Journal of Consumer Policy

, Volume 37, Issue 3, pp 397–411 | Cite as

Aiding Decision Making to Reduce the Impacts of Climate Change

  • Howard Kunreuther
  • Elke U. WeberEmail author
Original Paper


Utilizing theory and empirical insights from psychology and behavioural economics, this paper examines individuals’ cognitive and motivational barriers to adopting climate change adaptation and mitigation measures that increase consumer welfare. We explore various strategies that take into account the simplified decision-making processes used by individuals and resulting biases. We make these points by working through two examples: (1) investments in energy efficiency products and new technology and (2) adaptation measures to reduce property damage from future floods and hurricanes. In both cases there is a reluctance to undertake these measures due to high and certain upfront costs, delayed and probabilistic benefits, and behavioural biases related to this asymmetry. The use of choice architecture through framing and the use of default options coupled with short-term incentives and long-term contracts can encourage greater investment in these measures.


Climate change Decision processes Behavioural economics Energy efficiency Mitigation and adaptation measures Choice architecture 



Support for this research comes from the National Science Foundation (SES-1061882 and SES-1062039); the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science Foundation and Carnegie Mellon University (SES-0949710); the Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California; the Center for Research on Environmental Decisions (CRED; NSF Cooperative Agreement SES-0345840 to Columbia University), and the Wharton Risk Management and Decision Processes Center. We thank the referees for helpful comments and Carol Heller for editorial assistance.


