Climate Change 2022: Mitigation of Climate Change (IPCC, 2022).
Dahl, R. A. On Democracy (Yale Univ. Press, 1998).
Fiorino, D. J. Environmental risk and democratic process: a critical review. Columbia J. Environ. Law 14, 501–548 (1989).
Yeganeh, A. J., McCoy, A. P. & Schenk, T. Determinants of climate change policy adoption: a meta-analysis. Urban Clim. 31, 100547 (2020).
Wong-Parodi, G., Krishnamurti, T., Davis, A., Schwartz, D. & Fischhoff, B. A decision science approach for integrating social science in climate and energy solutions. Nat. Clim. Change 6, 563–569 (2016).
Bergquist, M., Nilsson, A., Harring, N. & Jagers, S. C. Meta-analyses of fifteen determinants of public opinion about climate change taxes and laws. Nat. Clim. Change 12, 235–240 (2022).
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 (2016).
Ejelöv, E. & Nilsson, A. Individual factors influencing acceptability for environmental policies: a review and research agenda. Sustainability 12, 2404 (2020).
Goldberg, M. H., Gustafson, A., Ballew, M. T., Rosenthal, S. A. & Leiserowitz, A. Identifying the most important predictors of support for climate policy in the United States. Behav. Public Policy 5, 480–502 (2021).
Clulow, Z., Ferguson, M., Ashworth, P. & Reiner, D. Comparing public attitudes towards energy technologies in Australia and the UK: the role of political ideology. Glob. Environ. Change 70, 102327 (2021).
Ding, D., Maibach, E. W., Zhao, X., Roser-Renouf, C. & Leiserowitz, A. Support for climate policy and societal action are linked to perceptions about scientific agreement. Nat. Clim. Change 1, 462–466 (2011).
Syropoulos, S. & Markowitz, E. M. Perceived responsibility to address climate change consistently relates to increased pro-environmental attitudes, behaviors and policy support: evidence across 23 countries. J. Environ. Psychol. 83, 101868 (2022).
Fairbrother, M., Johansson Sevä, I. & Kulin, J. Political trust and the relationship between climate change beliefs and support for fossil fuel taxes: evidence from a survey of 23 European countries. Glob. Environ. Change 59, 102003 (2019).
Swim, J. K. & Geiger, N. Policy attributes, perceived impacts, and climate change policy preferences. J. Environ. Psychol. 77, 101673 (2021).
Ogunbode, C. A. et al. Climate justice beliefs related to climate action and policy support around the world. Nat. Clim. Change 14, 1144–1150 (2024).
Huber, R. A., Wicki, M. L. & Bernauer, T. Public support for environmental policy depends on beliefs concerning effectiveness, intrusiveness, and fairness. Environ. Polit. 29, 649–673 (2020).
Thaller, A. et al. When perceived fairness and acceptance go hand in hand—drivers of regulatory and economic policies for low-carbon mobility. PLoS Clim. 2, e0000157 (2023).
Boon-Falleur, M., Grandin, A., Baumard, N. & Chevallier, C. Leveraging social cognition to promote effective climate change mitigation. Nat. Clim. Change 12, 332–338 (2022).
Goldberg, M. H., Van Der Linden, S., Leiserowitz, A. & Maibach, E. W. Perceived social consensus can reduce ideological biases on climate change. Environ. Behav. 52, 495–517 (2019).
Sokoloski, R., Markowitz, E. M. & Bidwell, D. Public estimates of support for offshore wind energy: false consensus, pluralistic ignorance, and partisan effects. Energy Policy 112, 45–55 (2018).
Smith, N. & Leiserowitz, A. The role of emotion in global warming policy support and opposition: the role of emotion in global warming policy support and opposition. Risk Anal. 34, 937–948 (2014).
Todorova, B. et al. Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors. npj Clim. Action 4, 46 (2025).
Dechezleprêtre, A. et al. Fighting climate change: international attitudes toward climate policies. Am. Econ. Rev. 115, 1258–1300 (2025).
Rhodes, E., Axsen, J. & Jaccard, M. Exploring citizen support for different types of climate policy. Ecol. Econ. 137, 56–69 (2017).
