The spatiotemporal pattern and influencing factors of urban shrinkage: A comparative study of East Germany and Northeast China

doi:10.3969/j.issn.1004-9479.2024.08.20222417

Abstract

Abstract:

It is of great significance to carry out accurate identification of urban shrinkage and comparative analysis of its influencing factors in large national regions, to reduce the error based on administrative area identification. By constructing the VANUI index, calculating the one-dimensional linear regression coefficient of the DN value of night light and the WorldPop population grid data, and based on the overlay of the regression coefficient of the DN value of night light, and the regression coefficient of the WorldPop population grid data, the urban areas of East Germany and Northeast China in the two stages of 2000-2009 and 2010-2018 were divided into four types: absolute growth, smart shrinkage, negative growth and absolute shrinkage. This paper then studied and compared the spatio-temporal distribution and transformation path of the two stages of absolute shrinkage and smart shrinkage in the two regions, and constructed a panel regression model from population growth, scientific and technological development level, medical service level and other aspects to conduct a comparative analysis of the influencing factors of the two stages of urban shrinkage in the two regions. The results show that the absolute shrinkage areas in the two regions have a great similarity in spatiotemporal distribution and transformation path; the smart shrinkage areas are similar in spatiotemporal distribution, but different in transformation path; the absolute shrinkage areas in the two regions and two stages is relatively small; the mechanism of population growth in the two regions has both similarities and differences, and has different connotations due to differences in national conditions. This study shows that: (1)Urban shrinkage areas have the potential for short-term growth, and their development rights should be treated equally; (2)The absolute shrinking areas should be given special atten‐tion and governance, while the smart shrinking areas should be regarded as a reasonable development model for urban shrinking areas; (3)Continuously improving the level of medical, elderly care services and research investment, steadily promoting the transformation of living environment and infrastructure in existing areas, and reasonably controlling incremental development can help alleviate the phenomenon of urban shrinkage.

Key words: urban shrinkage, population, East Germany, Northeast China, urban area

摘要：

随着城市收缩研究的深入，从城市实体地域尺度出发进行国家区域间的精准识别和影响因素对比分析，对减少基于行政地域识别的误差和借鉴国外成功经验具有重要意义。研究通过构建VANUI指数、计算夜间灯光DN值和WorldPop人口栅格数据的一元线性回归系数，对“夜间灯光DN值回归系数≥0或＜0，WorldPop人口栅格数据回归系数≥0或＜0”进行两两叠加，将德国东部地区和中国东北地区2000—2009年和2010—2018年两阶段的城市实体地域划分为绝对增长型、精明收缩型、消极增长型和绝对收缩型四类。基于分类结果，对比了两地区两阶段绝对收缩型和精明收缩型区域的时空分布和转变路径，并从人口增长、科技发展水平、医疗服务水平等方面构建面板回归模型对两地区两阶段城市收缩的影响因素进行了分析。结果表明：两地区绝对收缩型区域在时空分布和转变路径上具有较大的相似性；精明收缩型区域在时空分布上具有相似性，但在转变路径上具有差异性；两地区两阶段绝对收缩型区域均占比较少；两地区人口增长机制兼具异同点，并由于国情差异而具有不同内涵。总的来说，城市收缩区域具有短期内实现增长的可能性；应当给予重点关注和治理的是绝对收缩型区域，而精明收缩型区域应被视作城市收缩区域的良性发展模式；持续提升医疗养老服务水平和科研投入，稳步推进存量区域的人居环境改造和基础设施配套，合理管控增量开发，有助于缓解城市收缩现象。

关键词: 城市收缩, 人口, 德国东部地区, 中国东北地区, 城市实体地域

Yuanzhao LIANG, Ye WEI. The spatiotemporal pattern and influencing factors of urban shrinkage: A comparative study of East Germany and Northeast China[J]. World Regional Studies, 2024, 33(8): 40-51.

梁源钊, 魏冶. 城市收缩的时空格局与影响因素：德国东部地区与中国东北地区的对比研究[J]. 世界地理研究, 2024, 33(8): 40-51.

