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Learning dependent subsidies for lithium-ion electric vehicle batteries

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  • Matteson, Schuyler
  • Williams, Eric

Abstract

Governments subsidize diffusion of a variety of energy technologies believed to provide social benefits. These subsidies are often based on the idea that stimulating learning and industry development will lower costs to make the technology competitive, after which point the subsidy can be removed. We investigate two questions related to the design of subsidy programs. One question is how net public investment changes with the time interval over which subsidies are reduced, i.e. semi-annually, annually, etc. Governments prefer to reduce subsidies more often to lower public costs, producers prefer longer time periods for a more stable investment environment. The second question addressed is uncertainty in learning rates. Learning rates describe the fractional cost reduction per doubling of cumulative production; slower learning implies more government investment is needed to reach a cost target. We investigate these questions via a case study of subsidizing electric vehicles (EV) in the United States. Given the importance of lithium battery cost in the price of an EV, we gather historical data to build an experience curve that describes cost reductions for lithium-ion vehicle batteries as a function of cumulative production. Our model assumes vehicle batteries experience the same learning as consumer electronics, yielding a learning rate of 22%. Using learning rates ranging from 9.5–22%, we estimate how much public subsidy would be needed to reach a battery cost target of $300/kWh battery. For a 9.5% learning rate, semi-annual, annual and biannual tapering costs a total of 24, 27, and 34 billion USD respectively. For 22% learning, semi-annual, annual and biannual tapering costs a total of 2.1, 2.3, and 2.6 billion USD respectively. While the tapering does affect program cost, uncertainty in learning rate is the largest source of variability in program cost, highlighting the importance of finding realistic ranges for learning rates when planning technology subsidies.

Suggested Citation

  • Matteson, Schuyler & Williams, Eric, 2015. "Learning dependent subsidies for lithium-ion electric vehicle batteries," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 322-331.
    • Handle: RePEc:eee:tefoso:v:92:y:2015:i:c:p:322-331
    DOI: 10.1016/j.techfore.2014.12.007
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    Citations

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    Cited by:

    1. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    2. Shao, Liuguo & Kou, Wenwen & Zhang, Hua, 2022. "The evolution of the global cobalt and lithium trade pattern and the impacts of the low-cobalt technology of lithium batteries based on multiplex network," Resources Policy, Elsevier, vol. 76(C).
    3. 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).
    4. Matteson, Schuyler & Williams, Eric, 2015. "Residual learning rates in lead-acid batteries: Effects on emerging technologies," Energy Policy, Elsevier, vol. 85(C), pages 71-79.
    5. Safari, M., 2018. "Battery electric vehicles: Looking behind to move forward," Energy Policy, Elsevier, vol. 115(C), pages 54-65.
    6. Jaiswal, Abhishek, 2017. "Lithium-ion battery based renewable energy solution for off-grid electricity: A techno-economic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 922-934.
    7. Ranjit R. Desai & Eric Hittinger & Eric Williams, 2022. "Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market," Energies, MDPI, vol. 15(13), pages 1-25, June.
    8. Tibebu, Tiruwork B. & Hittinger, Eric & Miao, Qing & Williams, Eric, 2022. "Roles of diffusion patterns, technological progress, and environmental benefits in determining optimal renewable subsidies in the US," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Turan, Fikret Korhan, 2024. "A theoretical stakeholder model of automotive industry and policy implications for sustainable transport after Dieselgate," Transport Policy, Elsevier, vol. 148(C), pages 192-205.

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