Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Predictors of public climate change awareness and risk perception around the world

Nature Climate Change volume 5, pages 1014–1020 (2015)Cite this article

Subjects

Abstract

Climate change is a threat to human societies and natural ecosystems, yet public opinion research finds that public awareness and concern vary greatly. Here, using an unprecedented survey of 119 countries, we determine the relative influence of socio-demographic characteristics, geography, perceived well-being, and beliefs on public climate change awareness and risk perceptions at national scales. Worldwide, educational attainment is the single strongest predictor of climate change awareness. Understanding the anthropogenic cause of climate change is the strongest predictor of climate change risk perceptions, particularly in Latin America and Europe, whereas perception of local temperature change is the strongest predictor in many African and Asian countries. However, other key factors associated with public awareness and risk perceptions highlight the need to develop tailored climate communication strategies for individual nations. The results suggest that improving basic education, climate literacy, and public understanding of the local dimensions of climate change are vital to public engagement and support for climate action.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Geographic patterns of global climate change perceptions opinion poll.
Figure 2: Classification tree models for predicting climate change perceptions.
Figure 3: Top-ranked predictors of climate change perceptions worldwide.
Figure 4: Ordination of important predictors of climate change perceptions worldwide.

Similar content being viewed by others

References

  1. Leiserowitz, A. A. Human Development Report 2007/2008 (Human Development Office, 2007).

    Google Scholar 

  2. Brechin, S. R. & Bhandari, M. Perceptions of climate change worldwide. WIREs-Clim. Change 2, 871–885 (2011).

    Article  Google Scholar 

  3. Bord, R. J., Fisher, A. & O’Connor, R. E. Public perceptions of global warming: United States and international perspectives. Clim. Res. 11, 75–84 (1998).

    Article  Google Scholar 

  4. Bord, R. J., O’Connor, R. E. & Fisher, A. In what sense does the public need to understand global climate change? Public Underst. Sci. 9, 205–218 (2000).

    Article  Google Scholar 

  5. Nisbet, M. C. & Myers, T. The polls—Trends—Twenty years of public opinion about global warming. Public Opin. Q. 71, 444–470 (2007).

    Article  Google Scholar 

  6. Brulle, R. J., Carmichael, J. & Jenkins, J. C. Shifting public opinion on climate change: An empirical assessment of factors influencing concern over climate change in the U.S. 2002–2010. Climatic Change 114, 169–188 (2012).

    Article  Google Scholar 

  7. Weber, E. U. & Stern, P. C. Public understanding of climate change in the United States. Am. Psychol. 66, 315–328 (2011).

    Article  Google Scholar 

  8. Wolf, J. & Moser, S. C. Individual understandings, perceptions, and engagement with climate change: Insights from in-depth studies across the world. WIREs-Clim. Change 2, 547–569 (2011).

    Article  Google Scholar 

  9. Borick, C. P. & Rabe, B. G. A reason to believe: Examining the factors that determine individual views on global warming. Soc. Sci. Q. 91, 777–800 (2010).

    Article  Google Scholar 

  10. Leiserowitz, A. A. American risk perceptions: Is climate change dangerous? Risk Anal. 25, 1433–1442 (2005).

    Article  Google Scholar 

  11. Grothmann, T. & Patt, A. Adaptive capacity and human cognition: The process of individual adaptation to climate change. Glob. Environ. Change 15, 199–213 (2005).

    Article  Google Scholar 

  12. Lorenzoni, I. & Pidgeon, N. F. Public views on climate change: European and USA perspectives. Climatic Change 77, 73–95 (2006).

    Article  Google Scholar 

  13. Whitmarsh, L. Scepticism and uncertainty about climate change: Dimensions, determinants and change over time. Glob. Environ. Change-Hum. Policy Dimens. 21, 690–700 (2011).

    Article  Google Scholar 

  14. Kvaloy, B., Finseraas, H. & Listhaug, O. The publics’ concern for global warming: A cross-national study of 47 countries. J. Peace Res. 49, 11–22 (2012).

    Article  Google Scholar 

  15. Norgaard, K. M. Living in Denial: Climate Change, Emotions and Everyday Life (MIT Press, 2011).

    Book  Google Scholar 

  16. Pugliese, A. & Ray, J. A heated debate: Global attitudes toward climate change. Hav. Int. Rev. 31, 64 (2009).

    Google Scholar 

  17. Schultz, P. W., Zelezny, L. & Dalrymple, N. J. A multinational perspective on the relation between Judeo-Christian religious beliefs and attitudes of environmental concern. Environ. Behav. 32, 576–591 (2000).

