Communicating the scientific consensus that human-caused climate change is real increases climate change beliefs, worry and support for public action in the United States. In this preregistered experiment, we tested two scientific consensus messages, a classic message on the reality of human-caused climate change and an updated message additionally emphasizing scientific agreement that climate change is a crisis. Across online convenience samples from 27 countries (n = 10,527), the classic message substantially reduces misperceptions (d = 0.47, 95% CI (0.41, 0.52)) and slightly increases climate change beliefs (from d = 0.06, 95% CI (0.01, 0.11) to d = 0.10, 95% CI (0.04, 0.15)) and worry (d = 0.05, 95% CI (-0.01, 0.10)) but not support for public action directly. The updated message is equally effective but provides no added value. Both messages are more effective for audiences with lower message familiarity and higher misperceptions, including those with lower trust in climate scientists and right-leaning ideologies. Overall, scientific consensus messaging is an effective, non-polarizing tool for changing misperceptions, beliefs and worry across different audiences.

Barnard College Columbia University New York NY USA

Child and Adolescent Mental Health Center Copenhagen University Hospital Mental Health Services CPH Copenhagen Denmark

Cognitive Science Hub University of Vienna Vienna Austria

Columbia College Columbia University New York NY USA

Department of Behavioural and Cognitive Sciences University of Luxembourg Esch sur Alzette Luxembourg

Department of Cognitive Science Barnard College Columbia University New York NY USA

Department of Cognitive Science Columbia University New York NY USA

Department of Ecology Evolution and Environmental Biology Columbia University New York NY USA

Department of Health Policy and Management Mailman School of Public Health Columbia University New York NY USA

Department of Industrial Engineering and Operations Research Fu Foundation School of Engineering and Applied Science Columbia University New York NY USA

Department of Medicine and Psychology Sapienza University of Rome Rome Italy

Department of Psychology Behavioural and Economic Science University of Warwick Coventry UK

Department of Psychology Columbia University New York NY USA

Department of Psychology Faculty of Behavioural and Movement Sciences Vrije Universiteit Amsterdam Amsterdam the Netherlands

Department of Psychology Faculty of Education Kristianstad University Kristianstad Sweden

Department of Psychology Faculty of Psychology and Education Sciences University of Porto Porto Portugal

Department of Psychology Faculty of Social and Behavioural Sciences University of Amsterdam Amsterdam the Netherlands

Department of Psychology Faculty of Social Sciences Radboud University Nijmegen the Netherlands

Department of Psychology Heidelberg University Heidelberg Germany

Department of Psychology School of the Biological Sciences University of Cambridge Cambridge UK

Department of Psychology University of Ljubljana Ljubljana Slovenia

Department of Psychology University of Malta Msida Malta

Department of Psychology University of Maribor Maribor Slovenia

Department of Psychology Uppsala University Uppsala Sweden

Department of Women's and Children's Health Uppsala University Uppsala Sweden

Environmental Policy Group Wageningen University and Research Wageningen the Netherlands

Environmental Psychology Department of Cognition Emotion and Methods in Psychology Faculty of Psychology University of Vienna Vienna Austria

Faculty of Psychology Warsaw International Studies in Psychology University of Warsaw Warsaw Poland

Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic

Institute of Psychology Eötvös Loránd University Budapest Hungary

Institute of Psychology University of Innsbruck Innsbruck Austria

LAPCOS Université Côte d'Azur Nice France

Liberal Arts Program Faculty of Humanities Tel Aviv University Tel Aviv Israel

Motivation Psychology Department of Occupational Economic and Social Psychology Faculty of Psychology University of Vienna Vienna Austria

National Institute for Public Health and the Environment Bilthoven the Netherlands

Policy Research Group Centre for Business Research Judge Business School University of Cambridge Cambridge UK

Program in Cognitive Science Columbia University New York NY USA

School of General Studies Columbia University New York NY USA

Social Welfare Department and Center for CSR Social Entrepreneurship and Community Empowerment FISIP Universitas Padjadjaran Jatinangor Sumedang Indonesia

University of Mannheim Department of Psychology School of Social Sciences Mannheim Germany

University of Trento Department of Psychology and Cognitive Science Trento Italy

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Lynas, M., Houlton, B. Z. & Perry, S. Greater than 99% consensus on human caused climate change in the peer-reviewed scientific literature. Environ. Res. Lett.16, 114005 (2021).

