| Since the reform and opening up,China’s high-tech industry has developed rapidly,especially in fields such as information technology,biotechnology,new materials,and new energy.It has become an important driving force to support sustained and rapid growth of the national economy.China is now in the economic "New Normal".To achieve economic transformation and industrial upgrading,we must drive economic development with innovation.One of the obstacles to the development of China’s high-tech industries is the lack of innovation capacity.There are two main ways to improve innovation efficiency: firstly,through personal knowledge accumulation and technological development;secondly,through external knowledge spillovers,regional innovation efficiency can be improved;With the improvement of regional innovation development level in China,relying solely on one’s own knowledge accumulation can no longer meet the needs of development.Therefore,how to utilize knowledge spillover effects to improve innovation performance has become a hot topic of current research.This article studies the impact of knowledge spillover effects on regional innovation efficiency from both spatial and nonlinear perspectives.First,based on the panel data of 30 provinces and cities in China from 2011 to 2020,the input-output index system of innovation efficiency is constructed,and the DEA method is used to measure and compare,and then the improved Verspagen Caniels knowledge spillover model is used to measure the effect of knowledge spillover;Secondly,in response to the spatial spillover effect of regional knowledge spillover on the innovation efficiency of high-tech industries,a spatial econometric model is used and three spatial weight matrices are constructed for analysis;Finally,a threshold model is used to test the nonlinear relationship between knowledge spillover and regional innovation efficiency,calculate the threshold value of knowledge spillover,and compare and analyze provinces and cities in different threshold ranges.Finally,policy recommendations are proposed on how local governments can effectively improve the innovation efficiency level of local high-tech industries and achieve balanced development of high-tech industries.The theoretical and empirical analysis results indicate that:(1)The innovation efficiency of China’s high-tech industry showed a gradual upward trend from 2011 to2020,with the average innovation efficiency of each province increasing from 0.307 to 0.610.The level of regional knowledge spillover in China also showed a clear upward trend,with an annual average growth rate of 3.5%.(2)The Moran test results show that there is a stable positive spatial spillover effect in the regional innovation efficiency of high-tech industries in China,that is,regions with high innovation efficiency can have a good demonstration effect on their surrounding regions,driving the surrounding regions to improve innovation efficiency.(3)By constructing different weight matrices and fixed effects models,it was found that there is a positive correlation between knowledge spillover and regional innovation efficiency in China as a whole.That is,knowledge spillover between regions can help prevent duplicate investment in surrounding regions,or enable other regions to improve innovation efficiency by learning and absorbing existing knowledge and technology in the region,but may be affected by absorption capacity locally,To some extent,it has resulted in a negative correlation between regional knowledge spillover and regional innovation efficiency under the regional fixed effects model.(4)After introducing absorption of funds,technology gap,and property rights protection as threshold variables into the panel model for regression analysis,it was found that there is a single threshold effect.The higher the regional knowledge digestion and absorption of funds,the smaller the technology gap,which is more conducive to the promotion of inter provincial knowledge spillover on regional innovation efficiency;However,a high level of property rights protection is not conducive to continuing to improve innovation efficiency. |
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