Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 61 (2014) 2493 – 2496
The 6th International Conference on Applied Energy – ICAE2014
The Role of China in the International Crude Oil Trade Network Weiqiong Zhonga,b,c Haizhong Ana,b,c * a School of Humanities and Economic Management, China University of Geosciences, Beijing, China b Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resource, Beijing, China c Lab of Resources and Environmental Management, China University of Geosciences, Beijing, China
Abstract We built up a network model of international crude oil trade based on complex network theory and studied the properties of China including: trade relation and trade volume, control and anti-control abilities, and selection of trade partners. And we came to the conclusion that both the control ability and anti-control ability of China are rising. Trading with partners who have closer relations among each other can improve the anti-control ability of China; however, it is not so obvious in improving the control ability. © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2014 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the Organizing Committee of ICAE2014
Keywords: Crude oil; International trade; China; Complex network
1. Introduction Crude oil plays an important role in the world energy market. The uneven distribution of crude oil production and consumption shapes the international crude oil trade network. China is a country whose domestic oil supply cannot meet its great demand, and in 2012 the dependence on foreign oil reached 56.4%. Thus, optimizing the international crude oil trade is crucial to the energy security of ChinaDŽ International trade is a network with numerous countries and trade relations, and complex network model has the advantage of analyzing numerous nodes and links topologically and dynamically [1-5]. Our study applies the tool of complex network to look into the role of an individual country, China, in the international crude oil system. We first introduced the coherence of clustering coefficient and closeness centrality, as well as the coherence of clustering coefficient and betweenness centrality, into the complex analysis. 2. Data and model * Haizhong An. Tel.: +86-138-109-10701. E-mail address:
[email protected].
1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICAE2014 doi:10.1016/j.egypro.2014.12.030
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The data on international crude oil trade is downloaded from UN Comtrade, which contains crude oil export and import flows among 181 countries in the world. We selected the annual crude oil trade data for all these countries from 1993 to 2012. We represented countries as nodes and trade relations between countries as edges. The directions of the edges are the directions of crude oil trade flows, and the weights of the edges are the trade volume of crude oil measured by tons. 3. The Properties of China 3.1. Trade relation and trade volume
In-degree
50 40 30 20 10 0
Out-degree Weighted Degree
Degree
The total number of trade relations of a country in complex network model is calculated by degree. Out-degree is the number of export relations a country has with others, and in-degree is the number of import relations. The values of out-degree and in-degree can reflect a country’s importance in the network considering only relationships. The total trade volume of a country in complex network model is calculated by weighted degree. Weighted degree reflects a country’s importance in the network considering both relations and volume. We calculated the degree and weighted degree of China from 1993 to 2012 and plotted them in Fig.1. From Fig. 1 we can see that the in-degree is higher than out-degree and both of them are ascending, which means that the crude oil import relations of China is more than export relations, the number of both relations are increasing. On the contrary, the importing volume of China is rocketing during the 2 decades while the exporting volume is descending.
Year
(a)
Weighted In-degree 2.5E+11 2E+11 1.5E+11 1E+11 5E+10 0
Weighted Out-degree
Year
(b)
Fig. 1. (a) Degree of China; (b) Weighted Degree of China
3.2. Control and anti-control abilities Betweenness centrality measures the capability of the nodes as mediums in the network. It can also be explained as the extent to which the node controls the trade flow. In the international crude oil trade network, betweenness centrality is the frequency a country stands on the shortest path between two other countries. Fig.2 shows the betweenness centralities of five countries. From Fig.2 we can see that USA has the highest control ability. The control ability of China ascends eventually with fluctuation before 2000, descends a little from 2002 to 2008, rises to a peak in 2010 and drops a little in 2011 and 2012. The entire tendency of China ascends and the level of China’s control ability in the international crude oil trade is more or less equal to Russia and Netherlands, far below USA and a little higher than Japan. Betweenness centrality
8000 6000
China USA Russia Japan Netherlands
4000 2000 0 Year
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Fig. 2. The Betweenness Centrality of 5 countries in the international crude oil trade
Closeness Centriality
