Skill transferability and the stability of transition pathways- A learning-based explanation for patterns of diffusion

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	Skill transferability and the stability of transition pathways- A learning-based explanation for patterns of diffusion
	Journal of Evolutionary Economics
	2021-03-19
出版年	2021
国家	英国
领域	地球科学 ; 资源环境
英文摘要	Understanding and governing technology transitions is essential to cope with major challenges of the 21st century such as climate change or digitization. In this paper, a learning-based approach is developed to explain the dynamics of different transition pathways. Technological know-how is necessary to make effective use of technical innovations embodied in capital. Firms and employees accumulate technology specific knowledge when working with specific machinery. Radical innovation differs by technology type and pre-existing knowledge may be imperfectly transferable across types. This paper addresses the implications of cross-technology transferability of skills for firm-level technology adoption and its consequences for the direction of macro-level technological change. A microeconomically founded model of technological learning is introduced. The model is based on empirical and theoretical insights from the innovation literature. In a simulation study using the macro-economic ABM Eurace@unibi-eco and applied to the context of green 2 technology diffusion, it is shown that a high transferability of knowledge has ambiguous effects. It accelerates the diffusion process initially but comes at the cost of long-term technological stability and specialization. For firms, it is easy to adopt new technology, but also easy to switch back to the incumbent type. Technological instability can be macroeconomically costly.
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来源平台	Oxford Martin School, University of Oxford
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/319980
专题	地球科学资源环境科学
推荐引用方式 GB/T 7714	Journal of Evolutionary Economics. Skill transferability and the stability of transition pathways- A learning-based explanation for patterns of diffusion,2021.