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Labeling energy cost on light bulbs lowers implicit discount rates


  • Min, Jihoon
  • Azevedo, Inês L.
  • Michalek, Jeremy
  • de Bruin, Wändi Bruine


Lighting accounts for nearly 20% of overall U.S. electricity consumption and 18% of U.S. residential electricity consumption. A transition to alternative energy-efficient technologies could reduce this energy consumption considerably. To quantify the influence of factors that drive consumer choices for light bulbs, we conducted a choice-based conjoint field experiment with 183 participants. We estimated discrete choice models from the data, and found that politically liberal consumers have a stronger preference for compact fluorescent lighting technology and for low energy consumption. Greater willingness to pay for lower energy consumption and longer life was observed in conditions where estimated operating cost information was provided. Providing estimated annual cost information to consumers reduced their implicit discount rate by a factor of five, lowering barriers to adoption of energy efficient alternatives with higher up-front costs; however, even with cost information provided, consumers continued to use implicit discount rates of around 100%, which is larger than that experienced for other energy technologies.

Suggested Citation

  • Min, Jihoon & Azevedo, Inês L. & Michalek, Jeremy & de Bruin, Wändi Bruine, 2014. "Labeling energy cost on light bulbs lowers implicit discount rates," Ecological Economics, Elsevier, vol. 97(C), pages 42-50.
  • Handle: RePEc:eee:ecolec:v:97:y:2014:i:c:p:42-50
    DOI: 10.1016/j.ecolecon.2013.10.015

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    References listed on IDEAS

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    Cited by:

    1. Schleich, Joachim & Gassmann, Xavier & Faure, Corinne & Meissner, Thomas, 2016. "Making the implicit explicit: A look inside the implicit discount rate," Energy Policy, Elsevier, vol. 97(C), pages 321-331.
    2. Haaf, C. Grace & Morrow, W. Ross & Azevedo, Inês M.L. & Feit, Elea McDonnell & Michalek, Jeremy J., 2016. "Forecasting light-duty vehicle demand using alternative-specific constants for endogeneity correction versus calibration," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 182-210.
    3. repec:eee:rensus:v:78:y:2017:i:c:p:731-742 is not listed on IDEAS
    4. repec:kap:jcopol:v:40:y:2017:i:4:d:10.1007_s10603-017-9361-0 is not listed on IDEAS
    5. repec:eee:enepol:v:110:y:2017:i:c:p:375-385 is not listed on IDEAS
    6. Farzan, Farbod & Jafari, Mohsen A. & Gong, Jie & Farzan, Farnaz & Stryker, Andrew, 2015. "A multi-scale adaptive model of residential energy demand," Applied Energy, Elsevier, vol. 150(C), pages 258-273.
    7. Qiu, Yueming & Colson, Gregory & Grebitus, Carola, 2014. "Risk preferences and purchase of energy-efficient technologies in the residential sector," Ecological Economics, Elsevier, vol. 107(C), pages 216-229.
    8. repec:eee:ecolec:v:144:y:2018:i:c:p:112-123 is not listed on IDEAS
    9. Julia Blasch & Nilkanth Kumar & Massimo Filippini, 2016. "Boundedly rational consumers, energy and investment literacy, and the display of information on household appliances," CER-ETH Economics working paper series 16/249, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    10. repec:eee:enepol:v:111:y:2017:i:c:p:414-426 is not listed on IDEAS
    11. Oleson, Kirsten L.L. & Barnes, Michele & Brander, Luke M. & Oliver, Thomas A. & van Beek, Ingrid & Zafindrasilivonona, Bienvenue & van Beukering, Pieter, 2015. "Cultural bequest values for ecosystem service flows among indigenous fishers: A discrete choice experiment validated with mixed methods," Ecological Economics, Elsevier, vol. 114(C), pages 104-116.
    12. Haq, Gary & Weiss, Martin, 2016. "CO2 labelling of passenger cars in Europe: Status, challenges, and future prospects," Energy Policy, Elsevier, vol. 95(C), pages 324-335.


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