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欠发达地区农村人口迁移的模式和地方推动因素:以河北省北部山区为例。

The Pattern and Local Push Factors of Rural Depopulation in Less-Developed Areas: A Case Study in the Mountains of North Hebei Province, China.

机构信息

Key Research Institute of Yellow River Civilization and Sustainable Development, Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China.

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

出版信息

Int J Environ Res Public Health. 2022 May 12;19(10):5909. doi: 10.3390/ijerph19105909.

DOI:10.3390/ijerph19105909
PMID:35627445
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9141062/
Abstract

Rural depopulation is the most significant geographical phenomenon in rural areas during the process of urbanization. Although many studies have investigated the driving force of rural depopulation based on rural-urban migration at the macro level, the local factors, and their impact on rural depopulation from the rural areas have been not fully revealed. This paper selected the northern mountains of China's Hebei province as a study area to explore the pattern and local push factors of rural depopulation at the rural-township levels based on GeoDetector. The main findings are summarized as follows. (1) Rural depopulation varies substantially, demonstrates spatial correlation, and is distributed in clusters. From a dynamic perspective, compare that in years 2000-2010, the population growth areas during 2010-2017 have been significantly expanded, while the sharp depopulation areas and severe depopulation areas experienced shrinkage in our study area. (2) The pattern of rural depopulation is in accordance with terrain. Rural depopulation tends to be stronger in plateaus and mountains, while relatively milder in intermontane basins, hills, and piedmont plains. (3) The agricultural suitability of natural environmental and rural economic opportunities together with climate changes were the most important driving forces of rural depopulation at local levels. Location, sparse population, and inadequate public services also contributed to rural depopulation. However, the dominant driving factors are different in the different periods. Rural depopulation was mainly driven by arable land per capita and natural environmental variables in the years 2000-2010, while the population density, location, and off-farm economic opportunities played a decisive role in the years 2010-2017. (4) Rural depopulation is a complex, multi-dimensional process driven by a combination of multiple factors including different environmental factors, economic opportunities, and location. This paper reveals the push factors of rural depopulation in underdeveloped mountainous areas by a quantitative empirical approach, inspiring increased attention to the impacts of local factors and spatial correlations on rural depopulation, and has many implications for the policy design of China's rural revitalization.

摘要

农村人口空心化是城市化进程中农村地区最显著的地理现象。尽管许多研究已经从宏观层面上基于城乡迁移探讨了农村人口空心化的驱动力,但农村地区的局部因素及其对农村人口空心化的影响尚未得到充分揭示。本文以中国河北省北部山区为研究区,基于 GeoDetector 探讨了乡镇层面农村人口空心化的格局和局部推力因素。主要发现总结如下:(1)农村人口空心化差异显著,具有空间相关性,呈集聚分布。从动态角度来看,相较于 2000-2010 年,2010-2017 年人口增长区显著扩大,而剧烈空心化区和严重空心化区则有所缩小。(2)农村人口空心化格局与地形相吻合。高原和山地的农村人口空心化趋势较强,山间盆地、丘陵和山前平原则相对较弱。(3)自然环境农业适宜性和农村经济机会变化以及气候变化是局部层面农村人口空心化的最重要驱动力。位置、人口稀少和公共服务不足也促成了农村人口空心化。然而,不同时期的主导驱动力不同。2000-2010 年,农村人口空心化主要受人均耕地和自然环境变量驱动,而 2010-2017 年,人口密度、位置和非农经济机会则起决定性作用。(4)农村人口空心化是一个复杂的、多维度的过程,由多种因素共同驱动,包括不同的环境因素、经济机会和位置。本文通过定量实证方法揭示了欠发达山区农村人口空心化的推力因素,提高了对局部因素和空间相关性对农村人口空心化影响的重视,对中国乡村振兴政策设计具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/922ad38995ff/ijerph-19-05909-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/871a62de8460/ijerph-19-05909-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/3386e1897f2a/ijerph-19-05909-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/976192bcd365/ijerph-19-05909-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/a4abb15ef1e2/ijerph-19-05909-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/82909ce58db5/ijerph-19-05909-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/2cd4703dbfcc/ijerph-19-05909-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/5c4f818ffcbb/ijerph-19-05909-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/29a41ae33610/ijerph-19-05909-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/d781a6250b46/ijerph-19-05909-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/f85a04537d96/ijerph-19-05909-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/922ad38995ff/ijerph-19-05909-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/871a62de8460/ijerph-19-05909-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/ef14c4a38f04/ijerph-19-05909-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/3386e1897f2a/ijerph-19-05909-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/976192bcd365/ijerph-19-05909-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/a4abb15ef1e2/ijerph-19-05909-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/82909ce58db5/ijerph-19-05909-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/2cd4703dbfcc/ijerph-19-05909-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/5c4f818ffcbb/ijerph-19-05909-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/29a41ae33610/ijerph-19-05909-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/d781a6250b46/ijerph-19-05909-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/f85a04537d96/ijerph-19-05909-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77fc/9141062/922ad38995ff/ijerph-19-05909-g012.jpg

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