Figures/Tables 8

References 43

1	高舒琦.收缩城市研究综述.城市规划学刊,2015(3):44-49.
	GAO S. A review of researches on shrinking cities. Urban Planning Forum,2015(3):44-49.
2	孙平军.城市收缩:内涵·中国化·研究框架.地理科学进展,2022,41(8):1478-1491.
	SUN P. Urban shrinkage: Connotation-sinicization-framework of analysis. Progress in Geography,2022,41(8):1478-1491.
3	张华,练云龙.国外收缩城市研究进展及其启示.西部人居环境学刊,2018,33(3):28-36.
	ZHANG H, LIAN Y. Progresses of shrinking cities and their implications. Journal of Human Settlements in West China,2018,33(3):28-36.
4	邓嘉怡,郑莎莉,李郇.德国收缩城市的规划应对策略研究——以原东德都市重建计划为例.西部人居环境学刊,2018,33(3):13-20.
	DENG J, ZHENG S, LI X. Research on the strategies to cope with shrinkage in Germany--A case study of "urban rebuilding east (stadtumbau ost)". Journal of Human Settlements in West China,2018,33(3):13-20.
5	EBERSTADT N. Demographic shocks after communism: Eastern Germany 1989-93. Population and Development Review,1994,20(1):137-152.
6	邓嘉怡,李郇.统一后原东德城市收缩现象及机制研究.世界地理研究,2018,27(4):90-99.
	DENG J, LI X. Research on the phenomena and mechanism of shrinking cities in the former East Germany after reunification. World Regional Studies,2018,27(4):90-99.
7	李诚固,李振泉."东北现象"特征及形成因素.经济地理,1996,16(1):34-38.
	LI C, LI Z. The characteristics and forming factors of "Northeast Phenomenon". Economic Geography,1996,16(1):34-38.
8	孙平军,张可秋,何田.东北三省收缩城市城乡一体化收缩效应及其作用机理研究.地理科学进展,2022,41(7):1213-1225.
	SUN P, ZHANG K, HE T. Shrinkage effect of urban-rural integration on shrinking cities in the three provinces of Northeast China and mechanism. Progress in Geography,2022,41(7):1213-1225.
9	杜志威,李郇.珠三角快速城镇化地区发展的增长与收缩新现象.地理学报,2017,72(10):1800-1811.
	DU Z, LI X. Growth or shrinkage: New phenomena of regional development in the rapidly-urbanising Pearl River Delta. Acta Geographica Sinica,2017,72(10):1800-1811.
10	WIECHMANN T. Errors expected--Aligning urban strategy with demographic uncertainty in shrinking cities. International Planning Studies,2008,13(4):431-446.
11	SCHILLING J, LOGAN J. Greening the rust belt: A green infrastructure model for right sizing America's shrinking cities. Journal of the American Planning Association,2008,74(4):451-466.
12	MARTINEZ-FERNANDEZ C, WEYMAN T, FOL S, et al. Shrinking cities in Australia, Japan, Europe and the USA: From a global process to local policy responses. Progress in Planning,2016,105:1-48.
13	WIRTH P, ELIS V, MULLER B, et al. Peripheralisation of small towns in Germany and Japan dealing with economic decline and population loss. Journal of Rural Studies,2016,47:62-75.
14	MACE A, VOLGMANN F. The role of Leipzig's narrative of shrinking. Urban Geography,2018,39:803-821.
15	杜志威,张虹鸥,叶玉瑶,等.2000年以来广东省城市人口收缩的时空演变与影响因素.热带地理,2019,39(1):20-28.
