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Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model

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  • Yueran Duan

    (China University of Geosciences)

  • Qing Guan

    (China University of Geosciences)

Abstract

The innovation and development of emerging technology mostly depend on the way of knowledge convergence defined as the blurring of previously distinct domain-specific knowledge. This paper aims to explore the potential motivation of knowledge convergence and find the law of knowledge convergence, taking the solar energy field as an example. We established Keywords co-occurrence networks of solar energy literature in 2008–2017, and then link prediction is introduced to study the structural mechanism of knowledge convergence. We found that: (1) the common neighbor index better characterizes the knowledge convergence pattern in the knowledge networks among four similarity indicators. (2) The keywords co-occurrence network could effectively mine the structural characteristics of knowledge convergence; (3) the convergence cycle of knowledge in the field of solar energy was about 4 years; (4) keywords with higher betweenness centrality or eigenvector centrality easily generated knowledge convergence; (5) a literature knowledge convergence prediction model is proposed based on these results; and (6) the prediction results showed that scholars should pay attention to six basic issues including energy storage, efficiency, cost, ecological effect, application scenarios, and hybrid photovoltaic systems. This work can provide guidance not only for scholars to grasp the research direction and to generate more innovations but for the government to formulate the policies of government funding.

Suggested Citation

  • Yueran Duan & Qing Guan, 2021. "Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3749-3773, May.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03901-6
    DOI: 10.1007/s11192-021-03901-6
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    1. JingJing Zhang & Jiancheng Guan, 2017. "Scientific relatedness and intellectual base: a citation analysis of un-cited and highly-cited papers in the solar energy field," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 141-162, January.
    2. Fthenakis, Vasilis & Mason, James E. & Zweibel, Ken, 2009. "The technical, geographical, and economic feasibility for solar energy to supply the energy needs of the US," Energy Policy, Elsevier, vol. 37(2), pages 387-399, February.
    3. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    4. McEachern, Menzie & Hanson, Susan, 2008. "Socio-geographic perception in the diffusion of innovation: Solar energy technology in Sri Lanka," Energy Policy, Elsevier, vol. 36(7), pages 2578-2590, July.
    5. Chae, Young Tae & Kim, Jeehwan & Park, Hongsik & Shin, Byungha, 2014. "Building energy performance evaluation of building integrated photovoltaic (BIPV) window with semi-transparent solar cells," Applied Energy, Elsevier, vol. 129(C), pages 217-227.
    6. Sick, Nathalie & Preschitschek, Nina & Leker, Jens & Bröring, Stefanie, 2019. "A new framework to assess industry convergence in high technology environments," Technovation, Elsevier, vol. 84, pages 48-58.
    7. Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
    8. Wenjia Zhu & Jiancheng Guan, 2013. "A bibliometric study of service innovation research: based on complex network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1195-1216, March.
    9. Du, Huibin & Li, Na & Brown, Marilyn A. & Peng, Yuenuan & Shuai, Yong, 2014. "A bibliographic analysis of recent solar energy literatures: The expansion and evolution of a research field," Renewable Energy, Elsevier, vol. 66(C), pages 696-706.
    10. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    11. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    12. Kebede, Kassahun Y. & Mitsufuji, Toshio, 2017. "Technological innovation system building for diffusion of renewable energy technology: A case of solar PV systems in Ethiopia," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 242-253.
    13. Ferreira, Agmar & Kunh, Sheila S. & Fagnani, Kátia C. & De Souza, Tiago A. & Tonezer, Camila & Dos Santos, Geocris Rodrigues & Coimbra-Araújo, Carlos H., 2018. "Economic overview of the use and production of photovoltaic solar energy in brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 181-191.
    14. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    15. Kumar Sahu, Bikash, 2015. "A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 621-634.
    16. Wen Zhou & Jiayi Gu & Yifan Jia, 2018. "h-Index-based link prediction methods in citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 381-390, October.
    17. Yang Li & Huajiao Li & Nairong Liu & Xueyong Liu, 2018. "Important institutions of interinstitutional scientific collaboration networks in materials science," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 85-103, October.
    18. Kose, Toshihiro & Sakata, Ichiro, 2019. "Identifying technology convergence in the field of robotics research," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 751-766.
    19. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    20. Jinseok Kim & Jana Diesner, 2019. "Formational bounds of link prediction in collaboration networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 687-706, May.
    21. Sanz-Casado, Elias & Lascurain-Sánchez, Maria Luisa & Serrano-Lopez, Antonio Eleazar & Larsen, Birger & Ingwersen, Peter, 2014. "Production, consumption and research on solar energy: The Spanish and German case," Renewable Energy, Elsevier, vol. 68(C), pages 733-744.
    22. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    23. Guan, Qing & An, Haizhong & Gao, Xiangyun & Huang, Shupei & Li, Huajiao, 2016. "Estimating potential trade links in the international crude oil trade: A link prediction approach," Energy, Elsevier, vol. 102(C), pages 406-415.
    24. Li, Huajiao & An, Haizhong & Wang, Yue & Huang, Jiachen & Gao, Xiangyun, 2016. "Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 657-669.
    25. N'Tsoukpoe, K. Edem & Liu, Hui & Le Pierrès, Nolwenn & Luo, Lingai, 2009. "A review on long-term sorption solar energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2385-2396, December.
    26. Stucki, Tobias & Woerter, Martin, 2019. "The private returns to knowledge: A comparison of ICT, biotechnologies, nanotechnologies, and green technologies," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 62-81.
    27. Boussemart, Jean-Philippe & Leleu, Hervé & Mensah, Edward & Shitikova, Karina, 2020. "Technological catching-up and structural convergence among US industries," Economic Modelling, Elsevier, vol. 84(C), pages 135-146.
    28. Sharon, H. & Reddy, K.S., 2015. "A review of solar energy driven desalination technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1080-1118.
    29. de Paulo, Alex Fabianne & Porto, Geciane Silveira, 2017. "Solar energy technologies and open innovation: A study based on bibliometric and social network analysis," Energy Policy, Elsevier, vol. 108(C), pages 228-238.
    30. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
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