Assessing learning in low carbon technologies

Toward a more comprehensive approach

Bibliographic Data

ID12929418
AuthorsJoanna I Lewis (0000-0002-5362-1308, Walsh University, corresponding author), Gregory F Nemet (0000-0001-7859-4580, University of Wisconsin–Madison)
Year2021
Volume12
Issue5
Publication date2021-08-13
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueWiley Interdisciplinary Reviews Climate Change (JOURNAL)
Journal identifiersISSN: 1757-7780 • E-ISSN: 1757-7799
PublisherWiley (PUBLISHER • GB)
DOI10.1002/wcc.730
OpenAlexW3193583397
LanguageEN
Citations received3
References cited107

With decades of experience developing and deploying low carbon technologies around the world, much has been learned. We assess six categories that represent the diversity of methodological approaches that have been used to study low carbon learning: (1) learning curves; (2) expert elicitations; (3) patent analysis; (4) engineering‐based decomposition; (5) policy intervention studies; and (6) case studies. Based on a review of low carbon learning studies in these six areas, we summarize what we know about low carbon learning, and what we have yet to fully understand, including the methodological strengths and limitations of key studies conducted to date. We find that a more comprehensive understanding of low carbon learning is necessary and timely given the massive scale and short time horizon of the low carbon transition, and that there are real benefits to employing a comprehensive approach using multiple methods. We find a need for better data sets, and for studies of a more diverse set of technologies, as well as of interactions among technologies. In addition, studies should be more explicit about local context, with a particular need for additional focus on emerging and developing countries. We identify key topics that warrant further research, including technology specific learning methods; spatial distinctions and the local and global linkages that influence learning; and an expanded study of the cultural, social, environmental, and political factors that influence learning. Finally, we recommend more nuance in the design of policies directed at accelerating low carbon learning. This article is categorized under: The Carbon Economy and Climate Mitigation > Future of Global Energy

Business · Context (archaeology · Data science · Geography · Knowledge management · Set (abstract data type · Warrant · Climate Change Policy and Economics · Computer Science · Energy, Environment, Economic Growth · Green IT and Sustainability

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