Complex Network Approach to International Trade of Fossil Fuel

Authors: Semanur Soyyiğit Kaya, Ercan Eren

Abstract:

Energy has a prominent role for development of nations. Countries which have energy resources also have strategic power in the international trade of energy since it is essential for all stages of production in the economy. Thus, it is important for countries to analyze the weaknesses and strength of the system. On the other side, international trade is one of the fields that are analyzed as a complex network via network analysis. Complex network is one of the tools to analyze complex systems with heterogeneous agents and interaction between them. A complex network consists of nodes and the interactions between these nodes. Total properties which emerge as a result of these interactions are distinct from the sum of small parts (more or less) in complex systems. Thus, standard approaches to international trade are superficial to analyze these systems. Network analysis provides a new approach to analyze international trade as a network. In this network, countries constitute nodes and trade relations (export or import) constitute edges. It becomes possible to analyze international trade network in terms of high degree indicators which are specific to complex networks such as connectivity, clustering, assortativity/disassortativity, centrality, etc. In this analysis, international trade of crude oil and coal which are types of fossil fuel has been analyzed from 2005 to 2014 via network analysis. First, it has been analyzed in terms of some topological parameters such as density, transitivity, clustering etc. Afterwards, fitness to Pareto distribution has been analyzed via Kolmogorov-Smirnov test. Finally, weighted HITS algorithm has been applied to the data as a centrality measure to determine the real prominence of countries in these trade networks. Weighted HITS algorithm is a strong tool to analyze the network by ranking countries with regards to prominence of their trade partners. We have calculated both an export centrality and an import centrality by applying w-HITS algorithm to the data. As a result, impacts of the trading countries have been presented in terms of high-degree indicators.

Keywords: Complex network approach, fossil fuel, international trade, network theory.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110862

References:

[1] G. Fagiolo, T. Squartini and D. Garlaschelli, “Null models of economic networks: the case of the world trade web”, Journal of Economic Interaction and Coordination, vol.8, pp.75-107, 2013.
[2] H. An, W. Zhong, Y. Chen, H. Li and X. Gao, “Features and evolution of international crude oil trade relationship: a trading-based network analysis”, Energy, vol. 74, pp.254-259, 2014.
[3] W. Zhong, H. An, X. Gao and X. Sun, “The evolution of communities in the international oil trade network”, Physica A, vol. 413, pp.45-52, 2014.
[4] W. Zhong and H. An, “The role of China in the international crude oil trade network”, Energy Procedia, vol. 61, pp.2493-2496, 2014.
[5] S. Cheng, L. Song and X. Li, “Evolution of spatial pattern of crude oil trade”, Studies in Sociology of Science, vol. 5, no. 1, pp. 1-7, 2014.
[6] H. Xiaoqing, A. Haizhong and Q. Hai, “Evolution of fossil energy international trade pattern based on complex network”, Energy Procedia, vol. 61, pp.476-479, 2014.
[7] J. Reichardt, “Introduction to complex networks”, in Structure in Complex Networks Lecture Notes in Physics, vol 766, Springer-Verlag Berlin Heidelberg, 2009: pp.1-11.
[8] W. Chow, “An anatomy of the world trade network”, http://www.hkeconomy.gov.hk/en/pdf/An%20Anatomy%20of%20the%20World%20Trade%20Network%20%28July%202013%29.pdf, (31.10.2013), pp. 1-20
[9] M. D. König and S. Battiston, “From graph theory to models of economic networks: a tutorial” in Networks, Topology and Dynamics, A.K.Naimzada et.al., Ed. Springer-Verlag Berlin Heidelberg, 2009, pp. 23-63.
[10] M. E. J. Newman, Networks An Introduction, Oxford University Press, 2010.
[11] A. Howell, “Network statistics and modeling the global trade economy: exponential random graph models and latent space models: is geography dead?”, University of California, 2012, unpublished thesis.
[12] G. Caldarelli, “Lectures in complex networks”, http://www.ifr.ac.uk/netsci08/Download/Invited/ws1_Caldarelli.pdf
[13] P. Csermely, A. London, L. Wu, B. Uzzi, “Structure and dynamics of core/periphery networks”, Journal of Complex Networks, vol. 1, pp.93- 123, 2013.
[14] X. F. Wang, G. Chen, “Complex networks: small-world, scale-free and beyond”, IEEE Circuits and Systems Magazine, pp. 6-20, 2003.
[15] G. Fagiolo, J. Reyes and S. Schiavo. “The evolution of the world trade web: a weighted-network analysis” Journal of Evolutionary Economics, vol .20, no. 4, pp. 479-514, 2010.
[16] Jon M. Kleinberg, “Authoritative sources in a hyperlinked environment”, Journal of the ACM, vol. 46, no. 5, pp.604-632, 1999.
[17] W. Wei and G. Liu, “Bringing order to the world trade network,” in Int. Conf. on Economics Marketing and Management, IPEDR, vol.28, 2012, pp. 88-92.
[18] T. Deguchi, K. Takahashi, H. Takayasu and M. Takayasu, “Hubs and authorities in the world trade network using a weighted HITS algorithm”, PLOSONE, vol.9, no. 7, pp. 1-16, 2014.
[19] E. Eren and S. Soyyiğit Kaya, “Network analysis of Turkey’s trade with EU-28 with regards to BEC classification,” in 1st Annual Int. Conf. on Social Sciences, Istanbul, 2015, pp. 39-64.
[20] International Energy Agency, Key World Energy Statistics 2014.
[21] M. Aktaş, “Türkiye’de kömür madenciliği ve enerjideki rolü”, http://www.tki.gov.tr/Dosyalar/Dosya/YAZILI%20B%C4%B0LD%C4 %B0R%C4%B0%20METN%C4%B0.pdf, p.1-16.