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Technology Trend Analysis of Japanese Green Vehicle Powertrains Technology Using Patent Citation Data

Author

Listed:
  • Jiaming Jiang

    (Center for Artificial Intelligence and Mathematical Data Science, Okayama University, 2-1-1 Tsushimanaka, Kitaku, Okayama 700-8530, Japan)

  • Yu Zhao

    (School of Management, Department of Management, Tokyo University of Science, Tokyo 162-8601, Japan)

Abstract

As automobiles are major contributors to greenhouse gas emissions, the technological shift towards vehicle powertrain systems is an attempt to lower problems such as emissions of carbon dioxide and nitrogen oxides. Patent data are the most reliable measure of business performance for applied research and development activities when investigating knowledge domains or technology evolution. This is the first study on Japanese patent citation data of the green vehicle powertrains technology industry, using the social network analysis method, which emphasizes centrality estimates and community detection. This study not only elucidates the knowledge by visualizing flow patterns but also provides a precious and congregative method for verifying important patents under the International Patent Classification system and grasping the trend of the new technology industry. This study detects leading companies, not only in terms of the number of patents but also the importance of the patents. The empirical result shows that the International Patent Classification (IPC) class that starts with “B60K”, which includes hybrid electric vehicle (HEV) and battery electric vehicle (BEV), is more likely to be the technology trend in the green vehicle powertrains industry.

Suggested Citation

  • Jiaming Jiang & Yu Zhao, 2023. "Technology Trend Analysis of Japanese Green Vehicle Powertrains Technology Using Patent Citation Data," Energies, MDPI, vol. 16(5), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2221-:d:1080043
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    References listed on IDEAS

    as
    1. Jiaming Jiang & Rajeev K. Goel & Xingyuan Zhang, 2020. "IPR policies and determinants of membership in Standard Setting Organizations: a social network analysis," Netnomics, Springer, vol. 21(1), pages 129-154, December.
    2. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
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    4. Faria, Lourenço Galvão Diniz & Andersen, Maj Munch, 2017. "Sectoral patterns versus firm-level heterogeneity - The dynamics of eco-innovation strategies in the automotive sector," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 266-281.
    5. Jiaming Jiang & Yu Zhao & Junshi Feng, 2022. "University–Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms," Sustainability, MDPI, vol. 14(15), pages 1-18, August.
    6. Loet Leydesdorff & Liwen Vaughan, 2006. "Co‐occurrence matrices and their applications in information science: Extending ACA to the Web environment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(12), pages 1616-1628, October.
    7. Cohen, Wesley M. & Goto, Akira & Nagata, Akiya & Nelson, Richard R. & Walsh, John P., 2002. "R&D spillovers, patents and the incentives to innovate in Japan and the United States," Research Policy, Elsevier, vol. 31(8-9), pages 1349-1367, December.
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    Cited by:

    1. Jian Xue & YiXue Fan & Yang Lv, 2024. "The evolution of patent cooperation network for new energy vehicle power battery," SN Business & Economics, Springer, vol. 4(7), pages 1-17, July.

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