Spatial-temporal variation characteristics and evolution of the global industrial robot trade: A complex network analysis

Autoři: Yaya Li aff001;  Yongtao Peng aff002;  Jianqiang Luo aff002;  Yihan Cheng aff001;  Eleonora Veglianti aff003
Působiště autorů: School of Finance & Economics, Jiangsu University, Zhenjiang, Jiangsu, P.R. China aff001;  School of Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China aff002;  Department of Economics, University of Uninettuno, Roma, Italy aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222785


Industrial robots are a strategic future technology and an important part of the development of artificial intelligence, and they are a necessary means for the intelligent transformation of manufacturing industry. Based on global industrial robot trade data from 1998 to 2017, this paper applies the dynamic complex network analysis method to reveal the spatial and temporal variation characteristics and trade status evolution of the global industrial robot trade network. The results show that the global industrial robot network density has steadily increased, and the industrial robot trade has been characterized by ‘diversification’. The number of major industrial robot exporters in the world is increasing, and the import market is increasingly diversified. The export market structure is relatively tight, the centrality of the global industrial robot trade network shows a downward trend, and the dissimilarity of the ‘core-edge’ clusters decreases year by year. The trade status of ‘catch-up’ countries represented by China has rapidly increased. However, Japan, Germany, and Italy are still in the central position of the industrial robot trade. Moreover, trade of the ‘catch-up’ countries’ is dominated by imports, and exports of industrial robot products are insufficient. Finally, policy suggestions are provided according to the results.

Klíčová slova:

Artificial intelligence – Centrality – Italy – Network analysis – Robots – Structure of markets – Robotics – International trade


1. International Federation of Robot(IFR).Executive Summary World Robotics 2018 Industrial Robots.2018.

2. Graetz G, Michaels G. Robots at work. Review of Economics and Statistics. 2018; 100(5): 753–768.

3. Chiacchio F, Petropoulos G, Pichler D. The impact of industrial robots on EU employment and wages: A local labour market approach. Bruegel; 2018.

4. Ju Y, Sohn SY. Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea. Technological Forecasting and Social Change. 2015; 94: 44–64.

5. Chesbrough HW. Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press; 2006.

6. Hacklin F, Marxt C, Fahrni F. Coevolutionary cycles of convergence: An extrapolation from the ICT industry. Technological Forecasting and Social Change. 2009; 76(6): 723–736.

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. Research Policy. 2014; 43(10): 1716–1729.

8. Lee WJ, Lee WK, Sohn SY. Patent network analysis and quadratic assignment procedures to identify the convergence of robot technologies. PLOS One. 2016;11(10): e0165091. doi: 10.1371/journal.pone.0165091 27764196

9. Lee K. Patterns and processes of contemporary technology fusion: the case of intelligent robots. Asian Journal of Technology Innovation. 2007; 15(2): 45–65.

10. Yun JHJ, Won DK, Jeong ES, Park KB, Yang JH, Park JY. The relationship between technology, business model, and market in autonomous car and intelligent robot industries. Technological Forecasting and Social Change. 2016; 103: 142–155.

11. Artuc E, Bastos P, Rijkers B. Robots, Tasks and Trade. Policy Research Working Paper Series 8674. The World Bank; 2018.

12. Fagiolo G, Reyes J, Schiavo S. On the topological properties of the world trade web: A weighted network analysis. Physica A: Statistical Mechanics and its Applications. 2008; 387(15): 3868–3873.

13. Gao C, Sun M, Shen B. Features and evolution of international fossil energy trade relationships: a weighted multilayer network analysis. Applied energy. 2015; 156: 542–554.

14. De Andrade RL, Rêgo LC. The use of nodes attributes in social network analysis with an application to an international trade network. Physica A: Statistical Mechanics and its Applications. 2018; 491: 249–270.

15. Cepeda-López F, Gamboa-Estrada F, León C, Rincón-Castro H. The evolution of world trade from 1995 to 2014: A network approach. The Journal of International Trade & Economic Development. 2019; 28(4): 452–485.

16. Zhang C, Fu J, Pu Z. A study of the petroleum trade network of countries along “The Belt and Road Initiative”. Journal of Cleaner Production. 2019; 222: 593–605.

17. Li Y, Luo P, Pin P. Utility-based model for characterizing the evolution of social networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017; 99: 1–12.

18. Dumor K, Yao L. Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis. Sustainability. 2019; 11(5): 1449.

19. An H, Zhong W, Chen Y, Li H, Gao X. Features and evolution of international crude oil trade relationships: A trading-based network analysis. Energy. 2014; 74: 254–259.

20. Hao X, An H, Qi H, Gao X. Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network. Applied Energy. 2016; 162: 1515–1522.

21. Geng JB, Ji Q, Fan Y. A dynamic analysis on global natural gas trade network. Applied Energy. 2014; 132: 23–33.

22. Wang X, Ge J, Wei W, Li H, Wu C, Zhu G. Spatial dynamics of the communities and the role of major countries in the international rare earths trade: a complex network analysis. PLOS One, 2016; 11(5): e0154575. doi: 10.1371/journal.pone.0154575 27137779

23. Ercsey-Ravasz M, Toroczkai Z, Lakner Z, Baranyi J. Complexity of the international agro-food trade network and its impact on food safety. PLOS One, 2012; 7(5): e37810. doi: 10.1371/journal.pone.0037810 22701535

24. Sturgeon T, Van Biesebroeck J, Gereffi G. Value chains, networks and clusters: reframing the global automotive industry. Journal of Economic Geography. 2008; 8(3): 297–321.

25. Cingolani I, Iapadre L, Tajoli L. International production networks and the world trade structure. International Economics. 2018;153: 11–33.

26. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998; 393(6684): 440. doi: 10.1038/30918 9623998

27. Li Y, Liu G, Pin P. Network-based risk measurements for interbank systems. PLOS One. 2018; 13(7): e0200209. doi: 10.1371/journal.pone.0200209 30001356

28. Li Y, Luo P, Fan Z, Chen K, Liu J. A utility-based link prediction method in social networks. European Journal of Operational Research. 2017; 260(2): 693–705.

29. Garlaschelli D, Loffredo MI. Structure and evolution of the world trade network. Physica A: Statistical Mechanics and its Applications. 2005; 355(1): 138–144.

30. Geng JB, Ji Q, Fan Y. A dynamic analysis on global natural gas trade network. Applied Energy. 2014; 132: 23–33.

31. Zhong W, An H, Fang W, Gao X, Dong D. Features and evolution of international fossil fuel trade network based on value of emergy. Applied Energy. 2016; 165: 868–877.

32. De Nooy W, Mrvar A, Batagelj V. Exploratory social network analysis with Pajek: Revised and expanded edition for updated software. Cambridge University Press; 2018.

Článek vyšel v časopise


2019 Číslo 9