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:
    Articles
https://doi.org/10.1038/s41560-019-0460-9
Institute for Economy and the Environment, University of St.Gallen, St.Gallen, Switzerland. *e-mail: adrian.rinscheid@unisg.ch
E
ighty per cent of the world’s coal reserves must stay in the ground
to reach the target of limiting global warming to well below 2 °C
compared with pre-industrial levels1. In 2008, climate scientists
had called for a complete divestment from coal-fired electricity by
20302, a proposition reiterated by a recent roadmap for rapid decar-
bonization3. Yet despite the strong growth of renewable energies, coal
still accounted for 28% of the world’s primary energy supply in 20174.
As Pfeiffer et al.5 point out, coal-fired power plants “will need to be
underutilized, retired early, or retrofitted […] or—in short—stranded”
if countries are serious about reaching the targets set in Paris.
As private markets will not spur such necessary developments
on their own, government policies play an important role in phas-
ing out coal6,7. However, in democratic countries, such policies may
face public opposition. While several studies suggest that the global
energy transition (that is, shifting from non-renewable to renewable
energies) is likely to lead to net job creation in most economies8,
the closure of coal mines and coal-fired power plants may lead to
temporary and regionally concentrated job losses. The anticipation
of negative employment effects could lead to opposition by those
working in the sector9. Moreover, the coal industry has become
an identity-shaping symbol deeply engrained in the culture of
some communities and countries, such as in the German region of
Lusatia10, Silesia in Poland11 or Appalachia in the United States12.
Opposition to a phase-out of coal is also likely to be fuelled by the
economic actors that have to bear parts of the costs, such as utilities
whose business models depend on coal, and labour unions repre-
senting coal workers. Given their desire to be re-elected, democratic
governments may be responsive to these concerns.
At the same time, recent assessments such as the
Intergovernmental Panel on Climate Change’s Special Report
‘Global Warming of 1.5 °C’ emphasize the urgency of ambitious
action to prevent irreversible climate change13. While some jurisdic-
tions have committed to phasing out coal, such as Canada and the
United Kingdom who launched the Powering Past Coal Alliance in
November 2017, many others are not delivering policies ambitious
enough to meet the climate challenge. Germany, the largest energy
consumer in the European Union, the world’s largest producer of
lignite and one of the top-ten coal-burning countries in the world4,
has recently started to organize its departure from coal.
Germany has relied heavily on coal for power generation for a
long time. In the 1950s, more than 500,000 people were employed
in the sector, contributing to the German Wirtschaftswunder after
World War II14. While the oil crisis in 1973–1974 aided the devel-
opment of nuclear power, domestic coal was viewed as important
to ensure energy security, and utilities were thus required to burn
a quota of domestic hard coal15. As operating costs in the coal
industry started to outpace market revenues in the 1960s, federal
and state governments introduced subsidies for hard-coal mining.
These amounted to more than €320 billion until they were phased
out in 201816.
In the 1970s, coal-fired energy generation started to become
more controversial. The environmental movement and (later) the
newly established Green Party opposed coal-fired power plants and
open-pit mining but were even more concerned with nuclear power,
largely avoiding simultaneous contestation on two fronts14. The coal
industry nurtured strong ties to the country’s two largest parties, the
Christian Democrats (CDU/CSU) and the Social Democrats (SPD),
with the SPD in particular aiming to keep coal mines open as long
as possible14. In the wake of the 1986 Chernobyl nuclear accident,
Germany adopted a policy framework in 1990 for the promotion of
renewable energies17, which fundamentally changed electricity mar-
kets. The share of power generated by renewables soared from 3.6%
to 35.2% between 1990 and 201818.
While Germany has been successful in transitioning from
nuclear to renewable energy, 35.3% of electricity generation still
came from coal in 2018 (Fig. 1). Policy support for renewables is
an important element in decarbonizing the energy sector19, but it is
an open question whether layering support schemes for sustainable
technologies on top of the existing institutions20 without address-
ing the legacy of fossil fuels is enough to “effectively lead energy
systems out of carbon lock-in”21. Given only gently decreasing emis-
sions (Fig. 1), the question of a coal phase-out gained prominence in
the aftermath of the adoption of the Paris Agreement. In November
2016, the German government adopted the Climate Action Plan
2050, outlining measures to achieve the country’s climate targets.
This plan failed to develop a phase-out strategy, and in 2018 the task
was delegated to the Coal Commission, an expert commission on
growth, structural change and employment consisting of a variety of
Germany’s decision to phase out coal by 2038
lags behind citizens’ timing preferences
Adrian Rinscheid   * and Rolf Wüstenhagen
Coal-fired power generation is the single most important source of carbon dioxide emissions in many countries, including
Germany. A government commission recently proposed to phase out coal by 2038, which implies that the country will miss its
2020 climate target. On the basis of a choice experiment that assessed 31,744 hypothetical policy scenarios in a representative
sample of German voters, we show that voters prefer a phase-out by 2025. They would uphold their support for greater climate
ambition up to an additional cost to society of €8.5 billion. Voters in Rhineland and Lusatia, the country’s main coal regions,
also support an earlier phase-out, but to a lesser extent than voters in other regions. By demonstrating that political decision-
makers are more reluctant than voters in overcoming energy path dependence, our analysis calls for further research to explain
the influence of particular stakeholders in slowing energy transitions.
