EconPapers    
Economics at your fingertips  
 

Multi-model methods for structural analysis of China’s green economy network based on input-output method

Yigang Guo, Shaoling Ding, Jingliang Huai, Jiayao Pan and Yan Meng

PLOS ONE, 2024, vol. 19, issue 9, 1-25

Abstract: The green economy has been advocated globally as a solution to environmental issues. In China, it is considered a national strategy for future economic development. This study utilizes methods such as Industry Network, Maximum Spanning Tree (MST) method, Leiden Community Clustering (LCC) algorithm, and Weaver-Thomas (WT) model to explore the contribution and position of the green economy and industries in China’s economic development. The findings are as follows: (1) The density of China’s green industry network has experienced a process of initially tightening and then loosening, ultimately tending towards stability. (2) The trunk structure of China’s industrial network remains relatively stable, forming an industrial structure with electricity, heat production and supply as the core. (3) China’s industrial and green industry communities continue to improve and become more cohesive, but some green industries are still on the periphery of communities. (4) The ability of green industries to pull other industries is weak, and the subsequent promotion momentum needs to be improved. However, the green industry still has enormous room for growth and potential to unleash its long-term positive multiplier effects. More attention and support need to be given by managers and decision-makers, so that it can make better contributions to society and the economy.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0309916 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 09916&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0309916

DOI: 10.1371/journal.pone.0309916

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().

 
Page updated 2026-03-22
Handle: RePEc:plo:pone00:0309916