Fairbrother, M., Johansson Sevä, I. & Kulin, J. How do Europeans want to fight climate change? Comparing and explaining public support for a wide variety of policies. J. Public Policy 45, 1–25 (2025).
Bell, C., Rhodes, E., Long, Z. & Salemi, C. Do economic trade-offs matter in climate policy support? Survey evidence from the United Kingdom and Australia. Energy Policy 197, 114430 (2025).
Melton, N., Axsen, J. & Moawad, B. Which plug-in electric vehicle policies are best? A multi-criteria evaluation framework applied to Canada. Energy Res. Soc. Sci. 64, 101411 (2020).
Brosch, T. & Steg, L. Leveraging emotion for sustainable action. One Earth 4, 1693–1703 (2021).
Brosch, T. & Sauter, D. Emotions and the climate crisis: a research agenda for an affective sustainability science. Emot. Rev. 15, 253–257 (2023).
Jobin, M. & Siegrist, M. We choose what we like—affect as a driver of electricity portfolio choice. Energy Policy 122, 736–747 (2018).
Jobin, M., Visschers, V. H. M., Van Vliet, O. P. R., Árvai, J. & Siegrist, M. Affect or information? Examining drivers of public preferences of future energy portfolios in Switzerland. Energy Res. Soc. Sci. 52, 20–29 (2019).
Rinscheid, A. & Wüstenhagen, R. Divesting, fast and slow: affective and cognitive drivers of fading voter support for a nuclear phase-out. Ecol. Econ. 152, 51–61 (2018).
Spampatti, T., Hahnel, U. J. J., Trutnevyte, E. & Brosch, T. Short and long-term dominance of negative information in shaping public energy perceptions: The case of shallow geothermal systems. Energy Policy 167, 113070 (2022).
Egler, M., Rhodes, E. & Huddart, E. Affective Climate Polarization and Public Support for Just Transition Policy Bundles in Western Canada (SSRN, 2025); https://ssrn.com/abstract=6069747
Geiger, N., Dwyer, T. & Swim, J. K. Hopium or empowering hope? A meta-analysis of hope and climate engagement. Front. Psychol. 14, 1139427 (2023).
Gregersen, T., Andersen, G. & Tvinnereim, E. The strength and content of climate anger. Glob. Environ. Change 82, 102738 (2023).
Marlon, J. R. et al. How hope and doubt affect climate change mobilization. Front. Commun. 4, 20 (2019).
Myers, T. A., Roser-Renouf, C., Leiserowitz, A. & Maibach, E. Emotional signatures of climate policy support. PLoS Clim. 3, e0000381 (2024).
Schneider, C. R., Zaval, L. & Markowitz, E. M. Positive emotions and climate change. Curr. Opin. Behav. Sci. 42, 114–120 (2021).
Wang, S., Leviston, Z., Hurlstone, M., Lawrence, C. & Walker, I. Emotions predict policy support: why it matters how people feel about climate change. Glob. Environ. Change 50, 25–40 (2018).
Weber, E. U. & Constantino, S. M. All hearts and minds on deck: hope motivates climate action by linking the present and the future. Emot. Rev. 15, 293–297 (2023).
Swim, J. K., Guerriero, J. G., Gasper, K., DeCoster, J. & Lengieza, M. L. Emotions and policy information predicting water-quality policy support. J. Environ. Psychol. 98, 102385 (2024).
Zeelenberg, M., Nelissen, R. M. A., Breugelmans, S. M. & Pieters, R. On emotion specificity in decision making: why feeling is for doing. Judgm. Decis. Mak. 3, 18–27 (2008).
Cologna, V., Berthold, A., Kreissel, A. L. & Siegrist, M. Attitudes towards technology and their relationship with pro-environmental behaviour: development and validation of the GATT scale. J. Environ. Psychol. 95, 102258 (2024).
Berthold, A., Cologna, V., Hardmeier, M. & Siegrist, M. Drop some money! The influence of income and subjective financial scarcity on pro-environmental behaviour. J. Environ. Psychol. 91, 102149 (2023).