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stakeholders including industry associations, labour unions, state-
level governments, environmental non-governmental organizations
and independent scientists appointed by the federal government. In
early 2019, this group recommended to phase out coal-fired power
generation by 2038, proposing an array of measures to support the
coal regions in restructuring their economies.
In light of recent concerns about populist backlash against cli-
mate policy22,23, some observers consider the proposed timeline a
reasonable compromise between public acceptance and climate
change mitigation. Although the compromise has been praised for
representing a broad societal consensus, given the urgency of ambi-
tious climate action3,13, some members of the Coal Commission
have criticized the plan as not ambitious enough (ref. 24, pages 118
and 119) to deliver on Germany’s climate policy targets.
Given these concerns and the view expressed in the German
Climate Action Plan that public support is a central precondition
for successful implementation of climate policies25, we investigate
whether the recommendations of the Coal Commission are in line
with voters’ preferences, particularly regarding the temporal dimen-
sion. First, we investigate how citizens’ support for a coal phase-out
is affected by different timelines and other features of a phase-out
and examine the moderating influences of political orientation and
climate change-related beliefs. Second, we explore the preferences
of citizens living in Germany’s two largest coal regions, Rhineland
and Lusatia. Our analysis suggests that compared with the recom-
mendations of the Coal Commission, a more ambitious timeline for
phasing out coal would actually have been better aligned with citi-
zens’ preferences.
Effects of phase-out design on public support
With data from a large-scale choice experiment, we examine how
public preferences for a coal phase-out in Germany are affected
by different proposed phase-out timelines and compare citizens’
preferences with the recommendations of the expert commis-
sion. We also investigate the role of other policy attributes (cost,
effects on jobs and supporting measures for the transformation of
the coal regions; see Table 1 and Methods for details). Our analysis
is based on an online survey administered to a nonprobability but
representative sample of 2,161 Germans who are eligible to vote
(see Supplementary Table 1). The choice experiment involved a
rating task whereby respondents were exposed to eight consecu-
tive pairs of hypothetical policy scenarios to phase out coal. In the
scenarios, the attribute levels of the phase-out policy were varied
70
60
50
40
30
Share of coal in German electricity mix (%)
Greenhouse gas emissons from energy sector
(in million tons of CO2e)
20
10
0
1990
1995
2000
2005
2010
2015
420
360
300
240
180
120
60
Lignite
Hard coal
0
Year
Fig. 1 | Share of coal in German electricity mix and energy-related greenhouse gas emissions, 1990–2018. Based on data from AG Energiebilanzen18 and
the German Environment Agency64.
Table 1 | Policy attributes and levels for the choice experiment
Policy attributes
Attribute levels
End date of the
phase-out
∙By 2025
∙By 2030
∙By 2040
∙By 2100
Annual cost
per two-person
household (overall
costs for the
economy)
∙€0
∙€6 (€250 million)
∙€12 (€500 million)
∙€18 (€750 million)
Number of lost jobs
in the coal industry
∙–5,000
∙–10,000
∙–15,000
∙–20,000
Number of newly
created jobs
∙5,000
∙10,000
∙15,000
∙20,000
Measures for
structural change
∙Investment in expansion of renewable energies
∙Investment in regional funding programmes for new
businesses (for example start-up funding)
∙Investment in modern infrastructure (electric
vehicles, digitalization)
∙Investment in research and development
∙Mixture of further training and early retirement for
coal industry employees
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randomly. Participants were asked to rate these scenarios on a
scale from 1 (‘very poor’) to 7 (‘very good’). To ease interpretation
of the marginal effects shown in Figs. 2, 3 and 4, the rating scale
was dichotomized, using the median value of 4 as a cut-off value.
The resulting dependent variable ‘Phase-out Support’ is hence
coded 0 for cases where a respondent rated a proposal as poor
to neutral (1 to 4) and 1 for cases where (s)he rated it as positive
(5 to 7). The fully randomized design allows us to estimate the
causal effects of multiple treatment components simultaneously
using simple linear regression26. The subsequent analyses are based
on a sample of 1,984 Germans evaluating 31,744 policy scenar-
ios. This sample is cleaned of respondents who failed to correctly
answer an attention check implemented in the choice experiment.
However, all results discussed in the paper remain substantively
the same when replicating the analyses to include the inattentive
respondents (see Supplementary Tables 2, 4, 6, 8 and 10).
