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Bending The Learning Curve

Author

Listed:
  • Witajewski-Baltvilks, Jan
  • Verdolini, Elena
  • Tavoni, Massimo

Abstract

This paper aims at improving the application of the learning curve, a popular tool used for forecasting future costs of renewable technologies in integrated assessment models (IAMs). First, we formally discuss under what assumptions the traditional (OLS) estimates of the learning curve can deliver meaningful predictions in IAMs. We argue that the most problematic of them is the absence of any effect of technology cost on its demand (reverse causality). Next, we show that this assumption can be relaxed by modifying the traditional econometric method used to estimate the learning curve. The new estimation approach presented in this paper is robust to the reverse causality problem but preserves the reduced form character of the learning curve. Finally, we provide new estimates of learning curves for wind turbines and PV technologies which are tailored for use in IAMs. Our results suggest that the learning rate should be revised downward for wind power, but possibly upward for solar PV.

Suggested Citation

  • Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, "undated". "Bending The Learning Curve," Climate Change and Sustainable Development 206836, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemcl:206836
    DOI: 10.22004/ag.econ.206836
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    Cited by:

    1. Fard, Amirhossein & Javadi, Siamak & Kim, Incheol, 2020. "Environmental regulation and the cost of bank loans: International evidence," Journal of Financial Stability, Elsevier, vol. 51(C).
    2. Lafond, François & Greenwald, Diana & Farmer, J. Doyne, 2022. "Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment," The Journal of Economic History, Cambridge University Press, vol. 82(3), pages 727-764, September.
    3. Jakub Sawulski & Jan Witajewski-Baltvilks, 2017. "Optimal RES differentiation under technological uncertainty," IBS Working Papers 07/2017, Instytut Badan Strukturalnych.
    4. de Miguel, Carlos & Labandeira, Xavier & Löschel, Andreas, 2015. "Frontiers in the economics of energy efficiency," Energy Economics, Elsevier, vol. 52(S1), pages 1-4.
    5. Tadeusz Skoczkowski & Sławomir Bielecki & Joanna Wojtyńska, 2019. "Long-Term Projection of Renewable Energy Technology Diffusion," Energies, MDPI, vol. 12(22), pages 1-24, November.
    6. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    7. Hann-Earl Kim & Yu-Sang Chang & Hee-Jin Kim, 2021. "Dynamic Electricity Intensity Trends in 91 Countries," Sustainability, MDPI, vol. 13(8), pages 1-26, April.
    8. Bello, S. & Reiner, 2024. "Experience Curves for Electrolysis Technologies," Cambridge Working Papers in Economics 2476, Faculty of Economics, University of Cambridge.
    9. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2017. "Induced technological change and energy efficiency improvements," Energy Economics, Elsevier, vol. 68(S1), pages 17-32.
    10. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    11. Siamak Javadi & Abdullah‐Al Masum & Mohsen Aram & Ramesh P. Rao, 2023. "Climate change and corporate cash holdings: Global evidence," Financial Management, Financial Management Association International, vol. 52(2), pages 253-295, June.
    12. De Cian, Enrica & Buhl, Johannes & Carrara, Samuel & Michela Bevione, Michela & Monetti, Silvia & Berg, Holger, "undated". "Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach," MITP: Mitigation, Innovation and Transformation Pathways 249784, Fondazione Eni Enrico Mattei (FEEM).
    13. Hötte, Kerstin & Pichler, Anton & Lafond, François, 2021. "The rise of science in low-carbon energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    14. Blazquez, Jorge & Nezamuddin, Nora & Zamrik, Tamim, 2018. "Economic policy instruments and market uncertainty: Exploring the impact on renewables adoption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 224-233.
    15. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    16. Maharjan, Prapti & Hauck, Mara & Kirkels, Arjan & Buettner, Benjamin & de Coninck, Heleen, 2024. "Deriving experience curves: A structured and critical approach applied to PV sector," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    17. Saheed Bello & David M Reiner, 2024. "Experience curves for electrolysis technologies," Working Papers EPRG2420, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    18. Bello, Saheed & Reiner, David, 2025. "Experience curve analyses for green hydrogen technology development," Technological Forecasting and Social Change, Elsevier, vol. 220(C).

    More about this item

    Keywords

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    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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