Learning pathways for energy supply technologies: Bridging between innovation studies and learning rates |
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| Affiliation: | 1. Institute of Energy Systems, School of Engineering, University of Edinburgh, Edinburgh, EH9 3JL, UK;2. School of Geosciences, University of Edinburgh, Edinburgh, EH9 3JW, UK;3. Imperial College Centre for Energy Policy and Technology, London, SW7 2AZ, UK;4. STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, UK;5. Dalton Nuclear Institute, University of Manchester, Manchester, M13 9PL, UK;6. Department of Earth Science and Engineering, Imperial College, London SW7 2AZ, UK;7. Culham Science Centre, Abingdon, Oxfordshire, OX14 3DB, UK;1. ETH Zurich, Energy Politics Group, Department of Humanities, Social and Political Sciences, Haldeneggsteig 4, CH-8092 Zurich, Switzerland;2. Zurich University of Applied Sciences (ZHAW), Research Group for Renewable Energy, Institute of Natural Resource Sciences, Campus Grüental, CH-8820 Wädenswil, Switzerland;1. Innovation Studies, Copernicus Institute of Sustainable Development, Utrecht University, Heidelberglaan 2, 3584CS Utrecht, The Netherlands;2. Netherlands Enterprise Agency, Croeselaan 15, 3503 RE Utrecht, The Netherlands;1. School of Finance, Tianjin University of Finance and Economics, Tianjin 300222, China;2. Laboratory for Fintech and Risk Management, Tianjin 300222, China;3. Center for Energy & Environmental Policy Research, Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | Understanding and supporting learning for different emerging low carbon energy supply technology fields is a key issue for policymakers, investors and researchers. A range of contrasting analytical approaches are available: energy system modelling using learning rates provides abstracted, quantitative and output oriented accounts, while innovation studies research offers contextualised, qualitative and process oriented accounts. Drawing on research literature and expert consultation on learning for several different emerging energy supply technologies, this paper introduces a ‘learning pathways’ matrix to help bridge between the rich contextualisation of innovation studies and the systematic comparability of learning rates. The learning pathways matrix characterises technology fields by their relative orientation to radical or incremental innovation, and to concentrated or distributed organisation. A number of archetypal learning pathways are outlined to help learning rates analyses draw on innovation studies research, so as to better acknowledge the different niche origins and learning dynamics of emerging energy supply technologies. Finally, a future research agenda is outlined, based on socio-technical learning scenarios for accelerated energy innovation. |
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