	DU Z, ZHANG H, YE Y, et al. Spatiotemporal evolution and influences of urban population shrinkage in Guangdong since 2000. Tropical Geography,2019,39(1):20-28.
16	李郇,杜志威,李先锋.珠江三角洲城镇收缩的空间分布与机制.现代城市研究,2015(9):36-43.
	LI X, DU Z, LI X. The spatial distribution and mechanism of city shrinkage in the Pearl River Delta. Modern Urban Research,2015(9):36-43.
17	吴康,龙瀛,杨宇.京津冀与长江三角洲的局部收缩:格局、类型与影响因素识别.现代城市研究,2015(9):26-35.
	WU K, LONG Y, YANG Y. Urban shrinkage in the Beijing-Tianjin-Hebei region and Yangtze River Delta: Pattern, trajectory and factors. Modern Urban Research,2015(9):26-35.
18	杨振山,杨定.城市发展指数指引下的我国收缩区域初步评判.人文地理,2019,34(4):63-72.
	YANG Z, YANG D. Exploring shrinking areas in China availing of city development index. Human Geography,2019,34(4):63-72.
19	林雄斌,杨家文,张衔春,等.我国城市收缩测度与影响因素分析——基于人口与经济变化的视角.人文地理,2017,32(1):82-89.
	LIN X, YANG J, ZHANG X, et al. Measuring shrinking cities and influential factors in urban China: Perspective of population and economy. Human Geography,2017,32(1):82-89.
20	刘贵文,谢芳芸,洪竞科,等.基于人口经济数据分析我国城市收缩现状.经济地理,2019,39(7):50-57.
	LIU G, XIE F, HONG J, et al. Urban shrinkage in China based on the data of population and economy. Economic Geography,2019,39(7):50-57.
21	ZHOU Y, LI C, MA Z, et al. Identification of shrinkage and growth patterns of a shrinking city in China based on nighttime light data: A case study of Yichun. Sustainability,2019,11(24):1-18.
22	LANG W, DENG J, LI X. Identification of "growth" and "shrinkage" pattern and planning strategies for shrinking cities based on a spatial perspective of the Pearl River Delta region. Journal of Urban Planning and Development,2020,146(4):1-15.
23	周一星,史育龙.建立中国城市的实体地域概念.地理学报,1995,50(4):289-301.
	ZHOU Y, SHI Y. Toward establishing the concept of physical urban area in China. Acta Geographica Sinica,1995,50(4):289-301.
24	吴康,戚伟.收缩型城市:认知误区、统计甄别与测算反思.地理研究,2021,40(1):213-229.
	WU K, QI W. Shrinking cities: Misunderstandings, identifications and reflections. Geographical Research,2021,40(1):213-229.
25	孟祥凤,马爽,项雯怡,等.基于百度慧眼的中国收缩城市分类研究.地理学报,2021,76(10):2477-2488.
	MENG X, MA S, XIANG W, et al. Classification of shrinking cities in China using Baidu big data. Acta Geographica Sinica,2021,76(10):2477-2488.
26	陈才,李文华.世界经济地理.北京:北京师范大学出版社,1999.
	CHEN C, LI W. World Economic Geography. Beijing: Beijing Normal University Press,1999.
27	李振泉,杨万忠,陆心贤.中国经济地理.上海:华东师范大学出版社,1998.
	LI Z, YANG W, LU X. Chinese Economic Geography. Shanghai: East China Normal University Press,1998.
28	CHEN Z, YU B, YANG C, et al. An extended time series(2000-2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data,2021(13):889-906.
29	孟梦,牛铮,马超等.青藏高原NDVI变化趋势及其对气候的响应.水土保持研究,2018,25(3):360-365.
