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Identification of Hydrogen-Energy-Related Emerging Technologies Based on Text Mining

Abstract

As a versatile energy carrier, hydrogen possesses tremendous potential to reduce greenhouse emissions and promote energy transition. Global interest in producing hydrogen from renewable energy sources and transporting, storing, and utilizing hydrogen is rising rapidly. However, the high costs of producing clean hydrogen and the uncertain application scenarios for hydrogen energy result in its relatively limited utilization worldwide. It is necessary to find new promising technological paths to drive the development of hydrogen energy. As part of technological innovation, emerging technologies have vital features such as prominent impact, novelty, relatively fast growth, etc. Identifying emerging hydrogen-energy-related technologies is important for discovering innovation opportunities during the energy transition. Existing research lacks analysis of the characteristics of emerging technologies. Thus, this paper proposes a method combining the latent Dirichlet allocation topic model and hydrogen-energy expert group decision-making. This is used to identify emerging hydrogen-related technology regarding two features of emerging technologies, novelty and prominent impact. After data processing, topic modeling, and analysis, the patent dataset was divided into twenty topics. Six emerging topics possess novelty and prominent impact among twenty topics. The results show that the current hotspots aim to promote the application of hydrogen energy by improving the performance of production catalysts, overcoming the wide power fluctuations and large-scale instability of renewable energy power generation, and developing advanced hydrogen safety technologies. This method efficiently identifies emerging technologies from patents and studies their development trends. It fills a gap in the research on emerging technologies in hydrogen-related energy. Research achievements could support the selection of technology pathways during the low-carbon energy transition.

Funding source: This research was funded by the National Natural Science Foundation of China [grant numbers 71974107, L2224059, L2124002, 91646102, L1924058]; the MOE (Ministry of Education in China) Project of Humanities and Social Sciences [grant number 16JDGC011]; the Construction Project of China Knowledge Center for Engineering Sciences and Technology [grant number CKCEST-2022-1- 30]; the Tsinghua University Initiative Scientific Research Program [grant numbers: 2022THZWYY09, 2019Z02CAU]; and the Tsinghua University Project of Volvo-Supported Green Economy and Sustainable Development [grant number 20183910020].
Related subjects: Applications & Pathways
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/content/journal5392
2023-12-22
2024-02-28
http://instance.metastore.ingenta.com/content/journal5392
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