基于新空间经济学理论,借助六维网络数据集和工具变量法,实证检验数据要素流动对城际合作创新的影响效应、作用机制及其异质性特征。研究发现:数据要素流动显著提升城际合作创新机遇和水平,其作用依托三类集聚网络外部性,即搜寻匹配效应、服务共享效应和知识溢出效应,强度依次递减;在嵌入实体空间互动网络后,核心城市间搜寻匹配效应最强,核心—边缘城市间服务共享效应最突出,知识溢出效应自核心向边缘梯度递减,表明数据要素流动可优化实体空间的信息共享与资源配置,促进虚实空间耦合协调;在嵌入虚拟空间互动网络后,三类效应强度自核心向边缘呈倒“U”型空间展布趋势,在边缘—边缘城市间失效,反映数据要素流动推动了虚拟空间的核心—边缘式创新分工;多维邻近性正向调节数据要素流动对城际合作创新的提升作用,其中,地理、技术、社会邻近性通过知识溢出和服务共享助力合作创新量质齐升,制度、数字邻近性通过搜寻匹配和服务共享助力合作创新数量增长。研究结论为优化创新要素空间配置、重塑虚实网络空间结构、畅通创新大循环提供了重要启示。
Data element flow is a key force for stimulating new-quality productive forces and driving intercity collaborative innovation in the digital-intelligent era. Based on the new spatial economics theory, this study empirically examines the impact effects, mechanism of action, and heterogeneous characteristics of data element flow on intercity collaborative innovation by employing a 6-dimensional network dataset and the instrumental variable method. The findings are as follows: Data element flow significantly enhances the opportunities and level of intercity collaborative innovation, with its role relying on three types of agglomeration network externalities—search and matching effect, service sharing effect, and knowledge spillover effect—whose intensities decrease in sequence. After embedding in the physical space interaction network, the search and matching effect is the strongest among core cities, the service sharing effect is the most prominent between core-peripheral cities, and the knowledge spillover effect shows a gradient decline from core to peripheral areas, demonstrating that data element flow can optimize information sharing and resource allocation in physical space and promote the coupling and coordination of physical and virtual spaces. When embedded in the virtual space interaction network, the intensities of the three effects present an inverted "U"-shaped spatial distribution trend from core to peripheral areas and become ineffective among peripheral-peripheral cities, reflecting that data element flow has driven the core-peripheral innovative division of labor in virtual space. Multidimensional proximity positively moderates the promoting effect of data element flow on intercity collaborative innovation. Specifically, geographic, technological, and social proximity facilitate the improvement of both the quantity and quality of collaborative innovation through knowledge spillover and service sharing, while institutional and digital proximity facilitate the growth of collaborative innovation quantity through search and matching and service sharing. This study provides important implications for optimizing the spatial allocation of innovative factors, adjusting the structure of physical and virtual network spaces, and smoothing the innovation cycle.
[1] 吕承超,姜延杰,何加豪,等.创新驱动下的数据要素流动网络演化机制研究[J].科研管理,2025(6):10-20
[2] 张贵,钱钰.数据要素市场化配置与城市间创新合作——基于数据交易平台设立的准自然试验[J].现代经济探讨,2025(6):1-15
[3] 刘晶,杨怡帆.数据要素流动对区域协调发展的影响机理研究[J].管理学刊,2025(3):133-148
[4] 杨仁发,盛云龙.数据要素流动对跨区域合作创新的影响[J/OL].商业研究,2025:1-10.[2025-11-08]
[5] 张辽,蒋楠.数据要素流动对城市创新活跃度的影响研究[J].杭州电子科技大学学报(社会科学版),2024(2):1-16
[6] 高明,魏浩,王晓祺.数据要素流动赋能企业绿色创新[J].中国人口·资源与环境,2024(11):120-129
[7] Farboodi M, Veldkamp L.Data and markets[J].Annual Review of Economics,2023(15):23-40
[8] 杨开忠,范博凯,董亚宁.空间品质、创新活力与中国城市生产率[J].经济管理,2022(1):47-64
[9] 杨开忠,刘威,董亚宁,等.区域经济发展新逻辑:纳入旅行成本的新空间经济学[J].世界经济,2024(1):30-56
[10] Doloreux D.Regional networks of small and medium sized enterprises:Evidence from the metropolhan area of Ottawa in Canada[J].European Planning Studies,2004(2):173-189
