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长江经济带人口空间分布的“逆城市化”趋势及影响因素研究 被引量:10

The Trend of “Counter-urbanization” of Population Spatial Distribution and Its Influencing Factors in the Yangtze River Economic Belt
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摘要 本文以长江经济带为例,运用ROXY指数模型及PLS-SEM模型,分别对长江经济带城市群、城市内部人口空间分布的"逆城市化"趋势及其影响因素进行分析。研究结果表明:从城市群层面来看,长江中、下游城市群均出现了人口由中心城市向外围扩散的"逆城市化"现象;从城市层面来看,出现人口空间分布"逆城市化"趋势的有上海市、武汉市、南京市、成都市、合肥市、南昌市及长沙市;从影响因素来看,户籍、家庭责任、净收入、社会融入与人口"逆城市化"意愿显著负相关,土地权益、迁入地子女教育与人口"逆城市化"意愿显著正相关,医疗保障负向影响人口"逆城市化"意愿,但结果并不显著。本研究的相关结论对厘清人口流动的新趋势,探寻"逆城市化"产生的动力机制,正确认识及合理利用"逆城市化"趋势具有一定的借鉴意义。 Taking the Yangtze River Economic Belt for example,this paper analyzes the counter-urbanization trend of population spatial distribution in different city groups as well as inside cities,and its influencing factors by applying the ROXY index and PLS-SEM model.The results show that in the middle and lower reaches of the Yangtze River,the phenomenon of"counter-urbanization"in which the population spreads from the central city to the periphery appears in cities including Shanghai,Wuhan,Nanjing,Chengdu,Hefei,Nanchang and Changsha.The influencing factors such as household registration,land policies,family responsibilities,children’s education and individual welfare are all significantly related to the population"counter-urbanization"trend.The results have important implications for clarifying the new trend of population migration,exploring the dynamic mechanism of"counter-urbanization",and understanding and making good use of the"counter-urbanization"trend.
作者 张启春 梅莹 Zhang Qichun;Mei Ying(College of Public Administration,Hubei Local Government Administration and Local Development Research Center,Central China Normal University,Wuhan 430079;College of Public Administration,Central China Normal University,Wuhan 430079)
出处 《华中师范大学学报(人文社会科学版)》 CSSCI 北大核心 2020年第1期43-53,共11页 Journal of Central China Normal University:Humanities and Social Sciences
基金 国家社会科学基金重点项目“历史交汇期基本公共服务均等化政策实施效果、影响因素及优化方案研究”(19AZZ012)
关键词 长江经济带 逆城市化 ROXY指数模型 PLS-SEM模型 影响因素 the Yangtze River Economic Belt counter-urbanization ROXY index PLS-SEM model influencing factors
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