a. School of Management Science and Engineering;
b. Natural Resource Asset Capital Research Center;
c. Hebei Mineral Resources Strategy and Management Research Base;
d. School of Management, Hebei University of Geosciences, Shijiazhuang 050031, China
Abstract:In order to study the evolution characteristics of conventional energy trade network, based on the energy international trade data from 2010 to 2019 and the complex network theory, this paper constructs a directed weighted network to analyze the overall characteristics of the network and the changes of the characteristics of trade core countries and China in the network. The results show that the scale of conventional energy trade is becoming larger and larger; The United States, the Netherlands and China have strong ability to control trade; China is the core node connecting the network. It is suggested that all countries should observe the trade trend of the United States, the Netherlands and China and adjust the trade volume; The United States, Japan and China are the preferred trading partners of trading countries; Countries with insufficient resources and high demand establish trade relations with Saudi Arabia, Russia and other countries.
[1]邵朝对.能源价格冲击对中国贸易结构的传递效应-基于投入产出法的实证研究[J].上海经济研究,2012,24(7):22-30,42. Shao Chaodui. Transmission effect of energy price shock on China′s trade structure-an empirical study based on input-output method [J]. Shanghai Economic Research, 2012,24 (7): 22-30,42. [2]蔡进洲.国际贸易中石油价格的波动性与风险分析[J].中国商贸,2012(29):184-186. Cai Jinzhou. Volatility and risk analysis of oil price in international trade [J]. China Business Journal, 2012(29): 184-186. [3]杨鑫,安海忠,高湘昀.国际天然气贸易关系网络结构特征研究:基于复杂网络理论[J].资源与产业,2012,14(2):81-87. Yang Xin, An Haizhong, Gao Xiangyun. Research on network structure characteristics of international natural gas trade relations: based on complex network theory [J]. Resources and Industry, 2012,14 (2): 81-87. [4]刘建.基于社会网络的国际原油贸易格局演化研究[J].国际贸易问题,2013,39(12):48-57. Liu Jian. Research on the evolution of international crude oil trade pattern based on social network [J]. International Trade Issues, 2013,39(12): 48-57. [5]肖建忠,彭莹,王小林.天然气国际贸易网络演化及区域特征研究——基于社会网络分析方法[J].中国石油大学学报(社会科学版),2013,29(3):1-8. Xiao Jianzhong, Peng Ying, Wang Xiaolin. Research on evolution and regional characteristics of natural gas international trade network based on social network analysis method [J]. Journal of China University of Petroleum (Social Science Edition), 2013,29 (3): 1-8. [6]Zhong W,An H,Gao X, et al. The evolution of communities in the international oil trade network[J]. Physica A Statistical Mechanics & Its Applications, 2014, 413(11):42-52. [7]Zhong W, An H, Fang W, et al. Features and evolution of international fossil fuel trade network based on value of emergy[J]. Applied Energy, 2016, 165(1):868-877. [8]邓富华,冯乾彬,田霖.“一带一路”倡议下中国石油进口贸易效率及潜力研究[J].重庆大学学报(社会科学版),2019,25(5):18-29. Deng Fuhua, Feng Qianbin, Tian Lin. "One belt, one road" the efficiency and potential of China′s oil import trade [J]. Journal of Chongqing University (Social Sciences), 2019,25 (5): 18-29. [9]何则,杨宇,刘毅,等.世界能源贸易网络的演化特征与能源竞合关系[J].地理科学进展,2019,38(10):1621-1632. He Ze, Yang Yu, Liu Yi, et al. Evolution characteristics of world energy trade network and the relationship between energy competition and cooperation [J]. Progress in Geographical Sciences, 2019,38 (10): 1621-1632. [10] Xiang L, Yu Y J, Chen G. Complexity and synchronization of the world trade web[J]. Physica A: Statistical Mechanics and Its Applications, 2003, 328(1/2):287-296. [11] Geng J B, Ji Q, Fan Y. A dynamic analysis on global natural gas trade network[J]. Applied Energy, 2014,132(1):23-33. [12] Zhang H Y, Ji Q, Fan Y. Competition, transmission and pattern evolution: a network analysis of global oil trade-ScienceDirect[J]. Energy Policy, 2014, 73(1):312-322. [13] Watts D J, Strogatz S H. Collective dynamics of ‘small-world' networks.[J]. Nature, 1998,393(6684):440-442. [14] Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439):509-512. [15] 张若凡,申怡然,刘泽华.基于复杂网络的中国管理学领域研究热点及演进[J].统计与管理,2019(8):104-109. Zhang ruofan, Shen Yiran, Liu Zehua. Research hotspots and evolution of Chinese management based on complex networks [J]. Statistics and Management, 2019(8): 104-109. [16] Beyza J, Ruiz-Paredes H F, Garcia-Paricio E, et al. Assessing the criticality of interdependent power and gas systems using complex networks and load flow techniques[J]. Physica A: Statistical Mechanics and Its Applications, 2020, 540(6):123169. [17] 孙永龙.基于复杂网络视角下的高纯碳酸锂应用与制备方法研究[J].化工理,2019, 533(26):128-129. Sun Yonglong. Application and preparation of high purity lithium carbonate from the perspective of complex network [J]. Chemical Engineering, 2019, 533(26): 128-129. [18] 王雪,杨栋婷,刘文娜,等.基于复杂网络分析的针刺治疗脑卒中后抑郁的俞穴配伍规律研究[J].中国医药导报,2019,16(33):111-115. Wang Xue, Yang Dongting, Liu Wenna, et al. Study on compatibility of Shu Points in acupuncture treatment of post-stroke depression based on complex network analysis [J]. China Medical Guide, 2019,16 (33): 111-115. [19] Xi X, Zhou J, Gao X, et al. Impact of the global mineral trade structure on national economies based on complex network and panel quantile regression analyses[J]. Resources, Conservation and Recycling, 2020, 154(4):104637.