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Energy-related behaviour and rebound when rationality, self-interest and willpower are limited


The extent to which adopting energy-efficient technologies results in energy savings depends on how such technologies are used, and how monetary savings from energy efficiency are spent. Energy rebound occurs when potential energy savings are diminished due to post-adoption behaviour. Here we review empirical studies on how six behavioural regularities affect three energy-relevant decisions and ultimately rebound: adoption of energy-saving products or practices, their intensity of use and spending of associated monetary savings. The findings suggest that behaviours that reflect limited rationality and willpower may increase rebound, while the effects of behaviours driven by bounded self-interest are less clear. We then describe how interventions associated with each of the behavioural regularities can influence rebound and thus serve to achieve higher energy savings. Future research ought to study energy-relevant decisions in a more integrated manner, with a particular focus on re-spending as this presents the greatest challenge for research and policy.

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Fig. 1: Illustration of the three types of rebound.
Fig. 2: A schematic representation of the review.
Fig. 3: Dominant effects of behavioural regularities on energy-relevant decisions and rebound.
Fig. 4: Smart meter adapted to indicate the days left for the initial investment on house insulation to be paid back.


  1. 1.

    Berkhout, P. H. G., Muskens, J. C. & W. Velthuijsen, J. Defining the rebound effect. Energy Policy 28, 425–432 (2000).

    Google Scholar 

  2. 2.

    Brookes, L. The greenhouse effect: the fallacies in the energy efficiency solution. Energy Policy 18, 199–201 (1990).

    Google Scholar 

  3. 3.

    Gillingham, K., Kotchen, M. J., Rapson, D. S. & Wagner, G. Energy policy: the rebound effect is overplayed. Nature 493, 475–476 (2013).

    Google Scholar 

  4. 4.

    Azevedo, I. M. L. Consumer end-use energy efficiency and rebound effects. Annu. Rev. Environ. Resour. 39, 393–418 (2014). A Review Article on the various definitions of rebound, the research gaps in the literature and the importance of scope in estimating rebound.

    Google Scholar 

  5. 5.

    Sorrell, S., Dimitropoulos, J. & Sommerville, M. Empirical estimates of the direct rebound effect: a review. Energy Policy 37, 1356–1371 (2009).

    Google Scholar 

  6. 6.

    Brockway, P. E., Sorrell, S., Semieniuk, G., Heun, M. K. & Court, V. Energy efficiency and economy-wide rebound effects: a review of the evidence and its implications. Renew. Sustain. Energy Rev. 141, 110781 (2021).

    Google Scholar 

  7. 7.

    Peters, A. & Dütschke, E. in Rethinking Climate and Energy Policies (eds Santarius, T., Walnum, H. J. & Aall, C.) 89–105 (Springer, 2016);

  8. 8.

    Girod, B. & De Haan, P. Mental Rebound (ETH Zurich, 2009);

  9. 9.

    Dütschke, E., Frondel, M., Schleich, J. & Vance, C. Moral licensing—another source of rebound? Front. Energy Res. 6, 38 (2018). This review suggests that consumers may feel morally licensed to consume more energy after adopting a more energy-efficient technology or making an energy-conservation decision.

    Google Scholar 

  10. 10.

    Santarius, T. & Soland, M. How technological efficiency improvements change consumer preferences: towards a psychological theory of rebound effects. Ecol. Econ. 146, 414–424 (2018). This study integrates rational-choice and psychological behavioural theories for the study of rebound and identifies multiple channels through which rebound can arise.

    Google Scholar 

  11. 11.

    Seebauer, S. The psychology of rebound effects: explaining energy efficiency rebound behaviours with electric vehicles and building insulation in Austria. Energy Res. Soc. Sci. 46, 311–320 (2018).

    Google Scholar 

  12. 12.

    Font Vivanco, D., McDowall, W., Freire-González, J., Kemp, R. & van der Voet, E. The foundations of the environmental rebound effect and its contribution towards a general framework. Ecol. Econ. 125, 60–69 (2016).

