北京市创新集聚的影响因素及其空间溢出效应

Influence factors and spillover effect of the innovation agglomeration in Beijing

创新对邻近性的高度依赖使得创新活动在城市层面最为活跃和丰富,但由于城市内部创新数据的缺乏,大多数的创新研究都停留在国家和区域尺度,而城市内部创新活动研究一直难以获取。利用北京市乡镇街道层面的专利数据,深入分析了城市内部创新集聚的空间特征、影响因素及其空间溢出效应。研究发现:(1)北京市的创新活动高度集聚并呈现出明显的"中心—外围"结构,在市域内形成了中关村—上地、望京、CBD、金融街、亦庄经济技术开区、丰台科技城6个创新集群。(2)城市内部创新集聚的空间分布主要受创新投入和创新环境两类因素的影响。企业、大学与研究机构的研发投入是影响本地创新产出的重要因素,地区的科技服务水平、产业多样化程度、制造业基础、大公司比例等创新环境因素也对本地的创新集聚有重要影响。(3)创新投入和创新环境对创新集聚的影响具有明显的空间溢出效应。企业、大学与研究机构的研发投入、地区科技服务业水平和产业多样化水平的提高都会促进周边地区的创新产出。其中,企业研发投入和地区产业多样性水平比大学和研究机构研发投入、地区科技服务业的空间溢出范围更大。

Innovation activities are most active and abundant at the city level, because innovation is highly dependent on the spatial proximity. But there are few in-depth studies on the urban internal innovative activities for the lack of micro-scale innovation data. Using the patent data at township and sub-district levels, this article analyzes the spatial structure, influencing factors and spillover effect of the innovation agglomeration in Beijing. The following conclusions can be drawn： Firstly, innovation shows a higher degree of spatial concentration than production. The spatial distribution of innovation activities presents a ＂core- periphery＂ pattern and the core area includes the six main districts such as Haidian, Chaoyang, Shijingshan, Fengtai, Dongcheng and Xicheng. However, innovation activities are not concentrated in the center of the city, instead an innovation ring forms in the range of 5-20 km from the city center. In the administrative region of Beijing, six innovation clusters are formed： Zhongguancun-Shangdi, Wangjing, CBD, Financial Street, Yizhuang Economic and Technological Development Zone, and Fengtai Science and Technology City. Secondly, we use SDM （Spatial Dubin Model） method to analyze the factors that can influence the innovative agglomeration. Result shows that the spatial distribution of urban internal innovation is mainly affected by innovation input and innovation environment factors. The more R＆D personnel and capital are injected by companies, universities and research institutions, the more patents the local innovation system has. Besides, the innovation environment factors including local technology service level, the degree of industrial diversity, the manufacturing base and the proportion of large companies have great influence on the local innovation agglomeration. Thirdly, we divided the influence into direct effect and indirect effect, while the indirect effect reflects the spillover effect of the innovative factors. Then, we found that the innovation input and innovation environment factors have obvious spatial spillover effects on the innovative agglomeration. R＆D input from the enterprises, universities and research institutions have positive externalities on the surrounding areas, and local technology service level and the degree of local industrial diversity also have positive influence on the innovation out of the surrounding areas. But the manufacturing base and the proportion of large companies do not show obvious spatial externalities. In addition, there are obvious differences in the spatial distance of innovative spillovers. The R＆D input from companies and the degree of local industrial diversity have a greater distance of spillover than R＆D input from universities and research institutions and local technology service industry.