  1. Allcott, H. (2011). Social norms and energy conservation. Journal of Public Economics, 95(9), 1082–1095.CrossRefGoogle Scholar
  2. Borenstein, S. (2012). The private and public economics of renewable electricity generation. Journal of Economic Perspectives, 26(1), 67–92.CrossRefGoogle Scholar
  3. Bostrom, A., Morgan, M. G., Fischhoff, B., & Read, D. (1994). What do people know about global climate change? 1. Mental models. Risk Analysis, 14, 959–970.CrossRefGoogle Scholar
  4. Choi, J. J., Laibson, D., Madrian, B. C., & Metrick, A. (2003). Optimal defaults. Behavioral Economics, Public Policy, and Paternalism, 93, 180–185.Google Scholar
  5. Creyts, J., Granade, H. C., & Ostrowski, K. J. (2010). U.S. energy savings: Opportunities and challenges. McKinsey Quarterly, January Issue.Google Scholar
  6. Cullen, H. (2010). The weather of the future: heat waves, extreme storms, and other scenes from a climate-changed planet. New York: Harper.Google Scholar
  7. Denholm, P., Margolis, R. M., Ong, S., & Roberts, B. (2009). Break-even cost for residential photovoltaics in the United States” Key drivers an sensitivities. National Renewable Energy Lab, Technical Report NREL/TP-6A2–46909.Google Scholar
  8. Dietz, T., Stern, P. C., & Weber, E. U. (2013). Reducing carbon-based energy consumption through changes in household behavior. Daedalus, 142, 1–12.CrossRefGoogle Scholar
  9. Dinner, I., Johnson, E. J., Goldstein, D. G., & Liu, K. (2011). Partitioning default effects: Why people choose not to choose. Journal of Experimental Psychology: Applied, 17(4), 332.Google Scholar
  10. Drury, E., Jenkins, T., Jordan, D., & Margolis, R. (2013). Photovoltaic investment risk and uncertainty for residential customers. IEEE Journal of Photovoltaics.Google Scholar
  11. Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40, 351–401.Google Scholar
  12. Gately, D. (1980). Individual discount rates and the purchase and utilization of energy-using durables: Comment. The Bell Journal of Economics, 11, 373–374.Google Scholar
  13. Goldstein, D. G., Johnson, E. J., Herrmann, A., & Heitman, M. (2008). Nudge your customer toward better choices. Harvard Business Review, 86(12), 99–105.Google Scholar
  14. Goodnough, A. (2006). As hurricane season looms, state aim to scare. The New York Times, May 31.Google Scholar
  15. Gromet, D. M., Kunreuther, H., & Larrick, R. P. (2013). Political identity affects energy efficiency attitudes and choices. PNAS, 110(23), 9314–9319.Google Scholar
  16. Hardisty, D. J., Johnson, E. J., & Weber, E. U. (2010). A dirty word or a dirty world? Attribute framing, political affiliation, and query theory. Psychological Science, 21(1), 86–92. doi: 10.1177/0956797609355572.CrossRefGoogle Scholar
  17. Hausman, J. (1979). Individual discount rates and the purchase and utilization of energy-using durables. The Bell Journal of Economics, 10(1), 33–54.CrossRefGoogle Scholar
  18. Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15(8), 534–539.CrossRefGoogle Scholar
  19. Hirst, E., & Brown, M. (1990). Closing the efficiency gap: Barriers to the efficient use of energy. Resources, Conservation and Recycling, 3(4), 267–281.Google Scholar
  20. Jaffe, A. B., & Stavins, R. N. (1994). The energy-efficiency gap. What does it mean? Energy Policy, 22(10), 804–810.CrossRefGoogle Scholar
  21. Jakob, M. (2006). Marginal costs and co-benefits of energy efficiency investments: The case of the Swiss residential sector. Energy Policy, 34(2), 172–187.Google Scholar
  22. Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science, 302, 1338.CrossRefGoogle Scholar
  23. Johnson, E. J., Häubl, G., & Keinan, A. (2007). Aspects of endowment: A query theory of value construction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(3), 461–474.Google Scholar
  24. Johnson, E. J., Hershey, J., Meszaros, J., & Kunreuther, H. (1993). Framing, probability distortions, and insurance decisions. Journal of Risk and Uncertainty, 7(1), 35–51.CrossRefGoogle Scholar
  25. Johnson, E. J., Shu, S. B., Dellaert, B. C. G., Fox, C., Goldstein, D. G., Häubl, G., et al. (2012). Beyond nudges: Tools of a choice architecture. Marketing Letters, 1–18.Google Scholar
  26. Kahneman, D. (2003). A psychological perspective on economics. The American Economic Review, 93(2), 162–168.CrossRefGoogle Scholar
  27. Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.Google Scholar
  28. Kempton, W. (1991). Public understanding of global warming. Society & Natural Resources, 4(4), 331–345.CrossRefGoogle Scholar
  29. Kunreuther, H., Meyer, R. J., & Michel-Kerjan, E. (2013a). Overcoming decision biases to reduce losses from natural catastrophes. In E. Shafir (ed.), Behavioral foundations of policy, (pp. 398–416). Princeton: Princeton University Press.Google Scholar
  30. Kunreuther, H., Pauly, M. V., & McMorrow, S. (2013b). Insurance and behavioral economics: improving decisions in the most misunderstood industry. New York: Cambridge University Press.Google Scholar
  31. Laibson, D. (1997). Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 112(2), 443–478.CrossRefGoogle Scholar