    Google Scholar 

  13. 13.

    Jolls, C., Sunstein, C. R. & Thaler, R. A behavioral approach to law and economics. Stanf. Law Rev. 50, 1471 (1998).

    Google Scholar 

  14. 14.

    Turrentine, T. S. & Kurani, K. S. Car buyers and fuel economy? Energy Policy 35, 1213–1223 (2007).

    Google Scholar 

  15. 15.

    Allcott, H. Consumers’ perceptions and misperceptions of energy costs. Am. Econ. Rev. 101, 98–104 (2011).

    Google Scholar 

  16. 16.

    Wang, S., Fan, J., Zhao, D., Yang, S. & Fu, Y. Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation 43, 123–143 (2016).

    Google Scholar 

  17. 17.

    Gerarden, T. D., Newell, R. G. & Stavins, R. N. Assessing the energy-efficiency gap. J. Econ. Lit. 55, 1486–1525 (2017).

    Google Scholar 

  18. 18.

    Cohen, F., Glachant, M. & Söderberg, M. Consumer myopia, imperfect competition and the energy efficiency gap: evidence from the UK refrigerator market. Eur. Econ. Rev. 93, 1–23 (2017).

    Google Scholar 

  19. 19.

    Halvorsen, B., Larsen, B. M., Wilhite, H. & Winther, T. Revisiting household energy rebound: perspectives from a multidisciplinary study. Indoor Built Environ. 25, 1114–1123 (2016).

    Google Scholar 

  20. 20.

    Waechter, S., Sütterlin, B. & Siegrist, M. The misleading effect of energy efficiency information on perceived energy friendliness of electric goods. J. Clean. Prod. 93, 193–202 (2015).

    Google Scholar 

  21. 21.

    Keefer, Q. & Rustamov, G. Limited attention in residential energy markets: a regression discontinuity approach. Empir. Econ. 55, 993–1017 (2018).

    Google Scholar 

  22. 22.

    Attari, S. Z., DeKay, M. L., Davidson, C. I. & De Bruin, W. B. Public perceptions of energy consumption and savings. Proc. Natl Acad. Sci. USA 107, 16054–16059 (2010).

    Google Scholar 

  23. 23.

    Camilleri, A. R., Larrick, R. P., Hossain, S. & Patino-Echeverri, D. Consumers underestimate the emissions associated with food but are aided by labels. Nat. Clim. Change 9, 53–58 (2019).

    Google Scholar 

  24. 24.

    Thaler, R. H. Mental accounting matters. J. Behav. Decis. Mak. 12, 183–206 (1999).

    Google Scholar 

  25. 25.

    Antonides, G., Manon de Groot, I. & Fred van Raaij, W. Mental budgeting and the management of household finance. J. Econ. Psychol. 32, 546–555 (2011).

    Google Scholar 

  26. 26.

    Kahneman, D. & Tversky, A. Prospect theory: an analysis of decision under risk. Econometrica 47, 263–292 (1979).

    MathSciNet  MATH  Google Scholar 

  27. 27.

    Epley, N., Mak, D. & Idson, L. C. Bonus of rebate?: the impact of income framing on spending and saving. J. Behav. Decis. Mak. 19, 213–227 (2006).

    Google Scholar 

  28. 28.

    Hahnel, U. J. J., Chatelain, G., Conte, B., Piana, V. & Brosch, T. Mental accounting mechanisms in energy decision-making and behaviour. Nat. Energy 5, 952–958 (2020). This Perspective examines the ways mental accounting can affect energy-related behaviour, from which implications for rebound are drawn.

    Google Scholar 

  29. 29.

    Schleich, J., Gassmann, X., Meissner, T. & Faure, C. A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies. Energy Econ. 80, 377–393 (2019).

    Google Scholar 

  30. 30.

