EGI OpenIR
中国人口老龄化多尺度空间格局及其内在机理
Alternative TitleMulti-scale spatial pattern of population ageing and its internal mechanism in China
武荣伟
Subtype博士
Thesis Advisor杨德刚
2019-06-30
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline理学博士
Keyword人口老龄化 多尺度 人口迁移 空间格局 中国 Population ageing Multi-scale Population migration Spatial pattern China
Abstract近 40 年来,中国正在经历前所未有的老龄化,并将一直持续至今后的几十年间。快速的老龄化将深刻影响中国的社会与经济发展。然而,老龄化在地理上是不同步的,其成因是多样性的。在不同的尺度上,开展老龄化分布格局及其成因的研究,不仅有助于理解中国人口老龄化的分布格局及其动态变化,而且可以丰富对老龄化成因的理解,对政府可以科学、有效应对人口老龄化,实施积极老龄化战略具有重要的参考意义。首先,对人口老龄化相关经典理论和国内外相关研究进展进行了回顾,随后针对已有研究进行了述评。其次,基于人口转变理论、老年人空间集聚学说以及人口流动理论,抽象出适用于省域尺度研究的两地模型和适用于县域尺度研究的老龄化路径分析框架。 随后, 汇总了全国、分省、分县的老龄化及其影响因素的数据,并建立了 1990 年、 2000 年、 2010 年三期分县尺度的人口空间数据库。 最后, 在不同尺度上,分别选用了最适宜的要素与分析方法。在全国尺度上,采用灰色关联分析方法分析了社会经济要素对老龄化的作用;在省域尺度,采用了面板数据回归方法分析了省际人口迁移和社会经济要素对老龄化的作用; 在县域尺度上,采用了空间计量模型对老龄化分布格局的成因进行了综合分析。 主要结论包括:(1) 全国尺度上的研究发现:中国人口老龄化具有四个特点,其一、人口年龄结构金字塔总体上从扩张型向缩减型转变;其二、 1990~2017 年,老龄化率加速上升;其三、老龄化呈现出城乡倒置现象;其四、老龄化有显著的民族差异。在全国尺度上,人口系统是一个近似封闭的系统,在不考虑人口国际迁移的情况下,采用灰色关联分析方法分析老龄化的影响因素。发现: 经济增长、人均基本养老保险基金支出和城镇化与老龄化率的关联程度很高,可见,在全国尺度,社会经济发展是推动老龄化的主要因素。(2) 省域尺度上,人口老龄化具有三个特点:其一、不同的省份, 老龄化的时序演变不尽相同。总体而言,老龄化的时序演变可以分为三大类:倒 U 型,波动型,上升型。其二、 近年来, 中国省域老龄化空间格局变化显著。 1982~2000年,老龄化从东部地区向中西部地区扩展, 2000 年后,老龄化的区域格局发生变化,发达地区老龄化呈减速上升,欠发达地区老龄化加速上升,老龄化的省域差异先扩大后缩小。其三、不同省份,不同的发展历程,老龄化的“城乡倒置”现象有不同的体现,省份的固有差异和人口的迁移流动是重要成因。在省域尺度上,人口系统是一个相对开放的系统,因此必须考虑人口的跨省迁移对老龄化的影响。 首先,针对人口的跨省迁移进行了细致分析,从人口迁移的规模来看: 1995~2000 年,广东、浙江、上海、江苏、北京、福建和新疆是主要的人口迁入省份。 2000~2005 年和 2005~2010 年期间,迁移人口的目的地集中在几个主要东部省份,包括广东、浙江、江苏、上海、北京和福建。 2010~2015年,天津成为迁入人口的主要目的地。 1995~2000 年,中西部地区的四川、湖南、安徽、江西、河南、湖北、广西、江苏、贵州、重庆是主要的人口迁出省份。2000~2005年,广东成为主要的人口迁出省份, 2005~2010 年,湖北成为主要的人口迁出省份, 2010~2015 年,浙江成为主要的人口迁出省份。从人口迁移强度来看:东部省份的迁入率较高,中部和西部省份的迁入率普遍较低。 2010~2015 年,东部地区大部分省份的迁入率在下降,迁出率在上升,而中西部地区大部分省份的迁入率上升,迁出率下降。从影响人口迁移的因素来看:迁出省份的农村居民人均纯收入和迁入省份的城镇居民人均可支配收入是引起迁移最重要的两个因素,此外,迁出省份农民人均收入中工资性收入的比例,人均耕地面积,以及迁入省份的外商直接投资强度、人口密度和气候适宜性都对省际迁移有所影响。其次,分析了跨省迁移对老龄化省域分布格局的影响,发现:在控制了经济发展因素、文化教育因素和医疗卫生因素后,迁入率显著地降低了老龄化率,迁出率显著地促进老龄化进程。这佐证了我们的设想,大规模持续性的跨省人口迁移,确实重塑了省域的老龄化格局。(3) 在县域尺度上,分析了县域老龄化的类型变化,速度分异,地区差异与时空格局。随后,从社会经济、人口迁移、自然要素三个视角,综合考虑了老龄化的成因。主要结论包括:其一、老龄化空间分布呈现从沿海向内陆、从东南向西北逐步扩展的态势。其二、老龄化速度存在显著的地区差异,老龄化低速度区域并存于最发达与最不发达区域。其三、 1990~2010 年, 在县域尺度上, 中国人口老龄化总体差异呈现不断扩大的趋势。西部地区是省间差异最大的地区,东部地区省间差异次之,东北地区省间差异最小。从演变趋势来看,东部、中部、西部地区省间差异有所扩大,东北地区省间差异有所缩小。从总体差异的贡献率来看,省域内差异是造成地区差异的主要来源。其四、 1990~2000 年,老龄化重心向东北方向偏移, 2000~2010 年,重心向西北方向偏移。其五、县域尺度上,中国人口老龄化呈现出显著地空间依赖性。从局部来看 HH 型县域主要分布在胡焕庸线东南半壁,而 LL 型县域主要分布在胡焕庸线西北半壁。其六、从老龄化的空间结构来看,呈现出核心边缘式、逆核心边缘式、临海内陆型三大分布格局。在县域尺度上,人口系统是一个非常开放的系统。而小尺度下,将自然要素纳入分析当中也变得切实可行。总体而言,以地形起伏度、二月份平均温度、植被覆盖等为代表的自然要素是老龄化分布格局形成的基本因素,以人均 GDP,每千人医院卫生院床位数、人均受教育年限等为代表的社会经济要素是老龄化分布格局形成的推动力,由于人口迁移,使得社会经济因素对人口老龄化的影响趋于多样性和复杂性,以迁入率和迁出率为代表的人口迁移重塑了老龄化的分布格局,这种影响主要发生在人口迁入与迁出较为活跃的县市。
Other AbstractChina has been experiencing ageing, which is unprecedented nearly 40 years, andwill continue in the next few decades. Rapid ageing will profoundly affect social andeconomic development. While ageing is uneven in geographically, and its causes arevaried. Clarifying the distribution pattern and driving factors of ageing at threedifferent-scale, is helpful for us to understand the distribution pattern of populationageing and its dynamic change in China, and to enrich our knowledge about the causesof ageing. The research is also of great significance for the Chinese government intaking measures to deal with population ageing scientifically and effectively. Wedeveloped this research as follows: Firstly, we gave a summary of classical theories ofpopulation ageing and researches both at home and abroad. Then we have a review onprevious researches. Secondly, we built two-Place model and analysis framework of theageing path, which was relied on the three theories of population transformation theory,spatial agglomeration theory of the elderly and theory of population flow, for researchat province scale and county scale, respectively. Next, we summarized the data ofageing at nation, province and county