“增长-收缩”矩阵视角下“一带一路”沿线城市人口与经济变动类型及影响因素研究

doi:10.3969/j.issn.1004-9479.2025.03.20240561

世界地理研究 ›› 2025, Vol. 34 ›› Issue (3): 101-116.DOI: 10.3969/j.issn.1004-9479.2025.03.20240561

• 地理学与区域国别研究专辑 • 上一篇

“增长-收缩”矩阵视角下“一带一路”沿线城市人口与经济变动类型及影响因素研究

张旻薇¹^,²(), 肖超伟³, 刘合林¹^,²()

^1.华中科技大学建筑与城市规划学院，武汉 430074
^2.湖北省城镇化工程技术研究中心，武汉 430074
^3.中国人民大学国家发展与战略研究院，北京 100872

收稿日期:2024-07-15 修回日期:2024-12-21 出版日期:2025-03-15 发布日期:2025-03-26
通讯作者: 刘合林
作者简介:张旻薇（1996—），女，博士研究生，研究方向为城市收缩，E-mail：zhangmw@hust.edu.cn。
基金资助:
国家自然科学基金项目(52278063);国家社科基金重大项目(23&ZD100);教育部人文社科基金项目(23YJAZH154)

Population and economic change classification and influencing factors of cities along the Belt and Road from a "growth-shrinkage" matrix perspective

Minwei ZHANG¹^,²(), Chaowei XIAO³, Helin LIU¹^,²()

^1.School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
^2.Hubei Engineering & Technology Research Center of Urbanization, Wuhan 430074, China
^3.National Academy of Development and Strategy, Renmin University of China, Beijing 100872, China

Received:2024-07-15 Revised:2024-12-21 Online:2025-03-15 Published:2025-03-26
Contact: Helin LIU

摘要/Abstract

摘要：

了解“一带一路”沿线地区的城市人口与经济变动状况，有利于增进对沿线地区发展差异的认识，制定针对性的投资与合作计划。本研究通过构建“增长-收缩”矩阵，利用LandScan人口网格与NPP-VIIRS夜间灯光反衍的GDP数据，从人口与经济两个维度归纳首批“一带一路”合作名录中63个国家的4 269个城市的增长和收缩类型；然后采用地理加权回归法，从城市规模、经济水平、老龄化、城镇化、区位条件、灾害及争端风险共6个方面分析影响人口与经济变动的主要因素，并在此基础上总结各类型城市的主要特征。研究发现：①2013—2021年，研究区域内的城市整体呈人口小幅收缩，经济较快增长的趋势，但变动特征及其影响因素均存在显著的空间异质性；②4 269个城市可分为8种不同类型：人口增长主导型、经济增长主导型、经济精明增长型、马尔萨斯型、人口温和收缩型、经济危机型、经济衰退型、人口衰退型；③沿线的城市人口与经济变动存在明显的区域与国别差异，欧洲地区的高老龄化水平及大城市虹吸效应导致中小城市的人口普遍收缩，城市多为经济精明增长与人口温和收缩型；埃及、巴基斯坦等快速城镇化地区，城市多为人口增长主导型、马尔萨斯型；东南亚国家内部差异较大，各类增长型、收缩型城市交错分布；而在叙利亚、俄-乌边境等争端风险地区，则存在“争端中心为人口衰退型、外围圈层为人口增长主导型+马尔萨斯型”的特殊情况。本研究对于揭示“一带一路”沿线地区的国情与区域差异，制定相关地区的精准投资与合作计划具有一定参考价值。

关键词: “一带一路”, “增长-收缩”矩阵, 城市类型, LandScan, 夜间灯光

Abstract:

Analyzing population and economic dynamics in cities along the Belt and Road Initiative (BRI) provides insights about regional development heterogeneity, and helps develop targeted investment and cooperation plans. This study constructs a growth-shrinkage matrix and utilizes LandScan population grid data and GDP estimates derived from NPP-VIIRS nighttime light data to analyze the growth and shrinkage types of 4 269 cities across 63 countries included in the first batch of the Belt and Road Initiative (BRI) cooperation list. We adopt geographic weighted regression (GWR) to analyze factors influencing population and economic changes from six aspects: city size, economic level, national aging and urbanization levels, locational conditions, disaster risk, and battle risk. Based on this, the main characteristics of different types of cities are summarized. The findings reveal that: ①Between 2013 and 2021, cities in the study region exhibit a trend of "slight population shrinkage and rapid economic growth"; ②4,269 cities are classified into eight types: population-driven growth, economy-driven growth, economic-smart-growth, Malthusian type, population-mitigated-shrinkage, economic crisis, economic recession, and population recession. ③Regional and national heterogeneity in urban population and economic changes along the route are evident. In Europe, the population-economy types are predominantly classified as economic-smart-growth or population-mitigated-shrinkage types. In rapidly urbanizing regions such as Egypt and Pakistan, cities are mostly classified as population-driven growth or Malthusian types. Southeast Asian countries show significant internal variation, with cities of various growth and shrinkage types interspersed. In conflict-prone areas like Syria and the Russia-Ukraine border, a unique pattern emerges: central areas are predominantly classified as population recession types, while peripheral zones are categorized as population-driven growth or Malthusian types. This study provides valuable insights into the national contexts and regional differences along the route, serving as a reference for formulating targeted investment and cooperation plans.

Key words: Belt and Road Initiative, growth-shrinkage matrix, city types, LandScan, Nighttime Lights

张旻薇, 肖超伟, 刘合林. “增长-收缩”矩阵视角下“一带一路”沿线城市人口与经济变动类型及影响因素研究[J]. 世界地理研究, 2025, 34(3): 101-116.

