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

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41558-020-00914-6
1Department of Political Science, Washington University in St. Louis, St. Louis, MO, USA. 2Swiss Institute for International Economics and Applied
Economic Research, St. Gallen, Switzerland. 3Department of Political Science, Yale University, New Haven, CT, USA. 4Department of Political Science,
Stanford University, Stanford, CA, USA. ✉e-mail: mbechtel.mail@gmail.com; kenneth.scheve@yale.edu
T
wo decades ago, over a dozen national academies of sciences
urged policymakers to take prompt action to reduce emis-
sions of greenhouse gases1. Although scholars have contin-
ued to assess the numerous ways in which climate change affects
humans2,3, animals4,5 and plants6,7 on Earth, governments have been
hesitant to pursue policies able to successfully reduce emissions8,9
because of a costs–participation dilemma: to be effective, climate
policy must raise the price of carbon and include most countries of
the world. Realizing both of these objectives is challenging because
climate action is voluntary and existing studies demonstrate that
publics are averse to costs10–13. A potential response to this problem
is the ‘ramping-up principle’, that is, the idea that the price of car-
bon should be gradually increased to give publics time to adjust to
stricter regulations8. Although intuitively appealing, it is not clear
whether publics do indeed prefer increasing cost paths over alterna-
tive cost schedules or whether they simply favour climate policies
with average low costs. Previous work has explored the willingness
of individuals to invest in energy efficiency improvements14,15 and
public approval of costly climate policy initiatives16–20. So far, there
exists no systematic evidence on which intertemporal cost paths
maximize public support for climate action.
Mapping mass support for climate cost schedules
To characterize mass preferences over climate cost paths, we
devised a survey that we conducted among representative samples
of adult populations (n = 10,075) in four major economies (France,
Germany, the United Kingdom and the United States). The sociode-
mographic margins for the raw and weighted samples along with the
population margins for each country are shown in Supplementary
Table 1. We first studied support for different climate cost schedules
using a direct question item to introduce the idea of an international
agreement that would entail certain average costs per month and
per household. The survey then presented respondents with four
different ways to distribute the costs of implementing an inter-
national climate agreement over time and asked them to indicate
which cost schedule they would select in a referendum given a cer-
tain cost level average (Fig. 1; see the Methods for the wording of
the questions). The cost level was held constant for respondents in
the United States, but we randomized whether the average monthly
household costs for implementing the agreement were expected to
be low (€28, €39 and £15) or high (€113, €154 and £60) in France,
Germany and the United Kingdom, respectively. These values have
been used in previous research10 and correspond to approximately
0.5% and 2.0% of gross domestic product (GDP) expressed in aver-
age monthly costs per household.
The share of respondents who preferred a constant allocation
of climate costs over time as opposed to other options, including
an increasing cost path, is shown in Figure 2. The results are virtu-
ally identical for the weighted data (see Extended Data Fig. 1). The
pooled responses show that 58% of all individuals selected a con-
stant cost path whereas only 12% preferred an increasing cost path
(Fig. 2a). When we inspected responses separately by country, we
found that the constant cost schedule received majority support in
each of the four countries (Fig. 2b). We also explored whether pref-
erences for climate cost paths reflected expectations about average
cost levels. When breaking out the results by cost treatment, we still
found that a majority preferred a constant cost path over increasing,
decreasing and inverse U-shaped allocations in both the low-cost
and the high-cost conditions. These results indicate that ramping
up climate costs over time may provoke more public opposition
than a policy that keeps the costs of climate action stable, even if the
average cost level is relatively high (€113, €154 and £60 per month
and per household in France, Germany and the United Kingdom,
respectively).
The causal effects of cost schedules
Our mapping of the distribution of support for different climate
cost paths suggested a correlation between a constant distribution
of costs and climate policy support. However, it remained unclear
whether a constant cost path does in fact cause a higher level of
Constant carbon pricing increases support for
climate action compared to ramping up costs
over time
Michael M. Bechtel   1,2 ✉, Kenneth F. Scheve   3 ✉ and Elisabeth van Lieshout   4
The introduction of policies that increase the price of carbon is central to limiting the adverse effects of global warming.
Conventional wisdom holds that, of the possible cost paths, gradually raising costs relating to climate action will receive the
most public support. Here, we explore mass support for dynamic cost paths in four major economies (France, Germany, the
United Kingdom and the United States). We find that, for a given level of average costs, increasing cost paths receive little sup-
port whereas constant cost schedules are backed by majorities in all countries irrespective of whether those average costs are
low or high. Experimental evidence indicates that constant cost paths significantly reduce opposition to climate action relative
to increasing cost paths. Preferences for climate cost paths are related to the time horizons of individuals and their desire to
smooth consumption over time.
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public approval than that caused by the widely discussed option
of an increasing cost schedule, and how sizeable this effect is rela-
tive to the level of costs. To answer these questions, we devised a
randomized climate cost conjoint experiment10,21. The experiment
presented respondents with two climate policy scenarios and asked
them to indicate which of the two they prefer. Each respondent
completed eight conjoint tasks, each of which specified both the
temporal allocation of costs that each scenario entailed (increasing,
constant or decreasing) and the associated average costs to house-
holds (0.5%, 1.0%, 2.0% or 2.5% of GDP), along with other policy
features (see Methods). By independently randomizing both the
level of costs and their temporal distribution, this approach allowed
us to separate and directly compare the causal effects of these two
forces (see Methods). The instructions to respondents, along with
an example conjoint task, are shown in Supplementary Fig. 1. For
our analysis, each policy profile was treated as one observation. We
estimate the causal effects of climate cost paths on policy support
by regressing whether a scenario was chosen on indicator variables
for each randomly assigned attribute value. The full results of the
conjoint analysis are shown in Extended Data Fig. 2.
