中国省际人口空间格局演化的分析方法与实证

doi:10.11821/dlxb201410007

摘要/Abstract

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

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.

Key words: China, inter-province, population, migration, spatial pattern

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.

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.

图/表 8

表1

表2

表3

表4

图1

图2

表5

图3

参考文献 40

[1]	Zhang Kevin Honglin, Song Shunfeng.Rural-urban migration and urbanization in China: Evidence from time-series and cross-section analyses. China Economic Review, 2003, 14: 386-400.
[2]	National Bureau of Statistics (NBS). China Statistical Yearbook. Beijing: China Statistics Press, 2001.
[3]	National Bureau of Statistics (NBS). China Statistical Yearbook. Beijing: China Statistics Press, 2011.
[4]	Chen Daiyun..
[5]	Liu Shenghe, Deng Yu, Hu Zhang.Research on classification methods and spatial patterns of the regional types of China's floating population. Acta Geographica Sinica, 2010, 65(10): 1187-1197.
	[刘盛和, 邓羽, 胡章. 中国流动人口地域类型的划分方法及空间分布特征. 地理学报, 2010, 65(10): 1187-1197.]
[6]	Andrew M.Isserman: Projection, forecast, and plan on the future of population forecasting. Journal of the American Planning Association, 1984, 50(2): 208-221.
[7]	Keyfitz N.Introduction to the Mathematics of Population. Reading, Mass: Addison-Wesley, 1968.
[8]	Keyfitz N, Caswell H. Applied Mathematical Demography.3rd ed. New York: Springer, 2005
[9]	Nakicenovic.et al. Special Report on Emissions Scenarios, IPCC. Geneva and Cambridge University Press, 2000.
[10]	Dietrich Stauffer.Simple tools for forecasts of population ageing in developed countries based on extrapolations of human mortality, fertility and migration. Experimental Gerontology, 2002, 37: 1131-1136.
[11]	Ballard, Kenneth, Robert Wendling. The national-regional impact evaluation system: A spatial model of U S. economic and demographic activity. Journal of Regional Science, 1980, 20: 143-158.
[12]	Plaut Thomas.An econometric model for forecasting regional population growth. International Regional Science Reoieui, 1981, 6: 53-70.
[13]	Long John F.Migration and the phase of population redistribution. Journal of Development Economics, 1985, 17: 29-42.
[14]	Bach R L, Schraml L A.Migration, crisis, and theoretic conflict. International Migration Review, 1982, 16: 320-341.
[15]	Wood C H.Equilibrium and historical-structural perspectives on migration. International Migration Review, 1982, 6: 298-319.
[16]	de Gans H A. Population Forecasting 1895-1945: The Transition to Modernity. Dordrecht, The Netherlands: Kluwer, 1999.
[17]	Riahi K, Kolp P, Grubler A.National Scenarios of Economic Activity (GDP): A downscaling exercise based on SRES, IR-05-063. International Institute for Applied Systems Analysis, Laxenburg, Austria, 2005.
[18]	O'Neill B, Goujon A, Prommer I. et al. Downscaling of SRES population scenarios from regional to national level, IR-05-062. International Institute for Applied Systems Analysis, Laxenburg, Austria, 2005.
[19]	Gaffin S R, Rosenzweig C, Xing X.et al.Downscaling and geo-spatial gridding of socio-economic projections from the IPCC Special Report on Emissions Scenarios (SRES). Glob. Environ. Change, 2004, 14: 105-123.
[20]	Harris R S, Moore E G.A historical approach to mobility research. Professional Geographer, 1980, 32: 22-29.
[21]	Booth H, Hyndman R J, Tickle L.et al.Lee-Carter mortality forecasting: A multi-country comparison of variants and extensions. Demographic Research, 2006, 15(9): 289-310.
[22]	Hyndman R J, Ullah M S.Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 2007, 51: 4942-4956.
[23]	Grübler A, O'Neill B, Riahi K. et al. Regional, national, and spatially explicit scenarios of demographic and economic change based on SRES. Technological Forecasting & Social Change, 2007, 74: 980-1029.
