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Experience curves for power plant emission control technologies

Author

Listed:
  • Edward S. Rubin
  • Sonia Yeh
  • David A. Hounshell
  • Margaret R. Taylor

Abstract

This paper examines past experience in controlling emissions of sulphur dioxide (SO2) and nitrogen oxides (NOx) from coal-fired electric power plants. In particular, we focus on US and worldwide experience with two major environmental control technologies: flue gas desulphurisation (FGD) systems for SO2 control and selective catalytic reduction (SCR) systems for NOx control. We quantitatively characterise historical trends in the deployment and costs of these technologies over the past 30 years, and use these data to develop quantitative "experience curves" to characterise the rates of cost reduction as a function of cumulative installed capacity of each technology. We explore the key factors responsible for the observed trends, especially the development of regulatory policies for SO2 and NOx control and their implications for environmental control technology innovation. We further discuss some of the key technical innovations that have contributed to cost reductions over time. Finally, we discuss the relevance of these findings to other environmental issues of current interest, especially the outlook for technological progress in carbon capture and sequestration technologies applicable to fossil fuelled electric power plants.

Suggested Citation

  • Edward S. Rubin & Sonia Yeh & David A. Hounshell & Margaret R. Taylor, 2004. "Experience curves for power plant emission control technologies," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 52-69.
    • Handle: RePEc:ids:ijetpo:v:2:y:2004:i:1/2:p:52-69
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    Citations

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    Cited by:

    1. Neij, Lena, 2008. "Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments," Energy Policy, Elsevier, vol. 36(6), pages 2200-2211, June.
    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. Upstill, Garrett & Hall, Peter, 2018. "Estimating the learning rate of a technology with multiple variants: The case of carbon storage," Energy Policy, Elsevier, vol. 121(C), pages 498-505.
    4. Sakti, Apurba & Azevedo, Inês M.L. & Fuchs, Erica R.H. & Michalek, Jeremy J. & Gallagher, Kevin G. & Whitacre, Jay F., 2017. "Consistency and robustness of forecasting for emerging technologies: The case of Li-ion batteries for electric vehicles," Energy Policy, Elsevier, vol. 106(C), pages 415-426.
    5. Liu, Jiangfeng & Zhang, Qi & Li, Hailong & Chen, Siyuan & Teng, Fei, 2022. "Investment decision on carbon capture and utilization (CCU) technologies—A real option model based on technology learning effect," Applied Energy, Elsevier, vol. 322(C).
    6. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.

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