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Evaluation Of Innovation Efficiency And Analysis Of Influencing Factors In National High-tech Industrial Development Zones

Posted on:2024-09-12Degree:MasterType:Thesis
Country:ChinaCandidate:T N YangFull Text:PDF
GTID:2569307124992409Subject:Theoretical Economics
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In the context of China’s increasingly prominent structural contradictions,innovation has become the primary driving force for development.As an important position for technological innovation,the national high-tech zone is also a powerful engine for regional innovation and economic growth.However,China’s high-tech zones have problems such as unbalanced regional development,tightening resource constraints and closed factor flows,and the level of innovation investment transformation needs to be further improved.It is of great significance to evaluate the innovation efficiency of national high-tech zones and discuss how to improve the innovation efficiency of national high-tech zones to promote the high-quality development of high-tech zones.Based on the input-output data and environmental variables of 54 national high-tech zones from 2011 to 2019,this thesis uses the three-stage DEA model and the Malmquist index model to evaluate the innovation efficiency of national high-tech zones,respectively.The fixed-effect Tobit regression model was used to analyze the influencing factors of innovation efficiency in China and four regions,and the following conclusions were obtained:(1)From a static perspective,the traditional DEA model overestimates the comprehensive efficiency and scale efficiency of national high-tech zones and underestimates the pure technical efficiency.After removing the influence of external environmental factors,the efficiency values of national high-tech zones changed significantly,and the leading factor hindering the improvement of innovation efficiency of national high-tech zones was lower scale efficiency.From the regional level,the innovation efficiency of national high-tech zones shows the ladder-like distribution characteristics of "eastern>central> northeastern>western",and the gap between the northeast and western regions and the central and eastern regions is obvious.The internal equilibrium level of innovation efficiency of hightech zones in each region is manifested as central > east> northeast > west.(2)From the perspective of dynamics,the changes in total factor productivity in national high-tech zones are mainly affected by changes in technological progress;Changes in technical efficiency are mainly affected by pure technical efficiency.The difference in the total factor productivity index between regions is caused by the combination of the technological progress index and the technological efficiency index.(3)According to the results of SFA return,the level of regional economic development and cultural and educational environment are not conducive to the improvement of innovation efficiency of high-tech zones,while the degree of market openness,government support and infrastructure level have a promoting effect on the innovation efficiency of high-tech zones.Through Tobit regression analysis,the internal factors affecting the innovation efficiency of high-tech zones can significantly promote the improvement of innovation efficiency,among which the capital investment intensity,the openness of high-tech zones and the scale of high-tech zones mainly promote the overall efficiency of high-tech zones,while the quality of human input and profitability of high-tech zones are realized through the joint impact on scale efficiency and pure technical efficiency.At the same time,the influence of internal factors on the innovation efficiency of the four major regions is different in both positive and negative aspects and significance level.Finally,based on theoretical analysis and empirical research,targeted countermeasures and suggestions are given on how to improve the innovation efficiency of national hightech zones.
Keywords/Search Tags:National high-tech zone, Innovation efficiency, Influencing factors, Third-stage DEA-MALMQUIST model, TOBIT regression model
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