Exploring the complex structural evolution of global primary product trade network
Received date: 2021-11-03
Revised date: 2022-03-06
Accepted date: 2022-03-29
Online published: 2022-05-13
The production and trade of primary products had a growing impact on the economic security of all countries and regions, and the strategic position of these products in the global trade network was becoming increasingly prominent. Based on complex network theory, this paper explored the spatial pattern and complex structural evolution of the global primary product trade network (GPPTN) during 1985-2015 by using index methods, such as centrality, Sankey diagram, and structure entropy, focusing on the diversified spatial structure of China’s import and export markets for primary products (with exceptions of Taiwan of China, Hong Kong of China, and Macao of China due to a lack of data) and their geographical implications for China’s energy security. The research offered the following key findings. The GPPTN showed an obvious spatial heterogeneity pattern, and the area of import consumption was more concentrated; however, the overall trend was decentralized. The trade center of gravity shifted eastwards and reflected the rise of emerging markets. The overall flow of the GPPTN was from west to east and from south to north. In terms of the community detection of the GPPTN, North America, Europe, and Asia increasingly presented an unbalanced “tripartite confrontation”. China’s exports of primary products were mainly concentrated in the Association of Southeast Asian Nations (ASEAN) and other peripheral regions of Asia, and its imports undergone a major transformation, gradually expanding from the peripheral regions of Asia to Africa, the Middle East, Latin America, and other parts of the world. Energy fuels also became the largest imported primary products. Based on the changing trend of structural entropy and main market share, the analysis showed that the stable supply of China’s energy diversification was gradually realized. In particular, the cooperation dividend proposed by the Belt and Road initiative became an important turning point and a strong support for the expansion of China’s energy market diversification pattern and guarantee of energy security.
JIANG Xiaorong , LIU Qing , WANG Shenglan . Exploring the complex structural evolution of global primary product trade network[J]. Regional Sustainability, 2022 , 3(1) : 82 -94 . DOI: 10.1016/j.regsus.2022.03.006
| [1] | Alves L.A., Mangioni G., Rodrigues F., et al., 2018. Unfolding the complexity of the global value chain: strength and entropy in the single-layer, multiplex, and multi-layer international trade networks. Entropy. 20(12), 909, doi: 10.3390/e20120909. |
| [2] | An H.Z., Zhong W.Q., Chen Y.R., et al., 2014. Features and evolution of international crude oil trade relationships: a trading-based network analysis. Energy. 74, 254-259. |
| [3] | Angelidis G., Ioannidis E., Makris G., et al., 2020. Competitive conditions in global value chain networks: An assessment using entropy and network analysis. Entropy. 22(10), 1068, doi: 10.3390/e22101068. |
| [4] | Barabási A.L., Albert R., 1999. Emergence of scaling in random networks. Science. 286(5429), 509-512. |
| [5] | Bianconi G., 2008. The entropy of randomized network ensembles. Europhys. Lett. 81(2), 28005, doi: 10.1209/0295-5075/81/28005. |
| [6] | Cai M., Cui Y., Stanley H.E., 2017. Analysis and evaluation of the entropy indices of a static network structure. Sci. Rep. 7, 9340, doi: 10.1038/s41598-017-09475-9. |
| [7] | Chen Y., Li E., 2019. Spatial pattern and evolution of cereal trade networks among the Belt and Road countries. Progress in Geography. 38(10), 1643-1654. (in Chinese) |
| [8] | Chen Z.H., An H.Z., An F., et al., 2018. Structural risk evaluation of global gas trade by a network-based dynamics simulation model. Energy. 159, 457-471. |
| [9] | Coe N.M., Yeung H.W.C., 2019. Global production networks: Mapping recent conceptual developments. J.Econ. Geogr. 19(4), 775-801. |
| [10] | Danilov-Danil’ yan V.I., 2013. The natural resources sector in the structure of the world economy and the causes of the global economic crisis. Her. Russ. Acad. Sci. 83(2), 115-122. |
| [11] | de Andrade R.L., Rêgo L.C., 2018. The use of nodes attributes in social network analysis with an application to an international trade network. Phys. A Stat. Mech. Appl. 491, 249-270. |
| [12] | Fagiolo G., 2007. Clustering in complex directed networks. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys. 76(2), 26107, doi: 10.1103/physreve.76.026107. |
| [13] | Fronczak A., Fronczak P., Janusz A.H., 2004. Average path length in random networks. Phys. Rev. E. 70(52), 056110, doi: 10.1103/physreve.70.056110. |