Our estimates of how costs paths affect policy support, along
with 95% and 99% robust confidence intervals, are shown in Fig. 3a.
Moving from an increasing to a constant cost path caused a
significant increase in policy support by 7 percentage points com-
pared to the widely discussed option of ramping up costs over
time (the reference category), even when we explicitly specified
and fixed the cost level of the policy proposal. At the same time,
decreasing cost schedules also raised climate policy support com-
pared to the increasing cost path. The causal effects estimated from
the conjoint analysis show how cost paths changed the average
level of support, but may as such not be informative about the level
of support for a particular policy package, which was 50% across
all profiles by design in the forced-choice, paired-profile conjoint
experiment. We also note that the causal effect estimates are a func-
tion of both the preference for a policy feature and the intensity of
that preference, which contrasts to the analysis above in which the
estimates are simply a function of the number of individuals who
prefer a given cost schedule (Fig. 2). Understanding the rationale
for the positive estimate of the decreasing cost path in the conjoint
experiment seems a productive inquiry for future research, but we
focus primarily on the comparison between constant costs and
increasing costs in the remainder of this paper because constant
costs are preferred to an increasing time path across both measure-
ment strategies.
To put the sensitivity to cost paths into perspective, we also
visualized the causal effects of cost levels with support for climate
action (Fig. 3a). First, consistent with previous research, costs had
a significant and substantively noteworthy effect on public support.
Second, when using these estimates to benchmark the sensitivity to
an increasing domestic cost path, the effect of switching from a con-
stant to an increasing schedule was similar to doubling the average
monthly household costs from 0.5% of GDP (€28, €39, £15 and $53)
to 1.0% of GDP (€56, €77, £30 and $107). These results are robust
across all four countries (Fig. 3b). Overall, both the pooled esti-
mates and the results by country suggest that constant and decreas-
ing cost schedules increase the willingness to support climate action
compared to a policy with increasing costs over time.
Although the results provide information about the causal effect
of cost paths on the support of publics for climate action, opposi-
tion to an increasing cost schedule could reflect concerns about ris-
ing cost levels. To address this, we explicitly informed respondents
to the survey that, in the various scenarios, costs were distributed
differently over time but average household costs across the plans
remained unchanged. Furthermore, we re-estimated the cost path
effects separately by cost level to explore whether the aversion to an
increasing cost schedule depended strongly on costs (Fig. 4).
Monthly household costs
2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040
Year
Monthly household costs
2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040
Year
Monthly household costs
2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040
Year
Monthly household costs
2020
2022 2024 2026 2028 2030 2032 2034 2036 2038 2040
Year
Fig. 1 | Cost paths presented to respondents. Respondents were asked to select their preferred time path from four options: constant, increasing,
decreasing and inverse U-shaped. The order in which these options were shown was randomized across respondents.
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A constant cost schedule increased support by 8 percentage
points if costs were low (0.5% of GDP) and by 6 percentage points
if costs were high (2.5% of GDP). This suggests that the aversion to
increasing cost paths is unlikely to be explained by concerns about
average cost levels. Comparison of decreasing cost paths to con-
stant cost paths showed that decreasing schedules were only pre-
ferred over constant plans when costs were particularly high (2.0%
of GDP or more). In addition, we considered the possibility that
respondents would have different expectations about when costs
would commence depending on the cost schedule. We devised an
alternative version of the conjoint experiment that replaced the cost
path attribute with information about the year in which household
contributions would start. These values were drawn from a continu-
ous set of integers ranging from 2020 to 2040. This version of the
climate action conjoint experiment was completed by 680 randomly
chosen respondents from the United States.
We examined the causal effect of start year by regressing policy
support on a full set of indicator values that binned the start year
integers into 2-year periods. If publics were mostly concerned about
cost avoidance, a later starting period should significantly increase
public approval. The results (Fig. 5) indicate that changing the start
year had no significant effect on support for climate action. Overall,
this evidence is consistent with the idea that publics prefer cost sta-
bility, a tendency that seems to be relatively independent of con-
cerns related to cost levels.
Stability and predictability drive support for constant costs
To assess potential explanations for why publics prefer constant cost
paths over the intuitively appealing ramping-up approach, our sur-
vey included an open-ended question that asked respondents why
they selected a certain cost path. We then performed an exploratory
text analysis of the responses (see Methods) to determine which
58%
12%
19%
11%
Pooled
a
b
c
d
56%
13%
18%
13%
64%
13%
15%
8%
56%
12%
23%
9.7%
59%
11%
21%
9.7%
United States
United Kingdom
Germany
France
Constant
Increasing
Decreasing
Inverse U
By country
61%
11%
20%
8.7%
Low-cost treatment
(France, Germany, United Kingdom)
59%
13%
19%
9.6%
High-cost treatment
(France, Germany, United Kingdom)
Fig. 2 | Preferences for distributing climate costs over time. a,b, The percentage of respondents who prefer constant, increasing, decreasing or inverse
U-shaped intertemporal allocations of climate costs. Data are pooled (a) (n = 10,075) and by country (b) (France, n = 2,000; Germany, n = 2,000; United
Kingdom, n = 2,000; United States, n = 4,075). c, Low average household costs for implementing the agreement were set to €28, €39 and £15 for France,
Germany and the United Kingdom, respectively. d, High average household costs for implementing the agreement were set to €113, €154 and £60 for
France, Germany and the United Kingdom, respectively.
