 6 p.p. in support
and reduced local air pollution, which is associated with a reduction
of 5 p.p. in support.
Variation in ownership explains the greatest variation in support
compared to other attributes. Respondents prefer cooperative com-
munity ownership, associated with a 5 p.p. increase in support rela-
tive to American private company ownership. There is no statistically
significant difference in support between the reference category and
state government ownership. The most pronounced result concerns
ownership by a foreign private company, which decreases support
by 17 p.p. PA residents appear to be indifferent about the type of land
on which energy projects are sited. We find no statistically significant
differences in support detected between the reference category, low
visual impact on scenic resources and other attributes including agri-
cultural land, decommissioned mining or industrial sites, and areas
that would have low impact on wildlife habitats. PA residents appear
to support solar projects over other types of energy project, with an
associated 7 p.p. increase in support relative to the reference category
of natural gas power plants with CCS. Nuclear projects are associated
with a 6 p.p. reduction in support compared with natural gas with CCS.
Figure 2 displays the results of a subgroup analysis by political
party, comparing preferences of Democrats, Republicans and Inde-
pendents. Here, we report marginal means for each party on each
attribute.
We find statistically significant differences in preferences as a func-
tion of political party affiliation between Republicans and Democrats,
Republicans and Independents, and Independents and Democrats.
While partisan differences are present, they are generally minimal and
not statistically significant, with their magnitudes being nearly identi-
cal across groups. Considering distance from residential areas and site
types, there is no statistically significant difference in marginal means
across respondents from any political party. Considering jobs, there
is not a statistically significant difference in marginal means between
Republicans and Democrats, suggesting similar preferences. There are
small differences between Independents and each other party that indi-
cate Independents do not prioritize jobs as heavily (6 p.p. difference
between Independents and Democrats for ‘no changes to number of
jobs’, 7 p.p. difference between Independents and Republicans for ‘50
jobs lost due to retirement of an old plant’). In both cases, Independents
lean further towards the middle.
Considering the ownership and fuel type attributes, there are sta-
tistically significant differences between Republicans’ and Democrats’
50 miles (not likely to be visable from your home, school or workplace)
10 miles (potentially visable during drives or commutes in and out of the area)
5 miles (potentially visable from your home, school or workplace)
2 miles (probably visable from your home, school or workplace)
No changes to number of local energy jobs
50 jobs lost due to retirement of an old plant
Creation of 50 permanent jobs with union-scale wages
Creation of 50 temporary hourly-wage jobs during construction
Reduced energy costs for all local residents
Increased access to afordable clean energy in marginalized communities
Increased overall economic activity in the community
Reduced local air pollution
Owned by an American private company
Owned by a foreign private company
Owned by state government
Owned cooperatively by community
Low visual impact on scenic resources
Agricultural land
Decommissioned mining or industrial site
Low impact on wildlife habitats
Natural gas power plant with CCS
Large-scale onshore wind turbines
Large-scale solar energy farm
Nuclear power plant
Marginal mean
a   Democrat leaning
b   Republican leaning
c   Republican − Democrat
Marginal mean
Distance
Jobs
Benefits
Ownership
Site
Type
0.3
0.4
0.5
0.6
0.7
0.3
0.4
0.5
0.6
0.7
−0.2
−0.1
0
0.1
0.2
Estimated diference
Fig. 2 | Marginal means by respondent party (experiment 1). a–c, This plot
reports differences in subgroup preferences between Democrats (blue circle,
a) and Republicans (red square, b), along with the difference in marginal means
between subgroups (black triangle, c) in an online sample of PA residents
(n = 894). Point estimates are marginal means with 95% confidence intervals
(mean values ± s.e.m.) for each level. Marginal means are centred around 50%,
indicating no difference between levels.

