Abstract
Industrial transfer is the inevitable trend of economic development. The traditional industrial transfer theory tends to adopt partial data and methodologies from reductionism, and thus can’t tackle with the highly non-linear systematic problems like the mechanism and evolution path of international, regional, and domestic industrial transfer. With the properties of structural complexity, dynamic evolution and multiple linkages, complex networks can better reflect the interdependent and mutually restricted relation between different levels and components of the industrial structure, pinpoint the optimization and control nodes. Currently, there are only a few available researches on such weighted, directed and dense networks reflecting the topological complexity of global value chain, with the results being unsystematic and impractical. This paper utilizes the available ICIO data to build the Binary GISRN model in accordance with crucial flows of materials, energy, and information among industrial sectors all over the world. Also, methods of defining and measuring the networks’ redundancies are devised to figure out the trigger of worldwide industrial transfer pattern according to the link prediction method, thus blazing a new trail for the evolutionary economics.
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Acknowledgment
The authors acknowledge support from National Natural Science Foundation of China (Grant No. 71971006), Beijing Natural Science Foundation (Grant No. 9194024) and Study Abroad Foundation of China Scholarship Council (CSC No. 201806545030).
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Xing, L., Han, Y. (2020). Finding the Worldwide Industrial Transfer Pattern Under the Perspective of Econophysics. In: Barbosa, H., Gomez-Gardenes, J., Gonçalves, B., Mangioni, G., Menezes, R., Oliveira, M. (eds) Complex Networks XI. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-40943-2_26
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DOI: https://doi.org/10.1007/978-3-030-40943-2_26
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