	MENG M, NIU Z, MA C, et al. Variation trend of NDVI and response to climate change in Tibetan Plateau. Research of Soil and Water Conservation,2018,25(3):360-365.
30	ZHANG Q, SCHAAF C, SETO K. The vegetation adjusted NTL urban index: A new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sensing of Environment, 2013,129: 32-41.
31	厉飞.基于NPP/VIIRS亮度修正指数的城市建成区提取研究.徐州:中国矿业大学,2021.
	LI F. Research on urban built-up area extraction via brightness correction indexes based on NPP/VIIRS images. Xuzhou: China University of Mining and Technology,2021.
32	张建波,段利鹏,史晓楠,等.吉林省城市增长与收缩的分异特征、成因与对策.地域研究与开发,2022,41(1):53-59.
	ZHANG J, DUAN L, SHI X, et al. Characteristic, causes and strategies of urban growth or shrinkage in Jilin Province. Areal Research and Development,2022,41(1):53-59.
33	BARTHOLOMAE F, NAM C, SCHOENBERG A. Urban shrinkage and resurgence in Germany. Urban Studies,2017, 54(12):2701-2718.
34	YANG Y, WU J, WANG Y, et al. Quantifying spatiotemporal patterns of shrinking cities in urbanizing China: A novel approach based on time-series nighttime light data. Cities,2021: 103346.
35	ELVIDGE C, BAUGH K, KIHN E, et al. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing,1997,18(6):1373-1379.
36	HOLLANDER J, NEMETH J. The bounds of smart decline: A foundational theory for planning shrinking cities. Housing Policy Debate,2011,21(3):349-367.
37	方红生.面板数据分析与Stata应用.杭州:浙江大学出版社,2022.
	FANG H. Panel Data Analysis and Application Using STATA. Hangzhou: Zhejiang University Press,2022.
38	吕利丹,王涵,段成荣.国际移民最新趋势和政策应对.人口学刊,2021,43(6):68-84.
	LYU L, WANG H, DUAN C. Review of international immigration trends and policies. Population Journal,2021,43(6):68-84.
39	赵兵.德国统一后东部地区失业问题研究.兰州:兰州大学,2007.
	ZHAO B. Studies on unemployment in eastern regions of Germany after the reunification. Lanzhou: Lanzhou University,2007.
40	陈耀.新一轮东北振兴战略要思考的几个关键问题.经济纵横,2017(1):8-12.
	CHEN Y. Several key issues about the strategy of a new round of revitalization. Economic Review Journal,2017(1):8-12.
41	盛广耀.东北地区振兴战略实施效果评析.社会科学辑刊,2013(2):94-101.
	SHENG G. Evaluation and analysis of the implementation effect of the revitalization strategy in Northeast China. Social Science Journal,2013(2):94-101.
42	包世荣.国外医养结合养老模式及其对中国的启示.哈尔滨工业大学学报(社会科学版),2018,20(2):58-63.
	BAO S. The foreign pension mode of medical treatment and care and its inspiration to China. Journal of Harbin Institute of Technology(Social Sciences Edition),2018,20(2):58-63.
43	陈强,霍丹.德国创新驱动发展的路径及特征分析.德国研究,2013,28(4):86-100.
	CHEN Q, HUO D. Analyse des wegs und der merkmale der durch innovation angetriebenen entwicklung deutschlands. Deutschland-Studien,2013,28(4):86-100.

