IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i23p9857-d450907.html
   My bibliography  Save this article

Knowledge Discovering on Graphene Green Technology by Text Mining in National R&D Projects in South Korea

Author

Listed:
  • Ji Yeon Lee

    (Department of Science and Technology Management Policy, University of Science and Technology, Daejeon 34113, Korea
    NTIS Center, Korea Institute of Science and Technology Information, Daejeon 34113, Korea)

  • Richa Kumari

    (Department of Science and Technology Management Policy, University of Science and Technology, Daejeon 34113, Korea
    NTIS Center, Korea Institute of Science and Technology Information, Daejeon 34113, Korea)

  • Jae Yun Jeong

    (Policy Research Division, Busan Innovation Institute of Industry, Science & Technology, Busan 48058, Korea)

  • Tae-Hyun Kim

    (NTIS Center, Korea Institute of Science and Technology Information, Daejeon 34113, Korea)

  • Byeong-Hee Lee

    (Department of Science and Technology Management Policy, University of Science and Technology, Daejeon 34113, Korea
    NTIS Center, Korea Institute of Science and Technology Information, Daejeon 34113, Korea)

Abstract

This paper reviews the development of South Korea’s national research and development (R&D) in graphene technology, focusing on projects that have been classified as “green” technology. A total of 826 projects (USD 210 billion) from 2010 to 2019 were collected from the National Science and Technology Information Service (NTIS), which is full-cycle national R&D project management system in South Korea. Then we analyzed its R&D trend by conducting diverse text mining methods including frequency analysis, association rule mining, and topic modeling. The analysis suggests that the number of graphene green technology (GT) R&D projects and the research expenses will show a rising curve again in the incumbent government along with the implementation of the Korean New Deal policy, which integrates the Green New Deal and the Digital New Deal.

