Implications of Behavioral Economics for the Costs and Benefits of Fuel Economy Standards

  • David L. GreeneEmail author
Transportation (D Chen, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Transportation


Purpose of Review

This review focuses on recent developments in the application of behavioral economics to the evaluation of energy efficiency and greenhouse gas regulations. Transportation is the largest source of CO2 emissions from energy use in the US economy and a major and growing source worldwide. Regulating the efficiency of motor vehicles has been a core component of energy policy in the USA, the EU, China, Japan, Canada, and many other nations. Recent findings concerning consumers’ actual decision-making about energy efficiency indicate that the premises of the rational economic model are not appropriate for evaluating energy-efficiency standards.

Recent Findings

Progress in behavioral psychology and economics has shown that loss aversion, the principle that faced with a risky choice human beings tend to weigh potential losses about twice as heavily as gains, is strongly affected by framing. Simple, risky choices in which there is a status quo option generally provoke loss-averse responses. Recent analyses show that the choice to buy or not buy energy-efficiency technologies induces loss aversion and can result in systematic underinvestment in energy efficiency. Empirical investigation of consumers’ fuel economy decision-making contradicts the rational economic model and is consistent with loss aversion. However, recent economic evaluations of fuel economy and greenhouse gas regulations are explicitly or implicitly premised on rational economic behavior.


Insights developed by behavioral psychologists and behavioral economists about the decision-making of real consumers provide a coherent explanation that fundamentally alters the way fuel economy regulations should be evaluated. If consumers are assumed to make decisions according to the rational economic model and markets are reasonably efficient, regulations cannot produce large private fuel savings. The behavioral economic model explains not only why such savings do exist but why consumers strongly support fuel economy regulations. The private savings from fuel economy regulations can be large relative to the social benefits of fuel economy and greenhouse gas regulations.


Fuel economy standards Loss aversion Energy-efficiency gap Greenhouse gas regulations Behavioral economics Cost/benefit analysis 


