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运输设备网络分析:增值贡献

Transport equipment network analysis: the value-added contribution.

作者信息

Hernández García Luis Gerardo

机构信息

Graduate School of Economics, Kyushu University, Fukuoka, Japan.

出版信息

J Econ Struct. 2022;11(1):28. doi: 10.1186/s40008-022-00289-1. Epub 2022 Dec 6.

DOI:10.1186/s40008-022-00289-1
PMID:36530193
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9734606/
Abstract

Emerging in the twenty-first century, Network Science provides practical measures to interpret a system's interactions between the components and their links. Literature has focused on countries' interconnections on the final goods, but its application on the value-added from a network perspective in trade is still imitated. This paper applies network science properties and a multi-regional input-output analysis by using the UNCTAD-Eora Global Value Chain Database on the Transport Equipment value added on 2017 to unwrap the specific structural characteristics of the industry. Results show that the industry is highly centralized. The center of the network is dominated by developed countries, mainly from Europe, the United States, and Japan. Emerging countries such as China, Mexico, Thailand, and Poland also have an important position. In addition, the structure reveals two sub-hubs located in East Europe and North America. By extending to community detection, the network consists of three different communities led by Germany, the United States, and the United Kingdom, associated with more significant value-added flows. The study concludes that flows are not always consistent with the economy's geographical location as usually final goods analysis suggests, and highlight the need to continue using the complex network to reveal the world trade structure.

摘要

网络科学兴起于21世纪,它提供了实用的方法来解释系统中各组成部分及其链接之间的相互作用。文献主要关注各国在最终产品上的相互联系,但其在贸易中从网络视角对附加值的应用仍在探索之中。本文运用网络科学属性和多区域投入产出分析方法,利用联合国贸发会议-伊奥拉全球价值链数据库,对2017年运输设备的附加值进行分析,以揭示该行业的具体结构特征。结果表明,该行业高度集中。网络中心由发达国家主导,主要来自欧洲、美国和日本。中国、墨西哥、泰国和波兰等新兴国家也占据重要地位。此外,该结构显示出位于东欧和北美的两个子中心。通过扩展到社区检测,该网络由以德国、美国和英国为首的三个不同社区组成,这些社区伴随着更大量的附加值流动。研究得出结论,流动情况并不总是如通常的最终产品分析所表明的那样与经济地理位置一致,并强调需要继续使用复杂网络来揭示世界贸易结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/34180b0e62a5/40008_2022_289_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/3008290b5901/40008_2022_289_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/10e2d0ea5702/40008_2022_289_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/ca8047207df2/40008_2022_289_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/db97eb63b46a/40008_2022_289_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/34180b0e62a5/40008_2022_289_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/3008290b5901/40008_2022_289_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/10e2d0ea5702/40008_2022_289_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/ca8047207df2/40008_2022_289_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/db97eb63b46a/40008_2022_289_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8656/9734606/34180b0e62a5/40008_2022_289_Fig5_HTML.jpg

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2
Countries' positions in the international global value networks: Centrality and economic performance.各国在国际全球价值网络中的地位:中心性与经济表现。
Appl Netw Sci. 2017;2(1):21. doi: 10.1007/s41109-017-0041-4. Epub 2017 Jul 12.
3
Community Detection in Complex Networks via Clique Conductance.基于团导纳的复杂网络社团检测
Sci Rep. 2018 Apr 13;8(1):5982. doi: 10.1038/s41598-018-23932-z.
4
Quantitative microbiome profiling links gut community variation to microbial load.定量微生物组谱分析将肠道群落变化与微生物负荷联系起来。
Nature. 2017 Nov 23;551(7681):507-511. doi: 10.1038/nature24460. Epub 2017 Nov 15.
5
Disentangling Interactions in the Microbiome: A Network Perspective.从网络视角解析微生物组中的相互作用
Trends Microbiol. 2017 Mar;25(3):217-228. doi: 10.1016/j.tim.2016.11.008. Epub 2016 Dec 2.
6
Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.基于引用关系的科学出版物聚类:不同方法的系统比较
PLoS One. 2016 Apr 28;11(4):e0154404. doi: 10.1371/journal.pone.0154404. eCollection 2016.
7
World Input-Output Network.世界投入产出网络
PLoS One. 2015 Jul 29;10(7):e0134025. doi: 10.1371/journal.pone.0134025. eCollection 2015.
8
Scale-free models for the structure of business firm networks.商业公司网络结构的无标度模型。
Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Mar;81(3 Pt 2):036117. doi: 10.1103/PhysRevE.81.036117. Epub 2010 Mar 29.
9
A method of matrix analysis of group structure.一种群体结构的矩阵分析方法。
Psychometrika. 1949 Jun;14(2):95-116. doi: 10.1007/BF02289146.
10
Mixing patterns in networks.网络中的混合模式。
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Feb;67(2 Pt 2):026126. doi: 10.1103/PhysRevE.67.026126. Epub 2003 Feb 27.