Glenk Gunther, Meier Rebecca, Reichelstein Stefan
Mannheim Institute for Sustainable Energy Studies, University of Mannheim, Mannheim, Germany.
Graduate School of Business, Stanford University, Stanford, USA.
Schmalenbach Z Betriebswirtsch Forsch. 2021;73(2):179-206. doi: 10.1007/s41471-021-00114-8. Epub 2021 Sep 7.
The pace of the global decarbonization process is widely believed to hinge on the rate of cost improvements for clean energy technologies, in particular renewable power and energy storage. This paper adopts the classical learning-by-doing framework of Wright (1936), which predicts that cost will fall as a function of the cumulative volume of past deployments. We first examine the learning curves for solar photovoltaic modules, wind turbines and electrolyzers. These estimates then become the basis for estimating the dynamics of the life-cycle cost of generating the corresponding clean energy, i.e., electricity from solar and wind power as well as hydrogen. Our calculations point to significant and sustained learning curves, which, in some contexts, predict a much more rapid cost decline than suggested by the traditional 80% learning curve. Finally, we argue that the observed learning curves for individual clean energy technologies reinforce each other in advancing the transition to a decarbonized energy economy.
人们普遍认为，全球脱碳进程的速度取决于清洁能源技术，特别是可再生能源发电和储能技术的成本降低速度。本文采用了赖特（1936年）经典的干中学框架，该框架预测成本将随着过去部署的累计数量而下降。我们首先研究了太阳能光伏组件、风力涡轮机和电解槽的学习曲线。这些估计值随后成为估算相应清洁能源（即太阳能、风能发电以及氢能）生命周期成本动态变化的基础。我们的计算结果表明存在显著且持续的学习曲线，在某些情况下，这些曲线预测的成本下降速度比传统的80%学习曲线所表明的要快得多。最后，我们认为，观察到的单个清洁能源技术的学习曲线在推动向脱碳能源经济转型方面相互强化。
