Article | Published:

Meta-analyses of factors motivating climate change adaptation behaviour

Abstract

Adaptation behaviour is of critical importance to reduce or avoid negative impacts of climate change. Many studies have examined which factors motivate individuals to adapt. However, a comprehensive overview of the key motivating factors of various adaptation behaviours is lacking. Here, we conduct a series of meta-analyses using data from 106 studies (90 papers) conducted in 23 different countries to examine how 13 motivational factors relate to various adaptation behaviours. Descriptive norms, negative affect, perceived self-efficacy and outcome efficacy of adaptive actions were most strongly associated with adaptive behaviour. In contrast, knowledge and experience, which are often assumed to be key barriers to adaptation, were relatively weakly related to adaptation. Research has disproportionally focused on studying experience and risk perception, flooding and hurricanes, and preparedness behaviours, while other motivational factors, hazards and adaptive behaviours have been understudied. These results point to important avenues for future research.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

The datasets generated and/or analysed during the current study are available in the Open Science Framework repository: https://doi.org/10.17605/OSF.IO/G2JC3.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  2. 2.

    Patz, J. A., Campbell-Lendrum, D., Holloway, T. & Foley, J. A. Impact of regional climate change on human health. Nature 438, 310–317 (2005).

  3. 3.

    Stern, N. The Economics of Climate Change: The Stern Review (Cambridge Univ. Press, Cambridge, 2007).

  4. 4.

    Takao, K. et al. Factors determining residents’ preparedness for floods in modern megalopolises: the case of the Tokai flood disaster in Japan. J. Risk Res. 7, 775–787 (2004).

  5. 5.

    Vulturius, G. et al. The relative importance of subjective and structural factors for individual adaptation to climate change by forest owners in Sweden.Reg. Environ. Change 18, 511–520 (2017).

  6. 6.

    Kievik, M. & Gutteling, J. M. Yes, we can: motivate Dutch citizens to engage in self-protective behavior with regard to flood risks. Nat. Hazards 59, 1475–1490 (2011).

  7. 7.

    Michie, S. et al. From theory-inspired to theory-based interventions: a protocol for developing and testing a methodology for linking behaviour change techniques to theoretical mechanisms of action. Ann. Behav. Med. 52, 501–512 (2018).

  8. 8.

    Bamberg, S., Masson, T., Brewitt, K. & Nemetschek, N. Threat, coping and flood prevention—a meta-analysis. J. Environ. Psychol. 54, 116–126 (2017).

  9. 9.

    Rogers, R. W. in Social Psychophysiology: A Sourcebook (eds. Cacioppo, B. L. & Petty, L. L.) 153–176 (Guildford Press, New York, 1983).

  10. 10.

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

  11. 11.

    Mulilis, J.-P. & Duval, T. S. The PrE model of coping and tornado preparedness: moderating effects of responsibility. J. Appl. Soc. Psychol. 27, 1750–1766 (1997).

  12. 12.

    Lindell, M. K. & Perry, R. W. The protective action decision model: theoretical modifications and additional evidence. Risk Anal. 32, 616–632 (2012).

  13. 13.

    Renn, O. The social amplification/attenuation of risk framework: application to climate change. Wiley Interdiscip. Rev. Clim. Chang. 2, 154–169 (2011).

  14. 14.

    Kasperson, R. E. et al. The social amplification of risk: a conceptual framework. Risk Anal. 8, 177–187 (1988).

  15. 15.

    Baan, P. J. A. & Klijn, F. Flood risk perception and implications for flood risk management in the Netherlands. Int. J. River Basin Manag. 2, 113–122 (2004).

  16. 16.

    Demuth, J. L., Morss, R. E., Lazo, J. K. & Trumbo, C. The effects of past hurricane experiences on evacuation intentions through risk perception and efficacy beliefs: a mediation analysis. Weather Clim. Soc. 8, 327–344 (2016).

  17. 17.

    Sharma, U. & Patt, A. Disaster warning response: the effects of different types of personal experience. Nat. Hazards 60, 409–423 (2012).

  18. 18.

    Reynaud, A., Aubert, C. & Nguyen, M. H. Living with floods: protective behaviours and risk perception of Vietnamese households. Geneva Pap. Risk Insur. Pract. 38, 547–579 (2013).

  19. 19.

    Altman, I. & Low, S. Place Attachment (Plenum, New York, 1992).

  20. 20.

    De Dominicis, S. et al. Vested interest and environmental risk communication: improving willingness to cope with impending disasters. J. Appl. Soc. Psychol. 44, 364–374 (2014).

  21. 21.

