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Linking models of human behaviour and climate alters projected climate change


Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4–6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with the largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most.

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Fig. 1: Conceptual model.
Fig. 2: Regression tree partitioning of variation in mean global temperature change relative to the pre-industrial baseline period (approximately 1850).
Fig. 3: Effect of functional form on mean global temperature in 2100.
Fig. 4: Effect of PSN and PBC on mean global temperature in 2100 for simulations with a cumulative mitigation response in carbon emissions.


  1. IPCC Climate Change 2013: The Physical Science Basis (eds. Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  2. Nakicenovic, N. et al. Special Report Emissions Scenarios: Summary for Policymakers (IPCC, Cambridge Univ. Press, 2000).

  3. Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article  Google Scholar 

  4. Weber, E. U. What shapes perceptions of climate change? Wiley Interdiscip. Rev. Clim. Change 1, 332–342 (2010).

    Article  Google Scholar 

  5. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    CAS  Article  Google Scholar 

  6. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211 (1991).

    Article  Google Scholar 

  7. Sterman, J. et al. Climate interactive: the C-ROADS climate policy model. Syst. Dyn. Rev. 28, 295–305 (2012).

    Article  Google Scholar 

  8. Kahneman, D. Thinking, Fast and Slow (Farrar, Straus and Giroux, New York, 2011).

  9. Spence, A., Poortinga, W., Butler, C. & Pidgeon, N. F. Perceptions of climate change and willingness to save energy related to flood experience. Nat. Clim. Change 1, 46–49 (2011).

    Article  Google Scholar 

  10. Rudman, L. A., McLean, M. C. & Bunzl, M. When truth is personally inconvenient, attitudes change: the impact of extreme weather on implicit support for green politicians and explicit climate-change beliefs. Psychol. Sci. 24, 2290–2296 (2013).

    Article  Google Scholar 

  11. Howe, P. D., Markowitz, E. M., Lee, T. M., Ko, C.-Y. & Leiserowitz, A. Global perceptions of local temperature change. Nat. Clim. Change 3, 352–356 (2013).

    Article  Google Scholar 

  12. Myers, T. A., Maibach, E. W., Roser-Renouf, C., Akerlof, K. & Leiserowitz, A. A. The relationship between personal experience and belief in the reality of global warming. Nat. Clim. Change 3, 343–347 (2013).

    Article  Google Scholar 

  13. Kaufmann, R. K. et al. Spatial heterogeneity of climate change as an experiential basis for skepticism. Proc. Natl Acad. Sci. USA 114, 67–71 (2017).

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Whitmarsh, L. Are flood victims more concerned about climate change than other people? The role of direct experience in risk perception and behavioural response. J. Risk Res. 11, 351–374 (2008).

    Article  Google Scholar 

  17. Carlson, J. D., Burgan, R. E., Engle, D. M. & Greenfield, J. R. The Oklahoma Fire Danger Model: an operational tool for mesoscale fire danger rating in Oklahoma. Int. J. Wildland Fire 11, 183–191 (2002).

    Article  Google Scholar 

  18. Baker, J. et al. Explaining subjective risks of hurricanes and the role of risks in intended moving and location choice models. Nat. Hazards Rev. 10, 102–112 (2009).

    Article  Google Scholar 

  19. Carbone, J. C., Hallstrom, D. G. & Smith, V. K. Can natural experiments measure behavioral responses to environmental risks? Environ. Resour. Econ. 33, 273–297 (2006).

    Article  Google Scholar 

  20. Atreya, A., Ferreira, S. & Kriesel, W. Forgetting the flood? An analysis of the flood risk discount over time. Land Econ. 89, 577–596 (2013).

    Article  Google Scholar 

  21. Armitage, C. J. & Conner, M. Efficacy of the theory of planned behaviour: A meta-analytic review. Br. J. Soc. Psychol. 40, 471–499 (2001).

    CAS  Article  Google Scholar 

  22. Tikir, A. & Lehmann, B. Climate change, theory of planned behavior and values: a structural equation model with mediation analysis. Clim. Change 104, 389–402 (2010).

    Article  Google Scholar 

  23. Donat, M. G. & Alexander, L. V. The shifting probability distribution of global daytime and night-time temperatures. Geophys. Res. Lett. 39, L14707 (2012).