Compliance with Ethical Standards

Conflict of Interest

David L. Greene declares that he has no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Global Energy Assessment (GEA). Global Energy Assessment: Toward a Sustainable Future. Laxenburg, Austria: Cambridge University press, Cambridge, UK and New York, NY, USA and the International Institute for Applied Systems Analysis; 2012.Google Scholar
  2. 2.
    Bureau of Transportation Statistics (BTS), 2018. Transportation Statistics Annual Report 2018, p. 7–17, U.S. Department of Transportation, Washington, DC, accessed on 6/3/2019 at
  3. 3.
    U.S. Department of Transportation, Federal Highway Administration (USDOT/FHWA). Highway Statistics 2017, and earlier editions, 2017. Available at
  4. 4.
    Greene DL, Welch JG. Impacts of fuel economy improvements on the distribution of income in the US. Energy Policy. 2018;122:528–41.CrossRefGoogle Scholar
  5. 5.
    Consumer Reports Survey Group (CRSG). 2017 and 2018. “2018 Automotive Fuel Economy Survey Report” and “2017 Automotive Fuel Economy Survey Report”, provided by Shannon Baker-Brandstetter, Consumers Union, 2018.Google Scholar
  6. 6.
    National Research Council (NRC). Cost, effectiveness, and deployment of fuel economy technologies for light-duty vehicles. Washington, DC: National Academies Press; 2015.Google Scholar
  7. 7.
    Klier T, Linn J. The effect of vehicle fuel economy standards on technology adoption. J Public Econ. 2016;133(C):41–63.CrossRefGoogle Scholar
  8. 8.
    Jacobsen M, Benthem V. Vehicle scrappage and gasoline policy. Am Econ Rev. 2015;105(3):1312–38.CrossRefGoogle Scholar
  9. 9.
    Jacobsen MR. Evaluating US fuel economy standards in a model with producer and household heterogeneity. Am Econ J Econ Pol. 2013;5(2):148–87.CrossRefGoogle Scholar
  10. 10.
    Goulder LH, Jacobsen MR, van Benthem AA. Unintended consequences from nested state & federal regulations: the case of the Pavley greenhouse-gas-per mile limits. J Environ Econ Manag. 2012;63(2):187–207.CrossRefGoogle Scholar
  11. 11.
    Klier T, Linn J. New-vehicle characteristics and the cost of the corporate average fuel economy standard. RAND J Econ. 2012;43(1):186–213.CrossRefGoogle Scholar
  12. 12.
    Fischer C, Harrington W, Parry I. Do market failures justify tightening corporate average fuel economy (CAFEE) standards? Energy J. 2007;28(4):1–30.CrossRefGoogle Scholar
  13. 13.
    Austin D, Dinan T. Clearing the air: the costs and consequences of higher CAFE standards and increased gasoline taxes. J Environ Econ Manag. 2005;50(3):562–82.zbMATHCrossRefGoogle Scholar
  14. 14.
    West SE, Williams RC. The cost of reducing gasoline consumption. Am Econ Rev. 2005;95(2):294–9.CrossRefGoogle Scholar
  15. 15.
    Kleit AN. Impacts of long-range increases in the fuel economy (CAFE) standard. Econ Inq. 2004;42(2):279–94.CrossRefGoogle Scholar
  16. 16.
    U.S. Environmental Protection Agency (USEPA). Regulatory impact analysis: final rulemaking for 2017–2025 light-duty vehicle greenhouse gas emission standards and corporate average fuel economy standards. EPA-420-R-12-016, 2012. Available at .
  17. 17.
    National Research Council (NRC). Automotive fuel economy: how far can we go? Report of the committee on automobile and light truck fuel economy. Washington, DC: National Academy Press; 1992.Google Scholar
  18. 18.
    National Research Council (NRC). Effectiveness and impact of corporate average fuel economy (CAFE) standards, Report of the Committee. Washington: National Academy Press; 2002. p. 2002.Google Scholar
  19. 19.
    National Research Council (NRC). Assessment of fuel economy technologies for light-duty vehicles, report of the committee on the assessment of technologies for improving light-duty vehicle fuel economy. Washington, D.C.: National Academies Press; 2011.Google Scholar
  20. 20.
