Evolutionary characteristics and multidimensional proximity mechanism of resilience in Chinese urban innovation networks: A dual perspective of structure and function

doi:10.3969/j.issn.1004-9479.2026.08.20250127

World Regional Studies ›› 2026, Vol. 35 ›› Issue (8): 110-125.DOI: 10.3969/j.issn.1004-9479.2026.08.20250127

Evolutionary characteristics and multidimensional proximity mechanism of resilience in Chinese urban innovation networks: A dual perspective of structure and function

Yushan WANG¹(), Guanghui WANG²(), Xiaoyu HU³^,⁴, Qin LIU⁵

^1.Economic and Management College, Zhaoqing University, Zhaoqing 526061, China
^2.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100190, China
^3.School of Economics and Management, Jingdezhen University, Jingdezhen 333400, China
^4.Institute of Sustainable Development, Macau University of Science and Technology, Macau 999087, China
^5.Digital Economy and Resource Management College, East China University of Technology, Nanchang 330000, China

Received:2025-02-28 Revised:2025-06-03 Online:2026-08-15 Published:2026-08-26
Contact: Guanghui WANG

中国城市创新网络韧性的演化特征与多维邻近机制——结构与功能的双视角

王玉珊¹(), 王光辉²(), 胡骁宇³^,⁴, 刘勤⁵

^1.肇庆学院经济与管理学院，肇庆 526061
^2.中国科学院地理科学与资源研究所，北京 100190
^3.景德;镇学院经济管理学院，景德镇 333400
^4.澳门科技大学可持续发展研究所，澳门 999087
^5.东华理工大学数字经济与资源管理学院，南昌 330000

通讯作者: 王光辉
作者简介:王玉珊（1990—），女，博士，讲师，研究方向为城市与区域经济，创新网络，E-mail：ysw0327@163.com
基金资助:
国家自然科学基金面上项目(72374191);国家自然科学基金地区项目(42461027);国家“万人计划”青年拔尖人才项目(E44S0000AJ);广东省哲学社会科学规划项目(GD25YYJ09);广东省哲学社会科学规划项目(GD26CYJ52);广东省普通高校创新团队项目(2024WCXTD004)

Abstract

Abstract:

Enhancing the resilience of urban innovation networks helps to build a secure national innovation system and safeguard urban innovation functions. Based on data on cooperative invention patent applications among 346 Chinese cities from 2011 to 2020, this paper constructs China's urban innovation network. From the dual perspective of "structure and function" and the three-dimension framework of "node-edge-overall resilience," it builds a network resilience system. In addition, this study adopts disruption simulation and GIS spatial analysis to characterize the temporal dynamics and spatial patterns of network structural resilience, and utilizes a negative binomial regression model to test the multidimensional proximity mechanisms. The results show that: ① From 2011 to 2020, China's urban innovation network formed a diamond?shaped network architecture with Beijing, Shanghai, Shenzhen, and Chengdu as its vertices, and developed a pattern of "one core leading, multiple poles interacting, and multiple regions coordinating". ② The network exhibited a trend of structural stability, functional robustness, and enhanced resilience. Key nodes and key edges exerted a decisive influence on structural stability and functional robustness. It was found that advantaged nodes and their network linkages are mostly located within the diamond framework, whereas vulnerable nodes are mostly found in small and medium?sized cities in the northwest and northeast, or in areas with concentrated ethnic minority populations. ③ Multidimensional proximity is a key mechanism driving network resilience. Geographic proximity, cognitive proximity, social proximity, and cultural proximity all contribute to enhancing both structural resilience and functional resilience among cities.

Key words: urban innovation networks, network resilience, structural resilience, functional resilience, spatiotemporal differentiation, multidimensional proximity

摘要：

提升城市创新网络韧性，有助于构建安全的国家创新体系，保障城市创新职能的发挥。本文基于2011—2020年中国346个城市间合作申请发明专利的数据，构建中国城市创新网络，从“结构-功能”的双视角和“节点韧性-连边韧性-整体韧性”三维韧性出发，构建网络韧性体系；并采用中断模拟仿真和GIS空间分析技术，刻画网络结构韧性的时序演化与空间格局，并运用负二项回归模型检验多维邻近机制。结果表明：①2011—2020年，中国城市创新网络形成以“北京-上海-深圳-成都”为顶点的菱形网络架构，并形成“一核引领、多极联动、多区协同”的发展格局。②中国城市创新网络呈现结构稳定、功能稳健与韧性增强的趋势，关键节点和关键连边对网络结构稳定与功能稳健具有决定性影响，而边缘城市的脆弱性亟待关注。研究发现，优势节点及其网络联系多位于菱形框架内，而脆弱节点多位于西北、东北部的中小城市或少数民族聚集区。③多维邻近是驱动网络韧性的关键机制。地理邻近、认知邻近、社会邻近和文化邻近均有助于城市间结构韧性与功能韧性的提升。

关键词: 城市创新网络, 网络韧性, 结构韧性, 功能韧性, 时空分异, 多维邻近

Yushan WANG, Guanghui WANG, Xiaoyu HU, Qin LIU. Evolutionary characteristics and multidimensional proximity mechanism of resilience in Chinese urban innovation networks: A dual perspective of structure and function[J]. World Regional Studies, 2026, 35(8): 110-125.