    Heutel, G. Prospect theory and energy efficiency. J. Environ. Econ. Manag. 96, 236–254 (2019).

    Google Scholar 

  31. 31.

    Energy Supply Probe–Initial Findings Report (Ofgem, 2008);

  32. 32.

    Antonides, G. & Ranyard, R. Mental accounting and economic behaviour. Econ. Psychol. 1, 123–138 (2017).

    Google Scholar 

  33. 33.

    Beatty, T. K. M., Blow, L., Crossley, T. F. & O’Dea, C. Cash by any other name? Evidence on labeling from the UK Winter Fuel Payment. J. Public Econ. 118, 86–96 (2014).

    Google Scholar 

  34. 34.

    Andor, M. A., Gerster, A., Gillingham, K. T. & Horvath, M. Running a car costs much more than people think—stalling the uptake of green travel. Nature 580, 453–455 (2020). This empirical study shows that a majority of car drivers do not consider initial purchase costs of a new car as part of—that is, in the same mental account as—total car costs.

    Google Scholar 

  35. 35.

    de Haan, P., Mueller, M. G. & Peters, A. Does the hybrid Toyota Prius lead to rebound effects? Analysis of size and number of cars previously owned by Swiss Prius buyers. Ecol. Econ. 58, 592–605 (2006).

    Google Scholar 

  36. 36.

    Cunha, M. Jr & Caldieraro, F. Sunk-cost effects on purely behavioral investments. Cogn. Sci. 33, 105–113 (2009).

    Google Scholar 

  37. 37.

    Henderson, P. W. & Peterson, R. A. Mental accounting and categorization. Organ. Behav. Hum. Decis. Process. 51, 92–117 (1992).

    Google Scholar 

  38. 38.

    Milkman, K. L. & Beshears, J. Mental accounting and small windfalls: evidence from an online grocer. J. Econ. Behav. Organ. 71, 384–394 (2009).

    Google Scholar 

  39. 39.

    Chitnis, M., Sorrell, S., Druckman, A., Firth, S. K. & Jackson, T. Who rebounds most? Estimating direct and indirect rebound effects for different UK socioeconomic groups. Ecol. Econ. 106, 12–32 (2014).

    Google Scholar 

  40. 40.

    Kahneman, D., Knetsch, J. L. & Thaler, R. H. The endowment effect, loss aversion, and status quo bias. J. Econ. Perspect. 5, 193–206 (1991).

    Google Scholar 

  41. 41.

    Sunstein, C. R. & Reisch, L. A. Greener by default. Trinity Coll. Law Rev. 21, 31–66 (2018).

    Google Scholar 

  42. 42.

    Verplanken, B. & Aarts, H. Habit, attitude, and planned behaviour: is habit an empty construct or an interesting case of goal-directed automaticity? Eur. Rev. Soc. Psychol. 10, 101–134 (1999).

    Google Scholar 

  43. 43.

    Huebner, G. M., Cooper, J. & Jones, K. Domestic energy consumption—what role do comfort, habit, and knowledge about the heating system play? Energy Build. 66, 626–636 (2013).

    Google Scholar 

  44. 44.

    Pichert, D. & Katsikopoulos, K. V. Green defaults: information presentation and pro-environmental behaviour. J. Environ. Psychol. 28, 63–73 (2008).

    Google Scholar 

  45. 45.

    Abrahamse, W., Steg, L., Vlek, C. & Rothengatter, T. A review of intervention studies aimed at household energy conservation. J. Environ. Psychol. 25, 273–291 (2005).

    Google Scholar 

  46. 46.

    Dinner, I., Johnson, E. J., Goldstein, D. G. & Liu, K. Partitioning default effects: why people choose not to choose. J. Exp. Psychol. Appl. 17, 332–341 (2011).

    Google Scholar 

  47. 47.

    Janssen, M. A. & Jager, W. Stimulating diffusion of green products—co-evolution between firms and consumers. J. Evol. Econ. 12, 283–306 (2002).

