IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v90y2015ipbp562-574.html
   My bibliography  Save this article

The dynamics of incremental costs of efficient television display technologies

Author

Listed:
  • Desroches, Louis-Benoit
  • Ganeshalingam, Mohan

Abstract

We study the evolution of the incremental cost and price of efficiency for televisions in the U.S. market. We focus on televisions due to their rapid technological evolution and large number of annual shipments, such that costs and prices evolve on short timescales as compared to other consumer durable goods. Using the experience curve approach, we compare manufacturing costs and selling prices of two liquid crystal display (LCD) technologies. We find a mean experience rate of 27% for less efficient cold cathode fluorescent lamp LCD televisions and 14% for more efficient light emitting diode LCD televisions, using price data. This corresponds to an annual decline of approximately 17% per year in price for both television types. Our results also suggest that the incremental cost or price of efficiency, holding other major features constant, declines much more rapidly than the baseline cost or price. We find that the incremental cost or price declines at roughly 50% per year. The fitted parameters do depend on the specific technology modeled, as well as on whether cost or price data are used. Our results for LCD televisions are qualitatively similar to other display technologies, even very mature ones, suggesting that the cost and price decline extends many years after a technology is considered mature. We also analyze the selling prices of ENERGY STAR® and non-ENERGY STAR televisions, which support our main findings. These results highlight the consumer benefits of efficient display technologies, and how the dynamics of incremental costs differ from baseline costs.

Suggested Citation

  • Desroches, Louis-Benoit & Ganeshalingam, Mohan, 2015. "The dynamics of incremental costs of efficient television display technologies," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 562-574.
  • Handle: RePEc:eee:tefoso:v:90:y:2015:i:pb:p:562-574
    DOI: 10.1016/j.techfore.2014.02.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162514000808
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2014.02.016?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 search for a different version of it.

    References listed on IDEAS

    as
    1. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    2. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    3. Dale, Larry & Antinori, Camille & McNeil, Michael & McMahon, James E. & Sydny Fujita, K., 2009. "Retrospective evaluation of appliance price trends," Energy Policy, Elsevier, vol. 37(2), pages 597-605, February.
    4. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    5. Desroches, Louis-Benoit & Garbesi, Karina & Kantner, Colleen & Van Buskirk, Robert & Yang, Hung-Chia, 2013. "Incorporating experience curves in appliance standards analysis," Energy Policy, Elsevier, vol. 52(C), pages 402-416.
    6. Bass, Frank M, 1980. "The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations," The Journal of Business, University of Chicago Press, vol. 53(3), pages 51-67, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Park, Won Young & Phadke, Amol A., 2017. "Adoption of energy-efficient televisions for expanded off-grid electricity service," Development Engineering, Elsevier, vol. 2(C), pages 107-113.

    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. Weiss, Martin & Patel, Martin K. & Junginger, Martin & Blok, Kornelis, 2010. "Analyzing price and efficiency dynamics of large appliances with the experience curve approach," Energy Policy, Elsevier, vol. 38(2), pages 770-783, February.
    2. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    3. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    4. Desroches, Louis-Benoit & Garbesi, Karina & Kantner, Colleen & Van Buskirk, Robert & Yang, Hung-Chia, 2013. "Incorporating experience curves in appliance standards analysis," Energy Policy, Elsevier, vol. 52(C), pages 402-416.
    5. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    6. Paul Lehmann & Patrik Söderholm, 2018. "Can Technology-Specific Deployment Policies Be Cost-Effective? The Case of Renewable Energy Support Schemes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(2), pages 475-505, October.
    7. Lecca, Patrizio & McGregor, Peter G. & Swales, Kim J. & Tamba, Marie, 2017. "The Importance of Learning for Achieving the UK's Targets for Offshore Wind," Ecological Economics, Elsevier, vol. 135(C), pages 259-268.
    8. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    9. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    10. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    11. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    12. Chakravorty, Ujjayant & Leach, Andrew & Moreaux, Michel, 2009. ""Twin Peaks" in Energy Prices: A Hotelling Model with Pollution Learning," Working Papers 2009-10, University of Alberta, Department of Economics.
    13. Lee, Shun-Chung & Shih, Li-Hsing, 2010. "Renewable energy policy evaluation using real option model -- The case of Taiwan," Energy Economics, Elsevier, vol. 32(Supplemen), pages 67-78, September.
    14. Criqui, P. & Mima, S. & Menanteau, P. & Kitous, A., 2015. "Mitigation strategies and energy technology learning: An assessment with the POLES model," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 119-136.
    15. Giovanni Dosi & Richard Nelson, 2013. "The Evolution of Technologies: An Assessment of the State-of-the-Art," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 3(1), pages 3-46, June.
    16. Bramoulle, Yann & Olson, Lars J., 2005. "Allocation of pollution abatement under learning by doing," Journal of Public Economics, Elsevier, vol. 89(9-10), pages 1935-1960, September.
    17. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    18. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.
    19. Béla Nagy & J Doyne Farmer & Quan M Bui & Jessika E Trancik, 2013. "Statistical Basis for Predicting Technological Progress," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    20. Kumbaroglu, Gürkan & Madlener, Reinhard & Demirel, Mustafa, 2008. "A real options evaluation model for the diffusion prospects of new renewable power generation technologies," Energy Economics, Elsevier, vol. 30(4), pages 1882-1908, July.

    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:tefoso:v:90:y:2015:i:pb:p:562-574. 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.sciencedirect.com/science/journal/00401625 .

    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.