Figure 2 shows the marginal effects associated with each attri-
bute level based on regression analysis (Supplementary Table 2),
using the dichotomized rating outcome as a dependent variable
and standard errors grouped at the level of the individual (clustered
standard errors). For the timing attribute, we take 2040 as the ref-
erence category, as this level most closely matches the 2038 time-
line ultimately recommended by the Coal Commission. We find
that policy scenarios with 2025 as an end date have a significantly
higher probability of being supported than policies with later end
dates. Postponing the phase-out to 2040 leads to a decrease in policy
support of 10.7 percentage points, and postponing it to 2100—as
reflected in the G7’s statement to phase out fossil fuels by the end
of the century—leads to a decrease in policy support of 15.3 per-
centage points, compared with the 2040 baseline. As is apparent in
the data, Germans are also sensitive to the cost of a coal phase-out.
Every increase in annual cost of €10 per household (or about €400
million per year for the German economy as a whole) decreases
public support by about seven percentage points. With regard to
employment effects, people prefer scenarios with lower job losses
over scenarios with higher job losses, but they value newly created
jobs slightly higher than lost old jobs. For instance, while the sce-
nario with 20,000 lost jobs decreases phase-out support by 9.2 per-
centage points compared with a scenario with only 5,000 lost jobs,
creating the same number of new jobs increases phase-out support
by 12.2 percentage points. The type of supportive measures for the
local economy is the least important of the five attributes. Among
the design options offered here, the preferred attribute level is an
expansion of renewable energies.
Partisan differences and gateway beliefs
As the discussions of the Coal Commission showed, the main ques-
tion about phasing out coal is not if, but when, the phase-out is
going to happen. Hence, the following analyses focus specifically on
the question of timing. While Fig. 2 indicates that the timeline does
indeed have a considerable effect on citizens’ preferences, there may
be differences between population subgroups. In particular, it has
been suggested that party identification structures people’s energy
policy preferences27,28. Germany’s party elites represent opposing
views on the coal phase-out, ranging from the Greens’ position for
an early phase-out to the conservative parties tending to defend the
status quo29. In the context of the 2017 federal elections, the partisan
divide on the topic became highly visible, and the question of tim-
ing was one of the reasons why the negotiations for a government
coalition of the Christian Democrats, the Liberal Democrats (FDP)
and the Green Party failed in November 201730. Figure 3a shows
that there is some variation among partisans with regard to the
strength of their timing preferences. Unsurprisingly, Green Party
supporters show the strongest preference for an early phase-out in
2025. What may be more surprising is that supporters of almost all
other parties also prefer 2025 over 2040. The only exception is the
relatively small subsample supporting the Bavarian arm of the CSU,
where the preference for 2025 is not significant. In contrast to public
statements by their party leaders, FDP and Green Party voters have
similar views on this issue. For all respondents, phasing out in 2100
is the least preferred timeline, although supporters of the right-wing
populist party Alternative für Deutschland (AfD) are comparatively
more positive about such a late phase-out date than supporters of
all other parties. In light of other surveys investigating public atti-
tudes on the German energy transition (either surveys with a broad
focus31 or those with a specific focus on the coal phase-out32), the
muted differences across different partisans actually reflect a recur-
ring pattern. See Supplementary Table 3 for the supporting regres-
sion analyses.
We also expected beliefs about climate change to influence citi-
zens’ support of different phase-out timing options. We assessed
climate change-related beliefs by asking respondents to estimate
the share of global climate scientists who think that the rise in the
atmospheric CO2 concentration since the mid-twentieth century is
primarily due to human activities. Perceived scientific consensus
about the anthropogenic nature of current climate change functions
as a ‘gateway belief’ that influences several other attitudes related to
climate change and energy33–36. While quantifications show that the
consensus is shared by 90–100% of publishing climate scientists37,
Training and early retirement
Research and development
Investment in modern infrastructure
Support for new businesses
Expansion of renewables
Supportive measures
 20,000
15,000
10,000
5,000
New jobs
−20,000
−15,000
−10,000
−5,000
Lost jobs
€ 18
€ 12
€ 6
€ 0
Annual cost per household
2100
2040
2030
2025
End date
–0.2
0
0.2
Phase-out support (binary indicator)
Fig. 2 | Average effects of policy attributes on respondents’ preferences
for a coal phase-out. Each dot represents an AMCE of randomly assigned
attribute levels on the probability of supporting a given policy scenario
relative to the reference scenario, all else being equal. The horizontal bars
represent the 95% confidence intervals. Dots without bars represent the
reference level for each policy attribute. There were n = 1,984 respondents
and 31,744 policy scenarios.