    Google Scholar 

  48. 48.

    Laibson, D. Golden eggs and hyperbolic discounting. Q. J. Econ. 112, 443–478 (1997).

    MATH  Google Scholar 

  49. 49.

    Baumeister, R. & Vohs, K. in Time and Decision: Economic and Psychological Perspectives on Intertemporal Choice (eds Loewenstein, G. et al.) 201–216 (Russell Sage Foundation, 2003).

  50. 50.

    Bradford, D., Courtemanche, C., Heutel, G., McAlvanah, P. & Ruhm, C. Time preferences and consumer behavior. J. Risk Uncertain. 55, 119–145 (2017).

    Google Scholar 

  51. 51.

    Fuerst, F. & Singh, R. How present bias forestalls energy efficiency upgrades: a study of household appliance purchases in India. J. Clean. Prod. 186, 558–569 (2018).

    Google Scholar 

  52. 52.

    Tsvetanov, T. & Segerson, K. Re-evaluating the role of energy efficiency standards: a behavioral economics approach. J. Environ. Econ. Manag. 66, 347–363 (2013).

    Google Scholar 

  53. 53.

    Allcott, H. & Taubinsky, D. Evaluating behaviorally motivated policy: experimental evidence from the lightbulb market. Am. Econ. Rev. 105, 2501–2538 (2015).

    Google Scholar 

  54. 54.

    Allcott, H. & Wozny, N. Gasoline prices, fuel economy, and the energy paradox. Rev. Econ. Stat. 96, 779–795 (2014).

    Google Scholar 

  55. 55.

    Harding, M. & Hsiaw, A. Goal setting and energy conservation. J. Econ. Behav. Organ. 107, 209–227 (2014).

    Google Scholar 

  56. 56.

    Lillemo, S. C. Measuring the effect of procrastination and environmental awareness on households’ energy-saving behaviours: an empirical approach. Energy Policy 66, 249–256 (2014).

    Google Scholar 

  57. 57.

    Wesley Schultz, P. The structure of environmental concern: concern for self, other people, and the biosphere. J. Environ. Psychol. 21, 327–339 (2001).

    Google Scholar 

  58. 58.

    Mohana, R., Turaga, R., Howarth, R. B., Borsuk, M. E. & Rosenwald, J. Pro-environmental behavior: rational choice meets moral motivation. Ann. N. Y. Acad. Sci. 1185, 211–224 (2010).

    Google Scholar 

  59. 59.

    Black, J. S., Stern, P. C. & Elworth, J. T. Personal and contextual influences on household energy adaptations. J. Appl. Psychol. 70, 3–21 (1985).

    Google Scholar 

  60. 60.

    Wolske, K. S. & Stern, P. C. in Psychology and Climate Change: Human Perceptions, Impacts, and Responses (eds Clayton, S. & Manning, C.) 127–160 (Elsevier, 2018);

  61. 61.

    Kollmuss, A. & Agyeman, J. Mind the gap: why do people act environmentally and what are the barriers to pro-environmental behavior? Environ. Educ. Res. 8, 239–260 (2002).

    Google Scholar 

  62. 62.

    Monin, B. & Jordan, A. in Personality, Identity, and Character: Explorations in Moral Psychology (eds Narvaez, D. & Lapsley, D.) Ch. 15 (Cambridge Univ. Press, 2009).

  63. 63.

    Hope, A. L. B., Jones, C. R., Webb, T. L., Watson, M. T. & Kaklamanou, D. The role of compensatory beliefs in rationalizing environmentally detrimental behaviors. Environ. Behav. 50, 401–425 (2018).

    Google Scholar 

  64. 64.

    Truelove, H. B., Carrico, A. R., Weber, E. U., Raimi, K. T. & Vandenbergh, M. P. Positive and negative spillover of pro-environmental behavior: an integrative review and theoretical framework. Glob. Environ. Change 29, 127–138 (2014).

    Google Scholar 

  65. 65.

