{"title":"中国式生态现代化的空间关联网络及其影响因素","authors":"Huiping Wang, Yuezhan Huang","doi":"10.1016/j.ecolind.2025.113297","DOIUrl":null,"url":null,"abstract":"<div><div>This paper constructs an evaluation index system of Chinese-style ecological modernization (CSEM) based on the idea of “six-in-one”, and measures the CSEM of 30 provinces in China from 2011 to 2021 by using entropy value method. The improved gravity model, social network analysis and QAP regression model are used to study the characteristics of the spatial correlation network of CSEM and its influencing factors. The study finds that: First, the CSEM demonstrates a consistent upward trajectory, yet there exists a notable spatial disparity, with the eastern region exhibiting higher CSEM compared to other regions. Second, the inter-provincial connection of CSEM has exhibited a network structure, albeit it has not attained the optimal state of spatial correlation yet. The network density remains low, and the spatial spillover effect demonstrates a west-to-east trend, where the western region has emerged as the “spillover highland”. Meanwhile, Beijing, Shanghai, Jiangsu, and Zhejiang occupy a central and dominant position within the network. Third, the network can be divided into several factions based on subordination, with obvious geographical proximity pointing between provinces, in which subgroup III was initially composed of six provinces, including Guangdong, and shrunk to Guangdong, Guangxi and Hainan after 2017, while Sichuan, Chongqing and Guizhou formed the new subgroup IV, demonstrating the dynamic characteristics of the subordination network over time. Fourth, the spatial network of CSEM is segmented into<!--> <!-->four blocks: net benefit, net spillover, two-way spillover and broker. The role division and linkage effect between the four blocks is obvious. Fifth, differences in the urbanization, geographical proximity, economic development, technological innovation and industrial advancement all contribute positively to the development<!--> <!-->of the network, while differences in resource consumption inhibit the formation of network.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113297"},"PeriodicalIF":8.7000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial correlation network of Chinese-style ecological modernization and its influencing factors\",\"authors\":\"Huiping Wang, Yuezhan Huang\",\"doi\":\"10.1016/j.ecolind.2025.113297\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper constructs an evaluation index system of Chinese-style ecological modernization (CSEM) based on the idea of “six-in-one”, and measures the CSEM of 30 provinces in China from 2011 to 2021 by using entropy value method. The improved gravity model, social network analysis and QAP regression model are used to study the characteristics of the spatial correlation network of CSEM and its influencing factors. The study finds that: First, the CSEM demonstrates a consistent upward trajectory, yet there exists a notable spatial disparity, with the eastern region exhibiting higher CSEM compared to other regions. Second, the inter-provincial connection of CSEM has exhibited a network structure, albeit it has not attained the optimal state of spatial correlation yet. The network density remains low, and the spatial spillover effect demonstrates a west-to-east trend, where the western region has emerged as the “spillover highland”. Meanwhile, Beijing, Shanghai, Jiangsu, and Zhejiang occupy a central and dominant position within the network. Third, the network can be divided into several factions based on subordination, with obvious geographical proximity pointing between provinces, in which subgroup III was initially composed of six provinces, including Guangdong, and shrunk to Guangdong, Guangxi and Hainan after 2017, while Sichuan, Chongqing and Guizhou formed the new subgroup IV, demonstrating the dynamic characteristics of the subordination network over time. Fourth, the spatial network of CSEM is segmented into<!--> <!-->four blocks: net benefit, net spillover, two-way spillover and broker. The role division and linkage effect between the four blocks is obvious. Fifth, differences in the urbanization, geographical proximity, economic development, technological innovation and industrial advancement all contribute positively to the development<!--> <!-->of the network, while differences in resource consumption inhibit the formation of network.</div></div>\",\"PeriodicalId\":11459,\"journal\":{\"name\":\"Ecological Indicators\",\"volume\":\"172 \",\"pages\":\"Article 113297\"},\"PeriodicalIF\":8.7000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Indicators\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1470160X25002286\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/3/3 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X25002286","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/3 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Spatial correlation network of Chinese-style ecological modernization and its influencing factors
This paper constructs an evaluation index system of Chinese-style ecological modernization (CSEM) based on the idea of “six-in-one”, and measures the CSEM of 30 provinces in China from 2011 to 2021 by using entropy value method. The improved gravity model, social network analysis and QAP regression model are used to study the characteristics of the spatial correlation network of CSEM and its influencing factors. The study finds that: First, the CSEM demonstrates a consistent upward trajectory, yet there exists a notable spatial disparity, with the eastern region exhibiting higher CSEM compared to other regions. Second, the inter-provincial connection of CSEM has exhibited a network structure, albeit it has not attained the optimal state of spatial correlation yet. The network density remains low, and the spatial spillover effect demonstrates a west-to-east trend, where the western region has emerged as the “spillover highland”. Meanwhile, Beijing, Shanghai, Jiangsu, and Zhejiang occupy a central and dominant position within the network. Third, the network can be divided into several factions based on subordination, with obvious geographical proximity pointing between provinces, in which subgroup III was initially composed of six provinces, including Guangdong, and shrunk to Guangdong, Guangxi and Hainan after 2017, while Sichuan, Chongqing and Guizhou formed the new subgroup IV, demonstrating the dynamic characteristics of the subordination network over time. Fourth, the spatial network of CSEM is segmented into four blocks: net benefit, net spillover, two-way spillover and broker. The role division and linkage effect between the four blocks is obvious. Fifth, differences in the urbanization, geographical proximity, economic development, technological innovation and industrial advancement all contribute positively to the development of the network, while differences in resource consumption inhibit the formation of network.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.