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Energy Technology Learning Through Deployment In Competitive Markets

机译:通过在竞争市场中进行部署来学习能源技术

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Technology learning as observed in experience and learning curves shows that government deployment programs can be efficient tools for technology-led strategies to manage the risk of climate change. But better theoretical understanding is needed to explain observed learning rates and make reliable extrapolations. A top-down theoretical approach is offered based on cybernetic concepts. The theory explains the clustering of measured learning rates around 20%. It also explains learning rates between 0 and 7% found in some compilations. The theory provides insights into the different roles of government deployment and R&D programs and the need to distinguish between effects of public and private industry R&D. Government R&D programs cannot substitute for deployment programs. But they can provide new technological options that the industry learning systems can turn into radical innovations, which manifest themselves as leaps in cost reduction and in improvements in technical performance.
机译:从经验和学习曲线中观察到的技术学习表明,政府部署计划可以成为以技术为主导的战略来管理气候变化风险的有效工具。但是需要更好的理论理解来解释观察到的学习率并做出可靠的推断。提供了一种基于控制论概念的自上而下的理论方法。该理论解释了测得的学习率的聚类在20%左右。它还解释了某些汇编中的学习率介于0到7%之间。该理论为政府部署和R&D计划的不同作用以及区分公共和私营行业R&D效果的需求提供了见解。政府研发计划不能替代部署计划。但是它们可以提供新的技术选择,使行业学习系统可以转变为根本性的创新,这在降低成本和提高技术性能方面表现出飞跃性的发展。

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