    Wagner, G. & Zizzamia, D. Green moral hazards. Ethics Policy Environ. (2021).

  66. 66.

    Fischbacher, U., Schudy, S. & Teyssier, S. Heterogeneous preferences and investments in energy saving measures. Resour. Energy Econ. 63, 101202 (2021).

    Google Scholar 

  67. 67.

    Di Maria, C., Ferreira, S. & Lazarova, E. Shedding light on the light bulb puzzle: the role of attitudes and perceptions in the adoption of energy efficient light bulbs. Scott. J. Polit. Econ. 57, 48–67 (2010).

    Google Scholar 

  68. 68.

    Harding, M. & Rapson, D. Does absolution promote sin? A conservationist’s dilemma. Environ. Resour. Econ. 73, 923–955 (2019).

    Google Scholar 

  69. 69.

    Clark, C. F., Kotchen, M. J. & Moore, M. R. Internal and external influences on pro-environmental behavior: participation in a green electricity program. J. Environ. Psychol. 23, 237–246 (2003).

    Google Scholar 

  70. 70.

    Andersson, D., Linscott, R. & Nässén, J. Estimating car use rebound effects from Swedish microdata. Energy Effic. 12, 2215–2225 (2019). This empirical study shows that when drivers switch to more efficient cars that are green-labelled, direct rebound is null.

    Google Scholar 

  71. 71.

    Matiaske, W., Menges, R. & Spiess, M. Modifying the rebound: It depends! Explaining mobility behavior on the basis of the German socio-economic panel. Energy Policy 41, 29–35 (2012).

    Google Scholar 

  72. 72.

    Klöckner, C. A., Nayum, A. & Mehmetoglu, M. Positive and negative spillover effects from electric car purchase to car use. Transp. Res. Part D 21, 32–38 (2013).

    Google Scholar 

  73. 73.

    Gatersleben, B., Steg, L. & Vlek, C. Measurement and determinants of environmentally significant consumer behavior. Environ. Behav. 34, 335–362 (2002).

    Google Scholar 

  74. 74.

    Vita, G. et al. Happier with less? Members of European environmental grassroots initiatives reconcile lower carbon footprints with higher life satisfaction and income increases. Energy Res. Soc. Sci. 60, 101329 (2020).

    Google Scholar 

  75. 75.

    Laroche, M., Bergeron, J. & Barbaro-Forleo, G. Targeting consumers who are willing to pay more for environmentally friendly products. J. Consum. Mark. 18, 503–520 (2001).

    Google Scholar 

  76. 76.

    Wolske, K. S., Gillingham, K. T. & Schultz, P. W. Peer influence on household energy behaviours. Nat. Energy 5, 202–2012 (2020).

    Google Scholar 

  77. 77.

    Brick, C., Sherman, D. K. & Kim, H. S. “Green to be seen” and “brown to keep down”: visibility moderates the effect of identity on pro-environmental behavior. J. Environ. Psychol. 51, 226–238 (2017).

    Google Scholar 

  78. 78.

    Uren, H. V., Roberts, L. D., Dzidic, P. L. & Leviston, Z. High-status pro-environmental behaviors: costly, effortful, and visible. Environ. Behav. 53, 455–484 (2021).

    Google Scholar 

  79. 79.

    Griskevicius, V., Tybur, J. M. & Van den Bergh, B. Going green to be seen: status, reputation, and conspicuous conservation. J. Pers. Soc. Psychol. 98, 392–404 (2010).

    Google Scholar 

  80. 80.

    Sexton, S. & Sexton, A. The Prius halo and willingness to pay for environmental bona fides. J. Environ. Econ. Manag. 67, 303–317 (2014).

    Google Scholar 

  81. 81.

    Bollinger, B. & Gillingham, K. Peer effects in the diffusion of solar photovoltaic panels. Mark. Sci. 31, 900–912 (2012).

    Google Scholar 

  82. 82.

