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A bibliometric-based technique to identify emerging photovoltaic technologies in a comparative assessment with expert review

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  • Moro, Alberto
  • Boelman, Elisa
  • Joanny, Geraldine
  • Garcia, Juan Lopez

Abstract

This paper compares the results of technology mapping from bibliometric analysis and results from expert review to identify emerging solar photovoltaic (PV) technologies. The bibliometric analysis is based on “Tools for Innovation Monitoring” (TIM), a new software code developed by the Joint Research Centre. With this text-mining software a set of relevant keywords is extracted through frequency analysis from a corpus of pertinent scientific publications. Keywords obtained by quantitative analysis by TIM are tested against results from qualitative cognitive analysis by an international panel of PV technology experts by means of a set of proposed indicators. The technologies identified by the PV experts are well represented amongst the most frequently occurring (highest ranked) keywords retrieved by TIM. The more salient keywords tend to correspond to the relatively more established technologies such as dye sensitised solar cells, organic PV and more recently-developed technologies such as perovskites. These high rated/developed keywords/technologies can be relatively straightforwardly detected through bibliometric analysis. Contrary to that, keywords designating the most emerging technologies like ferroelectric PV, hot carriers and multiple exciton generation solar cells tend to occur much less frequently and therefore provide weaker signals. These weak signals can be important in foresight.

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  • Moro, Alberto & Boelman, Elisa & Joanny, Geraldine & Garcia, Juan Lopez, 2018. "A bibliometric-based technique to identify emerging photovoltaic technologies in a comparative assessment with expert review," Renewable Energy, Elsevier, vol. 123(C), pages 407-416.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:407-416
    DOI: 10.1016/j.renene.2018.02.016
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    2. Yun, Sunyoung & Lee, Joosung & Lee, Sungjoo, 2019. "Technology development strategies and policy support for the solar energy industry under technological turbulence," Energy Policy, Elsevier, vol. 124(C), pages 206-214.
    3. Daria A. Pavlova & Yulia V. Milshina & Konstantin O. Vishnevskiy & Ozcan Saritas, 2018. "The Role Of Wild Cards Analysis In Foresight Studies: The Case Of Russia," HSE Working papers WP BRP 89/STI/2018, National Research University Higher School of Economics.
    4. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    5. Xiaoli Wang & Yun Liu & Lingdi Chen & Yifan Zhang, 2022. "Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network," SAGE Open, , vol. 12(4), pages 21582440221, December.
    6. Parraguez, Pedro & Škec, Stanko & e Carmo, Duarte Oliveira & Maier, Anja, 2020. "Quantifying technological change as a combinatorial process," Technological Forecasting and Social Change, Elsevier, vol. 151(C).

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