Cook, J. et al. Quantifying the consensus on anthropogenic global warming in the scientific literature. Environ. Res. Lett.8, 024024 (2013).

Powell, J. L. Climate scientists virtually unanimous: anthropogenic global warming is true. Bull. Sci. Technol. Soc.35, 121–124 (2015).

Egan, P. J. & Mullin, M. Climate change: US public opinion. Annu. Rev. Polit. Sci.20, 209–227 (2017).

Duffy, B., Malcolm, F., May, G., Hewlett, K. & Haggar, T. Public Perceptions of Climate Change (Policy Institute, 2022); https://www.kcl.ac.uk/policy-institute/assets/peritia-climate-change%E2%80%8B.pdf

Hornsey, M. J., Harris, E. A., Bain, P. G. & Fielding, K. S. Meta-analyses of the determinants and outcomes of belief in climate change. Nat. Clim. Change6, 622–626 (2016).

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. Change62, 102061 (2020).

McCright, A. M., Dunlap, R. E. & Xiao, C. Perceived scientific agreement and support for government action on climate change in the USA. Clim. Change119, 511–518 (2013).

Tom, J. C. Social origins of scientific deviance: examining creationism and global warming skepticism. Sociol. Perspect.61, 341–360 (2018).

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. Change1, 462–466 (2011).

Hornsey, M. J. & Fielding, K. S. Understanding (and reducing) inaction on climate change. Soc. Issues Policy Rev.14, 3–35 (2020).

Hornsey, M. J. & Lewandowsky, S. A toolkit for understanding and addressing climate scepticism. Nat. Hum. Behav.6, 1454–1464 (2022). PubMed PMC

Bolsen, T. & Druckman, J. N. Do partisanship and politicization undermine the impact of a scientific consensus message about climate change? Group Process. Intergroup Relat.21, 389–402 (2018).

Brewer, P. R. & McKnight, J. ‘A statistically representative climate change debate’: satirical television news, scientific consensus and public perceptions of global warming. Atl. J. Commun.25, 166–180 (2017).

Kobayashi, K. The impact of perceived scientific and social consensus on scientific beliefs. Sci. Commun.40, 63–88 (2018).

Goldberg, M. H., van der Linden, S., Ballew, M. T., Rosenthal, S. A. & Leiserowitz, A. The role of anchoring in judgments about expert consensus. J. Appl. Soc. Psychol.49, 192–200 (2019).

Kerr, J. R. & Wilson, M. S. Changes in perceived scientific consensus shift beliefs about climate change and GM food safety. PLoS ONE13, e0200295 (2018). PubMed PMC

Goldberg, M. H. et al. The experience of consensus: video as an effective medium to communicate scientific agreement on climate change. Sci. Commun.41, 659–673 (2019).

Deryugina, T. & Shurchkov, O. The effect of information provision on public consensus about climate change. PLoS ONE11, e0151469 (2016). PubMed PMC

Myers, T. A., Maibach, E., Peters, E. & Leiserowitz, A. Simple messages help set the record straight about scientific agreement on human-caused climate change: the results of two experiments. PLoS ONE10, e0120985 (2015). PubMed PMC

Rode, J. B., Iqbal, S., Butler, B. J. & Ditto, P. H. Using a news article to convey climate science consensus information. Sci. Commun.43, 651–673 (2021).

