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.

陆上风电的估计学习率跨度很大，约为 40 个百分点。我们提出了一个多因素经验曲线模型，该模型具有新的规模经济措施，并使用来自七个欧洲国家的国家级数据估算陆上风电的学习率。我们发现就 LCOE 而言，通过执行率为 2%–3% 来学习，通过搜索率为 7%–9% 来学习。在分解 LCOE 时，我们发现在安装成本方面没有显着的学习，但在容量因素方面有显着的学习。因此，通过搜索来考虑容量因素的改进和建模学习对于将技术变革内生化的能源模型来说是有希望的。我们在几次稳健性检查中确认了我们的结果，并表明知识库的折旧率对估计的学习率有很大影响。