    Farrow, K., Grolleau, G. & Ibanez, L. Social norms and pro-environmental behavior: a review of the evidence. Ecol. Econ. 140, 1–13 (2017).

    Google Scholar 

  83. 83.

    Allcott, H. & Rogers, T. The short-run and long-run effects of behavioral interventions: experimental evidence from energy conservation. Am. Econ. Rev. 104, 3003–3037 (2014).

    Google Scholar 

  84. 84.

    Demarque, C., Charalambides, L., Hilton, D. J. & Waroquier, L. Nudging sustainable consumption: the use of descriptive norms to promote a minority behavior in a realistic online shopping environment. J. Environ. Psychol. 43, 166–174 (2015).

    Google Scholar 

  85. 85.

    Peattie, K. Green consumption: behavior and norms. Annu. Rev. Environ. Resour. 35, 195–228 (2010).

    Google Scholar 

  86. 86.

    Jackson, T. Motivating Sustainable Consumption: A Review of Evidence on Consumer Behaviour and Behavioural Change. A Report to the Sustainable Development Research Network 30–40 (University of Surrey, 2005).

  87. 87.

    Biswas, A., Mukherjee, A. & Roy, M. Leveraging factors for consumers’ car purchase decisions—a study in an emerging economy. J. Manag. Policies Pract. 2, 99–111 (2014).

    Google Scholar 

  88. 88.

    Nisa, C. F., Bélanger, J. J., Schumpe, B. M. & Faller, D. G. Meta-analysis of randomised controlled trials testing behavioural interventions to promote household action on climate change. Nat. Commun. 10, 4545 (2019). This recent meta-analysis finds that the size of emissions reduction of various behavioural interventions is not as big as previously thought.

    Google Scholar 

  89. 89.

    Abrahamse, W. & Steg, L. Social influence approaches to encourage resource conservation: a meta-analysis. Glob. Environ. Change 23, 1773–1785 (2013).

    Google Scholar 

  90. 90.

    Karlin, B., Zinger, J. F. & Ford, R. The effects of feedback on energy conservation: a meta-analysis. Psychol. Bull. 141, 1205–1227 (2015).

    Google Scholar 

  91. 91.

    Delmas, M. A., Fischlein, M. & Asensio, O. I. Information strategies and energy conservation behavior: a meta-analysis of experimental studies from 1975 to 2012. Energy Policy 61, 729–739 (2013).

    Google Scholar 

  92. 92.

    Andor, M. A. & Fels, K. M. Behavioral economics and energy conservation—a systematic review of non-price interventions and their causal effects. Ecol. Econ. 148, 178–210 (2018). A systematic review of social comparison, commitment devices, goal setting and labelling as behavioural interventions aimed at achieving reductions in household energy use.

    Google Scholar 

  93. 93.

    Iweka, O., Liu, S., Shukla, A. & Yan, D. Energy and behaviour at home: a review of intervention methods and practices. Energy Res. Soc. Sci. 57, 101238 (2019).

    Google Scholar 

  94. 94.

    Camilleri, A. R. & Larrick, R. P. Metric and scale design as choice architecture tools. J. Public Policy Mark. 33, 108–125 (2014).

    Google Scholar 

  95. 95.

    Sintov, N. D. & Schultz, P. W. Unlocking the potential of smart grid technologies with behavioral science. Front. Psychol. 6, 410 (2015).

    Google Scholar 

  96. 96.

    Darby, S. The Effectiveness of Feedback on Energy Consumption: A Review for DEFRA of the Literature on Metering, Billing and Direct Displays (Environmental Change Institute, Univ. of Oxford, 2006).

  97. 97.

    Mogles, N. et al. How smart do smart meters need to be? Build. Environ. 125, 439–450 (2017). This study shows that to increase their effectiveness, smart energy meters should provide context to the feedback they provide and help to improve the energy literacy of households.

    Google Scholar 

  98. 98.

