Wind power learning rates: A conceptual review and meta-analysis

In energy system models endogenous technological change can be introduced by implementing so-called technology learning rates specifying the quantitative relationship between the cumulative experience of a technology and its cost. The objectives of this paper are to: (a) provide a conceptual review of learning curve model specifications; and (b) conduct a meta-analysis of wind power learning rates. This permits an assessment of a number of important specification and data issues that influence these learning rates. The econometric analysis builds on 113 estimates of the learning-by-doing rate presented in 35 studies. The meta-analysis indicates that the choice of the geographical domain of learning, and thus the assumed presence of learning spillovers, is an important determinant of wind power learning rates. We also find that the use of extended learning curve concepts, e.g., integrating public R&D effects, appears to result in lower learning rates than those generated by so-called single-factor learning curve studies. Overall the empirical findings suggest that future studies should pay increased attention to the issue of learning and knowledge spillovers in the renewable energy field, as well as to the interaction between technology learning and R&D efforts.

Research highlights► We review various learning curve approaches and conduct a meta-analysis of wind power learning rates. ► The choice of the geographical domain of learning greatly influences estimated learning rates. ► The single-factor learning curve generates higher learning rates than the extended model specifications.