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marginal means in some levels. There is a 12 p.p. difference between the
parties’ marginal means in the ‘owned by an American private company
level’, indicating that Republicans more strongly prefer projects that
are owned by American private companies, and an 8.4 p.p. difference
indicating that Democrats more strongly prefer projects owned by state
governments. Regarding project type, Democrats have a stronger pref-
erence for onshore wind turbines (10.6 p.p. difference) and Democrats
are less favourable towards nuclear energy than Republicans (8.6 p.p.
difference). Similarly to Republicans, Independents favour American
private companies more than Democrats (11 p.p. difference) and also
have a less favourable opinion of onshore wind (12 p.p. difference). We
tested for differences across other subgroups including gender (Sup-
plementary Fig. 1), education (Supplementary Fig. 2), income (Supple-
mentary Fig. 3) and policy framing conditions (Supplementary Fig. 4).
Elected official preferences for large-scale energy projects
In experiment 2, we conducted a reduced version of the conjoint experi-
ment with a sample of local elected officials. For this study, we ask
local elected officials not only about their own support of renewable
projects but also about their perceptions of constituent support for
these projects. As this experiment has a smaller sample size and may
be underpowered, we draw attention to substantive differences in
responses rather than solely assessing statistical significance.
Distance from residential areas, job opportunities and site type
do not have a statistically significant causal effect on personal support
for energy projects in the overall sample of PA local elected officials
(Fig. 3; see Supplementary Table 2 for model estimates). However,
respondents appear to favour projects farther away from residential
areas, projects that create permanent jobs and projects that reduce
residential energy costs rather than increasing economic activity.
Like the PA resident sample in experiment 1, ownership models are
the strongest predictors of local elected officials’ support compared
with other attributes. Compared with a baseline of ownership by an
American private company, ownership by a foreign private company
reduces support by 29 p.p. Unlike the public sample in experiment 1,
local elected officials in PA do not prefer solar energy to natural gas with
CCS, where we see no statistically significant difference between levels.
In fact, local elected officials prefer this baseline to other options—nota-
bly, onshore wind turbines are associated with a 26 p.p. reduction in
support. Natural gas without CSS is associated with a 19 p.p. decrease
in support compared with natural gas with CCS. Nuclear power plants
are associated with an 18 p.p. reduction in support compared with
natural gas with CCS.
Splitting the sample of public officials into Republicans and Demo-
crats (considering there were few Independents in this sample) and
looking at the marginal means, we find limited differences. In Fig. 4
(see Supplementary Table 3 for model estimates), the rightmost panel
displays differences in marginal means between parties, with positive
values indicating that Republicans prefer a level more than Democrats
and negative values indicating that Democrats prefer a level more
Distance
Jobs
Benefits
Ownership
Site
Type
−0.4
−0.2
0
0.2
50 miles
2 miles
No net increase of permanent jobs
Net decrease of 50 permanent jobs
Net increase of 50 permanent jobs
Reduced residential energy costs
Increased economic activity
Owned by an American private company
Owned by a foreign private company
Owned by state government
Owned cooperatively by a community
Minimal impact on scenic views
Minimal impact on agriculture
Minimal impact on wildlife habitats
Natural gas power plant with CCS
Large-scale onshore wind turbines
Large-scale solar energy farm
Natural gas power plant
Nuclear power plant
Estimated AMCE
Elected oficials
Fig. 3 | AMCEs of attributes on local elected official support for project
development. The figure reports average effects of each project element
on support for the project in an online sample of local elected officials in PA
(n = 206). Point estimates are AMCEs with 95% confidence intervals (mean
values ± s.e.m.) for each level. Each AMCE estimates how inclusion of the listed
project feature affects support for energy projects. Each element is compared
against a baseline category, represented by points without lines.

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than Republicans. For public officials, there are very few statistically
significant differences in marginal means between parties. This may
be due to the very small sample size and lack of power for these com-
parisons. The only attribute level that shows statistically significant
differences across parties is support for projects that have minimal
impact on wildlife habitats, where a 17 p.p. difference indicates that
Democrats are more concerned about protecting wildlife habitats
when siting energy projects. Not statistically significant but substan-
tively interesting, Republicans appear to support projects owned by
American private companies the most while Democrats appear to
prefer projects owned cooperatively by communities. Additionally,
Republicans appear to prefer natural gas with CCS to other energy
projects, while Democrats prefer solar.