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a recent study conducted in the United States highlights that only
15% of US citizens are aware of this high level of consensus38. In our
German sample, the mean estimate of consensus is 66% (s.d. = 22.9),
and 18.3% of respondents estimate the consensus to be 90% or
higher. Figure 3b shows that perceived consensus strongly moder-
ates the effect of phase-out timelines on preferences. Respondents
who think the consensus is below 50% are indifferent to whether
the proposed end date is 2025, 2030 or 2040, but their support still
decreases if the proposed end date is 2100. The closer respondents’
climate-related beliefs approximate the true level of scientific con-
sensus, the more pronounced their preference for an earlier phase-
out. Respondents who (accurately) estimate the consensus to be
90% or higher prefer a 2025 phase-out date by more than 40 per-
centage points over a phase-out date of 2100. See Supplementary
Table 5 for the supporting regression analyses.
Ties to coal industry weaken support for early phase-out
To explore the influence of social embeddedness on preferences for
a coal phase-out, we rely on two additional samples of residents of
the two main coal regions, Rhineland (n = 533) and Lusatia (n = 501),
who took the same survey. Within these independently collected
regional samples, we further investigate whether the preferences of
people having direct ties to the coal industry, for example, through
acquaintances or by being employed in the sector, differ from those of
other respondents in the region. The results (Fig. 4 and Supplementary
Tables 7 and 9 for supporting regression analyses) suggest that peo-
ple in the coal regions have less pronounced preferences for an early
phase-out than respondents in the nationwide sample. However,
there are some differences between the two regions. Phasing out
coal by 2025 or 2030 instead of 2040 has significantly higher sup-
port in Rhineland, while respondents in the eastern German region
of Lusatia tend to support a phase-out in 2030. Even here, later phase-
out dates are significantly less preferred. An analysis of respondents
with strong (red symbols in Fig. 4) and weak (blue symbols) social
ties to the coal industry suggests that in both regions, people with
strong ties are indifferent to whether the proposed phase-out date
is 2025, 2030 or 2040, as the confidence intervals around the point
estimates for 2025 and 2030 include the dotted reference line.
Conclusion
Addressing climate change effectively and rapidly requires not only
investing in new energy technologies but also divesting from car-
bon-intensive energy infrastructures39–43. Our study is among the
first to investigate citizens’ views on the second part of this equa-
tion. Using a large-scale survey, we assessed German voters’ prefer-
ences for different policy design options to phase out coal. We found
that the average respondent consistently prefers a more ambitious
timeline. All else being equal, the preference was to phase out coal
by 2025, which contrasts with the Coal Commission’s proposal to
phase out coal by 2038. A particular strength of our methodologi-
cal approach is that the choice experiments allow us to scrutinize
respondents’ timing preferences in relation to possible trade-offs
between the different attributes of an accelerated phase-out, such as
higher cost. By comparing preferences across attributes, we find that
support for an accelerated phase-out is upheld up to an additional
cost to society of €8.5 billion (see Supplementary Fig. 1).
The acceptance of policy proposals is also sensitive to the
employment effects of the energy transition. Cost matters, as do job
losses. If delaying the phase-out from 2025 to 2030 would result in
halving job losses from 20,000 to 10,000, voters would—with all else
being equal— accept the later phase-out. Our analysis also shows
that the creation of new jobs matters even more than the loss of
old jobs. Policymakers aiming to find support for ambitious climate
policies are therefore well advised to make credible claims about
how these policies will lead to new employment opportunities in
low-carbon industries.
Our results also shed light on the similarities and differences
among population segments. With respect to party identifica-
tion, preferences for earlier over later phase-out dates are wide-
spread among almost the entire political spectrum. Even voters
in Germany’s two largest coal mining regions share—to a large
extent—the preference for an earlier over a later phase-out. The
only notable exception are citizens with strong ties to the coal
industry, who have no significant preference for a 2025 phase-out
over one that happens in 2030 or 2040. Similarly, voters in the east-
ern German region of Lusatia slightly prefer 2030 over 2025 as the
phase-out date. Moreover, knowledge about the scientific consen-
sus on anthropogenic climate change is an important predictor
of supporting an ambitious phase-out. Slightly less than one-fifth
of respondents are aware that more than 90% of climate scientists
agree that climate change is human-made. These well-informed
respondents have a stronger preference for phasing out coal in 2025
than those who (erroneously) believe that no such consensus exists.
In light of our findings, the German Coal Commission’s pro-
posal to phase out coal by 2038 does not appear to correspond
well with voter preferences. This might be an indication that
Phase-out support (binary indicator)
End date
–0.4
–0.3
–0.2
–0.1
0.1
0.2
0.3
2100
2040
2030
2025
a
b
Die Grünen (n = 138)
SPD (n = 325)
Die Linke (n = 160)
CDU (n = 321)
CSU (n = 85)
FDP (n = 81)
AfD (n = 129)
Phase-out support (binary indicator)
End date
–0.3
–0.2
–0.1
0.1
0.2
0.3
2100
2040
2030
2025
0–49% perceived c. (n = 368)
50–69% perceived c. (n = 513)
70–89% perceived c. (n = 768)
90–100% perceived c. (n = 390)
0
0
Fig. 3 | Average effects of the timing attribute on respondents’ preference for a coal phase-out. a,b, Symbols represent AMCEs for the timing attribute
(base 2040), conditional on party identification (a) and perceived scientific consensus (‘perceived c.’) (b) on the anthropogenic nature of current climate
change. The horizontal bars represent the 95% confidence intervals.
