G2TT
来源类型FEEM working papers "Note di lavoro" series
规范类型论文
Bending The Learning Curve
Jan Witajewski-Baltvilks; Elena Verdolini; Massimo Tavoni
发表日期2015
出处Climate Change and Sustainable Development
出版年2015
语种英语
摘要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, Bending the learning curve, Energy Economics, Volume 52, Supplement 1, December 2015, Pages S86-S99, ISSN 0140-9883, http://dx.doi.org/10.1016/j.eneco.2015.09.007
特色分类Q42;Q55;C55;C26
关键词Learning Curve Renewable Technologies Integrated Assessment Models
URLhttps://www.feem.it/en/publications/feem-working-papers-note-di-lavoro-series/bending-the-learning-curve/
来源智库Fondazione Eni Enrico Mattei (Italy)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/118419
推荐引用方式
GB/T 7714
Jan Witajewski-Baltvilks,Elena Verdolini,Massimo Tavoni. Bending The Learning Curve. 2015.
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