复杂产业间网络的统计特性和稳定性研究

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Complexity Pub Date : 2024-02-26 DOI:10.1155/2024/9220756
Xinyu Cheng
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引用次数: 0

摘要

本研究将中国 31 个省区 42 个行业的投入产出数据整合成一个统一的数据集,涵盖八个主要经济区的 42 个工业行业。利用最大熵方法,我们确定了重要的产业间关系,随后将这些关系形成一个有向、加权的复杂网络。在这一复杂网络的基础上,我们分析了其基本的统计属性。通过模拟各种网络攻击,进一步评估了网络结构的稳定性。我们的研究结果表明,最大熵法善于提取工业部门之间的显著关系,有助于创建一个有说服力的复杂工业间网络。虽然这个已建立的网络表现出很高的稳定性,但仍需要有针对性的政策干预和风险管理,尤其是对那些具有明显的度中心性和间度中心性的产业。这些关键产业节点对网络的整体稳定性起着决定性作用。我们通过对复杂产业间网络的研究得出的见解阐明了产业网络的结构和功能,对政策制定和推动可持续、均衡的经济进步具有深远的影响。
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Research on the Statistical Properties and Stability of Complex Interindustrial Networks

This study consolidates input-output data from 42 sectors across 31 provinces and regions in China into a unified dataset for 42 industrial sectors within eight major economic zones. Leveraging the maximum entropy method, we identify significant interindustrial relationships, subsequently forming a directed, weighted, complex network of these ties. Building upon this intricate network, we analyze its foundational statistical attributes. The stability of the network’s structure is further assessed through simulations of varied network attacks. Our findings demonstrate that the maximum entropy method is adept at extracting notable relationships between industrial sectors, facilitating the creation of a cogent complex interindustrial network. Although this established network exhibits high stability, it calls for targeted policy interventions and risk management, especially for industries with pronounced degree centrality and betweenness centrality. These pivotal industry nodes play a decisive role in the overall stability of the network. The insights derived from our examination of complex interindustrial networks illuminate the structure and function of industrial networks, bearing profound implications for policymaking and propelling sustainable, balanced economic progress.

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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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