Learning Curves For Energy Technology: A Critical Assessment
Learning Curves For Energy Technology: A Critical Assessment
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能源技术的学习曲线：批判性评估
DOI：
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发表时间：
2007
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影响因子：
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通讯作者：
J. Kohler
中科院分区：
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作者：
T. Jamasb;J. Kohler
在这篇论文中，Jamasb 和 Kohler 重新审视了有关学习曲线及其在能源技术和气候变化政策分析和建模中的应用的文献，该论文构成了即将出版的《提供低碳电力系统：技术、经济和政策》一书的一章。近年来，学术文献和政策文件已经接受了学习曲线，并将该概念应用于技术分析和预测成本降低。我们认为，在技术分析中经常不加批判地使用或假设学习曲线，并将能源技术进步和气候变化建模中学习率的使用与社会成本效益分析中贴现率的使用进行比较。本文讨论了在应用学习曲线时需要小心，学习曲线最初是作为一种实证工具开发的，用于评估制造业中边干边学的效果，以分析创新和技术变革。最后，我们建议一些潜在的学习曲线扩展，例如通过将研发和扩散效应纳入学习模型以及学习曲线可能成为能源技术政策和分析中有用工具的其他领域。
In this paper, which forms a chapter in the forthcoming Book “Delivering a Low Carbon Electricity System: Technologies, Economics and Policy” † , Jamasb and Kohler revisit the literature on learning curves and their application to energy technology and climate change policy analysis and modeling. The academic literature and policy documents have in recent years embraced the learning curves and applied the concept to technology analysis and forecasting cost reductions. We argue that learning curves have often been used or assumed uncritically in technology analysis and draw parallels between the use of learning rates in energy technological progress and climate change modeling to that of discount rates in social cost benefit analysis. The paper discusses that care needs to be taken in applying learning curves, originally developed as an empirical tool to assess the effect of learning by doing in manufacturing, to analysis innovation and technical change. Finally, we suggest some potential extensions of learning curves, e.g. by incorporating R&D and diffusion effects into learning models, and other areas where learning curves may potentially be a useful tool in energy technology policy and analysis.