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Learning pathways for energy supply technologies: bridging between innovation studies and learning rates

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Learning pathways for energy supply technologies: bridging between innovation studies and learning rates. / Winskel, Mark; Markusson, Nils; Jeffrey, Henry et al.
In: Technological Forecasting and Social Change, Vol. 81, 01.2014, p. 96–114.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Winskel, M, Markusson, N, Jeffrey, H, Candelise, C, Dutton, G, Howarth, P, Jablonski, S, Kalyvas, C & Ward, D 2014, 'Learning pathways for energy supply technologies: bridging between innovation studies and learning rates', Technological Forecasting and Social Change, vol. 81, pp. 96–114. https://doi.org/10.1016/j.techfore.2012.10.015

APA

Winskel, M., Markusson, N., Jeffrey, H., Candelise, C., Dutton, G., Howarth, P., Jablonski, S., Kalyvas, C., & Ward, D. (2014). Learning pathways for energy supply technologies: bridging between innovation studies and learning rates. Technological Forecasting and Social Change, 81, 96–114. https://doi.org/10.1016/j.techfore.2012.10.015

Vancouver

Winskel M, Markusson N, Jeffrey H, Candelise C, Dutton G, Howarth P et al. Learning pathways for energy supply technologies: bridging between innovation studies and learning rates. Technological Forecasting and Social Change. 2014 Jan;81:96–114. Epub 2013 Jan 2. doi: 10.1016/j.techfore.2012.10.015

Author

Winskel, Mark ; Markusson, Nils ; Jeffrey, Henry et al. / Learning pathways for energy supply technologies : bridging between innovation studies and learning rates. In: Technological Forecasting and Social Change. 2014 ; Vol. 81. pp. 96–114.

Bibtex

@article{606b85345d4641929cdf60ebb6c8a897,
title = "Learning pathways for energy supply technologies: bridging between innovation studies and learning rates",
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 {\textquoteleft}learning pathways{\textquoteright} 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.",
keywords = "Innovation, Learning , Niches , Pathways , Energy , Electricity , Technology",
author = "Mark Winskel and Nils Markusson and Henry Jeffrey and Chiara Candelise and Geoff Dutton and Paul Howarth and Sophie Jablonski and Christos Kalyvas and David Ward",
year = "2014",
month = jan,
doi = "10.1016/j.techfore.2012.10.015",
language = "English",
volume = "81",
pages = "96–114",
journal = "Technological Forecasting and Social Change",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Learning pathways for energy supply technologies

T2 - bridging between innovation studies and learning rates

AU - Winskel, Mark

AU - Markusson, Nils

AU - Jeffrey, Henry

AU - Candelise, Chiara

AU - Dutton, Geoff

AU - Howarth, Paul

AU - Jablonski, Sophie

AU - Kalyvas, Christos

AU - Ward, David

PY - 2014/1

Y1 - 2014/1

N2 - 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.

AB - 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.

KW - Innovation

KW - Learning

KW - Niches

KW - Pathways

KW - Energy

KW - Electricity

KW - Technology

U2 - 10.1016/j.techfore.2012.10.015

DO - 10.1016/j.techfore.2012.10.015

M3 - Journal article

VL - 81

SP - 96

EP - 114

JO - Technological Forecasting and Social Change

JF - Technological Forecasting and Social Change

ER -