Suggested Citation

  • Ji Yeon Lee & Richa Kumari & Jae Yun Jeong & Tae-Hyun Kim & Byeong-Hee Lee, 2020. "Knowledge Discovering on Graphene Green Technology by Text Mining in National R&D Projects in South Korea," Sustainability, MDPI, vol. 12(23), pages 1-16, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9857-:d:450907
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/23/9857/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/23/9857/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Goio Etxebarria & Mikel Gomez-Uranga & Jon Barrutia, 2012. "Tendencies in scientific output on carbon nanotubes and graphene in global centers of excellence for nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 253-268, April.
    2. González, Ander & Goikolea, Eider & Barrena, Jon Andoni & Mysyk, Roman, 2016. "Review on supercapacitors: Technologies and materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1189-1206.
    3. Luca Lavagna & Giuseppina Meligrana & Claudio Gerbaldi & Alberto Tagliaferro & Mattia Bartoli, 2020. "Graphene and Lithium-Based Battery Electrodes: A Review of Recent Literature," Energies, MDPI, vol. 13(18), pages 1-28, September.
    4. Jae Yun Jeong & Inje Kang & Ki Seok Choi & Byeong-Hee Lee, 2018. "Network Analysis on Green Technology in National Research and Development Projects in Korea," Sustainability, MDPI, vol. 10(4), pages 1-12, April.
    5. Min Song & Su Yeon Kim, 2013. "Detecting the knowledge structure of bioinformatics by mining full-text collections," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 183-201, July.
    6. Peng Hui Lv & Gui-Fang Wang & Yong Wan & Jia Liu & Qing Liu & Fei-cheng Ma, 2011. "Bibliometric trend analysis on global graphene research," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 399-419, August.
    7. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
    8. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    9. Yongrok Choi, 2020. "Optimal Transition toward Innovation-led Sustainable Governance under the 2020 Paris Regime," Sustainability, MDPI, vol. 12(4), pages 1-6, February.
    10. Sangdeok Lee & Yongwoon Cha & Sangwon Han & Changtaek Hyun, 2019. "Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects," Sustainability, MDPI, vol. 11(3), pages 1-14, January.
    11. In Hyuk Son & Jong Hwan Park & Seongyong Park & Kwangjin Park & Sangil Han & Jaeho Shin & Seok-Gwang Doo & Yunil Hwang & Hyuk Chang & Jang Wook Choi, 2017. "Graphene balls for lithium rechargeable batteries with fast charging and high volumetric energy densities," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Myoungjae Choi & Ohjin Kwon & Dongkyu Won & Wooseok Jang, 2021. "Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach," Sustainability, MDPI, vol. 13(22), pages 1-17, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xi Yang & Xiang Yu & Xin Liu, 2018. "Obtaining a Sustainable Competitive Advantage from Patent Information: A Patent Analysis of the Graphene Industry," Sustainability, MDPI, vol. 10(12), pages 1-25, December.
    2. Argyrou, Maria C. & Christodoulides, Paul & Kalogirou, Soteris A., 2018. "Energy storage for electricity generation and related processes: Technologies appraisal and grid scale applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 804-821.
    3. Goio Etxebarria & Mikel Gomez-Uranga & Jon Barrutia, 2012. "Tendencies in scientific output on carbon nanotubes and graphene in global centers of excellence for nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 253-268, April.
    4. Bhagaban Behera, 2013. "Drug Trafficking as a Non-Traditional Security Threat to Central Asian States," Jadavpur Journal of International Relations, , vol. 17(2), pages 229-251, December.
    5. Michelle Dietzen & Haoran Zhai & Olivia Lucas & Oriol Pich & Christopher Barrington & Wei-Ting Lu & Sophia Ward & Yanping Guo & Robert E. Hynds & Simone Zaccaria & Charles Swanton & Nicholas McGranaha, 2024. "Replication timing alterations are associated with mutation acquisition during breast and lung cancer evolution," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    6. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    7. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    8. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    9. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
    10. Rolfe, John & Flint, Nicole, 2018. "Assessing the economic benefits of a tourist access road: A case study in regional coastal Australia," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 167-178.
    11. Caizán-Juanarena, Leire & Sleutels, Tom & Borsje, Casper & ter Heijne, Annemiek, 2020. "Considerations for application of granular activated carbon as capacitive bioanode in bioelectrochemical systems," Renewable Energy, Elsevier, vol. 157(C), pages 782-792.
    12. Jesus Crespo Cuaresma & Bettina Grün & Paul Hofmarcher & Stefan Humer & Mathias Moser, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Papers wuwp193, Vienna University of Economics and Business, Department of Economics.
    13. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    14. Jeffrey L. Furman & Florenta Teodoridis, 2020. "Automation, Research Technology, and Researchers’ Trajectories: Evidence from Computer Science and Electrical Engineering," Organization Science, INFORMS, vol. 31(2), pages 330-354, March.
    15. Xin Jin & John M. Maheu & Qiao Yang, 2019. "Bayesian parametric and semiparametric factor models for large realized covariance matrices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 641-660, August.
    16. Yoon, Jisung & Park, Jinseo & Yun, Jinhyuk & Jung, Woo-Sung, 2023. "Quantifying knowledge synchronization with the network-driven approach," Journal of Informetrics, Elsevier, vol. 17(4).
    17. Csereklyei, Zsuzsanna & Anantharama, Nandini & Kallies, Anne, 2021. "Electricity market transitions in Australia: Evidence using model-based clustering," Energy Economics, Elsevier, vol. 103(C).
    18. Muhammad Yaseen & Muhammad Arif Khan Khattak & Muhammad Humayun & Muhammad Usman & Syed Shaheen Shah & Shaista Bibi & Bakhtiar Syed Ul Hasnain & Shah Masood Ahmad & Abbas Khan & Nasrullah Shah & Asif , 2021. "A Review of Supercapacitors: Materials Design, Modification, and Applications," Energies, MDPI, vol. 14(22), pages 1-40, November.
    19. Shu-Ping Shi & Yong Song, 2012. "Identifying Speculative Bubbles with an Infinite Hidden Markov Model," Working Paper series 26_12, Rimini Centre for Economic Analysis.
    20. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9857-:d:450907. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.