IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v152y2025ics0140988325007911.html

The effects of the energy star program on consumer electronics

Author

Listed:
  • Savage, Scott J.

Abstract

I design choice experiments to estimate consumer preferences for energy-efficient televisions. I use my estimates to simulate the prices for the important strategic players following the removal of the blue energy star label from their television models. My results show four main findings. The representative household is willing to pay an additional $71 for a television with the label, which is 25 % more efficient than conventional models. Willingness-to-pay exceeds the typical household's utility-bill savings. Prices and profits decline for all models without the label. And, the overall decrease in welfare is about 14 % of total firm profits in the baseline, with over half of this decrease matching foregone utility-bill savings. On the one hand, my findings suggest that the blue energy star label helps firms and consumers make informed decisions about the branding and quality of energy efficiency. On the other hand, sizeable benefits appear to arise from mechanisms that are independent from cost savings, such as the green premium.

Suggested Citation

  • Savage, Scott J., 2025. "The effects of the energy star program on consumer electronics," Energy Economics, Elsevier, vol. 152(C).
  • Handle: RePEc:eee:eneeco:v:152:y:2025:i:c:s0140988325007911
    DOI: 10.1016/j.eneco.2025.108964
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988325007911
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2025.108964?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Ying Fan & Chenyu Yang, 2020. "Competition, Product Proliferation, and Welfare: A Study of the US Smartphone Market," American Economic Journal: Microeconomics, American Economic Association, vol. 12(2), pages 99-134, May.
    2. Thomas C. Kinnaman, 2006. "Policy Watch: Examining the Justification for Residential Recycling," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 219-232, Fall.
    3. Savage, Scott J. & Waldman, Donald M., 2015. "Privacy tradeoffs in smartphone applications," Economics Letters, Elsevier, vol. 137(C), pages 171-175.
    4. Stefanie Stantcheva, 2023. "How to Run Surveys: A Guide to Creating Your Own Identifying Variation and Revealing the Invisible," Annual Review of Economics, Annual Reviews, vol. 15(1), pages 205-234, September.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, Enero-Abr.
    6. Gautam Gowrisankaran & Marc Rysman, 2012. "Dynamics of Consumer Demand for New Durable Goods," Journal of Political Economy, University of Chicago Press, vol. 120(6), pages 1173-1219.
    7. Yue Liu & Rong Luo, 2023. "Network Effects and Multinetwork Sellers’ Dynamic Pricing in the U.S. Smartphone Market," Management Science, INFORMS, vol. 69(6), pages 3297-3318, June.
    8. Duch-Brown, Néstor & Grzybowski, Lukasz & Romahn, André & Verboven, Frank, 2017. "The impact of online sales on consumers and firms. Evidence from consumer electronics," International Journal of Industrial Organization, Elsevier, vol. 52(C), pages 30-62.
    9. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    10. Grajek, Michal, 2010. "Estimating network effects and compatibility: Evidence from the Polish mobile market," Information Economics and Policy, Elsevier, vol. 22(2), pages 130-143, May.
    11. Assele, Samson Yaekob & Meulders, Michel & Vandebroek, Martina, 2023. "Sample size selection for discrete choice experiments using design features," Journal of choice modelling, Elsevier, vol. 49(C).
    12. Grzybowski, Lukasz & Liang, Julienne, 2015. "Estimating demand for fixed-mobile bundles and switching costs between tariffs," Information Economics and Policy, Elsevier, vol. 33(C), pages 1-10.
    13. Abbott, Andrew & Nandeibam, Shasikanta & O'Shea, Lucy, 2013. "Recycling: Social norms and warm-glow revisited," Ecological Economics, Elsevier, vol. 90(C), pages 10-18.
    14. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    15. Greg M. Allenby & Jeff Brazell & John R. Howell & Peter E. Rossi, 2014. "Valuation of Patented Product Features," Journal of Law and Economics, University of Chicago Press, vol. 57(3), pages 629-663.
    16. Hiller, R. Scott & Savage, Scott J. & Waldman, Donald M., 2018. "Using aggregate market data to estimate patent value: An application to United States smartphones 2010 to 2015," International Journal of Industrial Organization, Elsevier, vol. 60(C), pages 1-31.
    17. Andreoni, James, 1990. "Impure Altruism and Donations to Public Goods: A Theory of Warm-Glow Giving?," Economic Journal, Royal Economic Society, vol. 100(401), pages 464-477, June.
    18. Ward, David O. & Clark, Christopher D. & Jensen, Kimberly L. & Yen, Steven T. & Russell, Clifford S., 2011. "Factors influencing willingness-to-pay for the ENERGY STAR® label," Energy Policy, Elsevier, vol. 39(3), pages 1450-1458, March.
    19. Céline Bonnet & Pierre Dubois, 2010. "Inference on vertical contracts between manufacturers and retailers allowing for nonlinear pricing and resale price maintenance," RAND Journal of Economics, RAND Corporation, vol. 41(1), pages 139-164, March.
    20. Berglund, Christer, 2006. "The assessment of households' recycling costs: The role of personal motives," Ecological Economics, Elsevier, vol. 56(4), pages 560-569, April.
    21. Sébastien Houde, 2018. "How consumers respond to product certification and the value of energy information," RAND Journal of Economics, RAND Corporation, vol. 49(2), pages 453-477, June.
