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Evolution of CCUS Technologies Using LDA Topic Model and Derwent Patent Data

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  • Liangchao Huang

    (Sino-German Research Institute of Carbon Neutralization and Green Development, Zhengzhou University, Zhengzhou 450001, China
    Institute of Subsurface Energy Systems, Clausthal University of Technology, 38678 Clausthal Zellerfeld, Germany
    Research Centre of Energy Storage Technologies, Clausthal University of Technology, 38640 Goslar, Germany)

  • Zhengmeng Hou

    (Institute of Subsurface Energy Systems, Clausthal University of Technology, 38678 Clausthal Zellerfeld, Germany
    Research Centre of Energy Storage Technologies, Clausthal University of Technology, 38640 Goslar, Germany)

  • Yanli Fang

    (Institute of Subsurface Energy Systems, Clausthal University of Technology, 38678 Clausthal Zellerfeld, Germany
    Research Centre of Energy Storage Technologies, Clausthal University of Technology, 38640 Goslar, Germany
    Sino-German Energy Research Center, Sichuan University, Chengdu 610065, China)

  • Jianhua Liu

    (Sino-German Research Institute of Carbon Neutralization and Green Development, Zhengzhou University, Zhengzhou 450001, China)

  • Tianle Shi

    (Sino-German Research Institute of Carbon Neutralization and Green Development, Zhengzhou University, Zhengzhou 450001, China)

Abstract

Carbon capture, utilization, and storage (CCUS) technology is considered an effective way to reduce greenhouse gases, such as carbon dioxide (CO 2 ), which is significant for achieving carbon neutrality. Based on Derwent patent data, this paper explored the technology topics in CCUS patents by using the latent Dirichlet allocation (LDA) topic model to analyze technology’s hot topics and content evolution. Furthermore, the logistic model was used to fit the patent volume of the key CCUS technologies and predict the maturity and development trends of the key CCUS technologies to provide a reference for the future development of CCUS technology. We found that CCUS technology patents are gradually transforming to the application level, with increases in emerging fields, such as computer science. The main R&D institutes in the United States, Europe, Japan, Korea, and other countries are enterprises, while in China they are universities and research institutes. Hydride production, biological carbon sequestration, dynamic monitoring, geological utilization, geological storage, and CO 2 mineralization are the six key technologies of CCUS. In addition, technologies such as hydride production, biological carbon sequestration, and dynamic monitoring have good development prospects, such as CCUS being coupled with hydrogen production to regenerate synthetic methane and CCUS being coupled with biomass to build a dynamic monitoring and safety system.

Suggested Citation

  • Liangchao Huang & Zhengmeng Hou & Yanli Fang & Jianhua Liu & Tianle Shi, 2023. "Evolution of CCUS Technologies Using LDA Topic Model and Derwent Patent Data," Energies, MDPI, vol. 16(6), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2556-:d:1091315
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    References listed on IDEAS

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    1. Zhang, Xian & Fan, Jing-Li & Wei, Yi-Ming, 2013. "Technology roadmap study on carbon capture, utilization and storage in China," Energy Policy, Elsevier, vol. 59(C), pages 536-550.
    2. Stefano Sbalchiero & Maciej Eder, 2020. "Topic modeling, long texts and the best number of topics. Some Problems and solutions," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(4), pages 1095-1108, August.
    3. Yawei Qi & Wenxiang Peng & Neal N. Xiong, 2020. "The Effects of Fiscal and Tax Incentives on Regional Innovation Capability: Text Extraction Based on Python," Mathematics, MDPI, vol. 8(7), pages 1-19, July.
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