    Article  Google Scholar 

  18. Doherty, T. J. & Clayton, S. The psychological impacts of global climate change. Am. Psychol. 66, 265–276 (2011).

    Article  Google Scholar 

  19. Scruggs, L. & Benegal, S. Declining public concern about climate change: Can we blame the great recession? Glob. Environ. Change-Hum. Policy Dimens. 22, 505–515 (2012).

    Article  Google Scholar 

  20. O’Connor, R. E., Bord, R. J. & Fisher, A. Risk perceptions, general environmental beliefs, and willingness to address climate change. Risk Anal. 19, 461–471 (1999).

    Google Scholar 

  21. Maibach, E. W. et al. Identifying like-minded audiences for global warming public engagement campaigns: An audience segmentation analysis and tool development. PLoS ONE 6 (2011).

  22. Dunlap, R. E. Lay perceptions of global risk—Public views of global warming in cross-national context. Int. Sociol. 13, 473–498 (1998).

    Article  Google Scholar 

  23. Howe, P. D. et al. Global perceptions of local temperature change. Nature Clim. Change 3, 352–356 (2013).

    Article  Google Scholar 

  24. Li, Y., Johnson, E. J. & Zaval, L. Local warming: Daily temperature change influences belief in global warming. Psychol. Sci. 22, 454–459 (2011).

    Article  Google Scholar 

  25. Zaval, L. et al. How warm days increase belief in global warming. Nature Clim. Change 4, 143–147 (2014).

    Article  Google Scholar 

  26. Slovic, P. Trust, emotion, sex, politics, and science: Surveying the risk-assessment battlefield. Risk Anal. 19, 689–701 (1999).

    CAS  Google Scholar 

  27. Barkan, S. E. Explaining public support for the environmental movement: A civic voluntarism model. Soc. Sci. Q. 85, 913–937 (2004).

    Article  Google Scholar 

  28. Tschakert, P. Views from the vulnerable: Understanding climatic and other stressors in the Sahel. Glob. Environ. Change-Hum. Policy Dimens. 17, 381–396 (2007).

    Article  Google Scholar 

  29. Hapfelmeier, A., Hothorn, T. & Ulm, K. Recursive partitioning on incomplete data using surrogate decisions and multiple imputation. Comput. Stat. Data Anal. 56, 1552–1565 (2012).

    Article  Google Scholar 

  30. Strobl, C., Malley, J. & Tutz, G. An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14, 323–348 (2009).

    Article  Google Scholar 

  31. Strobl, C. et al. Conditional variable importance for random forests. BMC Bioinform. 9 (2008).

  32. Hamilton, L. C. & Keim, B. D. Regional variation in perceptions about climate change. Int. J. Climatol. 29, 2348–2352 (2009).

    Article  Google Scholar 

  33. McCright, A. M. & Dunlap, R. E. The politicization of climate change and polarization in the American public’s views of global warming, 2001–2010. Sociol. Q. 52, 155–194 (2011).

    Article  Google Scholar 

  34. Worldwide Research Methodology and Codebook (January issue) (Gallup, 2012).

  35. Liu, J. C.-E. & Leiserowitz, A. A. From red to green? Environmental attitudes and behavior in urban China. Environment 51, 32–45 (2009).

    Google Scholar 

  36. Bostrom, A. et al. What do people know about global climate-change. 1. Mental models. Risk Anal. 14, 959–970 (1994).

    Article  Google Scholar 

  37. Anderson, A. & Strecker, M. Sustainable development: A case for education. Environment 54, 3–15 (2012).

    Google Scholar 

  38. Lutz, W., Muttarak, R. & Striessnig, E. Universal education is key to enhanced climate adaptation. Science 346, 1061–1062 (2014).

    Article  CAS  Google Scholar 

  39. Kaufmann, D., Kraay, A. & Mastruzzi, M. World Bank Policy Research Working Paper No. 5430 (World Bank, 2010).

    Google Scholar 

  40. Dreher, A. Does globalization affect growth? Evidence from a new index of globalization. Appl. Econ. 38, 1091–1110 (2006).

    Article  Google Scholar 

  41. Wackernagel, M. et al. Tracking the ecological overshoot of the human economy. Proc. Natl Acad. Sci. USA 99, 9266–9271 (2002).

    Article  CAS  Google Scholar 

  42. Wheeler, D. CGD Working Paper 240 (Center for Global Development, 2011).

    Google Scholar 

  43. Kruskal, J. B. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29, 115–129 (1964).

    Article  Google Scholar 

  44. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

  45. Faith, D. P., Minchin, P. R. & Belbin, L. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57–68 (1987).

    Article  Google Scholar 

  46. Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 26, 32–46 (2001).

    Google Scholar 

  47. Bowman, T. E. et al. Time to take action on climate communication. Science 330, 1044 (2010).

    Article  CAS  Google Scholar 

  48. Kahneman, D. & Deaton, A. High income improves evaluation of life but not emotional well-being. Proc. Natl Acad. Sci. USA 107, 16489–16493 (2010).