[11] Fan B K, Zang Y.High-speed rail networks and intercity collaborative innovation:An agglomeration-network externalities perspective[J].Research in Transportation Business & Management,2026(64):101538
[12] Luo Q F, Wang J Y.Cross-regional collaborative innovation in the era of data openness:A factor mobility perspective[J].Technology in Society,2025(83):102989
[13] 庞瑞芝,王洪岩.数据要素市场化、超大规模市场与企业高质量创新[J].求是学刊,2025(4):38-53
[14] 周密,王雷,郭佳宏.新质生产力背景下数实融合的测算与时空比较——基于专利共分类方法的研究[J].数量经济技术经济研究,2024(7):5-27
[15] 陈钰芬,陈锦颖.数字化发展何以提升创新要素配置效率?——基于要素流动视角[J].现代经济探讨,2024(8):14-26
[16] Goldfarb A, Tucker C.Digital economics[J].Journal of Economic Literature,2019(1):3-43
[17] Burger M J, Meijers E J.Agglomerations and the rise of urban network externalities[J].Papers in Regional Science,2016(1):5-16
[18] 吕爽,孙铁山,张洪鸣.高铁网络如何促进城市间合作创新——基于高铁网络通达性与合作专利的实证分析[J].经济评论,2024(3):92-108
[19] Bao Y M, Li J, Yang J Y.Green pathways:The role of international air connections in corporate green innovation[J].Economic Modelling,2025(149):107135
[20] 孔令池,高波.中国城市群协同创新多维邻近机制与制度优化路径[J].河北学刊,2025(4):191-199
[21] Jones C I, Tonetti C.Nonrivalry and the Economics of Data[J].American Economic Review,2020(110):2819-2858
[22] Wu X K, Yang J J.High-speed railway and patent trade in China[J].Economic Modelling,2023(123):106276
[23] 王媛玉,范博凯.流空间视角下数字经济赋能城市群空间体系均衡化发展研究[J].重庆大学学报(社会科学版),2024(2):16-32
[24] Hanley D, Li J C, Wu M Q.High-speed railways and collaborative innovation[J].Regional Science and Urban Economics,2022(93):103717
[25] Ciabuschi F, Dellestrand H, Martín O.Internal embeddedness, headquarters involvement, and innovation importance in multinational enterprises[J].Journal of Management Studies,2011(7):1612-1639
[26] Xu Y S, Zhang H P, Li Z H, et al.Integration of migration and attention flow data to reveal association of virtual-real dual intercity network structure[J].Cities,2023(143):104614
[27] Wang C, Hu Q.Knowledge sharing in supply chain networks:Effects of collaborative innovation activities and capability on innovation performance[J].Technovation,2020(94):94-95
[28] 员彦文,孙瑞东.高速铁路网络、交易成本与技术市场一体化[J].产业经济研究,2024(6):128-142
[29] Kim M.Geographic scope, isolating mechanisms and value appropriation[J].Strategic Management Journal,2016(4):695-713
[30] Ahmadpoor M, Jones B F.The dual frontier:Patented inventions and prior scientific advance[J].Science,2017(6351):583-587
[31] Bertani F, Ponta L, Raberto M, et al.The complexity of the intangible digital economy:An agent-based model[J].Journal of Business Research,2021(5):527-540
[32] 黄群慧,余泳泽,张松林.互联网发展与制造业生产率提升:内在机制与中国经验[J].中国工业经济,2019(8):5-23
[33] Zhu P Y.Does high-speed rail stimulate urban land growth? Experience from China[J].Transportation Research Part D:Transport and Environment,2021(98):102974
[34] 贺灿飞,郭佳宏,谢玉欢.基于认知邻近性的进口溢出效应对区域出口产业重构的影响[J].地理学报,2023(6):1323-1338
[35] 曹湛,朱晟君,戴靓,等.多维邻近性对区域创新合作网络形成的影响——基于江浙沪医学科研机构的实证[J].地理研究,2022(9):2531-2547
[36] 张青睿,毛艳华,柯蕴颖,等.城市群建设如何推动城际联合创新:基于多维邻近性视角的解释[J].中国软科学,2024(12):87-97
[37] Boschma R A.Proximity and innovation:A critical assessment[J].Regional Studies,2005(1):61-74
[38] Kang M L, Li Y C, Zhao Z K, et al.Travel costs and inter-city collaborative innovation:Evidence of high-speed railway in China[J].Structural Change and Economic Dynamics,2023(65):286-302
[39] North D C.Institutions[J].Journal of Economic Perspectives,1991(1):97-112
[40] Opsahl T, Agneessens F, Skvoretz J.Node centrality in weighted networks:Generalizing degree and shortest paths[J].Social Networks,2010(3):245-251
[41] 李宇,王竣鹤.产业集群数字化转型的“数字邻近”效应:概念内涵、结构维度与量表开发[J].中国软科学,2025(2):54-65
