Technological relatedness evolution and influencing factors of the Yangtze River Delta cities

doi:10.3969/j.issn.1004-9479.2023.10.20220067

Abstract

Abstract:

Technology relatedness plays a significant role in promoting regional innovation and regional economic development, which has attracted the attention of economic geographers. The construction of the innovation community in the Yangtze River Delta is accelerating, and it is of great significance to explore its technology relatedness evolution and influencing factors. Taking 41 cities in the Yangtze River Delta as an example, this paper uses the data of invention patent from 2000 to 2016 to analyze the evolution characteristics of the technology-related spatial pattern by co-occurrence analysis methods. At the same time, this article explores the driving mechanism of government support, university capabilities, and corporate capabilities on the evolution of technology associations based on regional innovation system theory. The research found that: ①The technology-related spatial pattern of the Yangtze River Delta presents a typical "core-periphery" structure, and has evolved from isolated and scattered to a "Z"-shaped spatial distribution centered on Hefei-Shanghai-Hangzhou-Ningbo. ②There are significant differences in the impact of government support, university capabilities, and corporate capabilities on the density of technology relatedness. The driving role of government support is not significant. Enterprises and universities have a positive role in promoting the density of technology relatedness, and the driving role of enterprises is stronger. ③For different types of cities, the positive promotion effects of enterprises, universities are heterogeneous. Through this research, it will help clarify technological relatedness and driving factors of the Yangtze River Delta, and provide a theoretical basis for the Yangtze River Delta to take the lead in realizing high-quality integration in the field of scientific and technological innovation. It also help enrich the regional innovation system and other related theories.

Key words: technology relatedness, regional innovation system, innovation network, influencing factors, Yangtze River Delta

摘要：

技术关联对区域创新和区域经济发展具有显著促进作用，引起了经济地理学者高度关注，长三角创新共同体建设正在加速推进，探究其技术关联演化特征及影响因素具有重要意义。本文以长三角41个地级及以上城市为例，利用2000—2016年发明专利数据，运用共存分析方法分析了长三角城市技术关联的时空演化特征，并重点探讨政府支持、大学能力和企业能力对技术关联演化的驱动作用机制。研究发现：①长三角城市“技术空间”逐步显现，空间格局呈现典型的“核心-边缘”结构，且从相对孤立分散向以合肥-上海-杭州-宁波为核心的“Z”字形空间分布演化。②政府支持、大学能力和企业能力对城市技术关联密度的影响存在显著差异。政府支持的影响作用不显著，企业、大学对城市技术关联密度具有正向促进作用，且企业能力的影响作用更强。③针对不同类型的城市，企业、大学的正向促进作用存在异质性。本研究有助于明晰长三角城市技术关联特征和影响因素，为长三角率先实现科技创新领域高质量一体化提供理论依据，同时，也有利于丰富完善区域创新系统等相关理论体系。

关键词: 技术关联, 区域创新系统, 创新网络, 影响因素, 长三角

Xianzhong CAO, Bo CHEN, Jun HAO. Technological relatedness evolution and influencing factors of the Yangtze River Delta cities[J]. World Regional Studies, 2023, 32(10): 110-121.

曹贤忠, 陈波, 郝均. 长三角城市技术关联演化特征与影响因素分析[J]. 世界地理研究, 2023, 32(10): 110-121.

Figures/Tables 10

Fig.1 Patent data in Yangtze River Delta cities， 2000-2016

Fig.2 Schematic diagram of the "Technical Space" of the Yangtze River Delta urban agglomeration in 2016

Tab.1 The density of technology relatedness in the Yangtze River Delta from 2000 to 2016

年份	观测值	均值	最大值
2000	1 399	0.065 0	0.465 0
2005	9 747	0.080 2	0.499 3
2010	33 133	0.089 8	0.558 5
2016	75 075	0.114 9	0.491 1

Fig. 3 The evolution of urban technological relatedness density in the Yangtze River Delta from 2000 to 2016

Tab.2 Main variables and measurement methods

变量类型	变量名称	测度方法
因变量	技术关联密度	计算所有专利类别技术关联密度平均值
自变量	政府支持	科技支出占财政收入的比例/%
	大学能力	万人大学生人数/人
	企业能力	规模以上企业工业总产值/万元
控制变量	经济发展水平	GDP增长率/%
	对外开放度	实际使用外资占GDP的比例/%
	产业结构	城市第二产业占比/%

Tab.3 Regression results from 2000 to 2016

g o v c, t

－0.019

(0.158)

－0.109

(0.099)

0.076 0

(0.084)

0.217

(0.141)

c o l l e g e c, t

0.813^***

(0.165)

0.559^***

(0.116)

0.374^**

(0.138)

0.569^***

(0.143)

e n t e r c, t

0.800^***

(0.192)

1.103^***

(0.138)

0.913^***

(0.154)

0.901^***

(0.110)

e x t r o c, t

0.085

(0.141)

0.086

(0.110)

－0.005

(0.118)