In addition to asking local elected officials which project they
preferred, we also asked them to pick which project they think their
constituents would prefer (see Supplementary Table 4 for model esti-
mates). Figure 5a displays AMCEs for experiment 1, compared with
what elected officials predicted constituents would prefer (Fig. 5b).
These are presented as AMCEs as they are not subgroups but samples
from two different populations in different surveys (with different,
though largely corresponding, attribute levels). Thus, we cannot make
direct comparisons across samples but can observe general patterns.
It appears that elected officials largely project their own preferences
when judging those of their constituents. Considering constituent sup-
port for jobs, local elected official predictions do not show statistically
significant differences between levels or a baseline of no changes to
number of jobs, while there are relatively large casual impacts across
levels of this attribute in PA residents. The most notable difference
is officials’ perceptions of energy type, where officials believe that
their constituents do not prefer other types of energy projects to the
baseline category of natural gas with CCS. In the PA resident sample,
respondents preferred solar energy to the same baseline, suggesting
that elected officials underestimate support for solar renewable energy
among their constituents.
Discussion
The results of our conjoint experiments, and samples of matched
public and elected officials in a key transition state, have implications
for both planning and policy development around energy projects
and how dimensions of proposed projects are both decided on and
communicated to local communities. Considering the results of two
experiments, we raise four key implications for discussion: (1) evidence
of shared priorities across political parties, (2) the importance of and
opportunity for labour–energy coalitions, (3) the role of alternative
ownership models in just transitions and (4) the importance of local
elected officials to energy transition research.
Considering increasing polarization in the USA and that federal
climate change policy is framed as a highly partisan issue, our results
are promising—we largely see similarity, rather than heterogeneity,
in preferences for energy infrastructure projects across Democrats
and Republicans in our PA resident sample. We only saw statistically
significant differences between the parties’ preferences across two
attributes—ownership and energy type. Considering classic partisan
ideological differences, it is not surprising that Republicans favour
project ownership by American private companies more than Demo-
crats and Democrats prefer government-owned projects more than
Republicans. Across energy types, Democrats have a stronger prefer-
ence for onshore wind turbines and are also less likely to prefer nuclear
50 miles
2 miles
No net increase of permanent jobs
Net decrease of 50 permanent jobs
Net increase of 50 permanent jobs
Reduced residential energy costs
Increased economic activity
Owned by an American private company
Owned by a foreign private company
Owned by state government
Owned cooperatively by a community
Minimal impact on scenic views
Minimal impact on agriculture
Minimal impact on wildlife habitats
Natural gas power plant with CCS
Large-scale onshore wind turbines
Large-scale solar energy farm
Natural gas power plant
Nuclear power plant
Marginal mean
Marginal mean
Distance
Jobs
Benefits
Ownership
Site
Type
0
0.25
0.50
0.75
0
0.25
0.50
0.75
−0.25
0
0.25
Estimated diference
a
Democrat
b
Republican
c
Republican − Democrat
Fig. 4 | Marginal means by respondent party (experiment 2). a–c, This plot
reports differences in subgroup preferences between Democrats (blue circle,
a) and Republicans (red square, b), along with the difference in marginal means
between subgroups (black triangle, c) in an online sample of local elected
officials in PA (n = 206). Point estimates are marginal means with 95% confidence
intervals (mean values ± s.e.m.) for each level. Marginal means are centred
around 50%, indicating no difference between levels. In c, positive differences
indicate that Republicans prefer the level.