    Article  Google Scholar 

  24. Therneau, T., Atkinson, B. & Ripley, B. rpart: Recursive Partitioning and Regression Trees (2017).

  25. IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2015).

  26. Schneider, S. H. et al. Climate Change 2007: Impacts, Adaptation and Vulnerability (eds Parry, M. L. et al.) (IPCC, Cambridge Univ. Press, 2007).

  27. Bernstein, L. et al. Climate Change 2007: Synthesis Report (eds The Core Writing Team, Pachauri, R. K. & Reisinger, A. (IPCC, 2008).

  28. IPCC Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C.B. et al.) (Cambridge Univ. Press, Cambridge, UK, 2012).

  29. Diffenbaugh, N. S. et al. Quantifying the influence of global warming on unprecedented extreme climate events. Proc. Natl Acad. Sci. USA 114, 4881–4886 (2017).

    CAS  Article  Google Scholar 

  30. National Academies of Sciences, Engineering, and Medicine Attribution of Extreme Weather Events in the Context of Climate Change (National Academies Press, 2016).

  31. Wiel, K. van der et al. Rapid attribution of the August 2016 flood-inducing extreme precipitation in South Louisiana to climate change. Hydrol. Earth Syst. Sci. 21, 897–921 (2017).

  32. Christidis, N., Stott, P. A. & Zwiers, F. W. Fast-track attribution assessments based on pre-computed estimates of changes in the odds of warm extremes. Clim. Dyn. 45, 1547–1564 (2015).

    Article  Google Scholar 

  33. Bloodhart, B., Maibach, E., Myers, T. & Zhao, X. Local climate experts: the influence of local TV weather information on climate change perceptions. PLoS ONE 10, e0141526 (2015).

    Article  Google Scholar 

  34. Zhao, X. et al. Climate change education through TV weathercasts: results of a field experiment. Bull. Am. Meteorol. Soc. 95, 117–130 (2014).

    Article  Google Scholar 

  35. Harlan, S. L. & Ruddell, D. M. Climate change and health in cities: impacts of heat and air pollution and potential co-benefits from mitigation and adaptation. Curr. Opin. Environ. Sustain. 3, 126–134 (2011).

    Article  Google Scholar 

  36. Friedrich, K., Eldridge, M., York, D., Witte, P. & Kushler, M. Saving Energy Cost-Effectively: A National review of the Cost of Energy Saved Through Utility-Sector Energy Efficiency Programs (American Council for an Energy-Efficient Economy, 2009).

  37. Wei, M., Patadia, S. & Kammen, D. M. Putting renewables and energy efficiency to work: how many jobs can the clean energy industry generate in the US? Energy Policy 38, 919–931 (2010).

    Article  Google Scholar 

  38. Noll, D., Dawes, C. & Rai, V. Solar community organizations and active peer effects in the adoption of residential PV. Energy Policy 67, 330–343 (2014).

    Article  Google Scholar 

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This work resulted from a working group jointly supported by both the National Institute for Mathematical Biological Synthesis—a synthesis centre sponsored by the National Science Foundation through award DBI-1300426 with additional support from The University of Tennessee, Knoxville—and the National Socio-Environmental Synthesis Center under funding received from National Science Foundation award DBI-1052875. B.B., J.M.W. and A.Z. additionally acknowledge support from the National Science Foundation through Vermont Established Program to Stimulate Competitive Research grant numbers EPS-1101317 and OIA-1556770.

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Authors and Affiliations



B.B. directed the development of the computational model, conducted the model analyses and wrote the final version of the manuscript. L.J.G. contributed to writing the manuscript, supported the model development and interpretation, and wrote the supplementary materials. K.L. helped write the manuscript and contributed psychological expertise to the model development. E.C. implemented the computational model and ran simulation experiments. S.S.M. provided dynamic modelling expertise to support model implementation and analysis. J.M.W. contributed climate modelling expertise and helped articulate model insights. P.D.H. contributed expertise regarding human perceptions of climate change. N.F. provided modelling and mathematical expertise, detailed feedback and references. T.F. developed a preliminary social model and provided the linkage to C-ROADS. A.Z. contributed ideas about how to model the theory of planned behaviour. A.K. helped to frame real-world implications of the model. F.H. provided climate modelling expertise that clarified the contribution of the model. All authors contributed to the development of the conceptual model.

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Correspondence to Brian Beckage.

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Supplementary Information

Additional details of model structure, Supplementary References and Supplementary Figures 1–9.

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Beckage, B., Gross, L.J., Lacasse, K. et al. Linking models of human behaviour and climate alters projected climate change. Nature Clim Change 8, 79–84 (2018).

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