    Thaler, R.H. & Sunstein. C. R. Nudge: Improving Decisions about Health, Wealth, and Happiness (Yale Univ. Press, 2008).

  99. 99.

    Tiefenbeck, V. et al. Overcoming salience bias: how real-time feedback fosters resource conservation. Manag. Sci. 64, 1458–1476 (2018).

    Google Scholar 

  100. 100.

    Ropret Homar, A. & Knežević Cvelbar, L. The effects of framing on environmental decisions: a systematic literature review. Ecol. Econ. 183, 106950 (2021). A systematic review regarding the effects of framing on environmental decisions, which describe the conditions under which loss versus gain frames are more effective in promoting behavioural change.

    Google Scholar 

  101. 101.

    Hermsen, S., Frost, J., Renes, R. J. & Kerkhof, P. Using feedback through digital technology to disrupt and change habitual behavior: a critical review of current literature. Comput. Hum. Behav. 57, 61–74 (2016).

    Google Scholar 

  102. 102.

    Fujii, S., Gärling, T. & Kitamura, R. Changes in drivers’ perceptions and use of public transport during a freeway closure. Environ. Behav. 33, 796–808 (2001).

    Google Scholar 

  103. 103.

    Fujii, S. & Gärling, T. Development of script-based travel mode choice after forced change. Transp. Res. Part F 6, 117–124 (2003).

    Google Scholar 

  104. 104.

    Bamberg, S. Is a residential relocation a good opportunity to change people’s travel behavior? Results From a theory-driven intervention study. Environ. Behav. 38, 820–840 (2006).

    Google Scholar 

  105. 105.

    Thomas, G. O., Poortinga, W. & Sautkina, E. Habit discontinuity, self-activation, and the diminishing influence of context change: evidence from the UK understanding society survey. PLoS ONE 11, e0153490 (2016).

    Google Scholar 

  106. 106.

    Brown, Z., Johnstone, N., Haščič, I., Vong, L. & Barascud, F. Testing the effect of defaults on the thermostat settings of OECD employees. Energy Econ. 39, 128–134 (2013).

    Google Scholar 

  107. 107.

    McCalley, L. T. From motivation and cognition theories to everyday applications and back again: the case of product-integrated information and feedback. Energy Policy 34, 129–137 (2006).

    Google Scholar 

  108. 108.

    Johnson, E. J. et al. Beyond nudges: tools of a choice architecture. Mark. Lett. 23, 487–504 (2012).

    Google Scholar 

  109. 109.

    Allcott, H. & Knittel, C. Are consumers poorly informed about fuel economy? Evidence from two experiments. Am. Econ. J. Econ. Policy 11, 1–37 (2019).

    Google Scholar 

  110. 110.

    Osbaldiston, R. & Paul Schott, J. Environmental sustainability and behavioral science: Meta-analysis of proenvironmental behavior experiments. Environ. Behav. 44, 257–299 (2012).

    Google Scholar 

  111. 111.

    Locke, E. A. & Latham, G. P. Building a practically useful theory of goal setting and task motivation: a 35-year odyssey. Am. Psychol. 57, 705–717 (2002).

    Google Scholar 

  112. 112.

    Deconinck, G. et al. An approach towards socially acceptable energy saving policies via monetary instruments on the smart meter infrastructure. In 3rd International Conference on Next Generation Infrastructure Systems for Eco-Cities (IEEE, 2010);

  113. 113.

    Asensio, O. I. & Delmas, M. A. Nonprice incentives and energy conservation. Proc. Natl Acad. Sci. USA 112, E510–E515 (2015).

    Google Scholar 

  114. 114.

    Bougherara, D., Grolleau, G. & Thiébaut, L. Can labelling policies do more harm than good? An analysis applied to environmental labelling schemes. Eur. J. Law Econ. 19, 5–16 (2005).

    Google Scholar 

  115. 115.

    Gneezy, U., Imas, A. & Madarász, K. Conscience accounting: emotion dynamics and social behavior. Manag. Sci. 60, 2645–2658 (2014).

