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Non-constant learning rates in retrospective experience curve analyses and their correlation to deployment programs

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  • Wei, Max
  • Smith, Sarah Josephine
  • Sohn, Michael D.

Abstract

A key challenge for policy-makers is estimating future technology costs and the rate of cost reduction versus production volume. A related critical question is what role state and federal governments should have in advancing energy efficient and renewable energy technologies. We derive learning rates for six technologies (electronic ballasts, magnetic ballasts, compact fluorescent lighting, general service fluorescent lighting, stationary fuel cells, and the installed price of residential solar PV) and provide an overview and timeline of historical deployment programs, such as state and federal standards and incentive programs, for each technology. Piecewise linear regimes are observed in a range of technology experience curves, and deployment programs are found to be strongly correlated to an increase in learning rate across multiple technologies. A downward bend in the experience curve is found in 5 out of the 6 energy-related technologies presented here. In each of the five downward-bending experience curves, we believe that an increase in the learning rate can be linked to deployment programs to some degree. This work sheds light on the endogenous versus exogenous contributions to technological innovation and can inform future policy investment direction and can shed light on market transformation and technology learning behavior.

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  • Wei, Max & Smith, Sarah Josephine & Sohn, Michael D., 2017. "Non-constant learning rates in retrospective experience curve analyses and their correlation to deployment programs," Energy Policy, Elsevier, vol. 107(C), pages 356-369.
    • Handle: RePEc:eee:enepol:v:107:y:2017:i:c:p:356-369
    DOI: 10.1016/j.enpol.2017.04.035
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    10. Yu Sang Chang & Dosoung Choi & Hann Earl Kim, 2017. "Dynamic Trends of Carbon Intensities among 127 Countries," Sustainability, MDPI, vol. 9(12), pages 1-21, December.
    11. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
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    14. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng & Vaz-Serra, Paulo, 2022. "Economic and environmental impacts of public investment in clean energy RD&D," Energy Policy, Elsevier, vol. 168(C).

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