中国人口老龄化空间格局演变及其驱动因素

doi:10.13249/j.cnki.sgs.2020.05.011

摘要/Abstract

摘要：

基于第五、六次人口普查数据,采用ESDA和空间计量模型分析中国地级单元人口老龄化的空间格局演变特征及驱动因素。结果表明：① 胡焕庸线是中国人口老龄化呈东高西低的显著分割线,随着老龄化不断加深,最初自西向东阶梯上升的空间格局逐渐模糊;② 各地级单元间人口老龄化存在较强的空间正相关性,呈现出高-高和低-低相似值集聚的空间分布特征。其中高-高集聚区主要分布在长三角、成渝、山东半岛、辽中南等地区,低-低集聚区主要分布在西北地区、西南部分地区、东北地区西部和珠三角部分地区,10 a间这种正向的空间集聚特征减弱;③ 中国人口老龄化空间格局演变是由人口因素和社会经济因素共同作用决定的,人口因素是直接原因,根本原因是地区社会经济发展的不平衡所致。对此,分别从人口和社会经济角度提出应对人口老龄化对策建议。

关键词: 人口老龄化, 空间格局, 影响因素, 空间统计分析, 空间计量模型, 中国

Abstract:

Based on the fifth and sixth Population Census data, this paper uses Exploratory Data Analysis (ESDA) and spatial econometric model to analyze the spatial pattern evolution characteristics and driving factors of China's prefecture population aging. The conclusions are as follows: 1) The Hu Line is a significant dividing line of the overall pattern of China's population aging rate between the east-high and low-west. The regions with a high population aging rate in China are located to the east of the Hu Line, and the lower regions are located to the west of the Hu Line. As the aging population continues to increase, the spatial pattern of the initial population aging from the west to the east gradually changed. 2) China's population aging has a strong spatial positive correlation, showing the spatial distribution characteristics of high-high and low-low similarity aggregation. The high-high agglomeration areas are mainly distributed in the Yangtze River Delta, Sichuan Province and Chongqing, Shandong Peninsula, Middle and South Liaoning Province, etc. The low-low agglomeration areas are mainly distributed in northwestern China, southwestern China, western China, and some prefecture-level cities of the Pearl River Delta. This positive spatial agglomeration has weakened over the past 10 years. 3)The spatial pattern of China's population aging is influenced by population factors and social and economic factors. Among them, the birth rate, mortality rate, education level and population density factors are significant, while the first three factors are always the determinants of population aging. Therefore the demographic factors are the direct factors affecting the evolution of the spatial pattern of China's population aging and the imbalance factors of social and economic development are the root cause. To this end, the recommendations on population aging are proposed from the aspects of population factors and social and economic factors.

Key words: population aging, spatial pattern, influencing factors, spatial statistical analysis, spatial econometric models, China

中图分类号:

K901.3/C922

吴媛媛, 宋玉祥. 中国人口老龄化空间格局演变及其驱动因素[J]. 地理科学, 2020, 40(5): 768-775.

Wu Yuanyuan, Song Yuxiang. The Evolution of China's Population Aging Spatial Pattern and Its Driving Factors[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(5): 768-775.