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words were predictive of an individual’s time path choice22. The dis-
tinctiveness and frequency of the 20 most distinguishing terms for
each country and time path are shown in Fig. 6.
Word stems such as ‘budget’, ‘easier’, and ‘know’ were systematic
predictors of choosing the constant cost path, which indicates that
the popularity of this option was related to the desire of individuals
to simplify budgeting and planning for the future. Additional word
stems were related to stability concerns that could be expected if
individuals tried to smooth consumption over time. Consistent with
the rationale underlying the ramping-up approach8, individuals that
supported an increasing cost path in part justified this preference by
highlighting that this schedule allowed publics to gradually adjust
to rising costs. However, respondents also chose this option in the
hope that delayed costs would be felt less strongly, arguably because
of wage increases and inflation, because they themselves would be
too old or no longer alive, or because they generally discounted
future income and consumption. Respondents who preferred
higher costs up front that decreased over time emphasized the need
to make investments now, which they believed to be essential to
confronting climate change.
The text analysis suggests at least two theoretical explanations
for why people prefer a constant cost path over an increasing sched-
ule: consumption smoothing and time discounting. Consumption
smoothing refers to the extent that individual utility in intertem-
poral choices is characterized by diminishing marginal utility so
that individuals prefer to consume similar amounts across time. A
recent study finds that experimental estimates of diminishing mar-
ginal utility predict preferences over large-stake payment plans (D.
Aycinena, S. Blazsek, L. Rentschler and C. Sprenger, personal com-
munication). Individuals with a strong preference for consumption
smoothing should prefer constant cost plans over increasing sched-
ules. Many studies have demonstrated the importance of time dis-
counting23,24 for understanding individual decisions on borrowing25
and saving26, the willingness to invest in clean energy14, as well as
the policy preferences of elected officials27. Individuals who exhibit
relatively high levels of patience (that is, low discounting) probably
also prefer constant to increasing plans.
To assess these two arguments, our survey instrument included
an item to capture an individual’s general preference for intertempo-
ral smoothing and time discounting (patience). The measurement
of these preferences relies on the convex time budget approach28,29 in
which respondents are asked to repeatedly choose a bundle of pay-
ments that will pay out some combination of a lower payout sooner
or a higher payout later. Our respondents chose from a set of six
options that included both extreme cases in which the full payment
is realized either sooner or later as well as mixed bundles. The dif-
ferences in those choices allowed separate identification of attitudes
toward consumption smoothing, present bias and time discounting.
See Methods for details about this measurement approach.
We investigated the importance of consumption smoothing
and discounting by regressing cost path choice on a dichotomized
Increasing
Constant
Decreasing
€28, €39, £15, $53
€56, €77, £30, $107
€113, €154, £60, $213
€141, €193, £75, $267
Cost path
Cost path
a
b
Cost level
Cost level
–30
–20
–10
0
10
Change in chosen policy profile (percentage points)
Pooled
Increasing
Constant
Decreasing
€28, €39, £15, $53
€56, €77, £30, $107
€113, €154, £60, $213
€141, €193, £75, $267
–30
–20
–10
0
10
Change in chosen policy profile (percentage points)
United States
United Kingdom
Germany
France
By country
Fig. 3 | Support for climate action as a function of cost paths and cost levels.  a,b, Results are pooled (a) (n = 129,280 policy profiles) or by country
(b) (France, n = 32,000; Germany, n = 32,000; United Kingdom, n = 32,000; United States, n = 33,280 policy profiles). Dots with horizontal lines are
point estimates from linear least squares regressions of climate policy chosen on randomly assigned cost path and cost level attributes. Error bars
indicate 95% and 99% confidence intervals computed from robust standard errors clustered by respondent. Results with weighting are shown in
Extended Data Figs. 3,4.
Increasing
Constant
Decreasing
Cost path
0
2
4
6
8
10
Change in chosen policy profile (percentage points)
0.5% of GDP: €28, €39, £15, $53
1% of GDP: €56, €77, £30, $107
2% of GDP: €113, €154, £60, $213
2.5% of GDP: €141, €193, £75, $267
Fig. 4 | Support for climate action as a function of cost paths by cost level.
Causal effects of climate cost paths on policy support estimated separately
for each randomly assigned cost level (0.5% of GDP, n = 32,305; 1% of
GDP, n = 32,373; 2% of GDP, n = 32,367; 2.5% of GDP, n = 32,235 policy
profiles). Dots with horizontal lines are point estimates from linear least
squares regressions of climate policy chosen on randomly assigned cost
path attributes. Error bars indicate 95% and 99% confidence intervals
computed from robust standard errors clustered by respondent. Results
with weighting are shown in Extended Data Fig. 5.
