SPATIAL AGGLOMERATION CHARACTERISTICS AND FORMATION MECHANISM OF KNOWLEDGE-INTENSIVE MANUFACTURING INDUSTRIES IN THREE MAJOR URBAN AGGLOMERATIONS IN CHINA: BASED ON BIG DATA ANALYSIS OF SCIENCE AND TECHNOLOGY-BASED ENTERPRISES
YU Ying-jie1,2, DU De-bin1,2, LI Qi-xiang1,2, XING He-xiang3
1. School of Geographic Sciences, East China Normal University, Shanghai 200241, China; 2. Institute of Global Innovation and Development, East China Normal University, Shanghai 200062, China; 3. Industrial Technology Research Institute, East China Normal University, Shanghai 200062, China
Abstract The study examines the clustering characteristics and formation mechanisms of knowledgeintensive manufacturing industries at various scales. The results show that: 1)The computer industry has the highest agglomeration intensity, and the agglomeration intensity of each industry in Beijing-Tianjin-Hebei is higher than that in the Yangtze River Delta and Pearl River Delta regions. 2)At the city cluster scale, industries are primarily concentrated within a 100 km radius. 3)City-scale industries are mostly concentrated within a 40 km radius, with a small number of industries clustering in peripheral areas. The Beijing-TianjinHebei region exhibits a 'spreading type' agglomeration, while the Yangtze River Delta is of 'local type' and 'multi-centre type', and the Pearl River Delta is of 'multi-centre type'. 4)The scale of city clusters is determined by regional resource endowment and comparative advantages. The characteristics of agglomeration in each city scale vary depending on spatial differences in production costs, industrial policies, and local protectionism.
YU Ying-jie,DU De-bin,LI Qi-xiang等. SPATIAL AGGLOMERATION CHARACTERISTICS AND FORMATION MECHANISM OF KNOWLEDGE-INTENSIVE MANUFACTURING INDUSTRIES IN THREE MAJOR URBAN AGGLOMERATIONS IN CHINA: BASED ON BIG DATA ANALYSIS OF SCIENCE AND TECHNOLOGY-BASED ENTERPRISES[J]. HUMAN GEOGRAPHY, 2024, 39(4): 57-65,105.
杜明月, 范德成.知识密集型制造业技术创新资源配置效率研究： 基于动态StoNED模型的半参数分析[J]. 经济问题探索, 2019(11): 142-150. [Du Mingyue, Fan Decheng. Research on technological innovation resource allocation efficiencyin knowledge intensive manufacturing industries: Semi parametric analysis based on dynamic StoNED model[J]. Inquiry into Economic Issues, 2019(11): 142-150.]
[2]
谢子远, 吴丽娟.产业集聚水平与中国工业企业创新效率：基于20个工业行业2000—2012年面板数据的实证研究[J]. 科研管理, 2017, 38(1): 91-99. [Xie Ziyuan, Wu Lijuan. Industrial agglomeration level and innovation efficiency of industrial enterprisesanempirical study based on the panel data of 20 industries from the year 2000 to 2012[J]. Science Research Management, 2017, 38(1): 91-99.]
[3]
李佳洺, 张文忠, 孙铁山, 等.中国城市群集聚特征与经济绩效[J]. 地理学报, 2014, 69(4): 474-484. [Li Jiaming, Zhang Wenzhong, Sun Tieshan, et al. Characteristics of clustering and economic performance of urban agglomerations in China[J]. Acta Geographica Sinica, 2014, 69(4):474-484.]
[4]
Marshall A. Principles of Economics[M]. London, UK: Macmillan, 1890:1-10.
[5]
Krugman P R. Increasing returns and econoomics geography[J]. Journal of Political Economy, 1991, 99(3):483-499.
[6]
Michael E. Chsters and the new economics of competition[J]. Harvard Business Review, 1998, 76(6):77-90.
[7]
史修松.产业集聚及其测度理论研究动态[J]. 科技管理研究, 2009, 29(9):267-270. [Shi Xiusong. Research trends on industrial agglomeration and its measurement theory[J]. Science and Technology Management Research, 2009, 29(9):267-270.]
[8]
贺灿飞, 潘峰华.中国制造业地理集聚的成因与趋势[J]. 南方经济, 2011(6):38-52. [He Canfei, Pan Fenghua. The causes and trends of Chinese manufacturing geography agglomeration[J]. South China Journal of Economics, 2011(6):38-52.]
