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干旱区地理 >

2023 , Vol. 46 >Issue 5: 834 - 845

DOI: https://doi.org/10.12118/j.issn.1000-6060.2022.383

区域发展

黄河流域城市生态福利绩效测算及驱动因素研究

  • 董洁芳 ,
  • 张凯莉 ,
  • 屈学书 ,
  • 阮征
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  • 1.运城学院黄河文化生态研究院/文化旅游系，山西 运城 044000
    2.西北大学城市与环境学院，陕西 西安 710127
    3.陕西省地质科技中心，陕西 西安 710065
董洁芳（1984-），女，博士，副教授，主要从事区域经济与生态旅游开发研究. E-mail: dongjiefang-2005@163.com

收稿日期: 2022-08-03

  修回日期: 2022-09-11

  网络出版日期: 2023-06-05

基金资助

国家社会科学基金项目(18BJY120);山西省社科联重点课题(SSKLZDKT2022147);山西省黄河文化生态研究院项目(HH202101);运城学院旅游管理重点学科(XK-2021031)

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Measurement and influencing factors of ecological well-being performance of cities in Yellow River Basin

  • Jiefang DONG ,
  • Kaili ZHANG ,
  • Xueshu QU ,
  • Zheng RUAN
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  • 1. Yellow River Cultural and Ecological Research Institute, Department of Cultural and Tourism, Yuncheng University, Yuncheng 044000, Shanxi, China
    2. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, Shaanxi, China
    3. Shaanxi Geological Science and Technology Center, Xi’an 710065, Shaanxi, China

Received date: 2022-08-03

  Revised date: 2022-09-11

  Online published: 2023-06-05

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摘要

生态福利绩效（EWP）的提升是生态文明建设的必然选择，对区域可持续发展具有重要意义。从生态福利视角出发，构建指标体系，基于面板数据，采用非期望产出超效率SBM模型对2006—2019年黄河流域59个地级城市EWP进行测算，运用空间探索方法和时空地理加权回归（GTWR）模型对流域EWP的空间分布特征及驱动因素进行解析。结果表明：（1） 黄河流域城市EWP值普遍较低，平均存在19.7%的提升空间。（2） 黄河流域城市EWP存在显著正向空间自相关，“热点”高-高型城市主要分布在人口密度较低的上游地区；“冷点”低-低型多为黄河中下游经济发展较快、人口相对集中的城市。（3） 降水量、教育发展水平和产业结构水平对城市EWP的提升具有显著促进作用；人口密度、经济强度及金融发展水平对城市EWP的改善具有明显抑制作用。其中，降水量、教育发展水平和人口密度对城市EWP的边际效应较大。研究结果弥补了EWP影响因子“时-空”非平稳性分析的不足，可为有关部门制定城市EWP政策提供参考依据。

关键词： 生态福利绩效; SBM模型; 遥感数据; GTWR模型; 黄河流域

本文引用格式

董洁芳 , 张凯莉 , 屈学书 , 阮征 . 黄河流域城市生态福利绩效测算及驱动因素研究[J]. 干旱区地理, 2023 , 46(5) : 834 -845 . DOI: 10.12118/j.issn.1000-6060.2022.383

Abstract

The improvement of ecological well-being performance (EWP) is an inevitable choice for the construction of urban ecological civilization and is of great significance to the sustainable development. This paper established the evaluation index system of EWP from the perspective of ecological well-being. Based on the panel data of 59 prefecture-level cities in the Yellow River Basin of China from 2006 to 2019, this article used the undesired output SBM model to measure the EWP of 59 prefecture-level cities. The spatial autocorrelation method and a geographically and temporally weighted regression model were then used to analyze spatial distribution characteristics and influencing factors of interurban EWP in the Yellow River Basin. the results showed the following: (1) The overall EWP level of prefecture-level cities in the Yellow River Basin is relatively low, with 19.7% room for improvement. (2) A significant positive spatial autocorrelation is observed in EWP in the Yellow River Basin. The “hot spot” high-high cities are mainly distributed in the upstream areas with low population density, and the “cold spots” low-low cities are mostly cities in the middle and lower reaches of the Yellow River Basin with rapid economic development and relatively concentrated population. (3) Precipitation, educational development level, and industrial structure level are the positive key factors affecting EWP, while population density, economic intensity, and financial development level contribute to negative effects. Among all the influencing factors, precipitation, educational development level, and population density have the largest marginal effect on urban EWP. The research results make up for the deficiency of the “time-space” non-stationarity analysis of EWP impact factors, and can provide reference for the relevant departments to formulate EWP policies in cities.

Key words： ecological well-being performance; SBM model; remote sensing data; GTWR model; Yellow River Basin

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