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中国省际人口空间格局演化的分析方法与实证
引用本文:邓羽,刘盛和,蔡建明,鲁玺.中国省际人口空间格局演化的分析方法与实证[J].地理学报,2014,69(10):1473-1486.
作者姓名:邓羽  刘盛和  蔡建明  鲁玺
作者单位:1. 中国科学院地理科学与资源研究所, 北京 1001012. 哈佛大学工程与应用科学学院, 美国, 马萨诸塞州 02138
基金项目:国家自然科学基金项目(41271174);国家科技支撑计划项目(2012BAI32B06)~~
摘    要:中国正在经历着史无前例的城镇化过程,2011年全国城镇人口已达6.91亿人,城镇化率攀升至51.27%。按2030年城镇化水平到70%计算,还将有接近3亿人口从农村流动至城市。与此同时,由于总生育率受社会经济发展水平、生态环境以及社会思潮的共同作用,人口自然增长率正在减少,“单独二胎”政策即是国家对人口发展战略的重大战略调整。由此可见,对正处在城镇化、工业化和全球化加速推进的中国而言,人口自然增长与空间迁移正在且必将持续重塑人口空间格局,因而科学地认识中国人口自然增长和空间迁移的区域差异、准确地把握人口空间格局及其演化趋势,对于科学编制城镇化发展、土地利用、生态环境保护等各类空间规划和制订流动人口、区域协作等各项空间管理政策具有重要意义。本文构建了一种全新的自上而下的人口预测方法,考虑人口自然增长和空间迁移两种影响因素,着重对一级单元 (全国) 和次级单元 (省级) 在2010-2050年间的人口总量进行预测,并进一步研判我国人口空间格局的发展情景和演化特征:从各省人口密度变化来看,中国人口空间分布密度继续保持了东部高、中部次之、东北再次、西部最低的总体格局。东部省份中上海、北京、天津及江苏的人口密度始终保持在一至四位,最末三位依次是新疆、青海和西藏。根据本文提出的省际人口变化强度及其主导类型的测算指标和量化结果,可将中国划分为人口快速变化区 (净迁入主导型、净迁出主导型、自然增长主导型)、人口低速变化区 (净迁入型、净迁出型) 以及人口平稳区。净迁入主导型人口快速变化区将吸纳99%的未来新增流动人口,其中上海、北京、浙江至2030年时人口密度达到顶峰,而广东的人口密度持续增加到2035年;净迁出主导型人口快速变化区,包括河南、安徽、重庆、湖北,75%的新增流动人口将由此类型区域迁出;辽宁和山东同属于自然增长主导型人口快速变化区,其新增流动人口比重极小,由人口自然增长变化主导区域的人口快速变化态势。在低速变化区中,除福建和海南作为少有的流动人口净迁入型省份,其余省份人口密度不断降低则缘于大量的劳动力输出和逐渐走低的生育率;大部西北和西南省份属于人口平稳区,其人口密度呈现出稳中有减的态势,人口密度平均值大多在100人/km2以下。

关 键 词:中国  省际  人口  城镇化  迁移  人口流动  空间格局  自然增长  
收稿时间:2013-12-05
修稿时间:2014-08-15

Spatial pattern and its evolution of Chinese provincial population and empirical study
Yu DENG,Shenghe LIU,Jianming CAI,Xi LU.Spatial pattern and its evolution of Chinese provincial population and empirical study[J].Acta Geographica Sinica,2014,69(10):1473-1486.
Authors:Yu DENG  Shenghe LIU  Jianming CAI  Xi LU
Institution:1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China2. School of Engineering and Applied Sciences, Harvard Cambridge, MA 02138, USA
Abstract:China has been experiencing unprecedented urbanization process. In 2011, China's urban population reached 691 million with an urbanization rate of 51.27%. Urbanization level is expected to increase to 70% in China in 2030, reflecting the projection that nearly 300 million people would migrate from rural areas to urban areas over this period. At the same time, the natural growth rate of China's population is declining due to the combined effect of economic growth, improvement in education quality, and modern social consciousness. As a result, Chinese government has loosened its "one-child policy" gradually by allowing childbearing couples to have a second child if either of them is from a one-child family. An accurate prediction of the future spatial pattern of population and its evolution is critical for a variety of key policy-making processes in China, such as industrial development, ecological conservation, industrial upgrading and transfer, and environmental protection. In this paper, a top-down method was developed to project the spatial distribution of China's future population with considerations of both natural population growth at provincial level and the population migration between provinces from 2010 to 2050. The results suggested that the overall spatial pattern of Chinese population is unlikely to change significantly over the next four decades. Shanghai, Beijing, Tianjin and Jiangsu will remain the top 4 provinces in terms of population density in China, and Xinjiang, Qinghai and Tibet continue to have the lowest density of population. China's population is projected to continue to concentrate in eastern coastal provinces. Population densities of Shanghai, Beijing, Zhejiang will peak in 2030, while that of Guangdong will keep increasing until 2035. Due to the large amount of moving-out labors and gradually declining fertility rates, population densities for Central and Northeast China will decrease from 292 persons per km2 in 2010 to 253 persons per km2 in 2050, exhibiting a downward trend. The majority of the western provinces, including Shaanxi, Chongqing, Guizhou, Yunnan, Sichuan, Gansu, Ningxia, Qinghai, Xinjiang, Tibet, Guangxi and Inner Mongolia are likely to remain sparsely populated, with an averaged population density no more than 100 persons per km2.
Keywords:China  inter-province  population  migration  spatial pattern  
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