0.074

(0.102)

e c o c, t

0.035

(0.157)

－0.073

(0.132)

－0.144

(0.163)

－0.336^**

(0.132)

s e c o n d c, t

－0.070

(0.112)

－0.097

(0.137)

0.021

(0.126)

－0.073

(0.137)

常数项

－0.055

(0.114)

0.042

(0.069)

－0.024

(0.092)

0.082

(0.110)

样本数

R 2

Tab.3 Regression results from 2000 to 2016

变量

模型1

模型2

模型3

模型4

g o v c, t

－0.019

(0.158)

－0.109

(0.099)

0.076 0

(0.084)

0.217

(0.141)

c o l l e g e c, t

0.813^***

(0.165)

0.559^***

(0.116)

0.374^**

(0.138)

0.569^***

(0.143)

e n t e r c, t

0.800^***

(0.192)

1.103^***

(0.138)

0.913^***

(0.154)

0.901^***

(0.110)

e x t r o c, t

0.085

(0.141)

0.086

(0.110)

－0.005

(0.118)

0.074

(0.102)

e c o c, t

0.035

(0.157)

－0.073

(0.132)

－0.144

(0.163)

－0.336^**

(0.132)

s e c o n d c, t

－0.070

(0.112)

－0.097

(0.137)

0.021

(0.126)

－0.073

(0.137)

常数项

－0.055

(0.114)

0.042

(0.069)

－0.024

(0.092)

0.082

(0.110)

样本数

R 2

0.782

0.837

0.697

0.805

Tab.4 The division of urban technology relatedness density in the Yangtze River Delta in 2016

技术关联类别	城市
高技术关联密度城市	上海、南京、无锡、苏州、宁波、杭州、常州、合肥、嘉兴、绍兴、芜湖、徐州、镇江、台州
低技术关联密度城市	南通、连云港、淮安、盐城、扬州、泰州、宿迁、温州、湖州、金华、衢州、舟山、丽水、蚌埠、淮南、淮北、马鞍山、铜陵、安庆、黄山、滁州、阜阳、宿州、六安、亳州、池州、宣城

Tab.5 Regression results of heterogeneity of urban technology relatedness density in the Yangtze River Delta in 2016

变量	模型4	模型5	模型6
$g o v c, t$	0.217	0.085	0.054
$s t u c, t$	0.569^***	0.477^*	0.101
$e n t e r c, t$	0.901^***	0.666^**	0.232^**
控制变量	包含	包含	包含
Constant	0.082	0.395	－0.041
城市	41	14	27
$R 2$	0.805	0.676	0.336

Tab.5 Regression results of heterogeneity of urban technology relatedness density in the Yangtze River Delta in 2016

变量	模型4	模型5	模型6
$g o v c, t$	0.217	0.085	0.054
$s t u c, t$	0.569^***	0.477^*	0.101
$e n t e r c, t$	0.901^***	0.666^**	0.232^**
控制变量	包含	包含	包含
Constant	0.082	0.395	－0.041
城市	41	14	27
$R 2$	0.805	0.676	0.336

Tab.6

变量	模型1	模型7	模型8
$g o v c, t$	0.217 (0.141)	0.190 (0.145)
$c o l l e g e c, t$	0.569^*** (0.143)		0.633^*** (0.141)
$e n t e r c, t$	0.901^*** (0.110)	0.760^*** (0.127)	0.853^*** (0.166)
$e x t r o c, t$	0.074 (0.102)	0.071 (0.103)	0.082 (0.106)
$e c o c, t$	－0.336^** (0.132)	－0.272^* (0.134)	－0.266^* (0.132)
$s e c o n d c, t$	－0.073 (0.137)	0.099 (0.149)	0.023 (0.146)
$a b_c o l l e g e c, t$		0.597^*** (0.156)
$a b_g o v c, t$			0.110 (0.252)
常数项	0.082 (0.110)	0.011 (0.114)	0.034 (0.114)
样本数	41	41	41
$R 2$	0.805	0.800	0.793

Tab.6

变量	模型1	模型7	模型8
$g o v c, t$	0.217 (0.141)	0.190 (0.145)
$c o l l e g e c, t$	0.569^*** (0.143)		0.633^*** (0.141)
$e n t e r c, t$	0.901^*** (0.110)	0.760^*** (0.127)	0.853^*** (0.166)
$e x t r o c, t$	0.074 (0.102)	0.071 (0.103)	0.082 (0.106)
$e c o c, t$	－0.336^** (0.132)	－0.272^* (0.134)	－0.266^* (0.132)
$s e c o n d c, t$	－0.073 (0.137)	0.099 (0.149)	0.023 (0.146)
$a b_c o l l e g e c, t$		0.597^*** (0.156)
$a b_g o v c, t$			0.110 (0.252)
常数项	0.082 (0.110)	0.011 (0.114)	0.034 (0.114)
样本数	41	41	41
$R 2$	0.805	0.800	0.793

Fig.4 Schematic diagram of driving mechanism of technological relatedness evolution in the Yangtze River Delta

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