Liquefied natural gas trade network changes and its mechanism in the context of the Russia–Ukraine conflict

Renrong Xiao, Pengjun Zhao, Kangzheng Huang, Tianyu Ma, Zhangyuan He, Caixia Zhang and Di Lyu

Journal of Transport Geography, 2025, vol. 123, issue C

Abstract: Liquefied natural gas (LNG), as a transitional fossil fuel, plays a vital role in the modern energy transition process. In the context of the Russia–Ukraine geopolitical conflict, studying the evolution pattern and mechanisms of LNG trade networks is crucial for maintaining global energy security, particularly for countries relying on LNG imports. The study is based on multisource big data from 2021 to 2022, including Automatic Identification System (AIS) data, Gdelt news data, and remote sensing satellite data. It utilizes complex network metrics and the temporal exponential random graph model (TERGM) method to analyze the evolution patterns and mechanisms of the global LNG trade network. The research findings indicate the following: (1) The Russia–Ukraine conflict has led to a significant increase in LNG imports by European countries. Many countries have opened temporary direct shipping routes, which has accelerated the decline in the average path length of the network and improved network efficiency. (2) The global LNG trade community trend has been strengthened by the Russia–Ukraine conflict. The number of members within the trading community in which the United States is located has increased, with some European countries moving into this community. (3) The reciprocal structural characteristics of the LNG trade network drive its development, and the expansion of the national port handling capacity ability also drives network growth. Conversely, large differences in geopolitical relations, culture, and levels of governance between countries can hinder global LNG network development. This study provides a theoretical basis and decision-making reference for energy security under major geopolitical conflicts.

Keywords: LNG networks; Geopolitical relations; Exponential random graph models; Multisource big data (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:123:y:2025:i:c:s0966692324003107

DOI: 10.1016/j.jtrangeo.2024.104101

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