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commission members over-estimated voters’ conservatism, as
political elites have been shown to do frequently44,45. However, even
assuming that the commission members gave constituents in coal-
mining regions precedence over voters in other parts of the coun-
try would not explain why such a late date was chosen, as even in
those regions respondents preferred phase-out dates between 2025
(western Germany) and 2030 (eastern Germany). An alternative
explanation for this mismatch is that voter preferences simply did
not play a decisive role in the consultations of the commission. As
Fig. 5 illustrates, citizen voices (represented by non-governmental
organizations, for example) were under-represented among the 28
commission members. Moreover, most commission members were
insulated from re-election pressures, and some might have empha-
sized short-term economic interests, such as the Confederation of
German Employers’ Associations (BGA) or the trade union repre-
senting workers in mining, chemicals and energy (IGBCE). While a
detailed analysis of the decision-making dynamics within the com-
mission is beyond this paper’s scope, the strong representation of
incumbent interests within the commission highlights an important
institutional barrier against overcoming energy path dependence.
Further work in this area could investigate the ability of corporatist
styles of decision-making to reform today’s carbon-intensive energy
systems46. To successfully manage “the next phase of the energy
transition”47, which implies making established technologies and
infrastructures redundant, we need to enhance our understand-
ing of incumbents’ survival strategies, including their corporate
political activity aimed at slowing down the transition. Moreover,
given the prevalence of particular stakeholders who stress job losses
rather than new opportunities, the nexus between employment con-
siderations and the political feasibility of decarbonization measures
needs more scholarly attention8,48. Energy transition researchers and
modellers would benefit from engaging with political scientists and
sociologists to unveil the interests and activities of various actors
who are shaping energy policies. Policymakers trying to develop
ambitious climate change mitigation policies should be encouraged
to find ways of being exposed to a balanced view of the risks and
opportunities of the energy transition. Our results suggest that in
a democratic setting, such action could be rewarded in future elec-
tions by voters.
Methods
Choice experiment rationale and design. To investigate voters’ policy preferences,
we conducted a choice experiment. Choice experiments were developed in
marketing research to investigate the importance of different product design
features in determining purchasing preferences. The idea is to put respondents
in a hypothetical yet realistic choice situation in which they are confronted with
bundles of relevant product attributes. By observing stated preferences with regard
to the presented alternatives, it is possible to examine the relevance of certain
product attributes and their characteristics to individual choices.
Political scientists have adopted the method to gauge citizens’ preferences
with regard to different policy proposals or scenarios26,49. Analytically, the design
features of a policy are similar to product attributes, which is why the method
provides a powerful approach to simultaneously estimate the individual effects of
several attributes of a policy proposal on voter preferences50. Choice experiments
require decision-makers to make trade-offs between different policy attributes
when evaluating various multidimensional alternatives. As a consequence, they
can mitigate the problem of social desirability bias in public opinion research on
environmental matters26. In our case, using choice experiments may reduce the
likelihood of overestimating voters’ appetite for an ambitious phase-out of coal.
At the beginning of the choice experiment, respondents were familiarized
with five attributes of a potential policy to phase out coal: the timescale of the
phase-out, estimated costs, effects on employment in terms of layoffs and newly
created jobs, and supporting measures for the transformation of the coal regions.
We selected these five attributes on the basis of the following considerations. In
2017 the German Advisory Council on the Environment (SRU), an expert advisory
panel to the federal government, recommended a staged approach in which the
coal-fired power plants with highest emissions would be disconnected from the
grid as early as 202051. The most efficient power plants would be successively shut
down in the 2030s, and the phase-out would be completed by 2040 at the latest.
Phase-out support (binary indicator)
End date
–0.2
–0.1
0
0.1
0.2
2100
2040
2030
2025
Lusatia, total (n = 473)
Working in coal industry
or having acquaintances (n = 258)
Not working in coal industry,
no acquaintances (n = 215)
Phase-out support (binary indicator)
End date
–0.2
–0.1
0
0.1
0.2
2100
2040
2030
2025
a
b
Rhineland, total (n = 491)
Working in coal industry
or having acquaintances (n = 191)
Not working in coal industry,
no acquaintances (n = 300)
Fig. 4 | Average effects of the timing attribute on respondents’ preference for a coal phase-out in Rhineland and Lusatia. a,b, Symbols represent AMCEs
for the timing attribute (base 2040) for the (a) Rhineland and (b) Lusatia samples, excluding inattentive respondents. The analyses also differentiate
between respondents employed by or with acquaintances in the coal industry and those without strong coal industry ties. The horizontal bars represent
the 95% confidence intervals.