Sparkman, G. & Walton, G. M. Dynamic norms promote sustainable behavior, even if it is counternormative. Psychol. Sci. 28, 1663–1674 (2017).
Sparkman, G. & Walton, G. M. Witnessing change: dynamic norms help resolve diverse barriers to personal change. J. Exp. Soc. Psychol. 82, 238–252 (2019).
Kriesi, H. Direct Democratic Choice: The Swiss Experience (Lexington Books, 2005).
Lutz, G. The interaction between direct and representative democracy in Switzerland. Representation 42, 45–57 (2006).
Stadelmann-Steffen, I. Citizens as veto players: climate change policy and the constraints of direct democracy. Environ. Polit. 20, 485–507 (2011).
Fowler, A. & Margolis, M. The political consequences of uninformed voters. Elect. Stud. 34, 100–110 (2014).
Flynn, D. J., Nyhan, B. & Reifler, J. The nature and origins of misperceptions: understanding false and unsupported beliefs about politics. Polit. Psychol. 38, 127–150 (2017).
Jost, J. T., Baldassarri, D. S. & Druckman, J. N. Cognitive–motivational mechanisms of political polarization in social-communicative contexts. Nat. Rev. Psychol. 1, 560–576 (2022).
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).
Bernauer, T. & Gampfer, R. How robust is public support for unilateral climate policy? Environ. Sci. Policy 54, 316–330 (2015).
Chong, D. & Druckman, J. N. Framing public opinion in competitive democracies. Am. Polit. Sci. Rev. 101, 637–655 (2007).
Dermont, C., Ingold, K., Kammermann, L. & Stadelmann-Steffen, I. Bringing the policy making perspective in: a political science approach to social acceptance. Energy Policy 108, 359–368 (2017).
Wiest, S. L., Raymond, L. & Clawson, R. A. Framing, partisan predispositions, and public opinion on climate change. Glob. Environ. Change 31, 187–198 (2015).
Brückmann, G., El-Ajou, W. & Stadelmann-Steffen, I. When citizens and researchers learn from a serious game—an experimental analysis of information and efficacy in political opinion formation. Preprint at https://osf.io/preprints/socarxiv/x5u6b_v1/ (2026).
Volken, S. P., Xexakis, G. & Trutnevyte, E. Perspectives of informed citizen panel on low-carbon electricity portfolios in Switzerland and longer-term evaluation of informational materials. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.8b01265 (2018).
Kunnas, S. & Trutnevyte, E. Informed minds, opinions aligned? Informed perceptions towards solar PV locations in Switzerland. Environ. Res. Commun. 7, 075030 (2025).
Blastland, M., Freeman, A. L. J., Van Der Linden, S., Marteau, T. M. & Spiegelhalter, D. Five rules for evidence communication. Nature 587, 362–364 (2020).
Kerr, J. R., Schneider, C. R., Freeman, A. L. J., Marteau, T. & van der Linden, S. Transparent communication of evidence does not undermine public trust in evidence. PNAS Nexus 1, pgac280 (2022).
Rahmani Azad, Z., Spampatti, T., Gluth, S., Tam, K.-P. & Hahnel, U. J. J. Sampling and processing of climate change information and disinformation across three diverse countries. Br. J. Psychol. 00, 1–23 (2025).
Ecker, U., Roozenbeek, J. & Lewandowsky, S. Misinformation remains a threat to democracy. Nature https://doi.org/10.1038/d41586-024-01587-3 (2024).
Brexit was wrong, say 57% of British voters. The Economist https://www.economist.com/graphic-detail/2023/07/19/brexit-was-wrong-say-57-of-british-voters (19 July 2023).
Why most people regret Brexit. The Economist. https://www.economist.com/britain/2024/04/11/why-most-people-regret-brexit (11 April 2024).
Pickering, B., Lombardi, F. & Pfenninger, S. Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system. Joule 6, 1253–1276 (2022).
Zheng, D. et al. Strategies for climate-resilient global wind and solar power systems. Nature 643, 1263–1270 (2025).
