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Estimating the learning rate of a technology with multiple variants: The case of carbon storage

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  • Upstill, Garrett
  • Hall, Peter

Abstract

Learning rates enable the generation of first-order estimates of the cost of a technology as cumulative production grows, and play an important role in energy-economic modelling. This paper extends the component-based approach to estimating future learning rates to the case of a technology with multiple variants. It sets out a bottom-up method for estimating the composite learning rate including the situation where the proportional contribution of the different variants changes as cumulative production increases. The method is demonstrated for carbon storage using representative cost and distribution data from recent studies of storage in the European region.

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  • 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.
    • Handle: RePEc:eee:enepol:v:121:y:2018:i:c:p:498-505
    DOI: 10.1016/j.enpol.2018.05.017
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    2. Zhou, Li & Duan, Maosheng & Yu, Yadong & Zhang, Xiliang, 2018. "Learning rates and cost reduction potential of indirect coal-to-liquid technology coupled with CO2 capture," Energy, Elsevier, vol. 165(PB), pages 21-32.
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    4. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
    5. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
    6. 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).

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