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Bending The Learning Curve

Author

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  • Jan Witajewski-Baltvilks

    (Fondazione Eni Enrico Mattei (FEEM) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC))

  • Elena Verdolini

    (Fondazione Eni Enrico Mattei (FEEM) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC))

  • Massimo Tavoni

    (Fondazione Eni Enrico Mattei (FEEM), Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) and Politecnico di Milano)

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

  • Jan Witajewski-Baltvilks & Elena Verdolini & Massimo Tavoni, 2015. "Bending The Learning Curve," Working Papers 2015.65, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2015.65
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    References listed on IDEAS

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    Cited by:

    1. de Miguel, Carlos & Labandeira, Xavier & Löschel, Andreas, 2015. "Frontiers in the economics of energy efficiency," Energy Economics, Elsevier, vol. 52(S1), pages 1-4.
    2. repec:eee:eneeco:v:68:y:2017:i:s1:p:17-32 is not listed on IDEAS
    3. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    4. Jakub Sawulski & Jan Witajewski-Baltvilks, 2017. "Optimal RES differentiation under technological uncertainty," IBS Working Papers 07/2017, Instytut Badan Strukturalnych.
    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. 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. 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

    Learning Curve; Renewable Technologies; Integrated Assessment Models;

    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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