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The dynamics of incremental costs of efficient television display technologies

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  • 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
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    References listed on IDEAS

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    1. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    2. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    3. 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.
    4. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    5. 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.
    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.
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    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.

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