| [14] | Garlaschelli D., Matteo T.D., Aste T., et al., 2007. Interplay between topology and dynamics in the world trade web. Eur. Phys. J. B. 57(2), 159-164. |
| [15] | Grilli E.R., Yang M.C., 1988. Primary commodity prices, manufactured goods prices, and the terms of trade of developing countries: What the long run shows. The World Bank Economic Review. 2, 1-47. |
| [16] | Hanousek J., Kočenda E., 2014. Factors of trade in Europe. Econ Syst. 38(4), 518-535. |
| [17] | Hao X.Q., An H.Z., Sun X.Q., et al., 2018. The import competition relationship and intensity in the international iron ore trade: from network perspective. Resour. Policy. 57, 45-54. |
| [18] | Hong Y., Yang Y.M., Mu X.W., 2018. Assessing the US trade policies in the primary products. DEStech Transactions on Social Science Education and Human Science.148-151. |
| [19] | Ikeda Y., Iyetomi H., 2018. Trade network reconstruction and simulation with changes in trade policy. Evol. Inst. Econ. Rev. 15(2), 495-513. |
| [20] | Jia J.S., Lu X., Yuan Y., et al., 2020. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 582(7812), 389-394. |
| [21] | Kitamura T., Managi S., 2017. Driving force and resistance: Network feature in oil trade. Appl. Energy. 208, 361-375. |
| [22] | Kulkarni S.S., Nathan H.S.K., 2016. The elephant and the tiger: Energy security, geopolitics, and national strategy in China and India’s cross border gas pipelines. Energy Res. Soc. Sci. 11, 183-194. |
| [23] | Li G.Z., Xue M.T., Lv J.W., 2008. The study on the effect of oil price on primary commodities’ prices. Journal of International Trade. 7, 9-15. (in Chinese) |
| [24] | Mahutga M.C., 2006. The persistence of structural inequality? A network analysis of international trade, 1965-2000. Soc. Forces. 84(4), 1863-1889. |
| [25] | Newman M.E.J., 2005. Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323-351. |
| [26] | Newman M.E.J., 2006. Modularity and community structure in networks. Proc. Natl. Acad. Sci. U. S. A. 103(23), 8577-8582. |
| [27] | Qiang W.L., Niu S.W., Liu A.M., et al., 2020. Trends in global virtual land trade in relation to agricultural products. Land Use Policy. 12(1), 192, doi: 10.1016/j.landusepol.2019.104439. |
| [28] | Saramäki J., Kivelä M., Onnela J.P., et al., 2007. Generalizations of the clustering coefficient to weighted complex networks. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys. 75(2), 027105, doi: 10.1103/physreve.75.027105. |
| [29] | Serrano M.A., Boguã M., 2003. Topology of the world trade web. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys. 68(2), 634-646. |
| [30] | Sturgeon T., van Biesebroeck J., Gereffi G., 2008. Value chains, networks and clusters: reframing the global automotive industry. J. Econ. Geogr. 8(3), 297-321. |
| [31] | Tegene A., 1990. Commodity concentration and export earnings instability: The evidence from African countries. Am. Econ. 34(2), 55-59. |
| [32] | United Nations, 2006. Standard international trade classification:revision 4. New York: United Nations Publications, 8-10. |
| [33] | Wang W., Fan L., Li Z., et al., 2021a. Measuring dynamic competitive relationship and intensity among the global coal importing trade. Appl. Energy. 303, 117611, doi: 10.1016/j.apenergy.2021.117611. |
| [34] | Wang J.Y., Dai C., Zhou M.Z., et al., 2021b. Research on global grain trade network pattern and its influencing factors. J. Nat. Resour. 36 (6), 1545-1556. (in Chinese) |
| [35] | Wang F., Tian M.H., Yin R.S., et al., 2021c. Change of global woody forest products trading network and relationship between large supply and demand countries. Resources Science. 43(5), 1008-1024. (in Chinese) |
| [36] | Watts D.J., Strogatz S.H., 1998. Collective dynamics of ‘small-world’ networks. Nature. 393(6684), 440-442. |
| [37] | Wilhite A., 2001. Blateral trade and ‘small-world’ networks. Comput. Econ. 18(1), 49-64. |
| [38] | Xu L.L., Wang Q., Li N., et al. 2017. Spatial-temporal evolution of global energy security since 1990s. Acta Geographica Sinica. 72(12), 2166-2178. (in Chinese) |
| [39] | Yang Y., Poon J.P.H., Liu Y., et al. 2015. Small and flat worlds: A complex network analysis of international trade in crude oil. Energy. 93, 534-543. |
| [40] | Zha D., 2015. Energy security in China-European Union relations: framing further efforts of collaboration. Contemp. Politics. 21, 308-322. |
| [41] | Zhong W.Q., An H.Z., Fang W., et al., 2016. Features and evolution of international fossil fuel trade network based on value of energy. Appl. Energy. 165, 868-877. |
| [42] | Zhu R., Wang Y., Lin D., et al., 2021. Exploring the rich-club characteristic in internal migration: evidence from Chinese Chunyun migration. Cities. 114(10), 103198, doi: 10.1016/j.cities.2021.103198. |
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