王玉珊, 王光辉, 胡骁宇, 刘勤. 中国城市创新网络韧性的演化特征与多维邻近机制——结构与功能的双视角[J]. 世界地理研究, 2026, 35(8): 110-125.

Figures/Tables 7

Tab. 1 Variables and their explanation of negative binomial regression model

类别	变量		变量说明与计算
被解释变量	Rds	结构韧性	连边失效后的网络连通损失，计算见式（1）、式（2）
被解释变量	Rde	功能韧性	连边失效后的网络效率损失，计算见式（3）、式（4）
解释变量	GeoPro	地理邻近	城市间的最短公路距离，使用百度地图官方API获取的最短距离表示
	CogPro	认知邻近	城市间认知环境的相似性，运用城市创新创业指数（IRIEC）的差值的绝对值表示。IRIEC由新建企业数量、吸引外来投资、吸引风险投资、专利授权数量和商标注册数量5个维度构建
	CulPro	文化邻近	根据城市 $i$ 与城市 $j$ 是否属于同一方言区而定。如果两个城市属于同一方言区，该数值设置为1，否则为0^[41]。方言数据来源于《中国语言地图集》，分类精度采用中类
	SocPro	社会邻近	城市间知识基础的相似性，使用知识专业化水平的差值的绝对值表示。城市知识专业化水平用城市知识型产业的区位熵来衡量^[42]
控制变量	IndSim	产业相似	城市 $i$ 与城市 $j$ 的产业结构高级化水平的差值的绝对值，产业结构高级化水平使用城市第三产业占GDP比值表示
	EcoSim	经济相似	城市 $i$ 与城市 $j$ 的夜间灯光的均值差值的绝对值
	ResSim	人资相似	城市 $i$ 与城市 $j$ 的研发行业的员工数量占比的差值的绝对值
	EduEnv	教育环境	城市 $i$ 与城市 $j$ 的科学和教育财政支出的乘积
	OpeEnv	对外环境	城市 $i$ 与城市 $j$ 的外商投资占比的乘积

Tab. 1 Variables and their explanation of negative binomial regression model

类别	变量		变量说明与计算
被解释变量	Rds	结构韧性	连边失效后的网络连通损失，计算见式（1）、式（2）
被解释变量	Rde	功能韧性	连边失效后的网络效率损失，计算见式（3）、式（4）
解释变量	GeoPro	地理邻近	城市间的最短公路距离，使用百度地图官方API获取的最短距离表示
	CogPro	认知邻近	城市间认知环境的相似性，运用城市创新创业指数（IRIEC）的差值的绝对值表示。IRIEC由新建企业数量、吸引外来投资、吸引风险投资、专利授权数量和商标注册数量5个维度构建
	CulPro	文化邻近	根据城市 $i$ 与城市 $j$ 是否属于同一方言区而定。如果两个城市属于同一方言区，该数值设置为1，否则为0^[41]。方言数据来源于《中国语言地图集》，分类精度采用中类
	SocPro	社会邻近	城市间知识基础的相似性，使用知识专业化水平的差值的绝对值表示。城市知识专业化水平用城市知识型产业的区位熵来衡量^[42]
控制变量	IndSim	产业相似	城市 $i$ 与城市 $j$ 的产业结构高级化水平的差值的绝对值，产业结构高级化水平使用城市第三产业占GDP比值表示
	EcoSim	经济相似	城市 $i$ 与城市 $j$ 的夜间灯光的均值差值的绝对值
	ResSim	人资相似	城市 $i$ 与城市 $j$ 的研发行业的员工数量占比的差值的绝对值
	EduEnv	教育环境	城市 $i$ 与城市 $j$ 的科学和教育财政支出的乘积
	OpeEnv	对外环境	城市 $i$ 与城市 $j$ 的外商投资占比的乘积

Fig. 1 Spatial pattern of China's urban innovation network （2011， 2020）

Fig. 2 Changes in network connectivity and transmission under different attacks （2011， 2020）

Tab. 2 Top 10 cities for structural resilience and functional resilience (2011,2020)

排名	2011年				2020年
排名	城市	结构韧性	城市	功能韧性	城市	结构韧性	城市	功能韧性
1	北京	0.044 6	北京	0.166 4	北京	0.032 1	北京	0.221 2
2	上海	0.033 4	深圳	0.129 4	上海	0.020 8	上海	0.051 9
3	南京	0.020 6	上海	0.104 9	南京	0.019 5	南京	0.050 6
4	广州	0.018 9	苏州	0.083 0	西安	0.019 4	深圳	0.035 7
5	武汉	0.018 6	南京	0.035 0	武汉	0.018 3	杭州	0.033 4
6	深圳	0.018 6	天津	0.027 7	深圳	0.017 4	广州	0.032 1
7	天津	0.017 6	广州	0.026 2	成都	0.016 0	成都	0.025 5
8	杭州	0.015 8	杭州	0.023 8	广州	0.015 7	天津	0.024 5
9	成都	0.015 3	成都	0.022 4	杭州	0.015 2	武汉	0.022 7
10	西安	0.013 5	武汉	0.018 4	天津	0.014 7	西安	0.022 1

