工业机器人贸易网络结构及其影响机制研究

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Abstract Based on global industrial robot trade data from 1998 to 2017,this paper applies the social network analysis method to reveal the network structure characteristics and influencing mechanism of industrial robot trade network.The results are as follows.The world industrial robot trade relationship shows a trend of ‘rapid growth in the early stage and steady growth in the later stage’.The trade relationship is increasingly close,and the accessibility and trade efficiency of intra-regional trade are constantly improving.Germany and Japan are always at the center of the world's industrial robot trade network.China,South Korea and the Netherlands are the most prominent industrial robot trade catch-up countries;especially China has gradually become an important “bridge”and “hub”of industrial robot trade.Technological distance is the main factor affecting the evolution of industrial robot trade network.The development distance of manufacturing industry,economic distance,common language and culture can promote the development of industrial robot trade.Population size difference has no significant impact on industrial robot trade.Moreover,the influence of various factors on different types of industrial robot trade network is heterogeneous.

Key words： Industrial robot Trade network Social network analysis QAP model

Received: 29 September 2020

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F740

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	Li Yaya
	Luo Jianqiang

Cite this article:

Li Yaya,Luo Jianqiang. The Structural Characteristics and Influencing Mechanism of Global Industrial Robot Trade Network[J]. , 2021, 2(7): 76-85.

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http://www.zgkjlt.org.cn/EN/ OR http://www.zgkjlt.org.cn/EN/Y2021/V2/I7/76

[1]IFR.Executive summary world robotics 2018 industrial robots [DB/OL].(2018-01-01)[2019-10-03].https://www.ifr.org/downloads/press2018/ExecutiveSummaryWR2018IndustrialRobots.pdf.
[2]R MANN M,LORENZ M,GERBERT P,et al.Industry 4.0:the future of productivity and growth in manufacturing industries [J].Boston consulting group,2015,9 (1):54-89.
[3]王才,周文斌,赵素芳.机器人规模应用与工作不安全感——基于员工职业能力调节的研究 [J].经济管理,2019,41 (4):111-126.
[4]ACEMOGLU D,RESTREPO P.Robots and jobs:evidence from US labor markets [J].Journal of political economy,2020,128 (6):2188-2244.
[5]杜传文,李晴,芮明杰,等.大规模工业机器人应用与异质性技能劳动力之间的替代互补关系 [J].中国科技论坛,2018 (8):174-182.
[6]王友发,周献中.基于专利地图的机器人产业知识产权发展对策[J].中国科技论坛,2015 (9):31-37.
[7]LECHEVALIER S,NISHIMURA J,STORZ C.Diversity in patterns of industry evolution:how an intrapreneurial regime contributed to the emergence of the service robot industry [J].Research policy,2014,43 (10):1716-1729.
[8]YUN J J,WON D,JEONG E,et al.The relationship between technology,business model,and market in autonomous car and intelligent robot industries [J].Technological forecasting and social change,2016,103:142-155.
[9]董桂才,巨小超.工业机器人出口技术复杂度影响因素——基于钻石模型的分析 [J].中国科技论坛,2017 (7):111-117+160.
[10]李丫丫,潘安.工业机器人进口对中国制造业生产率提升的机理及实证研究 [J].世界经济研究,2017 (3):87-96+136.
[11]杨文龙,杜德斌,马亚华,等. “一带一路”沿线国家贸易网络空间结构与邻近性 [J].地理研究,2018,37 (11):2218-2235.
[12]王博,陈诺,林桂军. “一带一路”沿线国家制造业增加值贸易网络及其影响因素 [J].国际贸易问题,2019 (3):85-100.
[13]徐斌.国际铁矿石贸易格局的社会网络分析 [J].经济地理,2015,35 (10):123-129.
[14]AN H,ZHONG W,CHEN Y,et al.Features and evolution of international crude oil trade relationships:a trading-based network analysis [J].Energy,2014,74:254-259.
[15]GE J,WANG X,GUAN Q.World rare earths trade network:patterns,relations and role characteristics [J].Resources policy,2016,50:119-130.
[16]PETRIDIS N E,PETRIDIS K,STIAKAKIS E.Global e-waste trade network analysis [J].Resources,conservation and recycling,2020,158:104742.
[17]马述忠,任婉婉,吴国杰.一国农产品贸易网络特征及其对全球价值链分工的影响——基于社会网络分析视角 [J].管理世界,2016 (3):60-72.
[18]CHRISTEN E.Time zones matter:the impact of distance and time zones on services trade [J].The world economy,2017,40 (3):612-631.
[19]王倩倩,杜德斌,张杨,等.全球手机贸易网络演化特征研究 [J].世界地理研究,2019,28 (2):170-178.
[20]刘林青,陈紫若,田毕飞.结构依赖如何影响贸易网络形成及演化:以 “一带一路”为例[J].世界经济研究,2020 (6):106-120+137.
[21]RICARDO D.Principles of political economy and taxation [M].London:John Murray,1817.
[22]VERNON R.International investment and international trade in the product cycle [J].The quarterly journal of economics,1966,80 (2):190-207.
[23]傅帅雄,罗来军.技术差距促进国际贸易吗?——基于引力模型的实证研究[J].管理世界,2017 (2):43-52.
[24]李婧,杨修.技术距离对中国高技术产品贸易的影响[J].中国科技论坛,2019 (10):43-51.
[25]KEUPPE M M,GASSMANN O.Resource constraints as triggers of radical innovation:longitudinal evidence from the manufacturing sector [J].Research policy,2013,42 (8):1457-1468.
[26]WANG M L,CHOI C H.How information and communication technology affect international trade:a comparative analysis of BRICS countries [J].Information technology for development,2019,25 (3):455-474.
[27]OHLIN B.Interregional and international trade [M].Cambridge:Harvard University Press,1935.
[28]BERGSTRAND J H.The gravity equation in international trade:some microeconomic foundations and empirical evidence[J].The review of economics and statistics,1985:474-481.
[29]BORCHERT I,YOTOV Y V.Distance,globalization,and international trade [J].Economics letters,2017,153:32-38.
[30]CHONG Z,QIN C,PAN S.The evolution of the Belt and Road trade network and its determinant factors [J].Emerging markets finance and trade,2019,55 (14):3166-3177.
[31]ZHANG C,FU J,PU Z.A study of the petroleum trade network of countries along “The Belt and Road Initiative”[J].Journal of cleaner production,2019,222:593-605.
[32]SELMIER W T,OH C H.The power of major trade languages in trade and foreign direct investment [J].Review of international political economy,2013,20 (3):486-514.
[33]GAO C,SUN M,SHEN B.Features and evolution of international fossil energy trade relationships:a weighted multilayer network analysis [J].Applied energy,2015,156:542-554.
[34]FREEMAN L C.Centrality in social networks conceptual clarification[J].Social networks,1978,1 (3):215-239.
[35]KIMURA F,LEE H H.The gravity equation in international trade in services [J].Review of world economics,2006,142 (1):92-121.
[36]孙天阳,肖皓,孟渤,等.制造业全球价值链网络的拓扑特征及影响因素——基于WWZ方法和社会网络的研究 [J].管理评论,2018,30 (9):49-60.
[37]彭羽,沈玉良,田肖溪. “一带一路”FTA 网络结构特征及影响因素:基于协定异质性视角 [J].世界经济研究,2019 (7):90-103+135-136.