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所属单位：经济与管理学院

发表刊物：JOURNAL OF CLEANER PRODUCTION

关键字：Learning curves Cost decomposition Lead-acid batteries Energy storage system

摘要：Learning curve has been recognized as an effective tool for tracking the technological change and widely used in assisting policy design for energy technologies. But uncertainties underlying input price and learning rate would greatly influence the accuracy and reliability of learning curves. Regression to a learning curve using lead-acid price and production (between 1989 and 2013) yields a poor fit performance. This study then integrates cost decomposition with learning curve to remove the influences from input price changes in lead-acid batteries. Total lead-acid batteries' cost is decomposed into materials cost and residual cost. Materials cost is calculated through multiplying material consumption and its price to isolate the influences from input price changes. Learning curve is then constructed to trace the declines of materials consumption and residual cost. Regression with the improved learning curves performs better than a standard one, with the fit performance improved greatly. The study also indicates a learning effect in the material consumption in the long term. Integrating cost decomposition into learning curve is then proved to be a meaningful trial for mitigating the bias from input price changes. Learning rates of mature technologies such like lead-acid batteries indicate the importance to understand the cost reduction in them, which might significantly affect the policies' performances for emerging ones. (C) 2019 Elsevier Ltd. All rights reserved.

ISSN号：0959-6526

是否译文：否

发表时间：2019-10-20

合写作者：Ding, Hao,周鹏,王群伟

通讯作者：周德群