投资(军事)
环境经济学
学习曲线
温室气体
风力发电
光伏系统
光伏
自然资源经济学
步伐
经济
清洁技术
能量建模
业务
环境科学
工程类
高效能源利用
地理
政治学
管理
大地测量学
生态学
电气工程
政治
法学
生物
作者
Omar Castrejon-Campos,Lu Aye,Felix Kin Peng Hui,Paulo Vaz‐Serra
出处
期刊:Energy Policy
[Elsevier]
日期:2022-09-01
卷期号:168: 113134-113134
被引量:9
标识
DOI:10.1016/j.enpol.2022.113134
摘要
Learning curve theory has been adopted for investigating the relationship between technological learning and technology cost developments. The aim of this paper is to explore the impacts of public investment in clean energy research, development, and demonstration (RD&D) on future technology cost developments by using a two-factor learning curve approach. Learning-by-deploying and learning-by-researching were chosen as the main sources of learning. The focus is on onshore wind and solar photovoltaics in the United States of America. By using publicly available data, we estimated learning-by-deploying rates of 31.4% and 27.6%; and learning-by-researching rates of 2.3% and 4.7% for onshore wind and solar PV, respectively. By adopting a logistic curve approach, an additional $1322 and $819 mil. were forecast to be spent by 2050 in RD&D for onshore wind and solar PV, respectively. We explored the plausible long-term effects of diverse RD&D investment scenarios on electricity generation and greenhouse gas (GHG) emissions using a system dynamics model. The findings reveal that public investment in RD&D for clean energy technologies may play a key role in the pace of capital cost reductions and technology diffusion. However, relatively little long-term effects of RD&D efforts alone were found on market dynamics and GHG emissions.
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