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energy projects. Considering the wide range of project characteristics
our experiment covered, it is encouraging that parties broadly share
values around other dimensions such as employment opportunities
and project benefits. Agreement on the importance of jobs suggests
that just transitions may be a bipartisan value. While this experiment
cannot provide insight into why energy preferences seem to tran-
scend partisanship in this sample, the results are in line with polling
research that suggests renewable energy is broadly popular across
parties nationally39. Some research has found that the reasons for
support across parties differ; for example, Democrats might prioritize
climate change mitigation while Republicans prioritize lower costs
and energy independence40. Further, benefits like increased employ-
ment and community development may act to bridge divides. Prefer-
ences for new development may also differ from support for fossil fuel
phase-out—one study in western Colorado found partisanship to be a
strong predictor of support for coal phase-out47.
The importance of net job creation to respondents across parties
echoes narratives that argue for a just energy transition that supports
labour in the face of fossil fuel job losses. A link between ‘green jobs’
and ‘good jobs’ has been central to climate policy under the Biden
Administration. It is also likely that this narrative is particularly salient
in PA, which has seen recent declines in nuclear, coal and natural gas
fuel jobs48. Echoing other calls, our results suggest an opportunity
for labour–energy coalitions and worker-led transitions49. Partner-
ships between renewable energy developers and labour organizations
may be one pathway towards developing projects with broad public
support, as well as other investments aimed at supporting job crea-
tion and minimizing structural unemployment, such as job training
programmes.
In addition to employment, project ownership appears to exert
influence on public support for energy projects. Projects owned coop-
eratively by communities were preferred to other forms of ownership,
specifically the baseline of projects owned by American private com-
panies. This result is supported by a literature on community energy
and energy sovereignty that examines a wide range of community
ownership and management structures50,51. Currently, most large-scale
energy projects require private capital for financing, but there have
been various recent calls among climate activists for public ownership
of renewable energy infrastructure52. Our findings suggest that these
calls may not just be from a vocal activist minority but could broadly
increase support for project development. It is also interesting to note
the lack of support for foreign-owned projects. This may have been
particularly salient for Americans in 2021, given increases in populist,
anti-globalization sentiment, particularly among the far right53.
Employment opportunities and ownership structures stand in con-
trast to attributes that did not have strong effects on support among
resident respondents, such as local project benefits, site types and
distance from residential areas. It is possible that responses to projects
sited close to residential areas may be different in practice; other stud-
ies point out that people may not understand distances accurately in
the abstract in which they are represented in survey questions21. Consid-
ering local project benefits, respondents narrowly preferred receiving
low-cost energy to other benefits, including increased overall economic
activity in the community and reduced local air pollution. Residential
energy costs in PA have continued to rise from 2021 through 2022, so
it is reasonable to believe that costs are top of mind for ratepayers in
the state54. This finding is interesting because state governments and
project developers often use narratives about local community benefits
when promoting projects and seeking to gain community acceptance.
For example, Cottontail Solar, a large project developed by Light-
sourceBP currently in progress in PA as a part of their state-wide solar
initiative, touts the local economic benefits of the project, including
$40 million in tax revenue over the project lifespan. The website also
points to the number of temporary local jobs created and metric tons
of carbon dioxide reduced each year55. Our findings suggest that other
possible aspects of this and other projects, including permanent jobs
created, electricity rate reductions and local ownership models, may
shift support more substantially.
