IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/zyb3j.html
   My bibliography  Save this paper

Using Machine Learning to Analyze Climate Change Technology Transfer (CCTT)

Author

Listed:
  • Kulkarni, Shruti

Abstract

The objective of the present paper is to review the current state of climate change technology transfer. This research proposes a method for analyzing climate change technology transfer using patent analysis and topic modeling. A collection of climate change patent data from patent databases would be used as input to group patents in several relevant topics for climate change mitigation using the topic exploration model in this research. The research questions we want to address are: how have patenting activities changed over time in climate change mitigation related technology (CCMT) patents? And who are the technological leaders? The investigation of these questions can offer the technological landscape in climate change-related technologies at the international level. We propose a hybrid Latent Dirichlet Allocation (LDA) approach for topic modelling and identification of relationships between terms and topics related to CCMT, enabling better visualizations of underlying intellectual property dynamics. Further, a predictive model for CCTT is proposed using techniques such as social network analysis (SNA) and, regression analysis. The competitor analysis is also proposed to identify countries with a similar patent landscape. The projected results are expected to facilitate the transfer process associated with existing and emerging climate change technologies and improve technology cooperation between governments.

Suggested Citation

  • Kulkarni, Shruti, 2020. "Using Machine Learning to Analyze Climate Change Technology Transfer (CCTT)," SocArXiv zyb3j, Center for Open Science.
  • Handle: RePEc:osf:socarx:zyb3j
    DOI: 10.31219/osf.io/zyb3j
    as

    Download full text from publisher

    File URL: https://osf.io/download/608747357166fa014de8b73b/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/zyb3j?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kim, Jeeeun & Lee, Sungjoo, 2015. "Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 332-345.
    2. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    3. Frances Stewart, 1992. "Technology Transfer for Development," Palgrave Macmillan Books, in: North-South and South-South, chapter 13, pages 311-338, Palgrave Macmillan.
    4. Frances Stewart, 1992. "North-South and South-South," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-37594-9.
    Full references (including those not matched with items on IDEAS)

    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. Jumi Hwang & Kyung Hee Kim & Jong Gyu Hwang & Sungchan Jun & Jiwon Yu & Chulung Lee, 2020. "Technological Opportunity Analysis: Assistive Technology for Blind and Visually Impaired People," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    2. Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    3. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    4. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    5. Demir, FIrat & Dahi, Omar S., 2011. "Asymmetric effects of financial development on South-South and South-North trade: Panel data evidence from emerging markets," Journal of Development Economics, Elsevier, vol. 94(1), pages 139-149, January.
    6. Rudra P. Pradhan & Mak B. Arvin & Mahendhiran Nair & Sara E. Bennett, 2020. "Sustainable economic growth in the European Union: The role of ICT, venture capital, and innovation," Review of Financial Economics, John Wiley & Sons, vol. 38(1), pages 34-62, January.
    7. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    8. Pieter E. Stek, 2021. "Identifying spatial technology clusters from patenting concentrations using heat map kernel density estimation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 911-930, February.
    9. AM.Priyangani Adikari & Haiyun Liu & MMSA. Marasinghe, 2021. "Inward Foreign Direct Investment-Induced Technological Innovation in Sri Lanka? Empirical Evidence Using ARDL Approach," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    10. Font-Julián, Cristina I & Ontalba-Ruipérez, José-Antonio & Orduña-Malea, Enrique & Thelwall, Mike, 2022. "Which types of online resource support US patent claims?," Journal of Informetrics, Elsevier, vol. 16(1).
    11. Rudra P. Pradhan & Rana P. Maradana & Danish B. Zaki & Saurav Dash & Manju Jayakumar & Kunal Gaurav, 2017. "Venture Capital and Innovation: Evidence from European Economic Area Countries," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1-30, December.
    12. Ardito, Lorenzo & Ernst, Holger & Messeni Petruzzelli, Antonio, 2020. "The interplay between technology characteristics, R&D internationalisation, and new product introduction: Empirical evidence from the energy conservation sector," Technovation, Elsevier, vol. 96.
    13. Jaehyun Choi & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Predictive Model of Technology Transfer Using Patent Analysis," Sustainability, MDPI, vol. 7(12), pages 1-21, December.
    14. Mario Corona & Youngjung Geum & Sungjoo Lee, 2017. "Patterns of Protecting Both Technological and Nontechnological Innovation for Service Offerings: Case of the Video-Game Industry," Service Science, INFORMS, vol. 9(3), pages 192-204, September.
    15. Ardito, Lorenzo & Natalicchio, Angelo & Appio, Francesco Paolo & Messeni Petruzzelli, Antonio, 2021. "The role of scientific knowledge within inventing teams and the moderating effects of team internationalization and team experience: Empirical tests into the aerospace sector," Journal of Business Research, Elsevier, vol. 128(C), pages 701-710.
    16. Heikkilä, Jussi T.S. & Peltoniemi, Mirva, 2023. "The changing work of IPR attorneys: 30 years of institutional transitions," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    17. Hadjimanolis, Athanasios & Dickson, Keith, 2001. "Development of national innovation policy in small developing countries: the case of Cyprus," Research Policy, Elsevier, vol. 30(5), pages 805-817, May.
    18. Rana P. Maradana & Rudra P. Pradhan & Saurav Dash & Kunal Gaurav & Manju Jayakumar & Debaleena Chatterjee, 2017. "Does innovation promote economic growth? Evidence from European countries," Journal of Innovation and Entrepreneurship, Springer, vol. 6(1), pages 1-23, December.
    19. Noh, Heeyong & Lee, Sungjoo, 2020. "What constitutes a promising technology in the era of open innovation? An investigation of patent potential from multiple perspectives," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    20. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:osf:socarx:zyb3j. 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: OSF (email available below). General contact details of provider: https://arabixiv.org .

    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.