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  • May. 14, 2025

Arid Land Geography ›› 2025, Vol. 48 ›› Issue (4): 704-716.doi: 10.12118/j.issn.1000-6060.2024.396

• Regional Development • Previous Articles     Next Articles

Characterization and influencing factors of ecological resilience linkage networks in China

DENG Guangyao1,2(), SHEN Yingchen1()   

  1. 1. School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, Gansu, China
    2. Economic Research Institute of the Belt and Road Initiative, Lanzhou University of Finance and Economics, Lanzhou 730020, Gansu, China
  • Received:2024-06-26 Revised:2024-09-16 Online:2025-04-25 Published:2025-04-18
  • Contact: SHEN Yingchen E-mail:dgy203316@163.com;18993766832@163.com

Abstract:

Using the entropy weight-TOPSIS method, this study evaluates national ecological resilience from 2008 to 2022. The structure and determinants of the provincial ecological resilience network are analyzed through a modified gravity model, social network analysis, and exponential random graph models. The results highlight the following key points: (1) A positive trend in national ecological resilience during the study period. (2) A substantial increase in interprovincial connections, resulting in a more complex spatial network. (3) The central role of Beijing, Shanghai, Jiangsu, Zhejiang, and Guangdong as central nodes, and the northwest China, northeast China, middle and lower Yellow River, and Central Plains as peripheral nodes. (4) The classification of regions into net beneficiaries (Beijing-Tianjin, Yangtze River Delta); brokers (Zhejiang, Pearl River Delta); net spillover contributors (northeast China, the middle and lower reaches of the Yellow River, parts of western China); and two-way spillover areas (middle and lower reaches of the Yangtze River, southwest China). (5) The significant impact of economic development, technological advancement, water source condition, and geographic proximity on network formation, as demonstrated by exponential random graph model (ERGM). These results can provide a scientific basis for improving the connection and stability of the spatial correlation network of ecological resilience.

Key words: ecological resilience, spatial network, social network analysis, exponential random graph model, China