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Author:

Mei, Q. (Mei, Q..) | Qinyou, H. (Qinyou, H..) | Hu, Y. (Hu, Y..) | Yang, Y. (Yang, Y..) | Liu, X. (Liu, X..) | Huang, Z. (Huang, Z..) | Wang, P. (Wang, P..)

Indexed by:

EI Scopus SCIE

Abstract:

With the proposal of the development goals for pollution control, the use of liquefied natural gas (LNG) as a clean and low-carbon energy source has received huge attention in the European energy market. This study examines the European LNG maritime supply chain network's structural evolution from 2018 to 2020 using AIS data. Our analysis reveals a marked quantitative increase in the network's scale, with European shipments climbing from 695 to 1337 and cargo volumes soaring from 40,101,000 tons to 87,129,740 tons, signifying annual growth rates of 92.4% and 117.3%, respectively. A graph deep learning approach unveiled enhanced connectivity and community consolidation among European LNG ports despite the dispersion suggested by a slight density decrease from 0.192 to 0.185. Simulated attack scenarios indicate heightened network robustness in 2020, yet emphasize the criticality of safeguarding nodes like Sebatta against targeted disruptions. Addressing these insights, we propose policies focused on energy diversification, fortified port security, and adaptive governance to bolster the network's resilience amidst dynamic global conditions. Our study thus offers a strategic framework for managing energy trade complexity, acknowledging the need for further research on the geopolitical impact on network dynamics and vulnerability. © 2024 Elsevier Ltd

Keyword:

Graph deep learning Maritime transportation network Liquefied natural gas Community evolution Supply chain security Vulnerability

Author Community:

  • [ 1 ] [Mei Q.]Merchant Marine Academy, Shanghai Maritime University, Shanghai, 200210, China
  • [ 2 ] [Mei Q.]Navigation College, Jimei University, Xiamen, 361021, China
  • [ 3 ] [Qinyou H.]Merchant Marine Academy, Shanghai Maritime University, Shanghai, 200210, China
  • [ 4 ] [Hu Y.]Xiamen Institute of Data Intelligence, Xiamen, 361021, China
  • [ 5 ] [Yang Y.]School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
  • [ 6 ] [Liu X.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 7 ] [Huang Z.]Merchant Marine Academy, Shanghai Maritime University, Shanghai, 200210, China
  • [ 8 ] [Wang P.]Merchant Marine Academy, Shanghai Maritime University, Shanghai, 200210, China
  • [ 9 ] [Wang P.]Institute of Computing Technology Chinese Academy of Sciences, Beijing, 100190, China

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Source :

Ocean and Coastal Management

ISSN: 0964-5691

Year: 2024

Volume: 253

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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