While respondents in the PA resident sample broadly preferred
solar to other types of energy projects, we find a different pattern in our
sample of local elected officials, who appear to favour natural gas with
CSS to other types of projects. Considering that many US states defer
siting authority to local jurisdictions, the preferences and perceptions
of local elected officials are an incredibly important and understudied
piece of the transition puzzle. A recent survey of utility-scale wind and
solar developers found that they consider local zoning ordinances and
Distance
Jobs
Benefits Ownership
Site
Type
50 miles (not likely to be visable from your home, school or workplace)
10 miles (potentially visable during drives or commutes in and out of the area)
5 miles (potentially visable from your home, school or workplace)
2 miles (probably visable from your home, school or workplace)
No changes to number of local energy jobs
50 jobs lost due to retirement of an old plant
Creation of 50 permanent jobs with union-scale wages
Creation of 50 temporary hourly-wage jobs during construction
Reduced energy costs for all local residents
Increased access to afordable clean energy in marginalized communities
Increased overall economic activity in the community
Reduced local air pollution
Owned by an American private company
Owned by a foreign private company
Owned by state government
Owned cooperatively by community
Low visual impact on scenic resources
Agricultural land
Decommissioned mining or industrial site
Low impact on wildlife habitats
Natural gas power plant with CCS
Large-scale onshore wind turbines
Large-scale solar energy farm
Nuclear power plant
Estimated AMCE
a    Actual constituent
response
b    Predicted constituent
response
−0.2
−0.1
0
0.1
−0.3 −0.2 −0.1
0
0.1
0.2
50 miles
2 miles
No net increase of permanent jobs
Net decrease of 50 permanent jobs
Net increase of 50 permanent jobs
Reduced residential energy costs
Increased economic activity
Owned by an American private company
Owned by a foreign private company
Owned by state government
Owned cooperatively by a community
Minimal impact on scenic views
Minimal impact on agriculture
Minimal impact on wildlife habitats
Natural gas power plant with CCS
Large-scale onshore wind turbines
Large-scale solar energy farm
Natural gas power plant
Nuclear power plant
Estimated AMCE
Fig. 5 | AMCEs of attributes on predicted constituent preferences compared
with actual resident preferences. a,b, Predicted constituent preferences
(online sample of local elected officials in PA, n = 206) (a) compared with
actual resident preferences from experiment 1 (online sample of PA residents,
n = 894) (b). Point estimates are AMCEs with 95% confidence intervals (mean
values ± s.e.m.) for each level. Each AMCE estimates how inclusion of the listed
project feature affects support for energy projects. Each element is compared
against a baseline category, represented by points without lines. Attribute
levels are indicated on the outside panels, noting that levels differed slightly
between experiments.

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community opposition to be two of the biggest reasons for project
delays and cancellations56. Thus, it matters that local elected offi-
cials have accurate perceptions of public preferences when they hold
decision-making power about these projects. Local elected officials
also report perceptions of their constituents’ preferences in line with
their own, underestimating support for solar projects. This suggests a
disconnect between local elected officials and their constituents who
appear more tolerant and interested in renewable energy projects like
solar, and echoes other studies that find that local elected officials
misperceive public preferences regarding climate policy57.
These implications point to several avenues for future research.
Future experimental survey research might examine additional renew-
able energy sources and legacy fossil fuel sources, including coal-fired
power plants. We also suggest similar studies be carried out in nation-
ally representative samples, both in the USA and internationally, to
examine geographic heterogeneity in preferences. Further, the impor-
tance of employment and project ownership suggest that respondent
choices were influenced by attributes that correspond with community
buy-in. The dimensions we measured do not represent every concern
that could be raised about a project, and these experiments do not
consider issues of process. For example, additional research could
measure preferences for type and duration of community engagement
for project development. Some existing research closely examines the
siting process and argues for the role of perceived distributional and
procedural justice in building community support23. These dimen-
sions might be better untangled in qualitative case study research that
pays close attention to processes and power dynamics across multiple
projects and communities58.
While our study points to important implications for energy
transitions research, we also acknowledge limitations. First, the
hypothetical nature of the experiment could differ from actual siting
decisions in practice. Some combinations of attribute levels are more
plausible than others. For example, we are not aware of any existing
community-owned large-scale energy projects in PA. That said, we
believe all combinations of attributes represent possibilities that grant
insight into preferences. Next, there are limitations to our sample of
local elected officials in experiment 2. Local elected officials are a
difficult group to survey, and our sample of 206 officials is probably
underpowered to detect statistically significant differences across
subgroups; this may suggest a lack of differences where differences
exist. Despite this, our results do provide some insight into prefer-
ences for energy projects for which local elected officials in PA often
are key decision-makers. In this experiment, we target PA for its high
potential for energy infrastructure development, precedent for local
decision-making, and historical economic ties to energy industries,
but results could reasonably be explored in other contexts, both within
and beyond the USA. Given the mix of rural and urban areas and relative
demographic diversity of the state, we expect that many of the pat-
terns we found could be transferable to other states. Environmental
and economic concerns are probably salient across the USA; however,
specific economic context (for example, coal communities), cultural
attitudes towards development, and political atmosphere and amount
of existing energy infrastructure may drive different preferences that
can be explored in future research.