    Google Scholar 

  116. 116.

    Thøgersen, J. & Crompton, T. Simple and painless? The limitations of spillover in environmental campaigning. J. Consum. Policy 32, 141–163 (2009).

    Google Scholar 

  117. 117.

    Nilsson, A., Bergquist, M. & Schultz, W. P. Spillover effects in environmental behaviors, across time and context: a review and research agenda. Environ. Educ. Res. 23, 573–589 (2017).

    Google Scholar 

  118. 118.

    Nash, N. et al. Climate-relevant behavioral spillover and the potential contribution of social practice theory. Wiley Interdiscip. Rev. Clim. Change 8, e481 (2017).

    Google Scholar 

  119. 119.

    Maki, A. et al. Meta-analysis of pro-environmental behaviour spillover. Nat. Sustain. 2, 307–315 (2019).

    Google Scholar 

  120. 120.

    Bollinger, B., Gillingham, K., Kirkpatrick, A. J. & Sexton, S. Visibility and peer influence in durable good adoption. SSRN (2019).

  121. 121.

    Sunter, D. A., Castellanos, S. & Kammen, D. M. Disparities in rooftop photovoltaics deployment in the United States by race and ethnicity. Nat. Sustain. 2, 71–76 (2019).

    Google Scholar 

  122. 122.

    Font Vivanco, D., Kemp, R. & van der Voet, E. How to deal with the rebound effect? A policy-oriented approach. Energy Policy 94, 114–125 (2016). This Review Article analyses strategies for curbing rebound, suggesting that economic instruments might be the most effective.

    Google Scholar 

  123. 123.

    Hanimann, R. Consumer Behaviour in Renewable Electricity: Can Identity Signaling Increase Demand for Renewable Electricity? (Uppsala Univ., 2013).

  124. 124.

    Allcott, H. Social norms and energy conservation. J. Public Econ. 95, 1082–1095 (2011).

    Google Scholar 

  125. 125.

    Bardsley, N. et al. Domestic thermal upgrades, community action and energy saving: a three-year experimental study of prosperous households. Energy Policy 127, 475–485 (2019).

    Google Scholar 

  126. 126.

    Freire-González, J. Energy taxation policies can counteract the rebound effect: analysis within a general equilibrium framework. Energy Effic. 13, 69–78 (2020).

    Google Scholar 

  127. 127.

    van den Bergh, J. C. J. M. Energy conservation more effective with rebound policy. Environ. Resour. Econ. 48, 43–58 (2011).

    Google Scholar 

  128. 128.

    Drews, S., Exadaktylos, F. & van den Bergh, J. C. J. M. Assessing synergy of incentives and nudges in the energy policy mix. Energy Policy 144, 111605 (2020).

    Google Scholar 

  129. 129.

    Becker, G. The Economic Approach to Human Behavior (Univ. Chicago Press, 1976).

  130. 130.

    Simon, H. A. A behavioral model of rational choice. Q. J. Econ. 69, 99 (1955).

    Google Scholar 

  131. 131.

    Kahneman, D. & Tversky, A. Choices, Values, and Frames (Cambridge Univ. Press, 2000).

  132. 132.

    White, M. D. in Economics and the Mind (ed. Barbara Montero, M. D. W.) 143–158 (Routledge, 2007).

  133. 133.

    Sen, A. Rational fools: a critique of the behavioral foundations of economic theory. Phil. Public Aff. 6, 317–344 (1977).

    Google Scholar 

  134. 134.

    Henrich, J. et al. ‘Economic man’ in cross-cultural perspective: behavioral experiments in 15 small-scale societies. Behav. Brain Sci. 28, 795–855 (2005).

    Google Scholar 

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Exadaktylos, F., van den Bergh, J. Energy-related behaviour and rebound when rationality, self-interest and willpower are limited. Nat Energy 6, 1104–1113 (2021).

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