Closeness centrality of a country is the average distance from this country to all the other countries in the complex network. It reflects the anti-control ability of a country. The closer a country is from others, the higher anti-control ability. Fig.3 shows the closeness centralities of five countries. From Fig.3 we can see that the anti-control abilities of USA and Russia are similar and both are higher than other countries. The anti-control ability of China is between Netherlands and Japan, and it is ascending slightly. China
1.5 1.75 2 2.25 2.5 2.75 3 3.25
USA Russia Japan Netherlands Year
Fig. 3. The Closeness Centrality of 5 countries in the international crude oil trade
3.3. Selection of trade partners Clustering coefficient of a country is the probability of trade relationships exist between the partners of this country. It reflects the closeness of the neighbors. If a country’s neighbor in the network is closely related, the country owns a higher clustering coefficient; on the contrary, if a country’s neighbor is loosely related, the clustering coefficient of this country is lower. The weighted clustering coefficient takes into account not only the gathering of the network, but also considers the weights of the edges. We plotted the weighted clustering coefficient of China with the closeness centrality and betweenness centrality respectively in Fig.4. From Fig.4 we can see that the clustering coefficient of China is more coherent with closeness centrality than with betweenness centrality. To quantify this result, we calculated the coherence of the two couple of curves. In Fig.5 we can see that the coherence of clustering coefficient and closeness centrality is higher than the coherence of clustering coefficient and betweenness centrality. This means trading with partners who have closer relations between them can improve the anti-control ability of China; however, it is not so obvious in improving the control ability. Clustering Coefficient
Clustering Coefficient
Closeness Centrality 4
0.3
0.3
3
0.2
Betweeness Centrality 2000 1500
0.2
1000
2 0.1 0
(a)
0.1
1
500
0
0
(b)
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
0 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
Year
Year
Fig.4. (a) Clustering Coefficient and Closeness Centrality; (b) Clustering Coefficient and Betweenness Centrality Coherence of Clustering Coefficient and Closeness Centrality Coherence of Clustering Coefficient and Betweenness Centrality
1 0.8 0.6 0.4 0.2 0 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Fig. 5. The coherence analysis results
0.8
0.9
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4. Conclusion Based on complex network model and analysis, we have studied the role of China in the international crude oil trade and have come to 3 conclusions. First, the import volume of China is rocketing from 1993 to 2012 while the export volume is descending. This may due to the increase of crude oil demand in the pace of fast economic growth of China. On the contrary, both of the export and import relations of China are increasing. This may due to the reform and opening of China, and China seek for more trade partners. Second, both the control ability and anti-control ability of China are ascending and ranked between 2 and 4, similar to Netherlands. The control ability indicates the extent to which a country controls the resource flows between countries. If a country with high control ability cuts off its trade links with some of its partners, the whole network will be significantly affected. The increase of control ability implies that as China expanding its trade relations with more countries, it stands between more pairs of countries in the network, thus it controls more crude oil flows between countries. However, the anti-control ability indicates the national energy security, the ability of making up supplies or demands from other sources when some trade relations suddenly cut off. The increase of anti-control ability implies that China paid more attention to the trade risks and energy security, and the crude oil trade strategy of China is being optimized. Third, trading with partners who have closer relations among each other can improve the anti-control ability of China; however, it is not so obvious in improving the control ability. This result is practical. For China is a huge country with rocketing oil demand to support its rapid economic growth, its priority is not control ability but anti-control ability. To enhance the energy security, China could choose crude oil trade partners who have many trade relations among each other. In the future study, we can expand the observed period to several decades and analyze more countries to obtain further results and conclusions. References [1] M.A. Serrano, M. Boguna, Topology of the world trade web, Physical Review E, 68 (2003). [2] D. Garlaschelli, M.I. Loffredo, Structure and evolution of the world trade network, Physica a-Statistical Mechanics and Its Applications, 355 (2005) 138-144. [3] G. Fagiolo, J. Reyes, S. Schiavo, World-trade web: Topological properties, dynamics, and evolution, Physical Review E, 79 (2009). [4] G. Fagiolo, J. Reyes, S. Schiavo, The evolution of the world trade web: a weighted-network analysis, Journal of Evolutionary Economics, 20 (2010) 479-514. [5] M. Barigozzi, G. Fagiolo, D. Garlaschelli, Multinetwork of international trade: A commodity-specific analysis, Physical Review E, 81 (2010).
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