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 support 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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Improving public support for climate action through multilateralism
Nature Communications Open Access 28 October 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Data and replication materials are available at the Harvard Dataverse (https://doi.org/10.7910/DVN/VXJPN5).
Statistical code are available as part of the replication materials at the Harvard Dataverse (https://doi.org/10.7910/DVN/VXJPN5).
Australian Academy of Sciences et al. The science of climate change. Science 292, 1261 (2001).
Obradovich, N., Tingley, D. & Rahwan, I. Effects of environmental stressors on daily governance. Proc. Natl Acad. Sci. USA 115, 8710–8715 (2018).
Stevanović, M. et al. The impact of high-end climate change on agricultural welfare. Sci. Adv. 2, e1501452 (2016).
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).
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).
Panetta, A. M., Stanton, M. L. & Harte, J. Climate warming drives local extinction: Evidence from observation and experimentation. Sci. Adv. 4, eaaq1819 (2018).
Cámara-Leret, R. et al. Climate change threatens New Guinea’s biocultural heritage. Sci. Adv. 5, eaaz1455 (2019).
Nordhaus, W. Climate change: the ultimate challenge for economics. Am. Econ. Rev. 109, 1991–2014 (2019).
Keohane, R. O. & Victor, D. G. Cooperation and discord in global climate policy. Nat. Clim. Change 6, 570–575 (2016).
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).
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).
Aklin, M. & Urpelainen, J. Debating clean energy: frames, counter frames, and audiences. Glob. Environ. Change 23, 1225–1232 (2013).
Egan, P. J. & Mullin, M. Climate change: US public opinion. Annu. Rev. Polit. Sci. 20, 209–227 (2017).
Newell, R. G. & Siikamäki, J. Individual time preferences and energy efficiency. Am. Econ. Rev. 105, 196–200 (2015).
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).
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).
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).
Tingley, D. & Tomz, M. Conditional cooperation and climate change. Comp. Polit. Stud. 47, 344–368 (2014).
Bernauer, T. & Gampfer, R. How robust is public support for unilateral climate policy? Environ. Sci. Policy 54, 316–330 (2015).
Mildenberger, M. Support for climate unilateralism. Nat. Clim. Change 9, 187–188 (2019).
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).
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).
Andersen, S., Harrison, G. W., Lau, M. I. & Rutström, E. E. Eliciting risk and time preferences. Econometrica 76, 583–618 (2008).
Frederick, S., Loewenstein, G. & O’Donoghue, T. Time discounting and time preference: a critical review. J. Econ. Lit. 40, 351–401 (2002).
Meier, S. & Sprenger, C. Present-biased preferences and credit card borrowing, Am. Econ. J. Appl. Econ. 2, 193–210 (2010).
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).
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).
Andreoni, J. & Sprenger, C. Estimating time preferences from convex budgets. Am. Econ. Rev. 102, 3333–3356 (2012).
Andreoni, J., Kuhn, M. A. & Sprenger, C. Measuring time preferences: a comparison of experimental methods. J. Econ. Behav. Organ. 116, 451–464 (2015).
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.
The authors declare no competing interests.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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).
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.
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.
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.
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.
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.
Supplementary Fig. 1 and Tables 1 and 2.
Rights and permissions
About this article
Cite this article
Bechtel, M.M., Scheve, K.F. & van Lieshout, E. Constant carbon pricing increases support for climate action compared to ramping up costs over time. Nat. Clim. Chang. 10, 1004–1009 (2020). https://doi.org/10.1038/s41558-020-00914-6
This article is cited by
Improving public support for climate action through multilateralism
Nature Communications (2022)
A preference for constant costs
Nature Climate Change (2020)