图/表 4

图1

表1

图2

表2

参考文献 22

[1]	陈明华, 郝国彩. 中国人口老龄化地区差异分解及影响因素研究[J]. 中国人口·资源与环境, 2014,24(4):136-141.
	[ Chen Minghua, Hao Guocai. Research on regional difference decomposition and influence factors of population aging in China. China Population, Resources and Environment, 2014,24(4):136-141.]
[2]	国务院人口普查办公室, 国家统计局人口和社会科技统计司. 中国2000年人口普查资料[M]. 北京: 中国统计出版社, 2002.
	[ Population Census Office under the State Council Department of Population, Social Science and Technology Statistics Under the National Bureau of Statistics (NBS). Tabulation on the 2000 population census of the People's Republic of China. Beijing: China Statistics Press, 2002.]
[3]	Rusk H A. The internist's number one problem-chronic disease in an aging population[J]. Annals of Internal Medicine, 1950,33(6):1341-1348. doi: 10.7326/0003-4819-33-6-1341 pmid: 14790515
[4]	Lawton M P, Ward M, Yaffe S. Indices of health in an aging population[J]. Journal of Gerontology, 1967,22(3):334-342. doi: 10.1093/geronj/22.3.334 pmid: 6028504
[5]	Rogerso P A. Geographic perspectives on elderly population growth[J]. Growth and Change, 1996,27(1):75-79. doi: 10.1111/j.1468-2257.1996.tb00897.x pmid: 12347251
[6]	Hanlon N. Geographies of ageing: Social processes and the spatial unevenness of population ageing[J]. Population Space and Place, 2013,19(5):645-646. doi: 10.1002/psp.1728
[7]	Goodman A C. Using Lorenz curves to characterize urban elderly population[J]. Urban Studies, 1987,24(1):77-80. doi: 10.1080/00420988720080071
[8]	Buffel T, Phillipson C. Can global cities be age-friendly cities? Urban development and ageing populations[J]. Cities, 2016,55(6):94-100. doi: 10.1016/j.cities.2016.03.016
[9]	林琳, 马飞. 广州市人口老龄化的空间分布及趋势[J]. 地理研究, 2007,26(5):1043-1054.
	[ Lin Lin, Ma Fei. Spatial distribution and trends of the aging of population in Guangzhou. Geographical Research, 2007,26(5):1043-1054.]
[10]	赵东霞, 韩增林, 王利. 中国老年人口分布的集疏格局及其形成机制[J]. 地理学报, 2017,72(10):1762-1775. doi: 10.11821/dlxb201710003
	[ Zhao Dongxia, Han Zenglin, Wang Li. The spatial pattern of aging population distribution and its generating mechanism in China. Acta Geographica Sinica, 2017,72(10):1762-1775.]
[11]	李日邦, 王五一, 谭见安, 等. 我国人口老龄化发展的阶段、趋势和区域差异[J]. 地理研究, 1999,18(2):113-121.
	[[ Li Ribang, Wang Wuyi, Tan Jianan et al. The development stages and trend of population ageing and its regional differences in China. Geographical Research, 1999,18(2):113-121.]
[12]	王志宝, 孙铁山, 李国平. 近20年来中国人口老龄化的区域差异及其演化[J]. 人口研究, 2013,37(1):66-77.
	[ Wang Zhibao, Sun Tieshan, Li Guoping. Regional differences and evolution of population aging in China. Population Research, 2013,37(1):66-77.]
[13]	李扬, 刘慧, 金凤君, 等. 北京市人口老龄化的时空变化特征[J]. 中国人口·资源与环境, 2011,21(11):131-138.
	[ Li Yang, Liu Hui, Jin Fengjun et al. Spatiotemporal transition of the aging population based on ESDA-GIS in Beijing City. China Population, Resources and Environment, 2011,21(11):131-138.]
[14]	周春山, 童新梅, 王珏晗, 等. 2000-2010年广州市人口老龄化空间分异及形成机制[J]. 地理研究, 2018,37(1):103-118. doi: 10.11821/dlyj201801008
	[ Zhou Chunshan, Tong Xinmei, Wang Juehan et al. Spatial differentiation and the formation mechanism of population aging in Guangzhou in 2000-2010. Geographical Research, 2018,37(1):103-118.]
[15]	王录仓, 武荣伟, 刘海猛, 等. 县域尺度下中国人口老龄化的空间格局与区域差异[J]. 地理科学进展, 2016,35(8):921-931. doi: 10.18306/dlkxjz.2016.08.001
	[ Wang Lucang, Wu Rongwei, Liu Haimeng et al. Spatial patterns and regional differences of population ageing in China based on the county scale. Progress in Geography, 2016,35(8):921-931.]
[16]	张开洲, 陈楠. 1990-2010年福建省县域人口老龄化时空演变特征及其驱动机制[J]. 地理科学进展, 2014,33(5):605-615. doi: 10.11820/dlkxjz.2014.05.002
	[ Zhang Kaizhou, Chen Nan. Characteristics of spatial-temporal evolution in population aging and driving mechanism at county level in Fujian Province during 1990-2010. Progress in Geography, 2014,33(5):605-615.]
[17]	吴连霞, 赵媛, 吴开亚, 等. 中国人口老龄化区域差异及驱动机制研究[J]. 地理科学, 2018,38(6):877-884. doi: 10.13249/j.cnki.sgs.2018.06.006
	[ Wu Lianxia, Zhao Yuan, Wu Kaiya et al. Regional variations and driving mechanism of aging population in China. Scientia Geographica Sinica, 2018,38(6):877-884.]
[18]	Tobler W R. A computer movie simulating urban growth in the Detroit region[J]. Economic Geography, 1970,46(2):234-240. doi: 10.2307/143141
[19]	国务院人口普查办公室, 国家统计局人口和就业统计司. 中国2010年人口普查资料[M]. 北京: 中国统计出版社, 2012.
	[ Population Census Office under the State Council Department of Population, Employment Statistics under the National Bureau of Statistics (NBS). Tabulation on the 2010 population census of the People's Republic of China. Beijing: China Statistics Press, 2012.]
[20]	中华人民共和国国家统计局. 中国区域经济统计年鉴2001,2011[M]. 北京: 中国统计出版社, 2001,2011.
	[ National Bureau of Statistics of the People's Republic of China. China statistical yearbook for regional economy. Beijing: China Statistics Press, 2001,2011.]
[21]	吴媛媛, 宋玉祥. 中国旅游经济空间格局演变特征及其影响因素分析[J]. 地理科学, 2018,38(9):1491-1498.
	[ Wu Yuanyuan, Song Yuxiang. Spatial pattern evolution and influence factors of tourism economy in China. Scientia Geographica Sinica, 2018,38(9):1491-1498.]
[22]	王建康, 谷国锋, 姚丽, 等. 中国新型城镇化的空间格局演变及影响因素分析——基于285个地级市的面板数据[J]. 地理科学, 2016,36(1):63-71. doi: 10.1329/j.cnki.sgs.2016.01.008
	[ Wang Jiankang, Gu Guofeng, Yao Li et al. Analysis of new urbanization's spatial pattern evolution and influence factors in China. Scientia Geographica Sinica, 2016,36(1):63-71.]