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smoothing parameter variable, a measure of patience (both set to
one for respondents with above-median values and zero otherwise),
and a full set of sociodemographic predictors in a multinomial pro-
bit model. We find that individuals with a higher desire to smooth
consumption are significantly more likely to select the constant cost
path over the increasing and decreasing climate cost paths. Similarly,
respondents who were more patient were significantly more likely
to support a constant climate cost schedule over time-varying cost
paths such as the ramping-up approach (Supplementary Table 2).
Overall, the predictive patterns indicate that, consistent with the
results from our analysis of respondents’ answers to our open-ended
question, general attitudes towards time seem to play a systematic
role in understanding mass preferences for constant over dynamic
cost paths.
Conclusion
Credible climate policies will have to raise the price of carbon, and
the public are concerned about these costs even if they believe the
science of climate change and generally would like governments to
address the issue. One intuitively appealing approach to this prob-
lem is to ramp up the costs of climate action over time. This lowers
costs in the near to medium term but also requires individuals to
keep adjusting to steadily increasing carbon prices. Our results indi-
cate that such cost plans run the risk of reducing support for climate
policies because many individuals prefer to smooth their consump-
tion over time. The ramping-up approach could remain politically
feasible if some voters focus on near-term costs or if policy experi-
ence causes mass preferences to become more favourable towards
costly climate action in general. But to the extent that policymakers
2020–2021
2022–2023
2024–2025
2026–2027
2028–2029
2030–2031
2032–2033
2034–2035
2036–2037
2038–2040
Start year of cost paths
–10
–5
0
5
10
Change in chosen policy profile (percentage points)
Fig. 5 | Support for climate action as a function of cost paths by start year.
Results from a conjoint experiment conducted in a separate section of
the United States survey that randomized the year in which contributions
would start (n = 10,880 policy profiles; see Methods for details). Dots with
horizontal lines are point estimates from linear least squares regressions
of climate policy chosen on randomly assigned cost path attributes. Error
bars indicate 95% and 99% confidence intervals computed from robust
standard errors clustered by respondent. Results with weighting are shown
in Extended Data Fig. 6.
Costs remain the same over the entire period
Costs are lower initially and higher later
Costs are higher initially and  lower later
Costs are lower initially,
higher in the middle and lower later
France
Germany
United Kingdom
United States
3
4
5
6
2
3
4
5
6
2
3
4
5
6
2
3
4
5
3
4
5
6
7
4
6
8
2.5
5.0
7.5
10.0
2.5
5.0
7.5
10.0
12.5
Log(word count)
Log odds ratio
Budget
Chaqu
Const
Depens
Equilibr
Equit
Facil
Ger
Mauvais
Mem
N'y
Quoi
Rest
Sait
Savoir
Simpl
Somm
Stabilit
Stabl
Surpris
Ausgab
Belast
Bess
Betrag
Bleib
Bleibt
Einstell
Genau
Gerecht
Gleich
Gleichbleib
Gleichma
Kalkuli
Kalkulierbar
Monat
Plan
Planbar
Planung
Rechn
Uberschaubar
Amount
Budget
Consist
Constant
Easier
Exact
Fair
Fairer
Fix
Keep
Know
Like
Manag
Month
Much
Outgo
Plan
Remain
Stay
Steadi
Amount
Budget
Chang
Consist
Constant
Easier
Equal
Even
Everi
Fix
Keep
Know
Month
Plan
Predict
Rate
Remain
Set
Stay
Steadi
Age
Alor
Augment
Augmentent
Auss
Courb
Esper
Hauss
L'augment
Petit
Peu
Peut
Peuvent
Prepar
Progress
Progressivit
Ras
Salair
Toujour
Vont
Alt
Ander
Ansteig
Ebenfall
Egal
Einkomm
Erhoh
Erhoht
Erleb
Ganz
Gewohn
Inflation
Jahr
Kostensteiger
Langsam
Steig
Steiger
Steigt
Teur
Vielleicht
Adapt
Addit
Allow
Dead
Earn
Give
Gradual
Hope
Household
Increas
Inflat
Live
Moment
Offset Old
Probabl
Rise
Slowli
Time
Wage
Adjust
Dead
Dollar
Economi
Everyth
Give
Gradual
Great
Hope
Increas
Inflat
Money
New
Old
Possibl
Prepar
Slowli
Time
Wage
Year
Agir
Baiss
Cher
Debut
Depart
Diminu
Don
Effort
Ensuit
Fil
Fur
Invest
Mainten
Mesur
Moin
Plac
Puis
Rapid
Suit
Vit
Anfang
Dass
Entlast
Erfolg
Geld
Gut
Investi
Investition
Lauf
Lieb
Mehr
Nahm
Positiv
Schnell
Sink
Sofort
Sollt
Spat
Wenig
Zeit
Benefit
Cheaper
Decreas
Drop
Eventu
First
Futur
Get
Goe
Improv
Initi
Invest
Later Less
Long
Now
Reduc
Technolog
Thing
Worst
Begin
Cheaper
Cost
Decreas
First
Front
Futur
Get
Goe
Initi
Later
Less Lower
Now
Older
Pay
Rather
Reduc
Sooner
Upfront
2040
Baiss
Commenc
Courb
Douc
Ensuit
Fais
Just
Mesur
Milieu
Mis
Necessair
Par
Permet
Permettr
Prepar
Progress
Puis
Repartit
Term
Allerding
Ansteig
Auswirk
Best
Erscheint
Erst
Gewohn
Irgendwann
Langsam
Logisch
Losung
Mal
Mensch
Mitt
Start
Weg
Zeig
Zeitpunkt
Ziel
Zuerst
Adjust
Benefit
Eas
End
Fund
Give
Gradual
Improv
Later
Look
Low
Lower
Might
Prepar
Promis
Reduc
Reward
Save
Start
Use
Adjust
Back
Build
Chanc
Curv
Eas
End
Get
Give
Gradual
Half
Initi
Look
Low
Lower
Middl
Prepar
Ramp
Slowli
Start
Fig. 6 | Words associated with justifications for a given cost path. This plot shows the top 20 most distinguishing words in respondents’ justifications for choos
ing a given time path by cost schedule and country. The vertical axis plots the most distinguishing words on the basis of the log odds ratio. The horizontal axis
shows the log frequency with which a term occurred. Words were reduced to their common stems (for example, surprise and surprising both became surpris).
