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基于中国投入产出数据的动态评估关键产业的社会网络分析。

A social network analysis in dynamic evaluate critical industries based on input-output data of China.

机构信息

School of Business Administration, Zhongnan University of Economics and Law, Wuhan, China.

出版信息

PLoS One. 2022 Apr 7;17(4):e0266697. doi: 10.1371/journal.pone.0266697. eCollection 2022.

DOI:10.1371/journal.pone.0266697
PMID:35390100
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8989312/
Abstract

As the Chinese economy grows, the imbalance of industrial structure is prominent, and the optimization of industrial structure has become an urgent problem. Evaluation of industry is an important step in industry optimization. To this end, this study proposes an integrated evaluation method combining social network analysis (SNA) and the multi-criteria decision making (MCDM) method. Specifically, SNA method are used to calculate indicators, the measurement weights are calculated by the Entropy Weight (EW) Method, and the rank of each industry is determined by the TOPSIS method. Critical industries are identified based on China's input-output data from 2002 to 2017. The results indicate that Manufacturing Industry and the Metal products have a high evaluation, but the Research and Development have a low evaluation value at all times. According to the results, we suggest that the government should optimize the allocation of resources and promote the transfer of resources to balance industrial development.

摘要

随着中国经济的增长,产业结构失衡问题日益突出,产业结构优化已成为当务之急。产业评价是产业优化的重要环节。为此,本研究提出了一种将社会网络分析(SNA)和多准则决策(MCDM)方法相结合的综合评价方法。具体来说,使用 SNA 方法计算指标,熵权(EW)法计算测度权重,TOPSIS 法确定各产业的排名。根据 2002 年至 2017 年中国的投入产出数据识别关键产业。结果表明,制造业和金属制品的评价较高,但研发的评价值一直较低。根据研究结果,建议政府优化资源配置,促进资源转移,平衡产业发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/93293694eef4/pone.0266697.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/cc390865139a/pone.0266697.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/70243adb8d49/pone.0266697.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/de9ced2772d0/pone.0266697.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/93293694eef4/pone.0266697.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/cc390865139a/pone.0266697.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/70243adb8d49/pone.0266697.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/de9ced2772d0/pone.0266697.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa82/8989312/93293694eef4/pone.0266697.g004.jpg

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