    Article  CAS  Google Scholar 

  49. Tortora, R. D., Srinivasan, R. & Esipova, N. in Survey Methods in Multinational, Multiregional, and Multicultural Contexts (eds Harkness, J. A. et al.) 535–543 (John Wiley, 2010).

    Book  Google Scholar 

  50. GFN Global Footprint Network: The 2010 National Footprint Accounts (Global Footprint Network, 2010).

    Google Scholar 

  51. Dietz, T., Rosa, E. A. & York, R. Driving the human ecological footprint. Front. Ecol. Environ. 5, 13–18 (2007).

    Article  Google Scholar 

  52. Jorgenson, A. K. & Clark, B. Societies consuming nature. A panel study of the ecological footprints of nations, 1960–2003. Soc. Sci. Res. 40, 226–244 (2010).

    Article  Google Scholar 

  53. Füssel, H.-M. Review and Quantitative Analysis of Indices of Climate Change Exposure, Adaptive Capacity, Sensitivity, and Impacts (World Bank, 2009).

    Google Scholar 

  54. Ehrlich, P. R., Kareiva, P. M. & Daily, G. C. Securing natural capital and expanding equity to rescale civilization. Nature 486, 68–73 (2012).

    Article  CAS  Google Scholar 

  55. Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: A conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006).

    Article  Google Scholar 

  56. Breiman, L. et al. Classification and Regression Trees (CRC Press, 1984).

    Google Scholar 

  57. Breiman, L. Random forests. Machine Learning 45, 5–32 (2001).

    Article  Google Scholar 

  58. Janitza, S., Strobl, C. & Boulesteix, A.-L. An AUC-based permutation variable importance measure for random forests. BMC Bioinform. 14, 119 (2013).

    Article  Google Scholar 

  59. Gauch, H. G. Jr Multivariate Analysis and Community Structure (Cambridge Univ. Press, 1982).

    Google Scholar 

  60. Minchin, P. R. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 69, 89–107 (1987).

    Article  Google Scholar 

  61. Zapala, M. A. & Schork, N. J. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl Acad. Sci. USA 103, 19430–19435 (2006).

    Article  CAS  Google Scholar 

  62. Legendre, P. & Legendre, L. Numerical Ecology 2nd edn (Elsevier, 1998).

    Google Scholar 

  63. R Development Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2008).

    Google Scholar 

Download references

Acknowledgements

This research was supported in part by the Earth Institute Fellows Program, Columbia University and the Yale Project on Climate Change Communication (T.M.L.). The authors wish to thank A. Pugliese (Gallup World Poll) for assistance with the survey data and D. Budescu (Fordham University) for comments on the manuscript.

Author information

Author notes

  1. Tien Ming Lee

    Present address: Present address: Program in Science, Technology and Environmental Policy, Woodrow Wilson School of International and Public Affairs, Princeton University, Princeton, New Jersey 08544, USA.,

Authors and Affiliations

  1. Department of Ecology, Earth Institute, Evolution and Environmental Biology, and Center for Research on Environmental Decisions, Columbia University, New York, New York 10027, USA

    Tien Ming Lee

  2. Yale Project on Climate Change Communication, School of Forestry & Environmental Studies, Yale University, New Haven, Connecticut 06511, USA

    Tien Ming Lee, Peter D. Howe, Chia-Ying Ko & Anthony A. Leiserowitz

  3. Earth Institute and Center for Research on Environmental Decisions, Columbia University, New York, New York 10027, USA

    Ezra M. Markowitz

  4. Princeton Institute for International and Regional Studies, Princeton University, Princeton, New Jersey 08544, USA

    Ezra M. Markowitz

  5. Department of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts 01003, USA

    Ezra M. Markowitz

  6. Department of Environment and Society, Utah State University, Logan, Utah 84322, USA

    Peter D. Howe

  7. Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA

    Chia-Ying Ko

  8. Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan

    Chia-Ying Ko

Authors
  1. Tien Ming Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ezra M. Markowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter D. Howe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chia-Ying Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anthony A. Leiserowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.M.L., E.M.M. and A.A.L. designed the research, T.M.L. conducted the analysis. T.M.L. wrote the initial draft with inputs from E.M.M., P.D.H., C.-Y.K. and A.A.L.

Corresponding authors

Correspondence to Tien Ming Lee or Anthony A. Leiserowitz.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, T., Markowitz, E., Howe, P. et al. Predictors of public climate change awareness and risk perception around the world. Nature Clim Change 5, 1014–1020 (2015). https://doi.org/10.1038/nclimate2728

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate2728

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing