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Mills of progress grind slowly? Estimating learning rates for onshore wind energy

  • Technical University of Munich

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Estimated learning rates for onshore wind span a large range of about 40 percentage points. We propose a multi-factor experience curve model with a new economies of scale measure and estimate learning rates for onshore wind using country-level data from seven European countries. We find learning by doing rates of 2%–3% and learning by searching rates of 7%–9% in terms of LCOE. When decomposing LCOE, we find no significant learning in installed costs but significant learning in capacity factors. Accounting for improvements in capacity factors and modeling learning by searching can hence be promising for energy models that endogenize technological change. We confirm our results in several robustness checks, and show that depreciation rates of the knowledge stock have large effects on estimated learning rates.

Original languageEnglish
Article number105642
JournalEnergy Economics
Volume104
DOIs
StatePublished - Dec 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Economies of scale
  • Learning by doing
  • Learning curves
  • Public R&D
  • Technological change

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