Bending The Learning Curve

Jan Witajewski-Baltvilks, Elena Verdolini and Massimo Tavoni ()

No 206836, Climate Change and Sustainable Development from Fondazione Eni Enrico Mattei (FEEM)

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.

Keywords: Research and Development/Tech Change/Emerging Technologies; Resource/Energy Economics and Policy (search for similar items in EconPapers)
Pages: 35
Date: 2015-07-14
References: Add references at CitEc
Citations: View citations in EconPapers (18)

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https://ageconsearch.umn.edu/record/206836/files/NDL2015-065.pdf (application/pdf)

Related works:
Journal Article: Bending the learning curve (2015)
Working Paper: Bending The Learning Curve (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:feemcl:206836

DOI: 10.22004/ag.econ.206836

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