scale and its driving factors, with establishing thespatial database of the population in 1990, 2000 and 2010 at county level. Finally, thesuitable factors and analysis methods were selected at different scale, respectively. Atnation scale, grey relational analysis was used to analyze the effect of social andeconomic factors on ageing; and at province scale, panel data regression was adoptedto analyze the effect of population migration and social and economic factors on ageing;at county scale, the spatial econometric model was used to analyze the causes of thedistribution pattern of population ageing. The results are as following:(1) There are four characteristics of population ageing at nation scale. First, apyramid of the age structure of population shows the transition from expansion toshrinkage. Second, ageing rate accelerated from 1990 to 2017. Third, ageing presents aphenomenon of urban-rural inversion. Fourth, there are significant differences in ageingamong different ethnic groups.The population system is an approximate closed system at nation scale. Greyrelational analysis was used to analyze influencing factors on ageing withoutconsidering the international migration of population. There was high correlation in economic development, expenditure on basic pension insurance fund per capita andurbanization with the ageing rate. Therefore, social and economic development is themain factor to accelerate ageing at nation scale.(2) There are several features of population ageing at province scale. First, thetemporal evolution of ageing was varied in different provinces. Temporal evolution ofageing can be summarized as three categories: inverted U shaped, wave type and risingtype; Second, the change of the spatial pattern of ageing was remarkable at provincescale. Ageing expanded from the eastern region to the central and western region from1982 to 2000. The regional pattern of ageing has changed since 2000, which indeveloped areas raised with a decreasing rate. while in underdeveloped areas with anincreasing rate. The provincial differences of ageing are enlarged first and thennarrowed. Third, the phenomenon of “urban-rural inversion” in ageing showdifferences in different provinces, therefore, considering the different developingprogress provincially, we can conclude the inherent differences in provinces andpopulation migration are important reasons.The population system was a relatively open system at province scale. Therefore,it was necessary to consider the impact of inter-provincial migration on ageing. Theinter-provincial migration of population was analyzed in detail at first. The results canbe attained from the scale of population migration: in 1995–2000, Guangdong, Zhejiang,Shanghai, Jiangsu, Beijing, Fujian and Xinjiang were the main provinces of inmigration. During the periods of 2000–2005 and 2005–2010, the destinations of inmigration were concentrated in several main eastern provinces, including Guangdong,Zhejiang, Jiangsu, Shanghai, Beijing, and Fujian. From 2010 to 2015, Tianjin becamea major in-migration province. From 1995 to 2000, Sichuan, Hunan, Anhui, Jiangxi,Henan, Hubei, Guangxi, Jiangsu, Guizhou and Chongqing, which are located in centraland western China, were the main provinces from where the population moved out.Guangdong became the main province of out-migration from 2000 to 2005, while itwas Hubei from 2005 to 2010 and Zhejiang from 2010 to 2015. The population systemwas a relatively open system at province scale. Therefore, it was necessary to considerthe impact of inter-provincial migration on ageing.In terms of the intensity of migration, the in-migration rates in the easternprovinces were high, and those in the central and western provinces were generally low.From 2010 to 2015, the in-migration rates in the eastern provinces decreased and the out-migration rates increased. While in the central and western provinces, the inmigration rates increased and out-migration decreased.In terms of the