时段	类型	德国东部		中国东北
时段	类型	栅格数	占比/%	栅格数	占比/%
2000—2009年	消极增长型	154	7.91	217	3.20
	精明收缩型	962	49.44	2 636	38.40
	绝对收缩型	186	9.56	92	1.30
	绝对增长型	644	33.09	3 926	57.10
2010—2018年	消极增长型	103	2.03	486	5.70
	精明收缩型	2 084	41.02	3 084	36.20
	绝对收缩型	110	2.17	429	5.00
	绝对增长型	2 783	54.78	4 529	53.10

时段	类型	德国东部		中国东北
时段	类型	栅格数	占比/%	栅格数	占比/%
2000—2009年	消极增长型	154	7.91	217	3.20
	精明收缩型	962	49.44	2 636	38.40
	绝对收缩型	186	9.56	92	1.30
	绝对增长型	644	33.09	3 926	57.10
2010—2018年	消极增长型	103	2.03	486	5.70
	精明收缩型	2 084	41.02	3 084	36.20
	绝对收缩型	110	2.17	429	5.00
	绝对增长型	2 783	54.78	4 529	53.10

项	FE模型（2001—2009年）	双向固定效应模型（2001—2009年）	FE模型（2010—2017年）	双向固定效应模型（2010—2017年）
截距	0.403^**(18.119)	0.445^**(14.203)	0.281^**(12.760)	0.133(1.679)
人口机械迁移量/人	0.008(0.880)	－0.008(－0.797)	0.004(0.200)	－0.011(－0.837)
人口自然增长量/人	0.041(1.120)	0.057(1.022)	0.056^*(2.600)	0.203^**(3.472)
人口老龄化率/%	－0.141^**(－7.144)	－0.201^**(－3.615)	－0.028(－1.433)	0.098(1.286)
研发支出/百万欧元	－0.055^*(－2.194)	－0.079(－1.605)	0.211^**(4.172)	0.327^**(4.127)
医院数量/间	0.197^**(5.325)	0.175^**(2.756)	0.158^**(2.723)	0.205^**(4.581)
人均住房津贴/欧元	－0.001(－0.067)	0.014(0.957)	0.033(1.333)	－0.055(－1.698)
R ²	0.211	0.093	0.525	0.620
R ²(within)	0.908	0.661	0.625	－2.283
样本量	54	54	48	48
检验	F(6,42)=6.035×10¹⁴, p=0.000	F(6,39)=1.785×10¹⁴, p=0.000	F(6,36)=7.208×10¹⁵,p=0.000	F(6,29)=4.821×10¹⁵, p=0.000

项	FE模型（2001—2009年）	双向固定效应模型（2001—2009年）	FE模型（2010—2017年）	双向固定效应模型（2010—2017年）
截距	0.403^**(18.119)	0.445^**(14.203)	0.281^**(12.760)	0.133(1.679)
人口机械迁移量/人	0.008(0.880)	－0.008(－0.797)	0.004(0.200)	－0.011(－0.837)
人口自然增长量/人	0.041(1.120)	0.057(1.022)	0.056^*(2.600)	0.203^**(3.472)
人口老龄化率/%	－0.141^**(－7.144)	－0.201^**(－3.615)	－0.028(－1.433)	0.098(1.286)
研发支出/百万欧元	－0.055^*(－2.194)	－0.079(－1.605)	0.211^**(4.172)	0.327^**(4.127)
医院数量/间	0.197^**(5.325)	0.175^**(2.756)	0.158^**(2.723)	0.205^**(4.581)
人均住房津贴/欧元	－0.001(－0.067)	0.014(0.957)	0.033(1.333)	－0.055(－1.698)
R ²	0.211	0.093	0.525	0.620
R ²(within)	0.908	0.661	0.625	－2.283
样本量	54	54	48	48
检验	F(6,42)=6.035×10¹⁴, p=0.000	F(6,39)=1.785×10¹⁴, p=0.000	F(6,36)=7.208×10¹⁵,p=0.000	F(6,29)=4.821×10¹⁵, p=0.000

项	RE模型（2000—2009年）	双向固定效应模型（2000—2009年）	RE模型（2010—2017年）	双向固定效应模型（2010—2017年）
截距	0.244^**(8.582)	0.248^**(43.427)	0.227^**(8.073)	0.258^**(36.971)
人口自然增长率/%	0.008^*(2.291)	0.012^*(2.340)	0.010^**(4.992)	0.011^**(3.175)
第二产业产值占比/%	－0.014^*(－2.359)	－0.017^*(－2.368)	0.030^**(6.767)	－0.018(－1.730)
固定资产投资额/万元	0.043^**(6.144)	0.040^**(4.549)	－0.015^**(－4.013)	－0.013^**(－3.538)
科研从业人员数/万人	0.070^**(2.909)	0.055^**(2.884)	0.050^*(2.514)	0.037^**(3.830)
医院卫生院数量/间	0.019(1.872)	0.009(0.884)	0.066^**(3.029)	0.045^*(1.999)
R ²	0.142	0.109	0.161	0.115
R ²(within)	0.548	0.545	0.235	－0.202
样本量	400	400	295	295
检验	χ²(5)=47.496, p=0.000	F(5,346)=7.696, p=0.000	χ²(5)=73.319, p=0.000	F(5,245)=5.366, p=0.000