年份	Moran’s I	Z(I)	P值
2000	0.639	17.055	0.001
2010	0.623	15.651	0.001

变量	2000年			2010年
变量	OLS	SLM	SEM	OLS	SLM	SEM
LnX₁	-0.169^***(-4.499)	-0.164^***(-4.422)	-0.090^***(-6.358)	-0.113^***(-3.083)	-0.104^***(-3.434)	-0.208^***(-5.437)
LnX₂	0.839^***(19.697)	0.825^***(19.174)	0.788^***(17.738)	0.719^***(17.458)	0.693^***(16.964)	0.609^***(13.502)
LnX₃	-0.030(-1.382)	-0.027(-1.292)	-0.037^**(-1.963)	-0.056^**(-2.263)	-0.050^**(-1.993)	-0.086^**(-3.047)
LnX₄	0.062^***(8.135)	0.056^***(8.194)	0.050^***(5.640)	0.038^***(4.568)	0.038^***(4.803)	0.025^***(3.290)
LnX₅	0.083^***(3.772)	0.082^***(3.798)	0.034^*(1.707)	-0.009(-0.357)	-0.011(-0.790)	-0.003(-0.589)
LnX₆	0.080^**(2.635)	0.072^**(2.697)	0.106^***(3.933)	0.055(1.365)	0.046(1.278)	0.075^**(2.962)
LnX₇	0.184^***(3.296)	0.180^***(3.284)	0.234^**(1.907)	0.428^***(3.907)	0.397^***(3.706)	0.478^***(4.851)
LnX₈	-0.005(-1.124)	-0.006(-1.118)	-0.009(-1.495)	-0.005(-0.910)	-0.004(-0.817)	-0.011^**(-2.632)
LnX₉	0.040(1.368)	0.032(1.417)	0.007(0.27)	0.079^**(2.404)	0.082^**(2.547)	0.050^**(2.580)
ρ	-	0.332^***(3.340)	-	-	0.350^***(3.656)	-
λ	-	-	0.347^***(3.491)	-	-	0.362^***(5.120)
R²	0.711	0.812	0.788	0.660	0.762	0.737
Log-L	186.378	247.299	236.678	164.197	227.766	218.130
LMLAG	-	193.192^***	-	-	208.543^***	-
R-LMLAG	-	105.688^***	-	-	97.691^***	-
LMERR	-	-	91.340^***	-	-	117.311^***
R-LMERR	-	-	3.836^**	-	-	6.458^**