Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/113970 
Erscheinungsjahr: 
2015
Schriftenreihe/Nr.: 
Nota di Lavoro No. 65.2015
Verlag: 
Fondazione Eni Enrico Mattei (FEEM), Milano
Zusammenfassung: 
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.
Schlagwörter: 
Learning Curve
Renewable Technologies
Integrated Assessment Models
JEL: 
Q42
Q55
C55
C26
Dokumentart: 
Working Paper

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