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A review on learning effects in prospective technology assessment

Gwenny Thomassen, Steven Van Passel and Jo Dewulf

Renewable and Sustainable Energy Reviews, 2020, vol. 130, issue C

Abstract: Global environmental problems have urged the need for developing sustainable technologies. However, new technologies that enter the market have often higher economic costs and potentially higher environmental impacts than conventional technologies. This can be explained by learning effects: a production process that is performed for the first time runs less smooth than a production process that has been in operation for years. To obtain a fair estimation of the potential of a new technology, learning effects need to be included. A review on the current literature on learning effects was conducted in order to provide guidelines on how to include learning effects in prospective technology assessment. Based on the results of this review, five recommendations have been formulated and an integration of learning effects in the structure of prospective technology assessment has been proposed. These five recommendations include the combined use of learning effects on the component level and on the end product level; the combined use of learning effects on the technical, economic and environmental level; the combined use of extrapolated values and expert estimates; the combined use of learning-by-doing and learning-by-searching effects and; a tier-based method, including quality criteria, to calculate the learning effect. These five complementary strategies could lead to a clearer perspective on the environmental impact and cost structure of the new technology and a fairer comparison base with conventional technologies, potentially resulting in a faster adoption and a shorter time-to-market for sustainable technologies.

Keywords: Learning effects; Life cycle assessment; Techno-economic assessment; Prospective technology assessment; Learning-by-doing; Learning curve; Progress rate; Experience curve (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.rser.2020.109937

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