    Gerarden TD, Newell RG, Stavins RN, Stowe RC. An assessment of the energy-efficiency gap and its implications for climate-change policy, working paper 20905. Cambridge, MA: National Bureau of economic research; 2015.Google Scholar
  21. 21.
    Gillingham K, Palmer K. Bridging the energy efficiency gap: policy insights from economic theory and empirical evidence. Rev Environ Econ Policy. 2014;8(1):18–38.CrossRefGoogle Scholar
  22. 22.
    Heutel G. Prospect theory and energy efficiency, NBER working paper 23692. Cambridge, MA: National Bureau of economic research; 2017.CrossRefGoogle Scholar
  23. 23.
    Häckel B, Pfosser S, Tränkler T. Explaining the energy efficiency gap – expected utility theory versus cumulative prospect theory. Energy Policy. 2017;111:414–26.CrossRefGoogle Scholar
  24. 24.
    Greene DL. Uncertainty, loss aversion and markets for energy efficiency. Energy Econ. 2011;33:608–16.CrossRefGoogle Scholar
  25. 25.
    Starmer C. Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk. J Econ Lit. 2000;38:332–82.CrossRefGoogle Scholar
  26. 26.
    Dellavigna S. Psychology and economics: evidence from the field. J Econ Lit. 2009;47(2):315–72.CrossRefGoogle Scholar
  27. 27.
    Kahneman D. Thinking fast and slow. New York: Farrar, Straus and Giroux; 2011.Google Scholar
  28. 28.
    Thaler RH. Misbehaving: the making of behavioral economics. New York: W.W. Norton & Company; 2015.Google Scholar
  29. 29.
    Novemsky N, Kahneman D. The boundaries of loss aversion. J Market Res. 2005;XLII:119–28.CrossRefGoogle Scholar
  30. 30.
    Ert E, Erev I. On the descriptive value of loss aversion in decisions under risk: six clarifications. Judgm Decis Mak. 2013;8(3):214–35.Google Scholar
  31. 31.
    National Academies. Daniel Kahneman’s thinking, fast and slow wins best book award from academies, 2012. Accessed on 10/17/2018 at
  32. 32.
    Tom SM, Fox CR, Trepel C, Poldrack RA. The neural basis of decision-making under risk. Science. 2007;315:515–8.CrossRefGoogle Scholar
  33. 33.
    Kahneman D, Tversky A. Prospect theory: an analysis of decision making under risk. Econometrica. 1979;47:263–91.MathSciNetzbMATHGoogle Scholar
  34. 34.
    Sallee JM. Rational inattention and energy efficiency. J Law Econ. 2014;57(3):781–820.CrossRefGoogle Scholar
  35. 35•.
    Greene DL, Khattak AJ, Liu J, Wang X, Hopson JL, Goeltz R. What is the evidence concerning the gap between on-road and Environmental Protection Agency fuel economy ratings? Transport Policy. 2017;53:146–60. This paper summarizes evidence from four U.S. nationwide random sample surveys that framed fuel economy choices as risky bets. The results were as predicted by the behavioral economic principle of loss aversion and were also consistent with the stated beliefs of automobile manufacturers.CrossRefGoogle Scholar
  36. 36.
    Hamilton JD. Understanding crude oil prices. Energy J. 2009;30(2):179–206.CrossRefGoogle Scholar
  37. 37.
    Energy Information Administration (EIA). Gasoline explained: gasoline price fluctuations, 2018. Available at https://wwweiagov/energyexplained/indexphp?page=gasoline_fluctuations as of November 14, 2018.
  38. 38.
    Lin Z, Greene DL. Predicting individual on-road fuel economy using simple consumer and vehicle attributes, SAE Technical Paper Series No. 11SDP-0014. Warrendale, PA: Society of Automotive Engineers; 2011.Google Scholar
  39. 39.
    Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science. 1981;211(4481):453–8.MathSciNetzbMATHCrossRefGoogle Scholar
  40. 40.
    Gal D, Rucker DD. The loss of loss aversion: will it loom larger than its gain. J Consum Psychol. 2018;28(3):497–516.CrossRefGoogle Scholar
  41. 41.
    Erev I, Ert E, Plonsky O, Cohen D, Cohen O. From anomalies to forecasts: toward a descriptive model of decisions under risk, under ambiguity, and from experience. Psychol Rev. 2017;124(4):369–409.CrossRefGoogle Scholar
  42. 42.
    Walsworth J. Average age of vehicles on road hits 11.3 years, Automotive News, 2016. 11/22/16 accessed on 8/29/18 at
  43. 43.
