• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Mei, Qiang (Mei, Qiang.) | Hu, Qinyou (Hu, Qinyou.) | Hu, Yu (Hu, Yu.) | Yang, Yang (Yang, Yang.) | Liu, Xiliang (Liu, Xiliang.) | Huang, Zishuo (Huang, Zishuo.) | Wang, Peng (Wang, Peng.)

收录:

EI Scopus SCIE

摘要:

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.

关键词:

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

作者机构:

  • [ 1 ] [Mei, Qiang]Shanghai Maritime Univ, Merchant Marine Acad, Shanghai 200210, Peoples R China
  • [ 2 ] [Hu, Qinyou]Shanghai Maritime Univ, Merchant Marine Acad, Shanghai 200210, Peoples R China
  • [ 3 ] [Huang, Zishuo]Shanghai Maritime Univ, Merchant Marine Acad, Shanghai 200210, Peoples R China
  • [ 4 ] [Wang, Peng]Shanghai Maritime Univ, Merchant Marine Acad, Shanghai 200210, Peoples R China
  • [ 5 ] [Mei, Qiang]Jimei Univ, Nav Coll, Xiamen 361021, Peoples R China
  • [ 6 ] [Hu, Yu]Xiamen Inst Data Intelligence, Xiamen 361021, Peoples R China
  • [ 7 ] [Yang, Yang]East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China
  • [ 8 ] [Liu, Xiliang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 9 ] [Wang, Peng]Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China

通讯作者信息:

  • [Hu, Qinyou]Shanghai Maritime Univ, Merchant Marine Acad, Shanghai 200210, Peoples R China;;[Wang, Peng]Shanghai Maritime Univ, Merchant Marine Acad, Shanghai 200210, Peoples R China;;

查看成果更多字段

相关关键词:

来源 :

OCEAN & COASTAL MANAGEMENT

ISSN: 0964-5691

年份: 2024

卷: 253

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 9

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:243/5026637
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司