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

Winskel, Mark; Markusson, Nils; Jeffrey, Henry; Candelise, Chiara; Dutton, Geoff; Howarth, P; Jablonski, Sophie; Kalyvas, Christos; Ward, D.J.

doi:10.1016/j.techfore.2012.10.015

Cited by 58 publications

(34 citation statements)

References 67 publications

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“…Nemet [42] notes that key innovations in the PV industry originated in the microprocessor industry. Meanwhile, the development of combined cycle gas turbines (CCGT) benefited greatly from technological advances made inter alia in the field of jet aero engines [69,70].…”

Section: Knowledge Spillovers From Other Technologiesmentioning

confidence: 99%

A Review of Factors Influencing the Cost Development of Electricity Generation Technologies

Samadi

2016

Energies

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This article reviews the literature on the past cost dynamics of various renewable, fossil fuel and nuclear electricity generation technologies. It identifies 10 different factors which have played key roles in influencing past cost developments according to the literature. These 10 factors are: deployment-induced learning, research, development and demonstration (RD&D)-induced learning, knowledge spillovers from other technologies, upsizing, economies of manufacturing scale, economies of project scale, changes in material and labour costs, changes in fuel costs, regulatory changes, and limits to the availability of suitable sites. The article summarises the relevant literature findings for each of these 10 factors and provides an overview indicating which factors have impacted on which generation technologies. The article also discusses the insights gained from the review for a better understanding of possible future cost developments of electricity generation technologies. Finally, future research needs, which may support a better understanding of past and future cost developments, are identified.

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Section: Knowledge Spillovers From Other Technologiesmentioning

confidence: 99%

A Review of Factors Influencing the Cost Development of Electricity Generation Technologies

Samadi

2016

Energies

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“…The emergence of innovation occurs from the interaction of various distinct components of knowledge which demand complex technological research and development structures, organized in layers, as presented by Winskel et al (2014). Keasling (2017) shows that this is the case of most platforms related to biotechnology products, R&D--intensive activities (research and development) which seek to obtain liquid energy, and other economically interesting products from the transformation of biomass.…”

Section: Bioenergy Knowledge Networkmentioning

confidence: 99%

“…The central argument is that both to accomplish the mapping of future possibilities, given the evolution of the knowledge and technological frontier (exploration), and to decide as to the areas of technology application (exploitation), varied forms of knowledge arrangement are necessary, retrieving, at times, knowledge and technology that had been somewhat forgotten. It is what Winskel et al (2014) term layers which define learning pathways.…”

Section: Bioenergy Knowledge Networkmentioning

confidence: 99%

Aplicando rede de IPCs para identificar a fronteira tecnológica da bioenergia

et al. 2018

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This paper proposes to identify the formation of technological frontier clusters. In the present study, we applied the method for bioenergy technologies. The first methodological step uses patents extracted from the Derwent Innovation Index, in which a data mining treatment is done.The second step is the construction of the IPC (International Patents Classification) network of patents from a co-occurrence matrix. Three clusters of technological knowledge are identified.The first and main cluster is the ethanol technologies, and two clusters in the training phase, one of which uses by-products and the other of technologies based on cellulosic material. The work shows how a general knowledge base is appropriate and favors the configuration of new frontier technologies that amplify the knowledge base of the bioenergy paradigm. Aplicando rede de IPCs para identificar a fronteira tecnológica da bioenergia rEsumo Este artigo propõe um método para identificar clusters de fronteira tecnológica. No presente estudo, aplicamos o método para tecnologias da bioenergia. A primeira etapa metodológica utiliza patentes extraídas da Derwent Innovation Index, em que é feito um tratamento de data mining. A segunda etapa é a construção da rede de International Patents Classifications (IPCs) a partir de uma matriz de co-ocorrência. Foram identificados três clusters de conhecimento tecnológico. O primeiro é o de tecnologias de etanol e os outros dois em etapa de formação, sendo um de tecnologias que utilizam subprodutos e outro de tecnologias a partir de material celulósico. O trabalho mostra como uma base de conhecimentos gerais vai sendo apropriada e favorece a configuração de novas frentes tecnológicas que amplificam a base de conhecimento representada pelo paradigma da bioenergia.

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“…However, the focus of this paper lies on demand-pull instruments. 5 Thus, besides some caveats [22,30,31], learning curves have been included in modeling of energy systems [32,33] as well as applied to policy implications [34,35]. 6 Due to these characteristics the concept of GPTs had the most impact on academic literature on economic growth.…”

Section: The Concept Of Multi-purpose Technologiesmentioning

confidence: 99%

Cost-efficient demand-pull policies for multi-purpose technologies – The case of stationary electricity storage

Battke

Schmidt

2015

Applied Energy

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

Cited by 58 publications

References 67 publications

A Review of Factors Influencing the Cost Development of Electricity Generation Technologies

A Review of Factors Influencing the Cost Development of Electricity Generation Technologies

Aplicando rede de IPCs para identificar a fronteira tecnológica da bioenergia

Cost-efficient demand-pull policies for multi-purpose technologies – The case of stationary electricity storage

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