In this study, we assess the marginal impact of various character-
istics of energy projects on support for project development among
both the public and local elected officials in a key transition state. We
find that the public is more likely to support solar projects, projects
that create permanent jobs and community-owned projects. Foreign
ownership reduces support for projects, as does job loss. On a positive
note, we find substantial overlap in project characteristics preferences
between Republican and Democrat respondents. We also find, how-
ever, that local elected officials misperceive the preferences of their
constituents, underestimating support for renewable energy and the
importance of job creation. Given the role of local elected officials as
key decision-makers regarding energy infrastructure development,
their preferences and perceptions of their constituents’ preferences
may play an important role when considering which energy projects
are approved and what benefits they deliver to local communities.
Methods
Experimental design
To examine preferences for low-carbon energy infrastructure projects
in PA, we conducted two conjoint experiments to test the effects of vari-
ous characteristics of energy projects on support for project develop-
ment. These attributes are displayed in Supplementary Table 5 and were
selected on the basis of the above literature of previous energy siting
studies along with considerations about current discourse around
energy projects in the USA and PA. We examine the effects of these
project features on project approval in our samples of residents and
policymakers and broken down by party affiliation.
Conjoint experiments allow researchers to compare trade-offs
across multiple dimensions by randomly generating bundles of attrib-
utes and asking respondents to pick between two such choice bundles.
This design more closely captures decision-making in realistic contexts
than asking respondents to independently evaluate options. Use of
conjoint experiments to measure public preferences has increased
in recent years across the social sciences59–61. Conjoint experiments
have high external validity and lower social desirability bias compared
with other methods of eliciting preferences62–64. In experiment 1, we
examine 24 project dimensions in a demographically representative
sample of PA residents. In experiment 2, we examine a reduced set of
19 dimensions in a smaller sample of local elected officials. We use a
limited number of attributes and increase the number of choice pairs
that respondents evaluate, to ensure that we are powered to detect
0.07 and 0.05 effect sizes despite the smaller sample size. We selected
attributes to drop on the basis of the results of experiment 1 (for exam-
ple, eliminating attribute levels that did not have statistically significant
variation, like in project benefits, and adding an additional attribute
level in response to feedback from researchers experienced with elite
samples). We asked respondents to choose between two energy infra-
structure projects in their area that varied on five dimensions. The full
list of attributes is displayed in Supplementary Table 5. In experiment 2,
we additionally asked local elected officials to report their perceptions
of constituent support for each proposed pair of projects.
Sampling
For experiment 1, we collected a sample of 894 Pennsylvanians that
was demographically representative on age, gender, ethnicity and
household income (based on 2019 American Community Survey 1-year
estimates) and balanced on rural (25%), urban (25%) and suburban
residence (50%) (see demographics in Supplementary Table 6). Our
public sample was recruited by the survey firm Lucid. To recruit par-
ticipants, Lucid partners with multiple double opt-in panels that invite
participants through emails, push notifications and in-app pop-ups.
Respondents are incentivized through partner suppliers in the form
of cash, gift cards or loyalty reward points. All respondents from our
public sample completed the survey between 7 February and 24 Febru-
ary 2022. Fifty-seven respondents who failed an attention check were
removed from the sample and subsequent analyses (Supplementary
Fig. 5). Mean response time was 16.9 min. Each respondent was asked
to choose between five distinct pairs of randomly generated projects,
testing support across a total of 8,940 projects. For both experiments,
the attribute levels were fully randomized within and across project
pairs, ensuring the non-parametric identification of causal effects
of the attributes22,65. For this sample, probability weights were cre-
ated with a post-stratification raking procedure using Census vari-
ables (from 2019 American Community Survey 1-year estimates). This
procedure follows the methodology outlined in DeBell and Krosnick
(2009) for the American National Elections Study66. Robustness tests

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including comparison across attention check failure and profile order
are included in Supplementary Information.