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seek to design policies that are transparent and meet meaningful
emission reduction goals, constant cost plans promise more support
for climate action relative to ramping-up approaches. Moreover,
owing to the delay in large-scale policy responses to climate change,
countries will probably have to pursue more progressive and costly
climate action to limit the adverse effects of global warming. The
drop in support because of higher costs associated with these more
ambitious policy efforts may be at least partially mitigated by select-
ing a set of attractive design features such as the constant distribu-
tion of costs.
Online content
Any methods, additional references, Nature Research report-
ing summaries, source data, extended data, supplementary infor-
mation, acknowledgements, peer review information; details of
author contributions and competing interests; and statements of
data and code availability are available at https://doi.org/10.1038/
s41558-020-00914-6.
Received: 14 January 2020; Accepted: 20 August 2020;
Published online: 21 September 2020
References
	1.	 Australian Academy of Sciences et al. The science of climate change. Science
292, 1261 (2001).
	2.	 Obradovich, N., Tingley, D. & Rahwan, I. Effects of environmental stressors
on daily governance. Proc. Natl Acad. Sci. USA 115, 8710–8715 (2018).
	3.	 Stevanović, M. et al. The impact of high-end climate change on agricultural
welfare. Sci. Adv. 2, e1501452 (2016).
	4.	 Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid
range shifts of species associated with high levels of climate warming. Science
333, 1024–1026 (2011).
	5.	 Bonebrake, T. C. & Mastrandrea, M. D. Tolerance adaptation and
precipitation changes complicate latitudinal patterns of climate change
impacts. Proc. Natl Acad. Sci. USA 107, 12581–12586 (2010).
	6.	 Panetta, A. M., Stanton, M. L. & Harte, J. Climate warming drives local
extinction: Evidence from observation and experimentation. Sci. Adv. 4,
eaaq1819 (2018).
	7.	 Cámara-Leret, R. et al. Climate change threatens New Guinea’s biocultural
heritage. Sci. Adv. 5, eaaz1455 (2019).
	8.	 Nordhaus, W. Climate change: the ultimate challenge for economics. Am.
Econ. Rev. 109, 1991–2014 (2019).
	9.	 Keohane, R. O. & Victor, D. G. Cooperation and discord in global climate
policy. Nat. Clim. Change 6, 570–575 (2016).
	10.	Bechtel, M. M. & Scheve, K. F. Mass support for global climate agreements
depends on institutional design. Proc. Natl Acad. Sci. USA 110,
13763–13768 (2013).
	11.	Drews, S. & van den Bergh, J. C. J. M. What explains public support for
climate policies? A review of empirical and experimental studies. Clim. Policy
16, 855–876 (2015).
	12.	Aklin, M. & Urpelainen, J. Debating clean energy: frames, counter frames,
and audiences. Glob. Environ. Change 23, 1225–1232 (2013).
	13.	Egan, P. J. & Mullin, M. Climate change: US public opinion. Annu. Rev. Polit.
Sci. 20, 209–227 (2017).
	14.	Newell, R. G. & Siikamäki, J. Individual time preferences and energy
efficiency. Am. Econ. Rev. 105, 196–200 (2015).
	15.	Feldman, L. & Hart, P. S. Climate change as a polarizing cue: framing effects
on public support for low-carbon energy policies. Glob. Environ. Change 51,
54–66 (2018).
	16.	Stoutenborough, W., Bromley-Trujillo, R. & Vedlitz, A. Public support for
climate change policy: consistency in the influence of values and attitudes
over time and across specific policy alternatives. Rev. Policy Res. 31,
555–583 (2014).
	17.	Hammar, H. & Jagers, S. C. Can trust in politicians explain individuals’ support
for climate policy? The case of CO2 tax. Clim. Policy 5, 613–625 (2006).
	18.	Tingley, D. & Tomz, M. Conditional cooperation and climate change. Comp.
Polit. Stud. 47, 344–368 (2014).
	19.	Bernauer, T. & Gampfer, R. How robust is public support for unilateral
climate policy? Environ. Sci. Policy 54, 316–330 (2015).
	20.	Mildenberger, M. Support for climate unilateralism. Nat. Clim. Change 9,
187–188 (2019).
	21.	Hainmueller, J., Hopkins, D. J. & Yamamoto, T. Causal inference in conjoint
analysis: understanding multidimensional choices via stated preference
experiments. Polit. Anal. 22, 1–30 (2014).
