119 – Bending the Learning Curve

Jan Witajewski-Baltvilks, Elena Verdolini, Massimo Tavoni
Fondazione Eni Enrico Mattei, Italy

This paper aims at improving the application of the learning curve, a popular tool used for predicting future costs of renewable technologies in integrated assessment models (IAMs). First, we formally discuss the assumptions required for the traditional (OLS) estimation of the learning curve to deliver meaningful predictions in IAMs. The most problematic assumption 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: Alternative Energy Sources, Technological Innovation, Econometric model construction, Instrumental Variables Estimation