4
3
6
4
6
Scientists
Non-governmental organizations
Industry associations
Labour unions
Other politicians
Other businesses
Other civil society
Subnational governments and
administration
1
3
1
Fig. 5 | Composition of the Commission on Growth, Structural Change and
Employment. Numbers indicate number of members of the Coal Commission
entitled to vote per category based on the list of commission members24.
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The SRU stresses the climate–political necessity of immediately starting the phase-
out to achieve appropriate implementation of the Paris climate targets in Germany.
Other studies reach similar conclusions. Depending on the ambition of the first
stages of the phase-out, studies show that it is technically feasible to accomplish
a coal phase-out by 203552 or as soon as 203053. If the 1.5 °C target of the Paris
climate agreement is taken as the reference point, however, the phase-out of coal
in Germany must already occur by around 202554. A different time horizon for
the phase-out of coal was adopted by the G7 in 2015 when they decided to end
the use of fossil fuels by the end of the century55. Hence, reflecting these different
scenarios, our choice experiment uses 2025, 2030, 2040 and 2100 as attribute levels.
Arguments about costs, the second attribute of our choice experiment, play a
large role in the public debate on phasing out coal. During the negotiations for a
government coalition consisting of the CDU/CSU, the FDP and the Green Party
(the so-called Jamaica coalition) in November 2017, a number of energy-intensive
firms publicly warned that a phase-out of coal could mean a rise in electricity
prices by up to 30% (ref. 56). While the exact effect of reducing coal-fired power
generation on the electricity market depends on a variety of factors, including
demand response, growth in renewable power generation and cross-border trade,
it seems plausible that changing the demand–supply balance could have an effect
on prices. A study commissioned by the trade union ver.di indicates that an earlier
phase-out would entail considerable employment-related costs57. Various proposals
for financing the phase-out of coal have been articulated, such as a levy on the
electricity price or a structural transformation fund for the German coal regions58.
To make the costs a relevant choice consideration for individual respondents, we
made the assumption that they would be passed on to consumers. In the choice
tasks, we presented the attribute as cost per household or overall cost to the
economy, respectively, leaving open the concrete financing mechanism through
which those costs would be incurred by consumers. In the instructions to the
choice experiment, we mentioned electricity price increases as one such possibility,
which does not preclude other financing mechanisms that would ultimately also
affect consumers, such as CO2 taxes or emissions trading. As existing investigations
necessarily work with a number of assumptions, it is difficult to find clear reference
points for plausible cost scenarios. The 2016 study by Ecke, however, offers some
guidance57. This study estimates that the annual cost of a phase-out until 2040 will
amount to €499 million if industry and large customers were exempt from the
corresponding levy. Projected onto electricity prices, this corresponds to €0.0014
per kilowatt hour, implying an annual electricity price increase of €4.20 for a
typical two-person household with a consumption of 3,000 kWh. To leave space
for other factors not taken into account in estimates made to date, we defined
cost levels of €6, €12 and €18 (annual, per two-person household). As a reference
category, we assumed no costs.
The way that cost of a coal phase-out is presented to respondents might
influence the weight they assign to this attribute. To take the possibility of such
cost-framing effects into account, 50% of respondents (randomly assigned)
received the same information in a different format. In addition to the electricity
price increase per household, they were also informed of the corresponding overall
costs for the economy. Projecting the costs per household onto the entire economy
leads to cost levels of €250 million, €500 million and €750 million (see Table 1). As
it turns out, however, the way the costs were presented in the choice experiment
did not influence respondents’ responses. Hence, for all analyses reported in the
paper, we pooled the data of the respective subsamples.
Along with the timescale and the costs, employment effects are an important
consideration in planning a coal phase-out. More than 20,000 people are currently
employed in the coal industry51. Nearly 70% of those working in the lignite mining
sector are already over 46 years old and therefore will reach retirement age in the
mid-2030s. Taking existing early retirement programmes into account, around
5,000 to 7,500 people remain for whom new job perspectives would have to be
found with a phase-out of coal by 2040 at the latest (ref. 51, page 25). In the case
of an earlier phase-out, or if jobs that are only indirectly dependent on the coal
industry are considered, this number rises.
To get a complete picture of the effects on employment, the number of newly
created jobs, especially in the renewable sector, must be considered along with the
number of jobs lost51. According to the German government, 330,000 jobs had
been created in the country’s renewable energy sector as of 201559. Phasing out
coal could lead to the creation of new jobs not only in the renewable sector but
also in other sectors of the economy. Moreover, after closure of the current open-
cast mines, jobs in restoring the destroyed landscapes will be created or remain
in place for longer periods of time. Some studies therefore conclude that the net
employment effects of phasing out coal could indeed be positive51,60. To account for
both positive and negative employment effects, we included two separate attributes,
ranging from 5,000 to 20,000 jobs each.
Finally, as evidenced by the final report of the German Coal Commission, a
policy to phase out coal would need to entail specific measures for supporting
the transformation of the regional economy. For example, such a policy might
provide financing for early retirement and retraining programmes for coal industry
employees51 and/or prioritize deployment of renewable energy in the coal regions.