    Paton, D. Disaster preparedness: a social‐cognitive perspective. Disast. Prev. Manag. 12, 210–216 (2003).

  22. 22.

    Wachinger, G., Renn, O., Begg, C. & Kuhlicke, C. The risk perception paradox—implications for governance and communication of natural hazards. Risk Anal. 33, 1049–1065 (2013).

  23. 23.

    Weinstein, N. D., Rothman, A. J. & Nicolich, M. Use of correlational data to examine the effects of risk perceptions on precautionary behavior. Psychol. Health 13, 479–501 (1998).

  24. 24.

    Fox-Rogers, L., Devitt, C., O’Neill, E., Brereton, F. & Clinch, J. P. Is there really “nothing you can do”? Pathways to enhanced flood-risk preparedness. J. Hydrol. 543, 330–343 (2016).

  25. 25.

    Cialdini, R. B., Reno, R. R. & Kallgren, C. A. A focus theory of normative conduct: recycling the concept of norms to reduce littering in public places. J. Pers. Soc. Psychol. 58, 1015–1026 (1990).

  26. 26.

    Cialdini, R. B. Descriptive social norms as underappreciated sources of social control. Psychometrika 72, 263–268 (2007).

  27. 27.

    Adger, W. N. Vulnerability. Glob. Environ. Change 16, 268–281 (2006).

  28. 28.

    Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84, 191–215 (1977).

  29. 29.

    Samaddar, S., Chatterjee, R., Misra, B. & Tatano, H. Outcome-expectancy and self-efficacy: reasons or results of flood preparedness intention? Int. J. Disaster Risk Reduct. 8, 91–99 (2014).

  30. 30.

    Bjørnebekk, G. Positive affect and negative affect as modulators of cognition and motivation: the rediscovery of affect in achievement goal theory. Scand. J. Educ. Res. 52, 153–170 (2008).

  31. 31.

    Fu, R. et al. Conducting quantitative synthesis when comparing medical interventions: AHRQ and the Effective Health Care Program. J. Clin. Epidemiol. 64, 1187–1197 (2011).

  32. 32.

    Terpstra, T. Emotions, trust, and perceived risk: affective and cognitive routes to flood preparedness behavior. Risk Anal. 31, 1658–1675 (2011).

  33. 33.

    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).

  34. 34.

    De Wit, M. S., Van der Most, H., Gutteling, J. M. & Bočkatjova, M. in Safety, Reliability and Risk Analysis: Theories, Methods and Applications (eds Martorell, S., Guedes, C. & Barnett, J.) 1585–1593 (CRC Press, Boca Raton, 2008).

  35. 35.

    Sauerborn, R. & Ebi, K. Climate change and natural disasters: integrating science and practice to protect health. Glob. Health Action 5, 19295 (2012).

  36. 36.

    McCaffrey, S. Community wildfire preparedness: a global state-of-the-knowledge summary of social science research. Curr. For. Rep. 1, 81–90 (2015).

  37. 37.

    Bubeck, P., Botzen, W. J. W. & Aerts, J. C. J. H. A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal. 32, 1481–1495 (2012).

  38. 38.

    Poussin, J. K., Botzen, W. J. W. & Aerts, J. C. J. H. Factors of influence on flood damage mitigation behaviour by households. Environ. Sci. Pol. 40, 69–77 (2014).

  39. 39.

    Bonaiuto, M., Alves, S., De Dominicis, S. & Petruccelli, I. Place attachment and natural hazard risk: research review and agenda. J. Environ. Psychol. 48, 33–53 (2016).

  40. 40.

    Huang, S.-K., Lindell, M. K. & Prater, C. S. Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. Environ. Behav. 48, 991–1029 (2016).

  41. 41.

    Kellens, W., Terpstra, T. & De Maeyer, P. Perception and communication of flood risks: a systematic review of empirical research. Risk Anal. 33, 24–49 (2013).

  42. 42.

    Taylor, A. L., Dessai, S. & Bruine de Bruin, W. Public perception of climate risk and adaptation in the UK: a review of the literature. Clim. Risk Manag. 4–5, 1–16 (2014).

  43. 43.

    Thompson, R. R., Garfin, D. R. & Silver, R. C. Evacuation from natural disasters: a systematic review of the literature. Risk Anal. 37, 812–839 (2017).

  44. 44.

    Werg, J., Grothmann, T. & Schmidt, P. Assessing social capacity and vulnerability of private households to natural hazards: integrating psychological and governance factors. Nat. Hazards Earth Syst. Sci. 13, 1613–1628 (2013).

  45. 45.