    Statista. Average length of vehicle ownership in the United States in 2006 and 2016, by vehicle type (in months). 2018, accessed on 8/29/18 at
  44. 44•.
    Turrentine TS, Kurani KS. Car buyers and fuel economy? Energy Policy. 2007;35:1213–23. This descriptive investigation of the car purchase decisions of households in California found that the rational economic model was not employed by any household when making decisions about fuel economy. Instead, consumers’ fuel economy decisions were overwhelmingly intuitive or based on simple rules.CrossRefGoogle Scholar
  45. 45.
    Dharshing S, Hille SL. The energy paradox revisted: analyzing the role of individual differences and framing effects in information perception. J Consum Policy. 2017;40:485–508.CrossRefGoogle Scholar
  46. 46.
    Leard B. Consumer inattention and the demand for vehicle fuel cost savings. J Choice Model. 2018;29:1–16.CrossRefGoogle Scholar
  47. 47.
    Greene DL, Evans DH, Hiestand J. Survey evidence on the willingness of U.S. consumers to pay for automotive fuel economy. Energy Policy. 2013;61:1539–50.CrossRefGoogle Scholar
  48. 48•.
    Greene DL, DeCicco JM. Engineering-economic analyses of automotive fuel economy potential in the United States. Annu Rev Energ Environ. 2000;25:477–536. This paper finds that for consumers’ choices among energy efficiency options, the important aspects of cumulative prospect theory are loss aversion and reference dependence.CrossRefGoogle Scholar
  49. 49.
    Greene DL. How consumers value fuel economy: a literature review, EPA-420-R-10-008, U.S. Environmental Protection Agency, 2010.Google Scholar
  50. 50.
    U.S. Environmental Protection Agency (USEPA). Consumer willingness to pay for vehicle attributes: what is the current state of knowledge? Ann Arbor, MI: EPA-420-R-18-016, Assessment and Standards Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agency; 2018.Google Scholar
  51. 51.
    Greene DL, Hossain A, Hofmann J, Helfand G, Beach R. Consumer willingness to pay for vehicle attributes: what do we know? Transport Res A. 2018;118:258–79.CrossRefGoogle Scholar
  52. 52•.
    Helfand G, Wolverton A. Evaluating consumer response to fuel economy: a review of the literature. Int Rev Environ Resour Econ. 2011;5(2):103–46. The experiments analyzed in this paper show that loss aversion can explain consumers’ choices among energy efficiency options and that the private savings from cost-effective increases in energy efficiency can exceed the value of reduced externalities.CrossRefGoogle Scholar
  53. 53.
    Allcott H, Wozny N. Gasoline prices, fuel economy and the energy paradox. Rev Econ Stat. 2014;XCVI(5):779–95.CrossRefGoogle Scholar
  54. 54.
    Anderson ST, Kellogg R, Sallee JM. What do consumers believe about future gasoline prices? J Environ Econ Manag. 2013;66(3):383–403.CrossRefGoogle Scholar
  55. 55.
    Sallee JM, West SE, Fan W. Do consumers recognize the value of fuel economy? Evidence from used car prices and gasoline price fluctuations. J Public Econ. 2016;135:61–73.CrossRefGoogle Scholar
  56. 56.
    U.S. Department of Transportation, National Highway Traffic Safety Administration (USDOT NHTSA). Vehicle survivability and travel mileage schedules, Technical Report DOT HS 809 952, National Center for Statistics and Analysis, U.S. Department of Transportation, 2006.Google Scholar
  57. 57.
    Busse MR, Knittel CR, Zettelmeyer F. Are consumers myopic? Evidence from new and used car purchases. Am Econ Rev. 2013;103(1):220–56.CrossRefGoogle Scholar
  58. 58.
    Bento AM, Roth K, Zuo Y. Vehicle lifetime trends and scrappage behavior in the U.S. Used Car Market, UCLA. 2016, available at file:///C:/Users/HP%20USER/Desktop/Reviews/BentoetalFuelEconScrappage_18Jan2016.pdf.Google Scholar
  59. 59.
    Leard B, Linn J, Zhou YC. How much do consumers value fuel economy and performance?, RFF report. Washington, D.C., June: Resources for the Future; 2017.Google Scholar
  60. 60.