Williams, M. N. & Bond, C. M. C. A preregistered replication of ‘Inoculating the public against misinformation about climate change’. J. Environ. Psychol.70, 101456 (2020).

van der Linden, S., Leiserowitz, A., Rosenthal, S. & Maibach, E. Inoculating the public against misinformation about climate change. Glob. Chall.1, 1600008 (2017). PubMed PMC

van der Linden, S., Leiserowitz, A. & Maibach, E. The gateway belief model: a large-scale replication. J. Environ. Psychol.62, 49–58 (2019).

van der Linden, S. L., Leiserowitz, A. A., Feinberg, G. D. & Maibach, E. W. The scientific consensus on climate change as a gateway belief: experimental evidence. PLoS ONE10, e0118489 (2015). PubMed PMC

van Stekelenburg, A., Schaap, G., Veling, H., van’t Riet, J. & Buijzen, M. Scientific-consensus communication about contested science: a preregistered meta-analysis. Psychol. Sci.33, 1989–2008 (2022). PubMed

Rode, J. B. et al. Influencing climate change attitudes in the United States: a systematic review and meta-analysis. J. Environ. Psychol.76, 101623 (2021).

Tschötschel, R., Schuck, A., Schwinges, A. & Wonneberger, A. Climate change policy support, intended behaviour change and their drivers largely unaffected by consensus messages in Germany. J. Environ. Psychol.76, 101655 (2021).

Breakthrough Agenda Report 2022 (IEA, 2022); https://www.iea.org/reports/breakthrough-agenda-report-2022

Tam, K., Leung, A. K.-Y. & Clayton, S. Research on climate change in social psychology publications: a systematic review. Asian J. Soc. Psychol.24, 117–143 (2021).

Maertens, R., Anseel, F. & Van Der Linden, S. Combatting climate change misinformation: evidence for longevity of inoculation and consensus messaging effects. J. Environ. Psychol.70, 101455 (2020).

Bayes, R., Bolsen, T. & Druckman, J. N. A research agenda for climate change communication and public opinion: the role of scientific consensus messaging and beyond. Environ. Commun.17, 16–34 (2023).

van der Linden, S. The Gateway Belief Model (GBM): a review and research agenda for communicating the scientific consensus on climate change. Curr. Opin. Psychol.42, 7–12 (2021). PubMed

Eichhorn, J., Molthof, L. & Nicke, S. From Climate Change Awareness to Climate Crisis Action: Public Perceptions in Europe and the United States (Open Society Foundations, 2020); https://www.opensocietyfoundations.org/publications/from-climate-change-awareness-to-climate-crisis-action

IPCC. Summary for Policymakers. In Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O., et al.) 3–33 (Cambridge Univ. Press, 2022).

Tollefson, J. Top climate scientists are sceptical that nations will rein in global warming. Nature599, 22–24 (2021). PubMed

Sabherwal, A., Pearson, A. R. & Sparkman, G. Anger consensus messaging can enhance expectations for collective action and support for climate mitigation. J. Environ. Psychol.76, 101640 (2021).

Hofstede, G. Dimensionalizing cultures: the Hofstede model in context. Online Read. Psychol. Cult. 10.9707/2307-0919.1014 (2011).

Slovic, P. & Lichtenstein, S. Comparison of Bayesian and regression approaches to the study of information processing in judgment. Organ. Behav. Hum. Perform.6, 649–744 (1971).

Diamond, E., Bernauer, T. & Mayer, F. Does providing scientific information affect climate change and GMO policy preferences of the mass public? Insights from survey experiments in Germany and the United States. Environ. Polit.29, 1199–1218 (2020).

Bullock, J. G. Partisan bias and the Bayesian ideal in the study of public opinion. J. Polit.71, 1109–1124 (2009).

Goldberg, M., Linden, S., van der, Ballew, M. T., Rosenthal, S. A. & Leiserowitz, A. Convenient but biased? The reliability of convenience samples in research about attitudes toward climate change. Preprint at OSF10.31219/osf.io/2h7as (2019).

Kahan, D. M., Jenkins‐Smith, H. & Braman, D. Cultural cognition of scientific consensus. J. Risk Res.14, 147–174 (2011).

