Risk analysis and resilience assessment of China's oil imports after the Ukraine Crisis:A network-based dynamics model

被引:2
|
作者
Liu, Yi [1 ]
Wang, Jianliang [1 ,2 ,3 ]
机构
[1] China Univ Petr, Sch Econ & Management, Beijing 102249, Peoples R China
[2] China Univ Petr, Res Ctr Chinas Oil & Gas Ind Dev, Beijing 102249, Peoples R China
[3] China Univ Petr, Inst Carbon Neutral & Innovat Energy Dev, Beijing 102249, Peoples R China
基金
中国国家自然科学基金;
关键词
Cascading diffusion; Resilience; Oil trade network; Interrupt simulation; TRADE NETWORK;
D O I
10.1016/j.energy.2024.131502
中图分类号
O414.1 [热力学];
学科分类号
摘要
Global oil trade integration while creating a platform for risk spread. This study established a two-stage model for risk spread and recovery, simulating the cascading impact of oil supply shortage risks. On this basis, an assessment of the resilience of China's oil imports is conducted. The results indicate that, during the risk spread phase, countries causing a significant negative impact on China's oil imports may not necessarily be China's primary import sources. The proportion of indirect losses in Chinese oil imports due to Canadian oil supply shortages accounted for more than 70 % of the total losses. The United States, Singapore, Australia, the United Kingdom, and Brazil repeatedly act as intermediaries in risk spread. For the recovery phase, swiftly restoration of China's initial imports needs to establish new trade relationships with other non-traditional partners through competition. The top three countries in terms of accumulated new trade relationships are Korea (21), Turkmenistan (13), and Mexico (4). Regarding resilience, when the risk originates from Saudi Arabia, China demonstrates the weakest risk resistance, followed by Russia and Iran. The results not only serve as an early warning for China on regional oil supply risks but also provide policy directions for securing oil stable supply.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Risk diffusion of international oil trade cuts: A network-based dynamics model
    Chen, Zhihua
    Wang, Hui
    Liu, Xueyong
    Wang, Ze
    Wen, Shaobo
    ENERGY REPORTS, 2022, 8 : 11320 - 11333
  • [2] Coupling dynamics of urban flood resilience in china from 2012 to 2022: A network-based approach
    Chen, Zhang
    Zhu, Shiyao
    Feng, Haibo
    Zhang, Hongsheng
    Li, Dezhi
    SUSTAINABLE CITIES AND SOCIETY, 2025, 118
  • [3] Resilience Assessment of China's Natural Gas Supply System Based on Ecological Network Analysis
    Li Xueyi
    Zhang Jinjun
    Huai, Su
    Zio, Enrico
    2019 4TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS 2019), 2019, : 402 - 406
  • [4] Enhancing highway transportation safety resilience during emergencies: A network-based analysis and assessment
    Zhang, Xue
    Lu, Yi
    Wang, Jie
    Qi, Yongzheng
    PLOS ONE, 2024, 19 (07):
  • [5] Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data
    Yuan, Tangxiao
    Assilevi, Kossigan Roland
    Adjallah, Kondo Hloindo
    Ajavon, Ayite Senah A.
    Wang, Huifen
    ENERGIES, 2024, 17 (23)
  • [6] Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment
    Ming Li
    Mei Hong
    Ren Zhang
    International Journal of Disaster Risk Science, 2018, 9 : 237 - 248
  • [7] Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment
    Li, Ming
    Hong, Mei
    Zhang, Ren
    INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE, 2018, 9 (02) : 237 - 248
  • [8] Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment
    Ming Li
    Mei Hong
    Ren Zhang
    InternationalJournalofDisasterRiskScience, 2018, 9 (02) : 237 - 248
  • [9] Quantitative Assessment of Cyber Security Risk using Bayesian Network-based model
    Mo, Sheung Yin Kevin
    Beling, Peter A.
    Crowther, Kenneth G.
    2009 IEEE SYSTEMS AND INFORMATION ENGINEERING DESIGN SYMPOSIUM (SIEDS), 2009, : 183 - 187
  • [10] Structural risk evaluation of global gas trade by a network-based dynamics simulation model
    Chen, Zhihua
    An, Haizhong
    An, Feng
    Guan, Qing
    Hao, Xiaoqing
    ENERGY, 2018, 159 : 457 - 471