[9]
Koo J. Knowledge-based industry clusters: Evidenced by geographical patterns of patents in manufacturing[J]. Urban Studies, 2005, 42(9):1487-1505.
[10]
林兰. 重化工业集群式创新机制与空间响应研究[J]. 地理学报, 2016, 71(8):1400-1415. [Lin Lan. Innovation dynamics and spatial response of heavy-chemical industry: Rethinking the cluster innovation[J]. Acta Geographica Sinica, 2016, 71(8):1400-1415.]
高丽娜.产业差异与中小企业空间集聚[J]. 技术经济与管理研究, 2012(7): 125-128.[Gao Lina. Industry differences and spatial clusters of small and medium enterprise[J]. Journal of Technical Economics & Management, 2012(7):125-128.]
[13]
贺灿飞, 潘峰华.产业地理集中、产业集聚与产业集群：测量与辨识[J]. 地理科学进展, 2007, 26(2):1-13. [He Canfei, Pan Fenghua. Geographical concentration and agglomeration of industries: Measurement and identification[J]. Progress in Geography, 2007, 26(2): 1-13.]
[14]
刘桂梅, 王茂军.基于企业点数据的在京日企空间集聚模式[J]. 世界地理研究, 2021, 30(5):925-936. [Liu Guimei, Wang Maojun. Spatial agglomeration model of Japanese enterprises in Beijing based on enterprise point data[J]. World Regional Studies, 2021, 30(5):925-936.]
[15]
黄宇金, 盛科荣, 孙威.基于企业大数据的京津冀制造业集聚的影响因素[J]. 地理学报, 2022, 77(8):1953-1970. [Huang Yujin, Sheng Kerong, Sun Wei. Influencing factors of manufacturing agglomeration in the Beijing-Tianjin-Hebei region based on enterprise big data [J]. Acta Geographica Sinica, 2022, 77(8):1953-1970.]
[16]
蔺雪芹, 方创琳.城市群地区产业集聚的生态环境效应研究进展[J]. 地理科学进展, 2008, 27(3):110-118. [Lin Xueqin, Fang Chuanglin. Research progresson the eco-environmental effect of industry agglomeration in city group[J]. Progress in Geography, 2008, 27(3): 110-118.]
[17]
邹德玲, 丛海彬, 李钰, 等.长三角城市群内小城镇产业集聚效率时空演变与影响因素[J]. 经济地理, 2023, 43(4):73-82. [Zou Deling, Cong Haibin, Li Yu, et al. Spatio temporal evolution and influencing factors of industrial agglomeration efficiency in small towns of the Yangtze River Delta urban agglomeration[J]. Economic Geography, 2023, 43(4):73-82.
[18]
李汉青, 袁文, 马明清, 等.珠三角制造业集聚特征及基于增量的演变分析[J]. 地理科学进展, 2018, 37(9): 1291-1302. [Li Hanqing, Yuan Wen, Ma Mingqing, et al. Manufacturing industry agglomeration characteristics in the Pearl River Delta and evolution based on growth data[J]. Progressin Geography, 2018, 37(9):1291-1302.]
[19]
袁海红, 张华, 曾洪勇. 产业集聚的测度及其动态变化：基于北京企业微观数据的研究[J]. 中国工业经济, 2014(9):38-50.[Yuan Haihong, Zhang Hua, Zeng Hongyong. Measuring localization of manufacturing industries and its dynamics: Using Beijing firmlevel data[J]. China Industrial Economics, 2014(9):38-50.]
[20]
Rosenthal S S, Strange W C. Geography, industrial organization, and agglomeration[J]. Review of Economics and Statistics, 2003, 85(2):377-393.
[21]
Stuart S R, William C S. Evidence on the nature and sources of agglomeration economies(Volume 4)[M] //Vernon Henderson J, Thisse J. Handbook of Urban and Regional Economics. Amsterdam, The Netherlands, 2004:2119-2171.
[22]
吴家权, 谢涤湘, 方远平.珠三角城市群创新空间时空演进特征与影响因素：基于50981家高新技术企业数据的分析[J]. 城市发展研究, 2022, 29(10): 34-40. [Wu Jiaquan, Xie Dixiang, Fang Yuanping. Spatial-temporal evolution characteristics and influencing factors of urban lnnovation space development: Based on the analysis of 50981 high-tech enterprises[J]. Urban Development Studies, 2022, 29(10):34-40.]