Hart, P. S. & Nisbet, E. C. Boomerang effects in science communication: how motivated reasoning and identity cues amplify opinion polarization about climate mitigation policies. Commun. Res.39, 701–723 (2012).

Cook, J. & Lewandowsky, S. Rational irrationality: modeling climate change belief polarization using Bayesian networks. Top. Cogn. Sci.8, 160–179 (2016). PubMed

Chinn, S., Lane, D. S. & Hart, P. S. In consensus we trust? Persuasive effects of scientific consensus communication. Public Underst. Sci.27, 807–823 (2018). PubMed

Dixon, G. & Hubner, A. Neutralizing the effect of political worldviews by communicating scientific agreement: a thought-listing study. Sci. Commun.40, 393–415 (2018).

Rode, J. B., Dent, A. L. & Ditto, P. H. Climate change consensus messages may cause reactance in conservatives, but there is no meta-analytic evidence that they backfire. Environ. Commun. 10.1080/17524032.2022.2101501 (2022).

Sherman, D. K., Updegraff, J. A., Handy, M. S., Eom, K. & Kim, H. S. Beliefs and social norms as precursors of environmental support: the joint influence of collectivism and socioeconomic status. Pers. Soc. Psychol. Bull.48, 463–477 (2022). PubMed

Pornpitakpan, C. & Francis, J. N. P. The effect of cultural differences, source expertise and argument strength on persuasion: an experiment with Canadians and Thais. J. Int. Consum. Mark.13, 77–101 (2000).

Jung, J. M. & Kellaris, J. J. Responsiveness to authority appeals among young French and American consumers. J. Bus. Res.59, 735–744 (2006).

Sun, R. & Meng, J. Looking at young millennials’ risk perception and purchase intention toward GM foods: exploring the role of source credibility and risk attitude. Health Mark. Q.39, 263–279 (2022). PubMed

Hinne, M., Gronau, Q. F., van den Bergh, D. & Wagenmakers, E.-J. A conceptual introduction to Bayesian model averaging. Adv. Methods Pract. Psychol. Sci.3, 200–215 (2020).

Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. Bayesian model averaging: a tutorial. Stat. Sci.14, 382–401 (1999).

Liddell, T. M. & Kruschke, J. K. Analyzing ordinal data with metric models: what could possibly go wrong? J. Exp. Soc. Psychol.79, 328–348 (2018).

Schnuerch, M., Haaf, J. M., Sarafoglou, A. & Rouder, J. N. Meaningful comparisons with ordinal-scale items. Collabra Psychol.8, 38594 (2022).

Vanpaemel, W. Prior sensitivity in theory testing: an apologia for the Bayes factor. J. Math. Psychol.54, 491–498 (2010).

Zhang, B. et al. Experimental effects of climate messages vary geographically. Nat. Clim. Change8, 370–374 (2018).

Aklin, M. & Urpelainen, J. Perceptions of scientific dissent undermine public support for environmental policy. Environ. Sci. Policy38, 173–177 (2014).

Orchinik, R., Dubey, R., Gershman, S. J., Powell, D. & Bhui, R. Learning from and about climate scientists. Preprint at PsyArXiv10.31234/osf.io/ezua5 (2023).

Goldberg, M. H., Gustafson, A., van der Linden, S., Rosenthal, S. A. & Leiserowitz, A. Communicating the scientific consensus on climate change: diverse audiences and effects over time. Environ. Behav.54, 1133–1165 (2022).

Van Beynen, J. & Williams, M. N. Communicating the scientific consensus on climate change: a preregistered test of the Gateway Belief Model with a one-week time delay. Preprint at PsyArXiv10.31234/osf.io/f3svr (2023).

Treen, K. Md’I., Williams, H. T. P. & O’Neill, S. J. Online misinformation about climate change. WIREs Clim. Change11, e665 (2020).

Painter, J. et al. Climate delay discourses present in global mainstream television coverage of the IPCC’s 2021 report. Commun. Earth Environ.4, 118 (2023).

Funder, D. C. & Ozer, D. J. Evaluating effect size in psychological research: sense and nonsense. Adv. Methods Pract. Psychol. Sci.2, 156–168 (2019).