	22.	Monroe, B. L., Colaresi, M. P. & Quinn, K. M. Fightin’words: lexical feature
selection and evaluation for identifying the content of political conflict. Polit.
Anal. 16, 372–403 (2008).
	23.	Andersen, S., Harrison, G. W., Lau, M. I. & Rutström, E. E. Eliciting risk and
time preferences. Econometrica 76, 583–618 (2008).
	24.	Frederick, S., Loewenstein, G. & O’Donoghue, T. Time discounting and time
preference: a critical review. J. Econ. Lit. 40, 351–401 (2002).
	25.	Meier, S. & Sprenger, C. Present-biased preferences and credit card
borrowing, Am. Econ. J. Appl. Econ. 2, 193–210 (2010).
	26.	Sutter, M., Kocher, M. G., Glätzle-Rützler, D. & Trautmann, S. T. Impatience
and uncertainty: experimental decisions predict adolescents’ field behavior.
Am. Econ. Rev. 103, 510–531 (2013).
	27.	Sheffer, L., Loewen, P. J., Soroka, S., Walgrave, S. & Sheafe, T.
Nonrepresentative representatives: an experimental study of the decision
making of elected politicians. Am. Polit. Sci. Rev. 112, 302–321 (2018).
	28.	Andreoni, J. & Sprenger, C. Estimating time preferences from convex budgets.
Am. Econ. Rev. 102, 3333–3356 (2012).
	29.	Andreoni, J., Kuhn, M. A. & Sprenger, C. Measuring time preferences: a
comparison of experimental methods. J. Econ. Behav. Organ. 116,
451–464 (2015).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
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Methods
Sample. We carried out our survey in four major economies (France, Germany,
the United Kingdom and the United States). The survey was conducted online by
YouGov using matched sampling. Propensity scores were used to match available
internet respondents (participants of the YouGov online panel) to a stratified
random sample of the target adult population. The propensity score model
included age, gender, years of education and region (European countries), or age,
gender, race or ethnicity, years of education and region (the United States).
United States. The survey period was 18 December 2018 to 3 January 2019. The
sampling frame for the target population was constructed from the full 2016
American Community Survey. All matched respondents were then assigned
weights stratified on 2016 presidential vote, age, gender, race and education to
correct for remaining imbalances. The final number of respondents was 4,075.
France, Germany and the United Kingdom. The survey period was 31 March 2019
to 4 April 2019. The sampling frames for the target populations were constructed
from the 2018 Eurobarometer survey for selection within strata by weighted
sampling with replacements (using the person weights on the public use file). The
final number of observations was 2,000 for France, 2,000 for Germany, and 2,000
for the United Kingdom. The distributions of sociodemographic characteristics in
the population, the raw samples and the weighted samples by country are reported
in Supplementary Table 1.
Measurements. We measured individual tendency to discount future events
and the preference for consumption smoothing using the convex time budget
method28,29. The allocation of payments xt and xt+k between period t and t + k was
considered. The utility function of an individual was defined as:
U xt; xtþk
ð
Þ ¼
xα
t þ βδkxα
tþk if t ¼ 0
xα
t þ δkxα
tþk if t>0

where α measures consumption smoothing as the utility function curvature, δ
denotes long-run time discounting, and β captures present bias. We estimated
these parameters using the choices of respondents in a convex time budget task.
In this task, respondents were asked to select a bundle of payments that would be
received at time t and t + k, where each choice comprised the cases in which the full
payment occurred at time t and t + k and also included four convex combinations.
For example, a choice task might ask respondents to choose between a combination
of US$19 today and $0 in 5 weeks, a combination of $0 today and $20 in 5 weeks,
as well as the following four convex combinations of these two: $15.20 today and
$4.00 in 5 weeks, $11.40 today and $8.00 in 5 weeks, $7.60 today and $12.00 in 5
weeks, and $3.80 today and $16.00 in 5 weeks.
This example compares payments today to payments in 5 weeks. Following
existing work28, we asked respondents to also assess budgets in which the earlier
payment would occur at t > 0 and the later payment after that. Variation in
individual choices provided information to estimate the consumption smoothing
parameter α and the other parameters mentioned above using individual-level
regressions.
Parameters were estimated by regressing the natural log of the ratio of the
chosen earlier and later payments on the number of days to the first payment,
the number of days that the payment is delayed, and the natural log of the price
ratio of the later payments to the earlier payments. Our estimate of the smoothing
parameter α was the inverse of the coefficient on the price ratio. The time
discounting (or patience) parameter was the exponent of the ratio of the coefficient
on the temporal delay to the coefficient on the natural log of the price ratio. To
deal with extreme values we trimmed the estimated parameters at the 5th and
95th percentiles. We then transformed the estimated parameters into an indicator
variable that equals one if a respondent’s consumption smoothing measure was
greater than the median and is zero otherwise (Supplementary Table 2). Similarly,
we constructed an indicator variable that took a value of one if a respondent’s time
discounting estimated parameter was above the median, thus corresponding to a
relatively high degree of patience.
The sociodemographic predictors that were used included gender, age, income,
education and children. Age categories were 18–34 years, 35–49 years, 50–64 years,
and 65 years and above. Respondents were divided into three income categories
such that the sample in each country was equally split between these groups.
Education was also split into three categories adjusted to the education system of
each country, with groupings broadly corresponding to less than a high school
degree, a high school degree or more and a bachelor’s degree or more. ‘Children’
was measured as a binary variable that took a value of one if the respondent
indicated having any children.