    Pagerit S, Sharer P, Rousseau A. Fuel economy sensitivity to vehicle mass for advanced vehicle powertrains. Warrendale, PA: SAE-2006-01-0665, Society of Automotive Engineers; 2006.Google Scholar
  61. 61.
    Knittel CR. Automobiles on steroids: product attribute trade-offs and technological progress in the automobile sector. Am Econ Rev. 2012;101:3368–99.CrossRefGoogle Scholar
  62. 62.
    Larrick RP, Soll JB. The MPG illusion. Science. 2008;320:1593–4.CrossRefGoogle Scholar
  63. 63.
    Schoemaker PJH. The expected utility model: its variants, purposes, evidence and limitations. J Econ Literature. 1982;XX:529–63.Google Scholar
  64. 64.
    Katsikopolous KV. Bounded rationality: the two cultures. J Econ Methodol. 2014;21(4):361–74.CrossRefGoogle Scholar
  65. 65.
    Bernartzi S, Thaler R. Myopic loss aversion and the equity premium puzzle. Q J Econ. 1995;110:73–92.zbMATHCrossRefGoogle Scholar
  66. 66.
    Rabin M. Risk aversion and expected utility theory: a calibration theorem. Econometrica. 2003;68:1281–92.CrossRefGoogle Scholar
  67. 67.
    Varian H. Microeconomic analysis. New York: W.W. Norton & Co., Inc.; 1992.Google Scholar
  68. 68.
    Wali B, Greene DL, Khattak AJ, Liu J. Analyzing within garage fuel economy gaps to support vehicle purchasing decisions–a copula-based modeling & forecasting approach. Transp Res D. 2018;63:186–208.CrossRefGoogle Scholar
  69. 69.
    Federal Reserve Bank of St. Louis (FRED). Finance rate on consumer installment loans at commercial banks, New Autos 48 and 60 month loans. 2019, accessed on 6/7/2019 at and
  70. 70.
    U.S. Department of Transportation, Federal Highway Administration (USDOT/FHWA). National Household Travel Survey 2017, 2018. Available at
  71. 71.
    Davis SC, Williams SE, Boundy RG. Transportation energy data book: edition 36. ORNL/TM-2017/513-R2. Oak Ridge, TN: Oak Ridge National Laboratory; 2018. Available at as of August 2018Google Scholar
  72. 72.
    U.S. Environmental Protection Agency (USEPA). Light-duty automotive technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 Through 2017, EPA-420-R-18-001, Ch. 5, section B, pp. 57-59, EPA-420-R-18-001, 2017.Google Scholar
  73. 73.
    U.S. Department of Transportation, National Highway Traffic Safety Administration, U.S. Environmental Protection Agency (USDOT, USEPA), 2018. Notice of proposed rulemaking (NPRM): The safer affordable fuel-efficient (SAFE) vehicles rule for model year 2021–2026 passenger cars and light trucks, 2018b. Available at
  74. 74.
    U.S. Department of Transportation, National Highway Traffic Safety Administration (USDOT, NHTSA). Preliminary regulatory impact analysis (PRIA): The safer affordable fuel-efficient (SAFE) vehicles rule for model year 2021–2026 passenger cars and light trucks, 2018a. Available at
  75. 75.
    Tsvetanov T, Segerson K. Re-evaluating the role of energy efficiency standards: a behavioral economics approach. J Environ Econ Manag. 2013;66:347–63.CrossRefGoogle Scholar
  76. 76.
    Bernheim BD. The good the bad and the ugly: a unified approach to behavioral welfare economics. J Benefit-cost Anal. 2016;7(1):12–68.CrossRefGoogle Scholar
  77. 77.
    Kahneman D, Sugden R. Experienced utility as a standard of policy evaluation. Environ Resour Econ. 2005;31:161–81.CrossRefGoogle Scholar
  78. 78.
    Allcott H, Mullainathan S, Taubinsky D. Energy policy with externalities and internalities. J Public Econ. 2014;112:72–88.CrossRefGoogle Scholar
  79. 79.
    Greene DL, Patterson PD, Singh M, Li J. Feebates, rebates and gas-guzzler taxes: a study of incentives for increased fuel economy. Energy Policy. 2005;33(6):721–827.CrossRefGoogle Scholar
  80. 80.
    Liu C, Greene DL, Bunch DS. Vehicle manufacturer technology adoption and pricing strategies under fuel economy/emissions standards and Feebates. Energy J. 2014;35(3):71–89.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Howard H. Baker, Jr. Center for Public PolicyThe University of TennesseeKnoxvilleUSA

Personalised recommendations