[24]	Akimoto K, Homma T, Kosugi T.et al. Analysis of energy strategy by country for global warming mitigation beyond Kyoto with a world energy systems model//Paper Presented at the International Energy Workshop, Held June　2022 at the International Institute for Applied Systems Analysis, Laxenburg, Austria, 2004, .
[25]	George M V, Perreault J.Methods of external migration projections and forecasts//Keilman N, Cruijsen H. National Population Forecasting in Industrialized Countries. Amsterdam: Swets and Zeitlinger, 1992: 87-103.
[26]	Rob J Hyndman, Heather Booth.Stochastic population forecasts using functional data models for mortality, fertility and migration. International Journal of Forecasting, 2008, 24: 323-342.
[27]	Wu Jing, Wang Zheng.Agent-based simulation on the evolution of population geography of China during the past 2000 years. Acta Geographica Sinica, 2008, 63(2): 185-194.
	[吴静, 王铮. 2000年来中国人口地理演变的Agent模拟分析. 地理学报, 2008, 63(2): 185-194.]
[28]	Duan Xuejun, Wang Shuguo.Evolution of population distribution and growth shift in Changjiang River Delta. Acta Geographica Sinica, 2008, 28(2): 139-144.
	[段学军, 王书国. 长江三角洲地区人口分布演化与偏移增长. 地理学报, 2008, 28(2): 139-144.]
[29]	Cheng Xiaoliang, Rao Fenfang.Analysis of influence of road, river on population distribution under different terrain environment: Take Huangshan City as an example. Resource Development & Market, 2008, 24(5): 417-419.
	[程晓亮, 饶芬芳. 不同地形环境下道路、河流对人口分布影响的分析: 以黄山市为例. 地理学报, 2008, 24(5): 417-419.]
[30]	Zhu Baoshu.The changes in fertility of the migrants in the urban from the rural. South China Population, 2011, 26(6): 42-50.
	[朱宝树. 乡—城转移人口的生育率水平问题. 南方人口, 2011, 26(6): 42-50.]
[31]	梁秋生. 外来流入人口的分母效应与大城市育龄妇女的超低总和生育率: 以京、津、沪为例. 人口研究, 2004, (5): 19-27.
	[Liang Qiusheng.The denominator effect of migration and low fertility of women in metropolitan: Case study on Beijing, Tianjin and Shanghai. Population Research, 2004, (5): 19-27.]
[32]	Cai Jianming, Wang Guoxia, Yang Zhenshan.Future trends and spatial patterns of migration in China. Population Research, 2007, 31(5): 9-20.
	[蔡建明, 王国霞, 杨振山. 我国人口迁移趋势及空间格局演变. 人口研究, 2007, 31(5): 9-20.]
[33]	National Health and Family Planning Commission of PRC. China Floating Population Development Report, 2010. Beijing: China Population Press, 2010.
	[人口计生委流动人口服务管理司. 中国流动人口发展报告, 2010. 北京: 中国人口出版社, 2010.]
[34]	Northam R M. Urban Geography.New York: J. Wiley Sons, 1975: 65-67.
[35]	Wang Guixin, Pan Zehan.China's floating population spatial distribution and influencing factors: Evidence from year 2010 population census of China. Modern Urban Research, 2013, (3): 4-13.
	[王桂新, 潘泽瀚. 我国流动人口的空间分布及其影响因素: 基于第六次人口普查资料的分析. 现代城市研究, 2013, (3): 4-13.]
[36]	CPC Central Committee and State Council. National New-type Urbanization Planning, 2014-2020. 2014.
	[中共中央国务院. 国家新型城镇化规划 (2014-2020年). 2014.]
[37]	Liu Ruiwen, Feng Zhiming, You Zhen.Research on the spatial pattern & formation mechanisms of population agglomeration & shrinking in China. China Population, Resource and Environment, 2010, 20(3): 89-94.
	[刘睿文, 封志明, 游珍. 中国人口集疏格局与形成机制研究. 中国人口·资源与环境, 2010, 20(3): 89-94.]
[38]	Han Hui, Liu Yong, Liu Ruiwen.The spatial patterns of China's population and their cause of formation. Journal of Lanzhou University: Social Sciences, 2000, 28(4): 16-21.
	[韩惠, 刘勇, 刘瑞雯. 中国人口分布的空间格局及其成因探讨. 兰州大学学报: 社会科学版, 2000, 28(4): 16-21.]
[39]	Liu Guilian, Wang Fulin.Forecast and analysis of population in Heilongjiang. Journal of Northeast Agricultural University, 2007, 5(2): 89-92.
	[刘桂莲, 王福林. 黑龙江省人口预测及分析. 东北农业大学学报: 社会科学版, 2007, 5(2): 89-92.]
[40]	Li Haizhen.The prediction of Shanxi population based on GM (1, 1) Model. Journal of Jiangnan University, 2012, 11(6): 370-374.
	[李海珍. 基于GM (1, 1) 模型的山西省人口预测. 江南大学学报, 2012, 11(6): 370-374.]