Cost schedule questions. The full survey instrument is available as part of the
replication archive for this study. Our main outcome variables were based on two
items, namely a climate agreement item and a climate cost path item.
The climate agreement item was as follows:
“As you probably know, many experts say that countries should take action to
address global warming.
Suppose [your country] is considering joining an international agreement to
reduce greenhouse gas emissions. Implementing the agreement would mean that
each household would have to pay on average [France, €28, €113; Germany, €39,
€154; the United Kingdom, £15, £60; the United States, $107] more per month
through, for example, higher energy prices.
Generally speaking, do you approve or disapprove of [France, Germany, the
United Kingdom, the United States] joining such an agreement?”
Respondents were asked to answer on a scale from 1 (strongly approve) to 10
(strongly disapprove).
The climate cost path item was as follows:
“Regardless of your previous answer, suppose [France, Germany, the United
Kingdom, the United States] is going to implement that international agreement
and the household costs would still be [same costs as above] per month on average.
However, there are different ways of distributing these costs over time.
The figures below indicate four alternatives. If you had to select one of the
options in a referendum, which would you chose? Please carefully consider the
available options.”
The options were presented to respondents as shown in Fig. 1. The preferences
for distributing climate costs over time, shown in Fig. 2, were calculated as the
share of respondents who selected a particular time path. We investigated the role
of different individual-level covariates using a multinomial probit regression. The
four possible values of the outcome variable corresponded to the four time path
options available to respondents. Our independent variables were the measures of
consumption smoothing, patience and the sociodemographic covariates described
above (the results are reported in Supplementary Table 2).
Climate cost conjoint experiment. The forced-choice conjoint experiment
started by explaining to respondents that they were about to see a pair of policies,
from which they would be asked to choose their preferred option. The full
introductory text is available in Supplementary Fig. 1a. Respondents then
saw two possible climate policies side by side, with information about eight
different aspects of each policy. They were then asked to choose which of the two
policy sets they would prefer to see enacted. An example of such a task is shown
in Supplementary Fig. 1b. Each respondent was asked to make a choice regarding
eight different pairs of policies. Our analysis treats one policy scenario or proposal
as one observation.
The randomized policy features comprised the policy’s cost to the average
household, the distribution of those costs over time, and the percentage of revenues
used for adaptation and mitigation efforts. Information was provided on each of
these features regarding the respondents’ own country and other major economies.
The temporal distribution feature could take increasing, decreasing or constant
values. The percentages of revenue spent varied between 0% and 100%, such that
adaptation and mitigation together summed to 100%. The different household cost
values for each country were equivalent to 0.5%, 1.0%, 1.5%, 2.0% and 2.5% of per
capita GDP (see Extended Data Fig. 2).
Each of the policy features was randomized separately. One of the strengths
of conjoint experiments is that this separate randomization allows for the direct
calculation of the treatment effect of different values of the features, that is, the
average marginal component effect21. This causal effect captures the change in
probability that a respondent will choose a particular profile with that value,
compared to a profile with the baseline value for that policy feature. We computed
this treatment effect using linear regression, where the unit of analysis was a
policy profile and the outcome of interest was whether the profile was selected
by the respondent. Standard errors were clustered by respondent to account for
within-individual correlation of the error term (see Extended Data Fig. 2 for the
resulting treatment effects). To investigate differences between populations of
respondents, we estimated this regression separately for different subgroups.
All respondents in France, Germany and the United Kingdom completed
the climate cost path conjoint experiment. In the United States, this conjoint
experiment was administered to 2,080 randomly chosen respondents (about 50%
of the total sample). Each respondent performed eight conjoint tasks in which
they assessed two profiles simultaneously. The results (Fig. 5) rely on an alternative
version of the climate conjoint experiment that replaced the cost schedule attribute
with the randomly assigned year in which household contributions would start.
This conjoint experiment was administered to 680 randomly chosen respondents
in the United States. Each respondent again performed eight conjoint tasks
in which they assessed two profiles simultaneously. Therefore, the number of
observations for this analysis is 10,880.
Quantitative text analysis. After selecting one of the four time paths, respondents
were asked an open-ended question that read “Please let us know why you chose
this response”. The text analysis that we performed on the responses to these
questions was not preregistered. We first applied standard text-as-data cleaning
procedures to these responses by removing all punctuation, capitalization and word
orderings to treat the responses as a ‘bag of words’. In each of the three languages,
we discarded common stop words (for example, ‘the’ and ‘and’) and we stemmed
each word to combine related terms (for example, ‘consistently’ and ‘consistency’
both became ‘consist’). We then discarded rare terms, namely those that occurred
less than five times across answers from a particular country. After this cleaning
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process, we were left with 9,428 responses containing at least one useable term
(United States, 3,844; United Kingdom, 1,887; France, 1,894; Germany, 1,803).