For experiment 2, a sample of 219 local elected officials was
recruited by the survey firm CivicPulse. Thirteen respondents did not
complete the conjoint experiment portion of the survey, bringing the
analysis sample to 206. The survey was fielded from 20 September
2022 to 9 November 2022. The sample of respondents consisted of
elected policymakers in PA from local governments (that is, township,
municipality and county governments) with a population over 1,000
residents (see demographics in Supplementary Table 7). Elected poli-
cymakers include top elected officials and governing board members.
Each respondent was asked to choose between four distinct pairs of
randomly generated projects, testing support across a total of 824
projects. For this sample, probability weights were created with a
post-stratification raking procedure using the Census and presidential
vote share variables. Weights are calculated using local governments in
PA with populations over 1,000 residents. This procedure follows the
methodology outlined in DeBell and Krosnick (2009) for the American
National Elections Study. Both surveys were approved by Princeton
University’s institutional review board (IRB #14367: ‘Origins, Func-
tions, and Mechanisms of Coalitions for Change: Linking Individual
Cognition and Collective Action’, principal investigator Elke Weber),
and all participants provided informed consent. We used Qualtrics XM
(version 2022) to collect data for both surveys.
Statistical analysis
Stefanelli and Lukac (2020)67 provide a framework for conducting
power analyses for conjoint experiments using simulation techniques.
Using their online power-analysis tool (https://mblukac.shinyapps.io/
conjoints-power-shiny/), we find that experiment 1 is well powered.
With 894 participants, 5 tasks and 4 variable levels (attribute with the
largest number of options), the experiment has 94% power to detect
an effect of 0.05 and 81% power to detect an effect of 0.04. Experiment
2 is not as well powered given the smaller available sample size of local
elected officials; with 206 participants, 4 tasks and 4 variable levels, the
experiment has 42% power to detect an effect of 0.05 and 78% power
to detect an effect of 0.07 (Supplementary Fig. 6).
We calculate AMCEs for each option across attributes63, which meas-
ure the average causal effect of each attribute level on support for the pro-
ject relative to a baseline. Thus, we can estimate how much each attribute
level increases or decreases support for a project, holding all other ele-
ments constant. The dependent variable is a binary indicator for whether
a given energy project was preferred (Y = 1) or not preferred (Y = 0). We use
ordinary least squares regression to calculate the AMCE for each attribute
level, clustering standard errors at the respondent level to account for
within-respondent correlations in responses. We use equation (1):
Yp = α + βDp + γJp + ηBp + ζOp + θLp + λFp + ϵp
(1)
where Y represents the binary variable for whether the project p was
selected. α is the intercept. D, J, B, O, L and F are indicators for the
distance from residential areas; job impacts; local benefits; ownership
model; site of land; and energy fuel type for p, respectively, followed
by an error term, ε. β, γ, η, ς, θ, and λ are the corresponding coefficients
for D, J, B, O, L, and F.
While AMCEs have a clear causal interpretation, they can be mis-
leading for subgroup analyses when one wishes to descriptively inter-
pret differences in preferences between two or more groups. Following
the advice of Leeper et al. (2020)68, we report marginal means instead
of differences in AMCEs as indicators of favourability towards a given
feature when conducting subgroup analyses. Because levels of each
attribute were randomly assigned, pairwise differences between two
marginal means for a given attribute have a direct causal interpretation.
Marginal means have the advantage of directly presenting differences
between subgroup preferences without relying on reference categories
for comparison, while AMCEs are interpreted relatively. Examples of
conjoint choices seen by respondents appear in Supplementary Figs. 7
and 8. All data were processed and analysed using R, version 4.3.2.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
Data supporting this study are openly available from OSF at https://
doi.org/10.17605/OSF.IO/WKGT9.
