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干旱区地理 ›› 2025, Vol. 48 ›› Issue (4): 704-716.doi: 10.12118/j.issn.1000-6060.2024.396 cstr: 32274.14.ALG2024396

• 区域发展 • 上一篇    下一篇

中国生态韧性关联网络的特征及影响因素研究

邓光耀1,2(), 沈迎辰1()   

  1. 1.兰州财经大学统计与数据科学学院,甘肃 兰州 730020
    2.兰州财经大学“一带一路”经济研究院,甘肃 兰州 730020
  • 收稿日期:2024-06-26 修回日期:2024-09-16 出版日期:2025-04-25 发布日期:2025-04-18
  • 通讯作者: 沈迎辰(2000-),女,硕士研究生,主要从事资源环境统计研究. E-mail: 18993766832@163.com
  • 作者简介:邓光耀(1985-),男,教授,硕士生导师,主要从事资源环境统计研究. E-mail: dgy203316@163.com
  • 基金资助:
    国家自然科学基金(72363021);甘肃省陇原青年英才项目(2022);兰州财经大学校级科研项目(Lzufe2024C-009);兰州财经大学丝绸之路经济研究院重点科研项目(JYYZ202102)

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 Published:2025-04-25 Online:2025-04-18

摘要:

基于熵权-TOPSIS法测算2008—2022年全国生态韧性,采用修正的引力模型、社会网络分析和指数随机图模型探究生态韧性网络关联特征及其影响因子。研究表明:(1) 样本期内全国生态韧性整体表现为上升趋势,展现出不断优化的态势。(2) 2008—2022年全国生态韧性关联强度显著提升,展现出较复杂、多线程的空间网络结构。(3) 网络中的“中心行动者”包括北京、上海、江苏、浙江和广东。西北、东北、黄河中下游以及中部平原是网络中的“边缘行动者”。(4) 净受益板块包括京津和长三角;经纪人板块由浙江和珠三角地区组成;净溢出板块集中在东北、黄河中下游和部分西部地区;双向溢出板块主要包括长江中下游以及西南地区。(5) 指数随机图模型(ERGM)估计结果显示:经济水平、科技创新以及水源条件对网络存在一定程度的影响,并且网络受到地理邻近效应的影响显著。研究成果可为提升生态韧性空间关联网络的联系及稳定性提供科学依据。

关键词: 生态韧性, 空间网络, 社会网络分析, 指数随机图模型, 中国

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