Wenger, A., Stauffacher, M. & Dallo, I. Public perception and acceptance of negative emission technologies—framing effects in Switzerland. Clim. Change 167, 53 (2021).
Cox, E., Spence, E. & Pidgeon, N. Public perceptions of carbon dioxide removal in the United States and the United Kingdom. Nat. Clim. Change 10, 744–749 (2020).
Lee, C.-Y., Perlaviciute, G. & Steg, L. Forest or machine? Public perceptions and acceptability of negative emissions technologies and practices across six European countries. Clim. Change 178, 188 (2025).
Trutnevyte, E. et al. Renewable Energy Outlook for Switzerland (2024); https://doi.org/10.13097/ARCHIVE-OUVERTE/UNIGE:172640
Xexakis, G. & Trutnevyte, E. Consensus on future EU electricity supply among citizens of France, Germany, and Poland: implications for modeling. Energy Strategy Rev. 38, 100742 (2021).
Yilmaz, S. et al. Analysis of demand-side response preferences regarding electricity tariffs and direct load control: key findings from a Swiss survey. Energy 212, 118712 (2020).
Yilmaz, S., Cuony, P. & Chanez, C. Prioritize your heat pump or electric vehicle? Analysing design preferences for Direct Load Control programmes in Swiss households. Energy Res. Soc. Sci. 82, 102319 (2021).
Bender, J., Fait, L. & Wetzel, H. Acceptance of demand-side flexibility in the residential heating sector—evidence from a stated choice experiment in Germany. Energy Policy 191, 114145 (2024).
Wen, C., Steadman, S., Rafaq, M. S., Vatougiou, P. & Deakin, M. Can reduction of local carbon emissions motivate participation in demand-side flexibility programs? Evidence from the United Kingdom. Appl. Energy 388, 125610 (2025).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Fife, D. A. & D’Onofrio, J. Common, uncommon, and novel applications of random forest in psychological research. Behav. Res. Methods 55, 2447–2466 (2022).
James, G., Witten, D., Hastie, T. & Tibshirani, R. An introduction to statistical learning with applications in R. Stat. Theory Relat. Fields 6, 87–87 (2022).
Pargent, F., Schoedel, R. & Stachl, C. Best practices in supervised machine learning: a tutorial for psychologists. Adv. Methods Pract. Psychol. Sci. 6, 1–35 (2023).
Kyselá, E., Ščasný, M. & Zvěřinová, I. Attitudes toward climate change mitigation policies: a review of measures and a construct of policy attitudes. Clim. Policy 19, 878–892 (2019).
Freiburghaus, R., Leemann, L., Wasserfallen, F., Willi, T. & Yin, J. 20 Minuten-/Tamedia-Abstimmungsumfrage – Eidgenössische Volksabstimmungen vom 9. Juni. https://drive.google.com/file/d/11jRBacfyXhzdXomET5TpY_dvedQNRH9S/view (2024).
Mousson, M. 2. SRG-Trendumfrage zur eidg. Abstimmung vom 9. Juni 2024. https://www.gfsbern.ch/de/news/2-srg-trendumfrage-zur-eidg-abstimmung-vom-9-juni-2024/ (2024).
Van Der Linden, S. The social-psychological determinants of climate change risk perceptions: towards a comprehensive model. J. Environ. Psychol. 41, 112–124 (2015).
Bouman, T. et al. When worry about climate change leads to climate action: how values, worry and personal responsibility relate to various climate actions. Glob. Environ. Change 62, 102061 (2020).
Lee, T. M., Markowitz, E. M., Howe, P. D., Ko, C.-Y. & Leiserowitz, A. A. Predictors of public climate change awareness and risk perception around the world. Nat. Clim. Change 5, 1014–1020 (2015).
Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12, 1100–1122 (2017).
Van Valkengoed, A. M., Steg, L. & Perlaviciute, G. Development and validation of a climate change perceptions scale. J. Environ. Psychol. 76, 101652 (2021).
Wilson, R. S., Zwickle, A. & Walpole, H. Developing a broadly applicable measure of risk perception. Risk Anal. 39, 777–791 (2019).