To identify the words that best predicted the time path option that a respondent
had selected, we applied the ‘Fightin’ Words’ algorithm22. The basic intuition of
this measure is captured by a normal log odds ratio. For a given time path k and a
given word j, we calculated the log odds ratio according to the probability of a text
string containing that word j being written by a respondent who chose option k or
another option:
Log odds ratioj;k ¼ log
P kjj
ð
Þ
1  P kjj
ð
Þ

 log
P :kjj
ð
Þ
1  P :kjj
ð
Þ

The approach developed in ref. 22 produces a similar measure, but with useful
regularization. The model sets up the text corpus as a multinomial distribution, with
a Dirichlet prior on the probabilities of different topics based on different words:
π Dirichlet α
ð Þ
yk Multinomial nk; πk
ð
Þ
Using an informative prior avoids overfitting to rare terms. We followed the
approach in ref. 22 and used the average number of words per text such that α = 5 in
our application. Furthermore, we emphasized words for which the estimates were
more certain by reweighting words using their variance. To this end, we employed
the z-score of the log odds ratio, which is the regularized log odds ratio divided by its
standard deviation. This z-score is the measure displayed on the vertical axis in Fig. 6.
Preregistration. This study has been preregistered at the American Economic
Association’s registry for randomized controlled trials, under AEARCTR-0004090.
Ethics oversight. This study was approved by the internal review boards of
Washington University in St. Louis (201803178) and Stanford University
(eProtocol 46325).
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
Data and replication materials are available at the Harvard Dataverse (https://doi.
org/10.7910/DVN/VXJPN5).
Code availability
Statistical code are available as part of the replication materials at the Harvard
Dataverse (https://doi.org/10.7910/DVN/VXJPN5).
Acknowledgements
We thank Clara Vandeweerdt for research assistance and audiences at Yale University and
the 2019 International Political Economy Society Conference for comments. M.M.B. and
K.F.S. gratefully acknowledge financial support from the Swiss Network for International
Studies and the Weidenbaum Center on the Economy, Government, and Public Policy at
Washington University in St. Louis. K.F.S. thanks the Institute for Research in the Social
Sciences at Stanford University for a faculty fellowship.
Author contributions
M.M.B., K.F.S. and E.vL. contributed equally to the study design, data collection and
analysis, interpretation of the results and writing of the manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/s41558-020-00914-6.
Supplementary information is available for this paper at https://doi.org/10.1038/
s41558-020-00914-6.
Correspondence and requests for materials should be addressed to M.M.B. or K.F.S.
Peer review information Nature Climate Change thanks Rebecca Bromley-Trujillo,
Christopher Warshaw and the other, anonymous, reviewer(s) for their contribution to
the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
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Extended Data Fig. 1 | Preferences for distributing climate costs over time (weighted data). The percentage of respondents who prefer constant,
increasing, decreasing, or inverse U-shaped intertemporal allocations of climate costs (n = 10,075).
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Extended Data Fig. 2 | The causal effects of cost path, cost level, and other policy attributes on public support. Dots with horizontal lines are point
estimates from a linear least squares regression of climate policy chosen (n = 129,280) on randomly assigned cost path, cost level, and revenue investment
attributes. Error bars indicate 95% and 99% confidence intervals computed from robust standard errors clustered by respondent.
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Extended Data Fig. 3 | Support for climate action as a function of cost paths and cost levels (weighted data). Dots with horizontal lines are point
estimates from linear least squares regressions of climate policy chosen on randomly assigned cost path and cost level attributes. Error bars indicate 95%
and 99% confidence intervals computed from robust standard errors clustered by respondent, n(policy profiles)=129,280.
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Extended Data Fig. 4 | Support for climate action as a function of cost paths and cost levels by country (weighted data). Dots with horizontal lines are
point estimates from linear least squares regressions of climate policy chosen on randomly assigned cost path and cost level attributes. Error bars indicate
95% and 99% confidence intervals computed from robust standard errors clustered by respondent, n(France, policy profiles)=32,000, n(Germany, policy
profiles)=32,000, n(United Kingdom, policy profiles)=32,000, n(United States, policy profiles)=33,280.
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Extended Data Fig. 5 | Support for climate action as a function of cost paths by cost level (weighted data). Causal effects of climate cost paths on policy
support estimated separately for each randomly assigned cost level, n(0.5% of GDP, policy profiles)=32,305, n(1% of GDP, policy profiles)=32,373,
n(2% of GDP, policy profiles)=32,367, n(2.5% of GDP, policy profiles)=32,235. Dots with horizontal lines are point estimates from linear least squares
regressions of climate policy chosen on randomly assigned cost path attributes. Error bars indicate 95% and 99% confidence intervals computed from
robust standard errors clustered by respondent.
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Extended Data Fig. 6 | Support for climate action as a function of cost paths by cost level (weighted data). Results from a conjoint experiment conducted
in a separate section of the United States survey that randomized the year in which contributions would start, n(policy profiles)=10,880, see Methods
section for details. Dots with horizontal lines are point estimates from linear least squares regressions of climate policy chosen on randomly assigned cost
path attributes. Error bars indicate 95% and 99% confidence intervals computed from robust standard errors clustered by respondent.
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    Policy Problem:

Answer: Devising effective responses to global warming requires policies that raise the price of carbon. However, as has been powerfully demonstrated by the Yellow Vests movement, costly climate action can provoke forceful public backlash. A widely held conjecture is that, among the possible cost schedules, that is, the ways in which costs can be distributed over time, policies that gradually increase the price of carbon will minimize public opposition to climate action as compared with constant costs. However, such claims lack empirical support. Knowing which cost schedule — increasing or constant — is more appealing to the general public is crucial for policymakers interested in designing carbon price policies, based on cap-and-trade or tax, that promise to be effective and politically sustainable.