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Wiley Interdisciplinary Reviews Energy and Environment
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Technological learning: Lessons learned on energy technologies

Authors: Reinhard Haas; Marlene Sayer; Amela Ajanovic; Hans Auer;

Technological learning: Lessons learned on energy technologies

Abstract

AbstractThe concept of technological learning is a method to anticipate the future development of the costs of technologies. It has been discussed since the 1930s as a tool for determining manufacturing cost reductions, starting in an airplane manufacturing plant, by means of learning curves and has been widely used since the 2000s in energy models to endogenize technological change. In this paper, the theoretical concept of technological learning based on energy technologies is analyzed based on examples from the literature. The main low‐carbon power generation technologies, photovoltaics, concentrated solar power, wind and nuclear energy were analyzed, showing different cost trends. Additionally, the impact of policy support on technological learning was discussed in concrete examples of bioethanol and heat pumps. We find that the homogeneity and the modularity of a technology are essential for high learning rates. A good proof is the manufacturing cost development of photovoltaics in recent decades, where a rather stable learning rate of 20% has been identified. On the contrary, nuclear power did not evolve into a homogeneous technology due to required environmental adaptations caused by accidents and the lack of standardization and application of new engineering approaches. In that case, the overall price further increased. Finally, another important condition is stable legal and regulatory conditions regarding the implementation.This article is categorized under: Policy and Economics > Green Economics and Financing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
hybrid