NLP based Research on Traditional Energy Trade of the “Belt and Road” Energy Cooperation Partnership Countries

Jun Gong, Duoyong Sun () and Liang Feng
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Jun Gong: National University of Defense Technology
Duoyong Sun: National University of Defense Technology
Liang Feng: National University of Defense Technology

A chapter in Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024), 2024, pp 523-531 from Springer

Abstract: Abstract With the establishment of the “Belt and Road” energy cooperation partnership in 2019, the opportunities for improving China’s energy supply structure are broader. Taking the traditional energy trade of partner countries as the entry point, relevant public data is collected, and natural language processing (NLP) technology is used to analyze and process it using the PEGASUS text summary model. The main conclusions include: (1) Currently, there is a structural imbalance in China’s traditional energy trade. In the partnership, countries with abundant oil and gas resources such as Iraq, Kuwait, and Venezuela have become the main targets of China’s traditional energy trade; (2) The fluctuation of multilateral exchange rates has a significant inhibitory effect on China’s traditional energy imports, with the strongest inhibitory effect being the imbalance of the unilateral exchange rate of the RMB; (3) The energy itself has a significant import promoting effect on China’s traditional energy imports, and the construction of international energy pipeline connectivity also has a significant promoting effect on this. On the one hand, it provides a foundation for the improvement of China’s traditional energy trade policy, and on the other hand, this study is a beneficial exploration of using NLP technology to analyze social science issues, providing new ideas and insights for the research of China’s traditional energy trade.

Keywords: NLP; The Belt and Road; Energy Cooperation Partnership; Traditional Energy Trade (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-570-6_52

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