Tab. 3 Top 10 city pairs for structural resilience and functional resilience (2011,2020)

排名	2011年				2020年
排名	城市对	结构韧性	城市对	功能韧性	城市对	结构韧性	城市对	功能韧性
1	北京-上海	0.030 2	北京-上海	0.002 6	北京-南京	0.018 3	北京-上海	0.000 7
2	北京-抚顺	0.018 3	北京-南京	0.002 2	北京-上海	0.015 7	北京-南京	0.000 7
3	北京-深圳	0.013 5	北京-武汉	0.002 1	北京-天津	0.014 6	北京-西安	0.000 7
4	北京-天津	0.013 3	北京-天津	0.002 1	北京-成都	0.011 3	北京-武汉	0.000 7
5	北京-南京	0.008 7	北京-广州	0.002 1	北京-杭州	0.009 5	北京-深圳	0.000 7
6	杭州-台州	0.008 6	北京-深圳	0.002 1	北京-石家庄	0.008 9	北京-成都	0.000 6
7	成都-攀枝花	0.006 4	北京-杭州	0.002 0	北京-济南	0.007 7	北京-广州	0.000 6
8	北京-东营	0.005 8	北京-成都	0.002 0	北京-西安	0.007 2	北京-杭州	0.000 6
9	上海-广州	0.005 7	北京-西安	0.001 9	北京-合肥	0.007 0	北京-天津	0.000 6
10	北京-成都	0.005 6	北京-沈阳	0.001 9	北京-武汉	0.006 5	北京-长沙	0.000 6

Fig. 3 Spatial distribution of dominant nodes and vulnerable nodes （2011， 2020）

Tab. 4 Negative binomial regression test results

变量	结构韧性				功能韧性
变量	模型1	模型 2	模型3	模型4	模型5	模型6	模型7	模型8
GeoPro	-0.001	-0.002	-0.001	-0.005	-0.327^***	-0.365^***	-0.776^***	-0.546^***
	(0.000)	(0.004)	(0.017)	(0.010)	(0.053)	(0.043)	(0.119)	(0.059)
CogPro	0.006^***	0.022^***	0.041^***	0.027^***	0.093^***	0.050^***	0.074^***	0.001
	(0.001)	(0.002)	(0.002)	(0.003)	(0.002)	(0.003)	(0.004)	(0.004)
SocPro	7.623^***	8.336^***	-1.856	6.143^***	37.526^***	13.144^***	-1.497	22.166^***
	(0.739)	(0.770)	(1.564)	(1.162)	(3.987)	(3.636)	(6.134)	(5.436)
CulPro	0.000^*	0.000	0.000	-0.000	0.155^***	0.092^***	0.069^**	-0.092
	(0.000)	(0.000)	(0.000)	(0.000)	(0.031)	(0.027)	(0.028)	(0.064)
IndSim		0.038^**	0.085^***	0.031^*		1.670^***	1.599^***	1.503^***
		(0.019)	(0.019)	(0.019)		(0.084)	(0.088)	(0.084)
EcoSim		0.043^***	0.036^***	0.047^***		0.306^***	0.191^***	0.284^***
		(0.012)	(0.013)	(0.012)		(0.058)	(0.063)	(0.058)
ResSim		0.327^***	0.337^***	0.349^***		1.395^***	1.349^***	1.304^***
		(0.045)	(0.046)	(0.046)		(0.200)	(0.201)	(0.195)
EduEnv		-2.335^***	-2.520^***	-2.322^***		1.078^***	0.944^***	1.004^***
		(0.119)	(0.123)	(0.119)		(0.059)	(0.062)	(0.057)
OpeEnv		0.157^*	0.348^***	0.183^*		1.193^***	1.247^***	1.146^***
		(0.094)	(0.097)	(0.095)		(0.114)	(0.112)	(0.112)
GeoPro²			0.003				0.395^***
			(0.013)				(0.097)
CogPro²			-0.002^***				-0.001^***
			(0.000)				(0.000)
SocPro²			1.178^***				1.992^***
			(0.142)				(0.568)
GeoPro×CogPro				-0.052^***				-0.555^***
				(0.014)				(0.039)
GeoPro×SocPro				16.407^**				82.590^***
				(6.498)				(19.215)
GeoPro×CulPro				0.025				0.002
				(0.031)				(0.190)
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
R²	0.202	0.253	0.410	0.137	0.274	0.351	0.286	0.287

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Evolutionary characteristics and multidimensional proximity mechanism of resilience in Chinese urban innovation networks: A dual perspective of structure and function

中国城市创新网络韧性的演化特征与多维邻近机制——结构与功能的双视角

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