欢迎访问《应用生态学报》官方网站,今天是

应用生态学报 ›› 2026, Vol. 37 ›› Issue (8): 2741-2752.doi: 10.13287/j.1001-9332.202608.027

• • 上一篇    下一篇

黄河流域城市生态脆弱性的空间关联网络特征及影响因素

邓光耀*, 韩福孝   

  1. 兰州财经大学统计与数据科学学院, 兰州 730020
  • 收稿日期:2026-03-06 修回日期:2026-07-02 出版日期:2026-08-18 发布日期:2027-02-18
  • 通讯作者: *E-mail: dgy203316@163.com
  • 作者简介:邓光耀, 男, 1985年生, 博士, 教授。主要从事资源环境经济学研究。E-mail: dgy203316@163.com
  • 基金资助:
    国家自然科学基金项目(72363021)、甘肃省科技厅软科学专项(25JRZA094)和甘肃省高校青年博士“入企入园”项目(2026QB-053)

Spatial correlation network characteristics and influencing factors of urban ecological vulnerability in the Yellow River Basin, China

DENG Guangyao*, HAN Fuxiao   

  1. School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China
  • Received:2026-03-06 Revised:2026-07-02 Online:2026-08-18 Published:2027-02-18

摘要: 探究黄河流域生态脆弱性空间关联网络特征及形成机制,对于识别区域生态风险传导路径、推进协同治理有重要价值。本研究基于敏感性-恢复力-压力度模型构建生态脆弱性评价指标体系,采用熵值法测算2013—2024年黄河流域生态脆弱性指数,使用社会网络分析和指数随机图模型探究生态脆弱性空间关联网络特征及影响因素。结果表明:研究期间,黄河流域生态脆弱性呈西低东高的空间格局;生态脆弱性网络的关联度均值为1,等级度均值为0.01,呈现高度连通、结构扁平的网络特征;省会及周边城市在关联网络中兼具较强的内向吸引和外向辐射力;除经济发展水平、人口密度等节点属性显著促进关联网络形成外,网络的互惠性等内生结构变量的影响也同样显著,表明城市间生态脆弱性关联不仅受外源因素的影响,也存在显著的内生依赖机制,且网络受到地理邻接和行政从属关系的显著影响。本研究结果为从网络协同视角制定区域生态治理策略提供了实证参考。

关键词: 生态脆弱性, 黄河流域, 社会网络分析, 指数随机图模型

Abstract: Investigating the characteristics and formation mechanisms of the spatial association network of ecological vulnerability in the Yellow River Basin is of great significance for identifying regional ecological risk transmission pathways and promoting coordinated governance. We constructed an evaluation index system for ecological vulnerability based on the sensitivity-resilience-pressure model, calculated the ecological vulnerability index for the Yellow River Basin from 2013 to 2024 using the entropy method, and employed social network analysis and exponential random graph models to explore the characteristics and influencing factors of the spatial association network of ecological vulnerability. The results showed that ecological vulnerability in the Yellow River Basin exhibited a spatial pattern of low in the west and high in the east. The average connectivity of the ecological vulnerability network was 1, with an average hierarchy of 0.01, indicating a highly connected and flat-structured network. The capi-tals of the related provinces and their surrounding cities demonstrated strong inward attraction and outward radiation within the network. Apart from node attributes such as economic development level and population density significantly facilitating network formation, endogenous structural variables like reciprocity were also significant, indicating that inter-city ecological vulnerability associations were influenced not only by exogenous factors but also by notable endogenous dependencies. Moreover, the network was significantly affected by geographical adjacency and administrative subordination relationships. These findings provide empirical support for formulating regional ecological governance strategies from a network collaboration perspective.

Key words: ecological vulnerability, Yellow River Basin, social network analysis, exponential random graph model