Based on the innovation agglomeration characteristics of the Yangtze River Delta urban agglomeration, using the forward citation data of strategic emerging industries patents from 2003 to 2022, the innovation function indicators of the central cities were constructed to analyze the heterogeneous impact of the improvement of the innovation function of the central cities on the economic growth of the peripheral cities. The research found that the innovation agglomeration trend in the Yangtze River Delta urban agglomeration has strengthened, presenting a clear central-peripheral spatial pattern. The improvement of the innovation function of the central cities in the urban agglomeration significantly inhibits the economic growth of the peripheral cities, and this effect is particularly prominent within a 400-kilometer range. Beyond this range, it shows a positive promoting effect. As a national central city, Shanghai’s negative impact of innovation upgrading is stronger. Mechanism analysis indicates that reducing the level of technological innovation and inhibiting industrial transformation and upgrading are important mechanisms through which the improvement of the innovation function of the central cities in the urban agglomeration affects the economic development of the peripheral cities. Further analysis shows that the central cities can alleviate their inhibitory effect on the peripheral cities by leveraging their talent agglomeration advantages and enhancing the level of industry-university-research cooperation.
DUAN Si-jie
,
CHEN Hao
. Innovation function enhancement in central cities and economic growth in peripheral cities of city agglomeration: A case study of the Yangtze River Delta Urban Agglomeration[J]. Urban Problems, 2025
, 357(4)
: 34
-44
.
DOI: 10.13239/j.bjsshkxy.cswt.250403
[1] 叶琴,蒋海云,曾刚,等.中国城市关键数字技术创新格局:技术关联性的作用[J].地理科学进展,2025(5):896-907
[2] 王雨飞,王雅琦,赵佳涵,等.非中心城市嵌入高铁网络的经济增长效应——基于集聚外部性和网络外部性的双重视角[J].统计研究,2024(10):134-148
[3] 皮建才,罗禹涵.智能制造试点示范溢出与关键技术创新[J].经济理论与经济管理,2025(1):55-72
[4] Aghion P, Jaravel X. Knowledge spillovers, innovation and growth[J]. Economic Journal,2015(3):533-545
[5] Fujita M, Krugman P R. When is the economy monocentric: Von Thünen and Chamberlin unified[J]. Regional Science and Urban Economics,1995,25, 505-528
[6] 余振,李锦坡.中国区域创新集聚的测度及其影响因素分析——基于1985—2020年个体专利数据[J].江西社会科学,2023(12):23-35,206-207
[7] 苏屹,林周周.区域创新活动的空间效应及影响因素研究[J].数量经济技术经济研究,2017 (11):63-80
[8] 陈万灵,陈金源,温可仪.国际贸易通道建设对创新质量的影响——来自西部陆海新通道的经验研究[J].改革,2025(4):101-118
[9] 倪青山,卢彦瑾,贺筱君,等.中国城市创新绩效的差异及动态演进[J].数量经济技术经济研究,2021(12):67-84
[10] 宋永辉,马廷灿,冉从敬.中国省域基础研究发展的极核模式及其影响研究[J].科学管理研究,2025(1):19-29
[11] 冯兴华,高紫媛,徐美海,等.基于专利技术转移的城市群创新网络结构及其组织模式[J].经济地理,2024(10):66-75
