Dynamic Metamorphosis: Investigating Temporal Shifts in Global Crude Oil Trade Networks
DOI:
https://doi.org/10.47611/jsrhs.v13i4.7823Keywords:
Network, Abnormalities, Weighted, Eigenvector, Betweenness, Global Clustering Coefficient, EfficiencyAbstract
The vast disconnect between crude oil supply and demand markets has given rise to a worldwide crude oil trading system. This paper constructs and analyzes global crude oil trade networks, employing various network analysis techniques and visualization methods. This study aims to delve into the patterns and abnormalities in the global crude oil market, offering insights into its evolution over the past decade.
Based on the UN Comtrade Data from 2008-2023, we have constructed a weighted crude oil trade network. Through parameters such as Eigenvector Centrality, Betweenness Centrality and Global Clustering Coefficient we can arrive to certain conclusions regarding the evolving efficiency, criticality and robustness of economies and the trade relationships between nations and their primary crude oil trading partners.
Through the lens of geopolitical tensions, such as the ongoing Russia-Ukraine conflicts and the 2008 Financial Crisis, this paper aims to understand the behavior of the global crude oil trade network. It delves deep into the intricacies of specific nodes within the network, analyzing how these events have reshaped trade relationships, altered centrality measures, and impacted overall network resilience and adaptability. By examining these critical junctures, the paper sheds light on the dynamic responses and structural changes within the crude oil trade network during periods of significant geopolitical and economic upheaval.
Downloads
References or Bibliography
Bhattacharya, K., Mukherjee, G., Sarama ̈ki, J., Kaski, K., Manna, S. S., 2008. The international trade network: Weighted network analysis and modelling. J. Stat. Mech.-Theory Exp., P02002.
Barigozzi, M., Fagiolo, G., Garlaschelli, D., 2010. The multi-network of international trade: A commodity-specific analysis. Phys. Rev. E 81, 046104.
Giudici, P., Huang, B. H., Spelta, A., 2019. Trade networks and economic fluctuations in Asian countries. Economic Systems 43 (2).
Gleditsch, K. S., 2002. Expanded trade and GDP data. J. Conflict Resolution 46, 712–724.
Dablander, F., Hinne, M., 2019. Node centrality measures are a poor substitute for causal inference. Sci. Rep. 9.
Gao, C. X., Sun, M., Shen, B., 2015. Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis. Applied Energy 156, 542–554.
Du, R. J., Wang, Y., Dong, G. G., Tian, L. X., Liu, Y. X., Wang, M. G., Fang, G. C., 2017. A complex network perspective on interrelations and evolution features of international oil trade, 2002-2013. Appl. Energy 196, 142–151.
Sun, Q., Gao, X., Zhong, W., Liu, N., 2017. The stability of the international oil trade network from short-term and long-term perspectives. Physica A 482, 345–356.
Chen, B., Lam, W. H. K., Sumalee, A., Li, Q. Q., Li, Z. C., 2012. Vulnerability analysis for large-scale and congested road networks with demand uncertainty. Transportation Research Part A: Policy and Practice 46 (3), 501–516.
Annual crude oil production. https://www.opec.org/opec_web/en/data_graphs/330.htm
Shift towards renewable energy sources. https://www.researchgate.net/figure/Total-renewable-energy-usage-2010-2020-12_fig3_363026877
Maritime routes crucial for world oil trade. https://www.aa.com.tr/en/energy/oil/maritime-routes-crucial-for-world-oiltrade/12746
Deflation of crude oil prices following the Great Recession. https://www.sciencedirect.com/science/article/pii/S2211467X20300699
Wen-Jie Xie, Na Wei, Wei-Xing Zhou 2020, Evolving efficiency and robustness of global oil trade networks
Published
How to Cite
Issue
Section
Copyright (c) 2024 Rohan Agarwal
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