Minwei ZHANG, Chaowei XIAO, Helin LIU. Population and economic change classification and influencing factors of cities along the Belt and Road from a "growth-shrinkage" matrix perspective[J]. World Regional Studies, 2025, 34(3): 101-116.

图/表 10

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指标		代码	指标说明	预期作用方向
城市规模	人口规模	POP₁₃	城市的期初人口规模	+
	经济规模	GDP₁₃	城市的期初经济规模	+
	城市人口规模占比	PR	城市期初的人口规模占所在国家总城市人口的比例,反映城市在城镇体系中的重要程度	+
经济水平	人均GDP	PerGDP	城市人均GDP水平	+
经济水平	收入等级	Income	所属国家的收入等级，低收入、中低收入、中高收入、高收入分别为“1,2,3,4”	+
老龄化	老龄化水平	ELD₁₃	所属国家的期初老龄化水平	-
老龄化	老龄化增长率	ELDR	所属国家的老龄化增速	-
城镇化	城镇化水平	UR₁₃	所属国家的期初城镇化水平	-
城镇化	城镇化增速	AnUR	所属国家的年均城镇化增速	+
区位条件	城市可达性	ACCity	城市距50万以上人口的城市的通勤时间，2015	-
区位条件	交通可达性	ACPort	城市距中、大型港口的通勤时间，2015	-
风险因素	自然灾害	NaD	城市自然灾害点核密度	-
风险因素	政治争端	Bat	城市政治争端点核密度	-

指标		代码	指标说明	预期作用方向
城市规模	人口规模	POP₁₃	城市的期初人口规模	+
	经济规模	GDP₁₃	城市的期初经济规模	+
	城市人口规模占比	PR	城市期初的人口规模占所在国家总城市人口的比例,反映城市在城镇体系中的重要程度	+
经济水平	人均GDP	PerGDP	城市人均GDP水平	+
经济水平	收入等级	Income	所属国家的收入等级，低收入、中低收入、中高收入、高收入分别为“1,2,3,4”	+
老龄化	老龄化水平	ELD₁₃	所属国家的期初老龄化水平	-
老龄化	老龄化增长率	ELDR	所属国家的老龄化增速	-
城镇化	城镇化水平	UR₁₃	所属国家的期初城镇化水平	-
城镇化	城镇化增速	AnUR	所属国家的年均城镇化增速	+
区位条件	城市可达性	ACCity	城市距50万以上人口的城市的通勤时间，2015	-
区位条件	交通可达性	ACPort	城市距中、大型港口的通勤时间，2015	-
风险因素	自然灾害	NaD	城市自然灾害点核密度	-
风险因素	政治争端	Bat	城市政治争端点核密度	-

变量	OLS回归				GWR回归
变量	R_pop	t检验值	R_gdp	t检验值	R_pop	R_gdp
POP₁₃	－0.45	－4.31	0.36	4.20	－1.32	1.00
GDP₁₃	0.13	1.51	－0.68	－9.55	0.93	－1.01
PerGDP	－1.15	－13.84	0.46	6.76	－1.68	0.52
Income	0.06	7.52	0.00	－0.69	－0.09	－0.09
ELD₁₃	－0.30	－11.10	0.08	3.67	2.11	－0.11
ELDR	－0.13	－4.06	－0.02	－0.66	－1.43	－0.18
UR₁₃	－0.19	－7.54	－0.05	－2.61	0.39	0.02
AnUR	－0.16	－8.41	－0.04	－2.47	0.02	－0.13
PR	0.59	2.17	0.07	0.32	26.98	0.85
ACport	－0.41	－11.36	0.10	3.46	－0.51	－0.31
ACcity	0.14	1.54	－0.44	－6.11	－0.71	－0.40
NaD	0.09	3.16	0.06	2.63	0.26	－0.05
Bat	0.01	3.62	0.00	0.19	－43.61	25.73
R²	0.17		0.05		0.43	0.37
Ad R²	0.17		0.05		0.40	0.34
AICc	197.83		－1 468.61		－1 093.37	－2 938.25

变量	OLS回归				GWR回归
变量	R_pop	t检验值	R_gdp	t检验值	R_pop	R_gdp
POP₁₃	－0.45	－4.31	0.36	4.20	－1.32	1.00
GDP₁₃	0.13	1.51	－0.68	－9.55	0.93	－1.01
PerGDP	－1.15	－13.84	0.46	6.76	－1.68	0.52
Income	0.06	7.52	0.00	－0.69	－0.09	－0.09
ELD₁₃	－0.30	－11.10	0.08	3.67	2.11	－0.11
ELDR	－0.13	－4.06	－0.02	－0.66	－1.43	－0.18
UR₁₃	－0.19	－7.54	－0.05	－2.61	0.39	0.02
AnUR	－0.16	－8.41	－0.04	－2.47	0.02	－0.13
PR	0.59	2.17	0.07	0.32	26.98	0.85
ACport	－0.41	－11.36	0.10	3.46	－0.51	－0.31
ACcity	0.14	1.54	－0.44	－6.11	－0.71	－0.40
NaD	0.09	3.16	0.06	2.63	0.26	－0.05
Bat	0.01	3.62	0.00	0.19	－43.61	25.73
R²	0.17		0.05		0.43	0.37
Ad R²	0.17		0.05		0.40	0.34
AICc	197.83		－1 468.61		－1 093.37	－2 938.25

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“增长-收缩”矩阵视角下“一带一路”沿线城市人口与经济变动类型及影响因素研究

Population and economic change classification and influencing factors of cities along the Belt and Road from a "growth-shrinkage" matrix perspective

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