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中国乡村治理示范村的空间分布特征及其影响因素。

Spatial Distribution Characteristics and Influencing Factors of Rural Governance Demonstration Villages in China.

机构信息

The Institute of Geography and Resources, Sichuan Normal University, Chengdu 610066, China.

Key Laboratory of land Resources Evaluation and Monitoring in Southwest, Ministry of Education, Sichuan Normal University, Chengdu 610068, China.

出版信息

Int J Environ Res Public Health. 2023 Mar 3;20(5):4538. doi: 10.3390/ijerph20054538.

DOI:10.3390/ijerph20054538
PMID:36901548
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10001571/
Abstract

Rural governance plays a significant role in constructing national governance systems and promoting rural development. An accurate understanding of the spatial distribution characteristics and influencing factors of rural governance demonstration villages is conducive to giving full play to their leading, demonstration and radiating roles and further promoting the modernization of rural governance systems and governance capacity. Therefore, this study uses Moran's I analysis, local correlation analysis, kernel density analysis and a geographic concentration index to analyze the spatial distribution characteristics of rural governance demonstration villages. Moreover, this study proposes a conceptual framework to construct the cognition of rural governance and uses Geodetector and vector data buffer analysis methods to explore the internal influence mechanism of their spatial distribution. The results show the following: (1) The spatial distribution of rural governance demonstration villages in China is unbalanced. The distribution difference between the two sides of the "Hu line" is significant. The peak appears at 30° N and 118° E. (2) The rural governance demonstration villages in China are clustered, which forms a high-density core area, a sub-high density belt, two sub-high-density centers and several single core concentration areas. Additionally, the hot spots of rural governance demonstration villages in China are mostly located on the eastern coast, tending to cluster in places with superior natural conditions, convenient transportation, and excellent economic development. (3) Based on the distribution characteristics of Chinese rural governance demonstration villages, this study proposes a "one core, three axes and multiple centers" spatial structure to optimize the distribution of rural governance demonstration villages. (4) A rural governance framework system consists of a governance subject subsystem and influencing factor subsystem. The results of Geodetector show that under the mutual leading role of the three governance subjects, the distribution of rural governance demonstration villages in China is the result of multiple factors. Among them, nature is the basic factor, economy is the key factor, politics is the dominant factor, and demographic is the important factor. The interaction network formed by general public budget expenditure and total power of agricultural machinery affects the spatial distribution pattern of the rural governance demonstration villages in China.

摘要

乡村治理在构建国家治理体系和推进乡村发展中发挥着重要作用。准确把握乡村治理示范村的空间分布特征及其影响因素,有利于充分发挥其引领、示范和辐射作用,进一步推进乡村治理体系和治理能力现代化。因此,本研究采用 Moran's I 分析、局部相关分析、核密度分析和地理集中指数,分析了乡村治理示范村的空间分布特征。此外,本研究构建了乡村治理认知的概念框架,并采用地理探测器和向量数据缓冲区分析方法,探讨了其空间分布的内在影响机制。结果表明:(1)中国乡村治理示范村的空间分布不均衡,“胡焕庸线”两侧差异显著,峰值出现在 30°N、118°E 附近。(2)中国乡村治理示范村呈集聚分布,形成了一个高密度核心区、一个次高密度带、两个次高密度中心和多个单核集聚区,热点地区主要集中在东部沿海地区,倾向于聚集在自然条件优越、交通便利、经济发展良好的地区。(3)根据中国乡村治理示范村的分布特征,本研究提出了“一核、三轴、多点”的空间结构,以优化乡村治理示范村的布局。(4)乡村治理框架体系由治理主体子系统和影响因素子系统构成。地理探测器的结果表明,在三个治理主体的相互主导作用下,中国乡村治理示范村的分布是多种因素共同作用的结果。其中,自然是基础因素,经济是关键因素,政治是主导因素,人口是重要因素。一般公共预算支出和农业机械总动力的相互作用网络影响了中国乡村治理示范村的空间分布格局。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/1de2490e6dc4/ijerph-20-04538-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/dae59b8417cb/ijerph-20-04538-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/d322fe87264b/ijerph-20-04538-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/0863df919edc/ijerph-20-04538-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/f9db340a0298/ijerph-20-04538-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/5d9606d7e2ee/ijerph-20-04538-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/9d9455b048a3/ijerph-20-04538-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/210c1580ef1a/ijerph-20-04538-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/43bdf5619284/ijerph-20-04538-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/1f340bb2f93d/ijerph-20-04538-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/1de2490e6dc4/ijerph-20-04538-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/dae59b8417cb/ijerph-20-04538-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/d322fe87264b/ijerph-20-04538-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/0863df919edc/ijerph-20-04538-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/f9db340a0298/ijerph-20-04538-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/5d9606d7e2ee/ijerph-20-04538-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/9d9455b048a3/ijerph-20-04538-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/210c1580ef1a/ijerph-20-04538-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/43bdf5619284/ijerph-20-04538-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/1f340bb2f93d/ijerph-20-04538-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b792/10001571/1de2490e6dc4/ijerph-20-04538-g010.jpg

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