[23]
刘婧, 甄峰, 张姗琪, 等.新一代信息技术企业空间分布特征及影响因素：以南京市中心城区为例[J]. 经济地理, 2022, 42(2):114-123, 211. [Liu Jing, Zhen Feng, Zhang Shanqi, et al. Spatial distribution characteristics and influencing factors of new-generation information technology companies: A case of Nanjing central city[J]. Economic Geography, 2022, 42(2):114-123, 211.]
[24]
OECD. Science, Technology and Industry Score Board: Towards a Knowledge-Based Economy[M]. Paris: OECD, 2001:124-125.
[25]
Silverman B W. Density Estimation for Statistics and Data Analysis [M]. New York: Chapman and Hall, 1986:35-47.
[26]
Marcon E, Traissac S, Puech F, et al. Tools to characterize point patterns: Dbmss for R[J]. Journal of Statistical Software, 2015, 67(3):1-15.
[27]
Alfaro L, Chen M X. The global agglomeration of multinational firms[J]. Journal of International Economics, 2014, 94(2):263-276.
[28]
Balland P A, Boschma R, Frenken K. Proximity and innovation: From statics to dynamics[J]. Regional Studies, 2015, 49(6):907-920.
[29]
李琳, 雒道政.多维邻近性与创新：西方研究回顾与展望[J]. 经济地理, 2013, 33(6):1-7. [Li Lin, Luo Daozheng. Multi-proximity and innovation: The retrospect and prospect on western researches[J]. Economic Geography, 2013, 33(6):1-7.]
[30]
Scott A J. New Industrial Spaces: Flexible Production Organization and Regional Development in North America and Western Europe [M]. London: Pion, 1988:60-75.
[31]
马国霞, 石敏俊, 李娜.中国制造业产业间集聚度及产业间集聚机制[J]. 管理世界, 2007(8):58-65, 172. [Ma Guoxia, Shi Minjun, Li Na. The degree of co-agglomeration and the mechanism of spatial agglomeration in China's manufacturing industries[J]. Journal of Management World, 2007(8):58-65, 172.]
[32]
Weber A. On the location of industries[J]. Progress in Human Geography, 1982, 6(1):120-128.
[33]
肖凡, 王姣娥, 黄宇金, 等.中国高新技术企业分布影响因素的空间异质性与尺度效应[J]. 地理研究, 2022, 41(5): 1338-1351.[Xiao Fan, Wang Jiaoe, Huang Yujin, et al. Exploring the spatial and scale variation of factors affecting the geography of high-tech enterprises in China[J]. Geographical Research, 2022, 41(5):1338-1351].
[34]
李福柱, 李倩.知识密集型服务业集聚、高技术制造业集聚及二者协同集聚的创新驱动效应[J]. 科技进步与对策, 2019, 36(17):57-65. [Li Fuzhu, Li Qian. The innovation driving effect of knowledgeintensive business service agglomeration, high-tech manufacturing agglomeration and co-agglomeration of the two[J]. Science & Technology Progress and Policy, 2019, 36(17):57-65.]
[35]
Kirat T, Lung Y. Innovation and proximity territories as loci of collective learning processes[J]. European Urban and Regional Studies, 1999, 6(1):27-38.
[36]
Mansfield E. The speed and cost of industrial innovation in Japan and the United States:External vs. internal technology[J]. Management Science, 1988, 34(10):1157-1168.
[37]
Sosnovskikh S. Industrial clusters in Russia: The development of special economic zones and industrial parks[J]. Russian Journal of Economics, 2017(3):174-199.
[38]
Qin Xionghe, Wang X, Kwan M P. The contrasting effects of interregional networks and local agglomeration on R&D productivity in Chinese provinces: Insights from an empirical spatial Durbin model[J]. Technological Forecasting and Social Change, 2023, 193:122608.https://doi.org/10.1016/j.techfore.2023.122608.
[39]
林宏杰.市场效应、政府行为与科技服务业集聚发展的空间视角分析：以福建省为例[J]. 重庆大学学报(社会科学版), 2018, 24(5): 1-17. [Lin Hongjie. An analysis from a spatial perspective of market effect, government behavior and science and technology services industrial agglomeration: Take Fujian province as an example [J]. Journal of Chongqing University(Social Science Edition), 2018(5):1-17.]
2024, Vol. 39 