influencing factors of migration, the RURALI (Per Capita AnnualNet Income of Rural Household) of out-migration province and the URBANI (PerCapita Annual Disposable Income of Urban Households) of in-migration are the mostimportant factors affecting migration. In addition, Share of FDI in GDP (SFG), ArableLand Per Capita (ALPC), Share of Wages and Salaries in the Per Capita Income ofRural Households (SWR), Population Density (PD) and Temperature Difference Index(TDI) of destination were also important to inter-provincial migration in China.At second, we analysed the influence of inter-provincial migration on thedistribution pattern of population ageing at province scale. The conclusions are asfollows. The in-migration rate decreased the ageing rate significantly and the outmigration rate promoted the ageing process remarkably. Above situations appearedwhen factors of economic development, cultural educational and medical care werecontrolled. This result supported our assumption that the pattern of ageing at theprovince scale was reshaped by large-scale and continuous inter-provincial migration.(3) At county scale, type change, speed differentiation, regional differentiation andspatial-temporal patterns of ageing were analyzed firstly. Then the causes of ageingwere comprehensively considered from the perspective of economy, populationmigration and natural factors. The conclusions were as follows: Firstly, the spatialdistribution of ageing was gradually expanding from coastal to inland as well as fromsoutheast to northwest. Secondly, there were significant regional differences in ageingspeed. The low ageing speed was existed both in developed and least developed regions.Third, the overall disparity of the ageing showed a growing trend from 1990 to 2000.The western region was the area with the greatest inter-provincial differences, followedby the eastern region, and the northeast region with the smallest inter-provincialdifferences. The inter-provincial differences in eastern, central and western regionshave expanded, while the inter-provincial differences in Northeast China have narrowedfrom the perspective of evolution trend. The inter-provincial difference was the mainsource of the regional difference from the contribution rate of the overall difference.Fourth, the center of ageing, from 1990 to 2000, shifting to the northeast, while from2000 to 2010, it to the northwest. Fifth, population ageing shows significant spatialdependence at county scale. HH-type County was mainly distributed on the South-eastern side of Hu’s line, while LL-type County was mainly distributed on the Northwestern side of Hu’s line. Sixth, there are three distribution patterns of core-edge type,inverse core-edge type and coastal-inland type from the perspective of the spatialstructure of ageing.As the population system is a relatively open system at county scale, it is feasibleto incorporate natural factors into the analysis. In general, natural factors such astopographic relief, average temperature in February and NDVI are the basic factors forthe formation of the distribution pattern of ageing. Social and economic factors, suchas per capita GDP, number of hospital beds per thousand people and average educationattainment are the driving forces for the formation of the distribution pattern of ageing.The influence of socio-economic factors on population ageing tends to be diverse andcomplex due to population migration. The distribution pattern of ageing was reshapedby population migration including in-migration and out-migration, and the influencemainly occurred in the counties where the population moves in and out more actively.
Subject Area人文地理学
Language中文
Document Type学位论文
Identifierhttp://ir.xjlas.org/handle/365004/15304
Collection中国科学院新疆生态与地理研究所
研究系统
Affiliation中国科学院新疆生态与地理研究所
First Author Affilication中国科学院新疆生态与地理研究所
Recommended Citation
GB/T 7714
武荣伟. 中国人口老龄化多尺度空间格局及其内在机理[D]. 北京. 中国科学院大学,2019.
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