    Koerth, J., Vafeidis, A. T. & Hinkel, J. Household-level coastal adaptation and its drivers: a systematic case study review. Risk Anal. 37, 629–646 (2017).

  46. 46.

    McCaffrey, S., Toman, E., Stidham, M. & Shindler, B. Social science research related to wildfire management: an overview of recent findings and future research needs. Int. J. Wildl. Fire 22, 15–24 (2013).

  47. 47.

    Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. Introduction to Meta-Analysis (John Wiley & Sons, Hoboken, 2009).

  48. 48.

    Van Duinen, R., Filatova, T., Geurts, P. & van der Veen, A. Coping with drought risk: empirical analysis of farmers’ drought adaptation in the south-west Netherlands. Reg. Environ. Change 15, 1081–1093 (2015).

  49. 49.

    Rupinski, M. T. & Dunlap, W. P. Approximating Pearson product–moment correlations from Kendall’s tau and Spearman’s rho. Educ. Psychol. Meas. 56, 419–429 (1996).

  50. 50.

    Peterson, R. A. & Brown, S. P. On the use of beta coefficients in meta-analysis. J. Appl. Psychol. 90, 175–181 (2005).

  51. 51.

    Rosenberg, M. S. A generalized formula for converting chi-square tests to effect sizes for meta-analysis. PLoS ONE 5, e10059 (2010).

  52. 52.

    Walker, D. A. JMASM9: converting Kendall’s tau for correlational or meta-analytic analyses. J. Mod. Appl. Stat. Methods 2, 525–530 (2003).

  53. 53.

    Field, A. P. & Gillett, R. How to do a meta-analysis. Br. J. Math. Stat. Psychol. 63, 665–694 (2010).

  54. 54.

    R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

  55. 55.

    Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

  56. 56.

    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. Change 6, 622–626 (2016).

  57. 57.

    Kontopantelis, E. & Reeves, D. Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: a simulation study. Stat. Methods Med. Res. 21, 409–426 (2012).

  58. 58.

    Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. Br. Med. J. 315, 629–634 (1997).

  59. 59.

    Duval, S. & Tweedie, R. A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J. Am. Stat. Soc. 95, 89–98 (2000).

  60. 60.

    Viechtbauer, W. & Cheung, M. W.-L. Outlier and influence diagnostics for meta-analysis. Res. Synth. Methods 1, 112–125 (2010).

  61. 61.

    Paul, B. K. Factors affecting evacuation behavior: The case of 2007 cyclone Sidr, Bangladesh. Prof. Geogr. 64, 401–414 (2012).

  62. 62.

    Cahyanto, I. et al. Predicting information seeking regarding hurricane evacuation in the destination. Tour. Manag. 52, 264–275 (2016).

  63. 63.

    Baumann, D. D. & Sims, J. H. Flood insurance: Some determinants of adoption. Econ. Geogr. 54, 189–196 (1978).

  64. 64.

    McFarlane, B. L., McGee, T. K. & Faulkner, H. Complexity of homeowner wildfire risk mitigation: An integration of hazard theories. Int. J. Wildl. Fire 20, 921–931 (2011).

  65. 65.

    De Dominicis, S., Fornara, F., Ganucci Cancellieri, U., Twigger-Ross, C. & Bonaiuto, M. We are at risk, and so what? Place attachment, environmental risk perceptions and preventive coping behaviours. J. Environ. Psychol. 43, 66–78 (2015).

  66. 66.

    Stein, R. M., Dueñas-Osorio, L. & Subramanian, D. Who evacuates when hurricanes approach? The role of risk, information, and location. Soc. Sci. Q 91, 816–834 (2010).

Download references

Acknowledgements

We thank all the authors who corresponded with us to provide the necessary data for these meta-analyses or to clarify any questions about their work.

Author information

A.M.v.V. and L.S. developed the idea for the paper and defined the scope for the meta-analyses. A.M.v.V. conducted the literature search and analysed the data. A.M.v.V. and L.S. wrote the paper.

Competing interests

The authors declare no competing interests.

Correspondence to Anne M. van Valkengoed.

Supplementary information

  1. Supplementary Information

    Supplementary Tables 1–3, Supplementary Figures 1–3

  2. Reporting Summary

  3. Supplementary Data 1

    Contains an overview of all included studies, their sample sizes, the extracted data points for each analysis and references for all studies

Rights and permissions

To obtain permission to re-use content from this article visit RightsLink.

About this article

Fig. 1: Mean meta-analytical effect sizes.
Fig. 2: Types of climate-related hazards examined.
Fig. 3: Types of adaptive behaviours examined.