Other conceivable measures include investment in modern infrastructure (for
example public transport, electric vehicles and digitalization), incentives for the
creation of new businesses (such as start-up funding), and public investment
in research and development. Opinion surveys31 on phasing out coal have not
investigated whether the public has a pronounced preference for particular
measures in supporting the structural transformation, and whether this attribute is
more important than others.
Table 1 provides a summary of the policy attributes and levels. All attributes
and their levels were briefly explained to study participants before the choice
experiment started. The choice experiment itself consisted of eight successive
rounds. In each round, participants were presented with two policy scenarios for
a phase-out of coal in which the levels of attributes were randomly varied both
within and across the binary comparisons. To prevent order effects, the order in
which the attributes appeared in the description of scenarios was randomized
across respondents but fixed for each respondent. At the end of each round,
participants had to evaluate the scenarios using two different scales. First,
they were asked to indicate which of the two scenarios they preferred
(forced-choice outcome). Second, participants were asked to provide a more
detailed evaluation of the two scenarios, using a scale from 1 (‘very poor’) to 7
(‘very good’) (rating outcome).
Measurement of moderators. Party identification was measured with the two-
step approach used in the Socio-Economic Panel, which has been conducted since
1984 by the German Institute for Economic Research61. In a first step, respondents
were asked whether they hold a preference for a specific party. If they replied in the
affirmative (which was the case for 1,275 respondents, or 64% of the main sample),
they were then asked which of the seven parties represented in the lower house of
the German parliament they identify with, or whether they preferred another party.
To measure perceived scientific consensus, respondents were asked to indicate
the percentage of climate scientists worldwide who think that the increased
concentration of carbon dioxide in the atmosphere since the middle of the
20th century is primarily due to human activity. They used a slider to choose a
percentage between 0 and 100%.
Strong ties with the coal industry were measured by asking whether
respondents themselves or someone they know works in a coal mine or a coal-fired
power station or has done so in the past.
Data analysis. The fully randomized design allows us to simultaneously estimate
the causal effects of multiple treatment components based on simple linear
regression26. Hence, average marginal component effects (AMCEs) were calculated
using a simple linear regression estimator with standard errors clustered by
respondent, using Stata 14.2 (by StataCorp, see https://www.stata.com/stata14/).
The dependent variable is based on the rating scale, and the models include sets of
dummy variables for the values of all attribute levels.
To ease interpretation of the results, we dichotomized the obtained data with
the rating scale using the median (which is 4) as the cut-off value. The resulting
dependent variable ‘Phase-out Support’ is hence coded 0 for cases where a
respondent rated a proposal as poor to neutral (1 to 4) and 1 for cases where (s)he
rated a proposal as positive (5 to 7). The rationale for using the rating outcome as
the dependent variable (instead of the forced-choice outcome) is that it may allow
for a more fine-grained assessment of preferences. In the first task (forced choice),
respondents had to choose one out of two scenarios in each of eight rounds.
However, the comparison of scenarios may include instances where respondents
have either strong preferences for or against both proposals—a situation that
cannot be meaningfully ascertained by a forced-choice outcome. By contrast, in
the rating task respondents could appraise both scenarios independently and on a
more fine-grained scale. Nevertheless, replicating the analyses based on the forced-
choice outcome leads to substantively the same results (see Supplementary Fig. 2).
Samples. The choice experiment was implemented in an online survey, which was
fielded between December 2017 and January 2018. Study participants were drawn
from the opt-in online consumer panel operated by Kantar/Lightspeed, which
includes more than 230,000 registered individuals in Germany62. From this panel,
a nonprobability but representative sample of 2,161 Germans entitled to vote at
national elections was drawn using an algorithm to match the census population
as closely as possible on age, gender and household income. Supplementary Table
1 shows that the sample matches the German population well in terms of age and
gender. With regard to income, both low-income and high-income households are
under-represented. However, given the fact that we also allowed respondents to
provide no answer, the deviations appear to be relatively small overall.
The two additional regional samples for Rhineland (n = 533) and Lusatia
(n = 501) were drawn from the same consumer panel. As the two coal regions
do not by themselves constitute administrative units, the target population for
each region was defined on the basis of postal codes covering all towns and
municipalities that border the open-pit coal mines. The final lists include 53 postal
codes for the Rhineland and 92 postal codes for Lusatia. It is difficult to assess the
representativeness of the regional samples, as no data comprising the distribution
of socio-demographic variables for exactly these regions are readily available.
Compared to the German population as a whole, the two regional samples show
some deviations with regard to gender and age. The distribution of income varies
between both samples: while the Rhineland sample includes more high-income
individuals than the German sample, the Lusatia sample includes higher shares
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NATuRe EneRGy
of low-income individuals. This is in line with the different economic conditions
between western and eastern Germany.