Spampatti, T., Brosch, T., Trutnevyte, E. & Hahnel, U. J. J. A preregistered field study of the trust inoculation against a negative event involving geothermal energy systems. Collabra Psychol. 9, 89755 (2023).
Bouman, T., Steg, L. & Kiers, H. A. L. Measuring values in environmental research: a test of an environmental portrait value questionnaire. Front. Psychol. 9, 564 (2018).
ESS Round 8 Source Questionnaire (European Social Survey, 2016).
Wong-Parodi, G. & Berlin Rubin, N. Exploring how climate change subjective attribution, personal experience with extremes, concern, and subjective knowledge relate to pro-environmental attitudes and behavioral intentions in the United States. J. Environ. Psychol. 79, 101728 (2022).
Constantino, S. M. et al. Scaling up change: a critical review and practical guide to harnessing social norms for climate action. Psychol. Sci. Public Interest 23, 50–97 (2022).
Geiger, N., Swim, J. K. & Benson, L. Using the three-pillar model of sustainability to understand lay reactions to climate policy: a multilevel approach. Environ. Sci. Policy 126, 132–141 (2021).
Henninger, M., Debelak, R., Rothacher, Y. & Strobl, C. Interpretable machine learning for psychological research: opportunities and pitfalls. Psychol. Methods 30, 271–305 (2025).
Bowerman, B. L. & O’Connell, R. T. Linear Statistical Models: An Applied Approach (PWS-Kent Pub. Co., 1990).
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).
Robinson, D., Hayes, A. & Couch, S. broom: convert statistical objects into tidy tibbles. R package version 1.0.5. https://CRAN.R-project.org/package=broom (2023).
Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third edition (Sage, 2019); https://socialsciences.mcmaster.ca/jfox/Books/Companion/
Biecek, P. DALEX: explainers for complex predictive models in R. J. Mach. Learn. Res. 19, 1–5 (2018).
Maksymiuk, S., Gosiewska, A. & Biecek, P. Landscape of R packages for eXplainable Artificial Intelligence. Preprint at https://arxiv.org/abs/2009.13248 (2020).
Lüdecke, D. et al. easystats: framework for easy statistical modeling, visualization, and reporting. CRAN https://doi.org/10.32614/CRAN.package.easystats (2022).
Clarke, E., Sherrill-Mix, S. & Dawson, C. ggbeeswarm: categorical scatter (violin point) plots. R package version 0.7.2. https://CRAN.R-project.org/package=ggbeeswarm (2023).
Kay, M. ‘ggdist: visualizations of distributions and uncertainty in the grammar of graphics’. IEEE Trans. Vis. Comput. Graph. https://doi.org/10.1109/TVCG.2023.3327195 (2024).
Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots. R package version 0.6.0. https://CRAN.R-project.org/package=ggpubr (2023).
Friedman, J., Tibshirani, R. & Hastie, T. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
Simon, N., Friedman, J., Tibshirani, R. & Hastie, T. Regularization paths for Cox’s proportional hazards model via coordinate descent. J. Stat. Softw. 39, 1–13 (2011).
Tay, J. K., Narasimhan, B. & Hastie, T. Elastic net regularization paths for all generalized linear models. J. Stat. Softw. 106, 1–31 (2023).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn (Springer, 2002).
Lang, M. & Schratz, P. mlr3verse: easily install and load the ‘mlr3’ package family. R package version 0.2.8. https://CRAN.R-project.org/package=mlr3verse (2023).
Revelle, W. psych: procedures for psychological, psychometric, and personality research. R package version 2.4.3. https://CRAN.R-project.org/package=psych (2024).
Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).
Krainz, M., Trutnevyte, E. & Brosch, T. Data – Study 1. OSF https://doi.org/10.17605/OSF.IO/W782V (2026).
Krainz, M., Vallaeys Mora, I. & Brosch, T. Data – Study 2. OSF https://doi.org/10.17605/OSF.IO/953BE (2025).
Sorgato, V., Krainz, M., Brosch, T. & Trutnevyte, E. Data – Study 3. OSF https://doi.org/10.17605/OSF.IO/P2UQA (2025).