Anvari, F. et al. Not all effects are indispensable: psychological science requires verifiable lines of reasoning for whether an effect matters. Perspect. Psychol. Sci.18, 503–507 (2023). PubMed

Mellon, J. & Prosser, C. Twitter and Facebook are not representative of the general population: political attitudes and demographics of British social media users. Res. Polit.4, 205316801772000 (2017).

Marengo, D., Sindermann, C., Elhai, J. D. & Montag, C. One social media company to rule them all: associations between use of Facebook-owned social media platforms, sociodemographic characteristics and the big five personality traits. Front. Psychol.11, 936 (2020). PubMed PMC

Poortinga, W., Whitmarsh, L., Steg, L., Böhm, G. & Fisher, S. Climate change perceptions and their individual-level determinants: a cross-European analysis. Glob. Environ. Change55, 25–35 (2019).

Bertoldo, R. et al. Scientific truth or debate: on the link between perceived scientific consensus and belief in anthropogenic climate change. Public Underst. Sci.28, 778–796 (2019). PubMed

Coppock, A. Generalizing from survey experiments conducted on Mechanical Turk: a replication approach. Polit. Sci. Res. Methods7, 613–628 (2019).

Mullinix, K. J., Leeper, T. J., Druckman, J. N. & Freese, J. The generalizability of survey experiments. J. Exp. Polit. Sci.2, 109–138 (2015).

Said, N., Frauhammer, L. T. & Huff, M. Pre-registered replication of the Gateway Belief Model—results from a representative German sample. J. Environ. Psychol.84, 101910 (2022).

Van Valkengoed, A. M. & Steg, L. Meta-analyses of factors motivating climate change adaptation behaviour. Nat. Clim. Change9, 158–163 (2019).

Adger, W. N., Huq, S., Brown, K., Conway, D. & Hulme, M. Adaptation to climate change in the developing world. Prog. Dev. Stud.3, 179–195 (2003).

Ruggeri, K. et al. The general fault in our fault lines. Nat. Hum. Behav.5, 1369–1380 (2021). PubMed

Ruggeri, K. et al. The globalizability of temporal discounting. Nat. Hum. Behav.6, 1386–1397 (2022). PubMed PMC

Maibach, E. et al. A National Survey of Republicans and Republican-Leaning Independents on Energy and Climate Change (Center for Climate Change Communication, 2013); https://climatecommunication.yale.edu/wp-content/uploads/2016/02/2013_04_A-National-Survey-of-Republicans-and-Republican-%C2%AD%E2%80%90Leaning-Independents-on-Energy.pdf

Forscher, P. S. et al. PSACR: The Psychological Science Accelerator’s COVID-19 Rapid-Response Project. Preprint at PsyArXiv10.31234/osf.io/x976j (2020).

Jeffreys, H. Some tests of significance, treated by the theory of probability. Proc. Camb. Philos. Soc.31, 203–222 (1935).

Rouder, J. N. & Morey, R. D. Teaching Bayes’ theorem: strength of evidence as predictive accuracy. Am. Stat.73, 186–190 (2019).

Etz, A. & Wagenmakers, E.-J. J. B. S. Haldane’s contribution to the Bayes factor hypothesis test. Stat. Sci.32, 313–329 (2017).

Morey, R. D., Rouder, J. N., Jamil, T. & Morey, M. R. D. Package ‘bayesfactor’. (2015).

Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw.76, 1–32 (2017). PubMed PMC

Stan Development Team. RStan: the R interface to Stan. (2020).

Gronau, Q. F., Singmann, H. & Wagenmakers, E.-J. bridgesampling: an R package for estimating normalizing constants. J. Stat. Softw.92, 1–29 (2020).

Lee, M. D. & Wagenmakers, E.-J. Bayesian Cognitive Modeling: A Practical Course (Cambridge Univ. Press, 2013).

Jeffreys, H. Theory of Probability (Oxford Univ. Press, 1939).