  32. Leighty, W., & Meier, A. (2010). Short‐term Electricity Conservation in Juneau, Alaska: A Study of Household Activities. Proceedings of the 2010 Summer Study on Energy Efficiency in Buildings. Washington, DC: ACEEE, Scholar
  33. Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267–286.Google Scholar
  34. Meltzoff, M. A., & Moore, M. K. (1999). Persons and representation: Why infant imitation is important for theories of human development. In J. Nadel & G. Butterworth (Eds.), Imitation in infancy. Cambridge: Cambridge University Press.Google Scholar
  35. Meyer, E., Baker, J., Broad, K., Czajkowski, J. & Orlove, B. (2013). “The dynamics of hurricane risk perception: Real-Time Evidence from the 2012 Atlantic Hurricane Season”. Wharton Risk Center Working Paper #2013–07.Google Scholar
  36. Mills, B.F., and Schleich, J. (2008). Why don’t households see the light? Explaining the diffusion of compact fluorescent lamps. Working paper sustainability and innovation, No, S1/2008,
  37. Michel-Kerjan, E., Lemoyne De Forges, S., & Kunreuther, H. (2012). Policy tenure under the U.S. National Flood Insurance Program (NFIP). Risk Analysis, 32(4), 644–658.CrossRefGoogle Scholar
  38. Montgomery, H. (1989). From cognition to action: The search for dominance in decision making. In H. Montgomery & O. Svenson (Eds.), Process and structure in human decision making (pp. 23–49). New York: Wiley.Google Scholar
  39. Palm, R. (1995). Earthquake insurance: A longitudinal study of California homeowners. Westview Press.Google Scholar
  40. Patt, A. & Weber, E. U. (2013). Perceiving and communicating climate change uncertainty. WIREs: Climate Change. Doi 10.1002/wcc.259.Google Scholar
  41. Peters, E., & Slovic, P. (2000). The springs of action: Affective and analytical information processing in choice. Personality and Social Psychology Bulletin, 26(12), 1465–1475.Google Scholar
  42. Pichert, D., & Katsikopoulos, K. V. (2008). Green defaults: Information presentation and pro-environmental behaviour. Journal of Environmental Psychology, 28(1), 63–73.Google Scholar
  43. Reynolds, T. W., Bostrom, A., Read, D., & Morgan, M. G. (2010). Now what do people know about global climate change? Survey studies of educated laypeople. Risk Analysis, 30(10), 1520–1538.CrossRefGoogle Scholar
  44. Simon, H. (1957). Models of man. New York: Wiley.Google Scholar
  45. Slovic, P. (1987). Perception of risk. Science, 236, 280–285.CrossRefGoogle Scholar
  46. Slovic, P., Fischhoff, B., & Lichtenstein, S. (1978). Accident probabilities and seat belt usage: A psychological perspective. Accident Analysis & Prevention, 10, 281–285.Google Scholar
  47. Solomon, S., Plattner, G.-K., Knutti, R., & Friedlingstein, P. (2009). Irreversible climate change due to carbon dioxide emissions. Proceedings of the national academy of sciences, 106(6), 1704–1709.CrossRefGoogle Scholar
  48. Spence, A., Poortinga, W., Butler, C., & Pidgeon, N. F. (2011). Perceptions of climate change and willingness to save energy related to flood experience. Nature Climate Change, 1(1), 46–49.CrossRefGoogle Scholar
  49. Sterman, J. D., & Sweeney, L. B. (2007). Understanding public complacency about climate change: adults’ mental models of climate change violate conservation of matter. Climatic Change, 80(3), 213–238.CrossRefGoogle Scholar
  50. Thaler, R. (1999). Mental accounting matters. Journal of Behavioral Decision Making, 12, 183–206.CrossRefGoogle Scholar
  51. Thaler, R., & Sunstein, C. (2008). Nudge: the gentle power of choice architecture. New Haven: Yale University Press.Google Scholar
  52. Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: a reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061.CrossRefGoogle Scholar
  53. Weber, E. U. (2006). Experience-based and description-based perceptions of long-term risk: Why global warming does not scare us (yet). Climatic Change, 77(1), 103–120.Google Scholar
  54. Weber, E., & Johnson, E. (2009). Mindful judgment and decision making. Annual Review of Psychology, 60, 53–85.CrossRefGoogle Scholar
  55. Weber, E. U., & Lindemann, P. G. (2007). From intuition to analysis: Making decisions with our head, our heart, or by the book. In H. Plessner, C. Betsch, & T. Betsch (Eds.), Intuition in judgment and decision making (pp. 191–208). Mahwah: Lawrence Erlbaum.Google Scholar
  56. Weber, E. U., Johnson, E. J., Milch, K., Chang, H., Brodscholl, J., & Goldstein, D. (2007). Asymmetric discounting in intertemporal choice: A query theory account. Psychological Science, 18, 516–523.Google Scholar
  57. Weber, E. U., & Stern, P. (2011). The American public’s understanding of climate change. American Psychologist, 66, 315–328. doi: 10.1037/a0023253.CrossRefGoogle Scholar
  58. Weinstein, N. D., Kolb, K., & Goldstein, B. D. (1996). Using time intervals between expected events to communicate risk magnitudes. Risk Analysis, 16(3), 305–308.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Columbia UniversityNew YorkUSA

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