	2010	2015	2020	2025	2030	2035	2040	2045	2050
总人口 (万人)	133972.4	136974.3	138779.2	139525.6	139307.6	138158.8	136090.6	133176.8	129560.4
城镇人口 (万人)	66919.2	76157.9	84636.3	91180.4	95764.9	98444.6	99858.1	100409.0	100161.2
城镇化率	0.49	0.55	0.61	0.65	0.69	0.71	0.73	0.75	0.77

年份	空间单元	空间单元 (百万)
		总计	城镇			农村
		总计	小计	省内	省际	小计	省内	省际
2010	城镇	212.25	83.85	70.39	13.46	128.4	75.25	53.15
2000	城镇	98.12	43.39	37.93	5.46	54.74	35.44	19.30
净流动人口^a	城镇	114.13	40.47	32.46	8.01	73.66	39.81	33.85

省份	总人口 (2000)	自然增长率 (2000-2010)	预测人口 (2010)	六普人口 (2010)	估测流动人口 (2000-2010)	流动人口 (2000-2010)	估测^b 误差(%)
北京	13.82	1.81	14.07	19.61	+5.54	+4.40	5.83
天津	10.01	1.80	10.19	12.94	+2.75	+2.07	5.27
河北	67.44	5.99	71.48	71.85	+0.37	-1.81^a	3.03
山西	32.97	5.90	34.91	35.71	+0.80	-0.51^a	3.67
内蒙古	23.76	4.03	24.72	24.71	-0.01	+0.33^a	1.40
辽宁	42.38	1.11	42.85	43.75	+0.90	+0.09	1.85
吉林	27.28	2.33	27.91	27.46	-0.45	-0.62	0.60
黑龙江	36.89	2.35	37.76	38.31	+0.55	-1.26^a	4.73
上海	16.74	1.01	16.91	23.02	+6.11	+5.73	1.63
江苏	74.38	2.34	76.12	78.66	+2.54	+3.50	1.22
浙江	46.77	4.44	48.85	54.43	+5.58	+7.76	4.01
安徽	59.86	6.32	63.64	59.50	-4.14	-4.81	1.12
福建	34.71	6.05	36.81	36.89	+0.09	+1.31	3.32
江西	41.40	8.08	44.74	44.57	-0.18	-1.76	3.55
山东	90.79	5.27	95.57	95.79	+0.22	-0.91^a	1.18
河南	92.56	5.42	97.58	94.02	-3.56	-5.44	2.00
湖北	60.28	2.93	62.05	57.24	-4.81	-2.68	3.72
湖南	64.40	5.35	67.84	65.68	-2.16	-2.55	0.59
广东	86.42	7.65	93.03	104.30	+11.27	+5.98	5.07
广西	44.89	7.98	48.47	46.03	-2.45	-1.33	2.43
海南	7.87	9.07	8.58	8.67	+0.09	+0.05	0.43
重庆	30.90	3.21	31.89	28.85	-3.05	-1.96	3.77
四川	83.29	3.03	85.81	80.42	-5.39	-1.38	5.00
贵州	35.25	8.23	38.15	34.75	-3.40	-2.10	3.76
云南	42.88	8.10	46.35	45.97	-0.39	-1.07	1.48
西藏	2.62	11.17	2.91	3.00	+0.09	+0.02	2.26
陕西	36.05	4.07	37.52	37.33	-0.19	-0.61	1.12
甘肃	25.62	6.38	27.26	25.58	-1.68	-0.80	3.43
青海	5.18	9.76	5.69	5.63	-0.06	+0.05^a	1.88
宁夏	5.62	10.52	6.21	6.30	+0.09	+0.04	0.78
新疆	19.25	10.99	21.37	21.81	+0.45	+0.24	0.95

年份	总人口	城镇人口	城镇化率	城镇人口增长量	农村人口城镇化数量			省际流动人口增长量
年份	总人口	城镇人口	城镇化率	城镇人口增长量	省际		省内	合计
2000	1259.95	456.35	0.36	-	-	-	-	-
2010	1339.72	669.19	0.50	212.84	34.23	40.26	74.49	46.07
2015	1369.74	761.58	0.56	92.39	12.74	14.98	27.72	17.34
2020	1387.79	846.36	0.61	84.78	11.69	13.75	25.42	15.91
2025	1395.26	911.80	0.65	65.44	6.01	7.07	13.08	8.19
2030	1393.08	957.65	0.69	45.85	2.11	2.48	4.59	2.87
2035	1381.59	984.45	0.71	26.80	0.00	1.61	1.61	0.00
2040	1360.91	998.58	0.73	14.14	0.00	0.57	0.57	0.00
2045	1331.77	1004.09	0.75	5.51	0.00	0.11	0.11	0.00
2050	1295.60	1001.61	0.77	-2.48	0.00	0.00	0.00	0.00

区域类型		划分标准			新增累积流动人口比重(%)	新增累积自然增长人口比重(%)	省份
大类	亚类				A	B	C
人口快速变化区	净迁入主导型	a > 10	b > 5%	c>>1	99	18	北京、天津、江苏、上海、浙江、广东
	净迁出主导型		b < -5%	c<<-1	-75	14	河南、安徽、重庆、湖北
	自然增长主导型		b≈0%	\|c\|≈0	-1	6	辽宁、山东
人口低速变化区	净迁入型	5<a<10	0<b<5%	0<c<1	1	5	福建、海南
人口低速变化区	净迁出型	5<a<10	-5%<b<0	-1<c<0	-18	31	河北、山西、吉林、黑龙江、湖南、江西、广西、四川、陕西
人口平稳区		a < 5	0<b<5%	\|c\|≈0	-6	26	贵州、云南、宁夏、甘肃、内蒙古、新疆、青海、西藏