Analysis of Allocation Efficiency of Scientific and Technological Innovation Resources and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration from the Perspective of Government Intervention
Analysis of Allocation Efficiency of Scientific and Technological Innovation Resources and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration from the Perspective of Government Intervention
Implementing innovation-driven development strategy requires the optimal allocation of S&T innovation resources.Figuring out the differences in the allocation efficiency of S&T innovation resource and its influencing factors in the the Yangtze River Delta city cluster,the pioneer area of China's economic and scientific development,is vital for building an innovation-driven country.From the perspective of government intervention,this paper adopts the super-efficiency DEA model and DEA-Malmquist model to measure the allocation efficiency of S&T innovation resource of 26 cities in the Yangtze River Delta city cluster from 2011 to 2020,also adopts the Tobit model with government intervention as the core explanatory variable to empirically analyze the influencing factors.The study finds that the overall allocation efficiency of S&T innovation resource in the Yangtze River Delta city cluster shows an increasing trend,in which Shanghai,Ningbo,Suzhou,Hangzhou and Nanjing rank in the top 5.However,huge gaps remain over the allocation efficiency of S&T innovation resource between different cities,and the regional spatial pattern is extremely unbalanced.Among which,the government intervention,industrial structure adjustment and urbanization all have a significant positive impact on improving the allocation of S&T innovation resources.Therefore,it is suggested that local governments of the Yangtze River Delta should map out relevant laws and regulations aiming at the input and output of S&T innovation resource to further standardize the means for resource allocation,and strengthen exchanges and cooperation between cities.
维普
万方数据