[12] 王越,王承云.中国民营企业500强时空演化与影响因素分析[J].世界地理研究,2018(6):108-114
[13] Andersson U, Dasí A, Mudambi R, et al. Technology, innovation and knowledge: The importance of ideas and international connectivity[J]. Journal of World Business,2016(1):153-162
[14] 李松林,刘修岩,王峤.集聚与创新——来自摩天大楼建设的证据[J].经济学(季刊),2023(2):517-531
[15] Kemeny T, Petralia S, Storper M. Disruptive innovation and spatial inequality[J]. Regional Studies,2022(3):1-18
[16] 陆军,韦天宇.功能迭代中的国际科技创新中心:演进路径与风险应对[J].北京行政学院学报,2024(3):62-72
[17] Alonso W. Urban zero population growth[J]. Daedalus,1973(4):191-206
[18] 郑江淮,巫南杰,赖晓.城市间技术邻近缩小了劳动生产率差异吗? ——基于长三角地区41个城市的经验证据[J].经济评论,2024(5):56-74
[19] 张玉梅,吴先明,高厚宾.资源“集聚”与“辐射”视角下国际创新中心的成长机制研究——以粤港澳大湾区为例[J].中国工业经济,2022(11):97-115
[20] 吴思栩,李杰伟.“数字经济”时代城市的未来——互联网对中国城市生产性服务业集聚的影响研究[J].经济学(季刊),2024(2):431-447
[21] Chagas A L, Azzoni C R. Almeida A N. A spatial Difference-in-differences analysis of the impact of sugarcane production on respiratory diseases[J]. Regional Science and Urban Economics,2016(7):24-36
[22] GonÇalves S M, Da Silva R V, Teixeira N. Individual actors and embeddedness in business-to-business interactions[J]. Industrial Marketing Management,2019(1):181-191
[23] 曾婧婧,陶文清.科创走廊对经济的双刃剑效应:辐射还是虹吸[J].科学学研究,2024(12):2533-2544
[24] Mancusi M L. International spillovers and absorptive capacity: Across-country cross-sector analysis based on patents and citations[J]. Journal of International Economics,2008(2):155-165
[25] 朱丰毅,桂文林.粤港澳区域知识溢出与经济增长[J].数量经济技术经济研究,2022(3):44-65
[26] 于晓琳,石军伟,万凯.知识的力量:基础研究与区域技术创新[J].科学学与科学技术管理,2023(12):68-85
[27] 蔡庆丰,陈熠辉,严佳佳.“强省会”的创新外部性——基于省域知识交流与产业分工的研究视角[J].金融研究,2025(2):132-149
[28] Liang J C, Goetz S. Technology intensity and agglomeration economies[J]. Research Policy,2018(10):1990-1995
[29] Cuberes D, Desmet D, Rappaport J. Urban growth shadows[J]. Journal of Urban Economics.2021(5):103334
[30] Kogler D F, Rigby D L, Tucker I. Mapping knowledge space and technological relatedness in US cities[J]. European Planning Studies,2013(9):1374-1391
[31] Chattergoon B, Kerr W R. Winner takes all? Tech clusters, population centers, and the spatial transformation of U.S. invention[J]. Research Policy,2022(2):104418
[32] Hall B, Jaffe A, Trajtenberg M. Market value and patent citations[J]. The RAND Journal of Economics,2005(1):16-38
[33] 刘修岩,陈子扬.城市体系中的规模借用与功能借用——基于网络外部性视角的实证检验[J].城市问题,2017(12):12-19
[34] 王文凯,任元明.边界效应、市场潜力与经济发展差异:基于重庆市直辖的研究[J].财贸经济,2022(3):128-142
[35] 徐海东,黄徐亮,倪鹏飞.中国城市核心区对县域人力资本的影响研究[J].地理学报,2024(12):3177-3196
[36] 安树伟,李瑞鹏.城市群核心城市带动外围地区经济增长了吗? ——以京津冀和长三角城市群为例[J].中国软科学,2022(9):85-96
[37] 席强敏,黎思灏,张可云.大城市人口调控政策对毗邻区县第二产业发展的溢出效应——来自2014年户籍制度改革的证据[J].中国农村经济,2024(11):61-80
[38] 谢佳松,才国伟.金融网络何以推动创新扩散[J].财贸经济,2025(1):99-115
[39] 刘斐然,李世杰.产学研合作、内部研发与企业突破性创新——基于知识基础理论视角[J].管理评论,2025(3):101-110