Code availability
Code supporting this study (including replication code for analy-
ses, figures and tables) is openly available from OSF at https://doi.
org/10.17605/OSF.IO/WKGT9.
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Acknowledgements
This research was presented at the Association of Collegiate Schools
of Planning Conference in October 2023, and we thank discussant
M. Coffman for her generous feedback on the manuscript. It was
also presented at the 2024 American Political Science Association
Virtual Research Meeting, and we thank participants of the ‘Greener
Futures in Tense Times’ panel for their comments. We are grateful
for the assistance of various research assistants and lab managers
in Princeton’s Behavioral Science for Policy Lab and UBC’s School
of Community and Regional Planning in making this work possible,
especially K. Nichols and M. Lore. We appreciate the time and
expertise of the multiple stakeholders that reviewed early drafts of the
conjoint experiment. Finally, we acknowledge internal funding from
the Andlinger Center for Energy and the Environment at Princeton
University (H.C.).
Author contributions
H.C., S.M.C., E.U.W. and C.G. conceived of the presented idea and
research questions. H.C., S.M.C. and E.U.W. designed the survey
experiment. H.C. carried out data collection and analysis with
support from S.M.C. H.C., S.M.C., E.U.W. and C.G. contributed to the
interpretation of results. H.C. took the lead in writing the manuscript.
S.M.C., E.U.W. and C.G. provided critical feedback and helped shape
the research, analysis and manuscript.
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/s41560-024-01603-w.
Correspondence and requests for materials should be addressed to
Holly Caggiano.
Peer review information Nature Energy thanks Salil D Benegal, Shawn
Hazboun and Adam Mayer 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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Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with
the author(s) or other rightsholder(s); author self-archiving of the
accepted manuscript version of this article is solely governed by the
terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Springer Nature Limited
2024

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Corresponding author(s):
Holly Caggiano
Last updated by author(s): 6/17/2024
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All survey data were collected using Qualtrics XM (version 2022)
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All data were processed and analyzed using R, version 4.3.2. Code is available on OSF at DOI 10.17605/OSF.IO/WKGT9, https://osf.io/wkgt9/.
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Reporting on sex and gender
Participants in study 1 were asked to self-identify their gender. Data on sex was not collected in this study. Data on gender
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Participants for study 1 were recruited by the survey firm Lucid. Participants for study 2 were recruited by CivicPulse.
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Both surveys were approved by Princeton University’s Institutional Review Board (IRB#14367: “Origins, Functions, and
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Study description
This is a quantitative study that conducted a conjoint experiment in two online samples.
Research sample
The sample for study 1 (n=894) is a representative sample of Pennsylvania, USA residents based on 2019 ACS 1-year estimates,
balanced on rural (25%), urban (25%), and suburban (50%) residence. The sample was recruited by the firm Lucid using quota
sampling on age, gender, ethnicity, and household income to be representative of PA residents (reported in supplemental
information). The sample for study 2 is a sample of local elected officials in Pennsylvania collected by CivicPulse (n=206).  Sample
rationale: PA is a key transition state.
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Quota sampling was used to collect data for Study 1 by the survey firm Lucid. CivicPulse recruited participants for Study 2 through
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For study 1, Lucid only delivered data for completed surveys and did not maintain data on non-participation. For study 2, the
CivicPulse sample contained 219 participants, but 13 did not complete the conjoint experiment portion of the survey. Since this was
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Randomization
In both experiments, each respondent was asked to choose between five distinct pairs of randomly generated projects. The attribute
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Methods
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ChIP-seq
Flow cytometry
MRI-based neuroimaging

    Policy Implications:

Answer:
• Direct benefits like job creation and community ownership increase public support for large‑scale energy projects.
• Support for energy projects is similar across Democrats and Republicans in the general public, suggesting opportunities for bipartisan efforts.
• Elected officials underestimate how much their constituents support projects that generate solar energy and create permanent jobs.
• Open dialogue between community members and local elected officials about prioritized project benefits may help advance energy transitions.