To identify random responders, we implemented a short attention test
immediately after the choice experiment. All analyses shown in the paper are
based solely on the responses of all participants who passed this test. Hence, the
final samples consist of 1,984 (Germany), 491 (Rhineland) and 473 (Lusatia)
respondents (see Supplementary Table 1). As can be inferred from Supplementary
Table 1, 247 participants failed to answer the attention test correctly across samples.
Ethics. We have complied with all relevant ethical regulations and guidelines for
study procedures set forth by the Ethics Committee of the University of St.Gallen.
The survey for this study was fielded by Kantar/Lightspeed, and all respondents
were first informed about the nature of the study before being asked to consent.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
Replication data for the study are available in the Harvard Dataverse with the
identifier https://doi.org/10.7910/DVN/TEFCBL63.
Code availability
Replication code for the study is available in the Harvard Dataverse with the
identifier https://doi.org/10.7910/DVN/TEFCBL63.
Received: 16 April 2019; Accepted: 5 August 2019;
Published online: 16 September 2019
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Acknowledgements
We acknowledge support by the Andlinger Center for Energy and the Environment
at Princeton University, the Swiss Center of Competence for Energy Research SCCER
CREST, the Swiss National Science Foundation (grant no. P1SGP1_174939) and
Greenpeace Germany, who funded data collection. Design of the research project and
data analysis was the sole responsibility of the authors.
Author contributions
A.R. designed the study and analysed the data. A.R. and R.W. wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-019-0460-9.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to A.R.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2019
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Adrian Rinscheid
Last updated by author(s): Jul 26, 2019
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Behavioural & social sciences study design
All studies must disclose on these points even when the disclosure is negative.
Study description
The study uses quantitative data based on a survey with an embedded choice experiment.
Research sample
The main sample consists of 2,161 Germans entitled to vote at national elections. This sample is a nonprobability but representative
sample, matching  the German population well in terms of age and gender. Two additional samples were fielded in the coal regions
Rhineland (n = 533) and Lusatia (n = 501). It is difficult to assess the demographic representativeness of the additional samples, as no
data comprising the distribution of socio-demographic variables for exactly these regions are readily available. Supplementary Table 1
gives more details on how the 3 samples compare with the German population in terms of distribution of sex, age, and household
income. All samples were collected through online sampling, based on the panel operated by Kantar/Lightspeed, which includes 230,000
individuals in Germany.
Sampling strategy
Respondents were recruited online through the survey firm Kantar/Lightspeed. We used quotas to ensure representativeness of the
general population. Hard quotas were used on sex (male/female) and age (five groups). Soft quotas were used on household income
(deciles).
Data collection
The surveys were completed online individually by respondents.
Timing
18th December 2017 to 8th January 2018.
Data exclusions
We excluded the data from respondents who failed to respond correctly to an attention test implemented during the choice experiment
from the analyses shown in the paper. The rationale is that respondents failing the attention test may have provided random responses,
hence adding random noise to the data. This exclusion citerion was pre-established and reduced the sample size as follows: main sample
(Germany): 177 exclusions; Rhineland sample: 42 exclusions; Lusatia sample: 28 exclusions.
As a robustness check, we re-ran all analyses with the full samples, including inattentive respondents. None of the results described in
the paper change substantially if inattentive individuals are included in the analyses (for the full results, see Supplementary Tables 2, 4, 6,
8 and 10).
Non-participation
There were no drop-outs.
Randomization
For each participant, the choice experiment consisted of eight successive rounds with two pairs of policy scenarios. The levels of
attributes were randomly varied both within and across the binary comparisons. To prevent order effects, the order in which the
attributes appeared was randomized across respondents but fixed for each respondent.
Reporting for specific materials, systems and methods
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material,
system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
Materials & experimental systems
n/a Involved in the study
Antibodies
Eukaryotic cell lines
Palaeontology
Animals and other organisms
Human research participants
Clinical data
Methods
n/a Involved in the study
ChIP-seq
Flow cytometry
MRI-based neuroimaging
Human research participants
Policy information about studies involving human research participants
Population characteristics
See above.
Recruitment
Subjects were recruited through the online survey panel operated by Kantar/Lightspeed. This opt-in sample is likely to under-
represent individuals who have little or no internet access.
Ethics oversight
Ethics Committee of the University of St.Gallen
Note that full information on the approval of the study protocol must also be provided in the manuscript.

    Policy Implications:

Answer:
• A more ambitious timeline for phasing out coal would be better aligned with voters’ preferences in Germany.
• Although voters are willing to incur additional costs for a phase-out by 2025, every €10 increase in annual household cost decreases voter support by about seven percentage points.
• In coal-producing regions, creating new jobs in low-carbon industries is important to voters and can outweigh concerns about job losses.
• In the area of the energy transition, systematic incorporation of the views of the population into future government commission recommendations could lead to more publically acceptable policies.
• Raising awareness for the scientific consensus on climate change could foster public acceptance of ambitious climate policies.