    22. R. Scott Hiller & Scott J. Savage, 2021. "Tariff Pass‐Through and Welfare in the Tablet Computer Market," Journal of Industrial Economics, Wiley Blackwell, vol. 69(2), pages 369-409, June.
    23. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    24. Stefan Weiergraeber, 2022. "Network Effects And Switching Costs In The U.S. Wireless Industry," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(2), pages 601-630, May.
    25. Richard G. Newell & Juha Siikamäki, 2014. "Nudging Energy Efficiency Behavior: The Role of Information Labels," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 1(4), pages 555-598.
    26. Ohler, Adrienne M. & Loomis, David G. & Ilves, Kadi, 2020. "A study of electricity savings from energy star appliances using household survey data," Energy Policy, Elsevier, vol. 144(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olsthoorn, Mark & Schleich, Joachim & Guetlein, Marie-Charlotte & Durand, Antoine & Faure, Corinne, 2023. "Beyond energy efficiency: Do consumers care about life-cycle properties of household appliances?," Energy Policy, Elsevier, vol. 174(C).
    2. Zhang, Xiao-Bing & Fei, Yinxin & Duan, Hongbo & Soytas, Ugur & Crifo, Patricia & Sterner, Thomas, 2025. "Implicit discount rates and energy efficiency gap in air conditioning: Evidence from the Chinese market," Resource and Energy Economics, Elsevier, vol. 82(C).
    3. Hiller, R. Scott & Savage, Scott J. & Waldman, Donald M., 2018. "Using aggregate market data to estimate patent value: An application to United States smartphones 2010 to 2015," International Journal of Industrial Organization, Elsevier, vol. 60(C), pages 1-31.
    4. Schleich, Joachim & Faure, Corinne & Guetlein, Marie-Charlotte & Tu, Gengyang, 2020. "Conveyance, envy, and homeowner choice of appliances," Energy Economics, Elsevier, vol. 89(C).
    5. Leard, Benjamin, 2018. "Consumer inattention and the demand for vehicle fuel cost savings," Journal of choice modelling, Elsevier, vol. 29(C), pages 1-16.
    6. Cecere, Grazia & Mancinelli, Susanna & Mazzanti, Massimiliano, 2014. "Waste prevention and social preferences: the role of intrinsic and extrinsic motivations," Ecological Economics, Elsevier, vol. 107(C), pages 163-176.
    7. Lukasz Grzybowski & Ambre Nicolle, 2021. "Estimating Consumer Inertia in Repeated Choices of Smartphones," Journal of Industrial Economics, Wiley Blackwell, vol. 69(1), pages 33-82, March.
    8. Yue Liu & Rong Luo, 2023. "Network Effects and Multinetwork Sellers’ Dynamic Pricing in the U.S. Smartphone Market," Management Science, INFORMS, vol. 69(6), pages 3297-3318, June.
    9. Brent, Daniel A. & Ward, Michael B., 2018. "Energy efficiency and financial literacy," Journal of Environmental Economics and Management, Elsevier, vol. 90(C), pages 181-216.
    10. Faure, Corinne & Guetlein, Marie-Charlotte & Schleich, Joachim, 2021. "Effects of rescaling the EU energy label on household preferences for top-rated appliances," Energy Policy, Elsevier, vol. 156(C).
    11. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    12. Laura Mørch Andersen, 2013. "Obtaining reliable Likelihood Ratio tests from simulated likelihood functions," IFRO Working Paper 2013/1, University of Copenhagen, Department of Food and Resource Economics.
    13. R. Scott Hiller & Scott J. Savage, 2021. "Tariff Pass‐Through and Welfare in the Tablet Computer Market," Journal of Industrial Economics, Wiley Blackwell, vol. 69(2), pages 369-409, June.
    14. Gutsche, Gunnar & Ziegler, Andreas, 2019. "Which private investors are willing to pay for sustainable investments? Empirical evidence from stated choice experiments," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 193-214.
    15. Dalia Streimikiene & Tomas Balezentis & Ilona Alisauskaite-Seskiene & Gintare Stankuniene & Zaneta Simanaviciene, 2019. "A Review of Willingness to Pay Studies for Climate Change Mitigation in the Energy Sector," Energies, MDPI, vol. 12(8), pages 1-38, April.
    16. Kipperberg, Gorm & Bond, Craig A. & Hoag, Dana L., 2008. "An Application of Mixed Logit Estimation in the Analysis of Producers’ Stated Preferences," Working Papers 108719, Colorado State University, Department of Agricultural and Resource Economics.
    17. Gunnar Gutsche & Andreas Ziegler, 2016. "Are private investors willing to pay for sustainable investments? A stated choice experiment," MAGKS Papers on Economics 201640, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Charu Grover & Sangeeta Bansal & Adan L. Martinez-Cruz, "undated". "Influence of Social Network Effect and Incentive on Choice of Star Labeled Cars in India: A Latent Class Approach based on Choice Experiment," Centre for International Trade and Development, Jawaharlal Nehru University, New Delhi Discussion Papers 18-05, Centre for International Trade and Development, Jawaharlal Nehru University, New Delhi, India.
    19. D'Amato, Alessio & Mancinelli, Susanna & Zoli, Mariangela, 2016. "Complementarity vs substitutability in waste management behaviors," Ecological Economics, Elsevier, vol. 123(C), pages 84-94.
    20. Bae, Jeong Hwan & Rishi, Meenakshi, 2018. "Increasing consumer participation rates for green pricing programs: A choice experiment for South Korea," Energy Economics, Elsevier, vol. 74(C), pages 490-502.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L68 - Industrial Organization - - Industry Studies: Manufacturing - - - Appliances; Furniture; Other Consumer Durables

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:152:y:2025:i:c:s0140988325007911. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.