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Bending The Learning Curve

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  • 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, 2015. "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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    File URL: http://ageconsearch.umn.edu/record/206836/files/NDL2015-065.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. 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.
    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. Enrica De Cian & Johannes Buhl & Samuel Carrara & Michela Bevione & Silvia Monetti & Holger Berg, 2016. "Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach," Working Papers 2016.66, Fondazione Eni Enrico Mattei.
    6. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2017. "Induced technological change and energy efficiency improvements," Energy Economics, Elsevier, vol. 68(S1), pages 17-32.
    7. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    8. De Cian, Enrica & Buhl, Johannes & Carrara, Samuel & Michela Bevione, Michela & Monetti, Silvia & Berg, Holger, 2016. "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).

    More about this item

    Keywords

    Research and Development/Tech Change/Emerging Technologies; Resource /Energy Economics and Policy;

    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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