| With the rapid development of a new round of scientific and technological revolution and industrial structure optimization,China’s scientific and technological strength has made historic achievements from selective breakthroughs to system-wide capacity building,but the efficiency of the use of scientific and technological innovation resources is still very low,which seriously restricts the improvement and upgrading of regional scientific and technological innovation capacity.At the same time the landing of major national strategic regions to implement China’s science and technology innovation development has put forward a new mission and requirements.In this context,how to scientifically explore and evaluate the science and technology innovation efficiency of national major strategic regions and grasp the deep logic of their regional differences is of great significance to collaboratively enhance China’s regional science and technology innovation efficiency and promote the innovation-driven high-quality development of national major strategic regions.Based on the panel data of national strategic regions from 2010 to 2020,this paper studies the development level of technology and innovation efficiency and the causes of their differences.Firstly,it defines the basic concept of technology and innovation efficiency,outlines the basic theories related to this study,and summarises and analyses the literature from three aspects: regional technology and innovation efficiency,technology and innovation efficiency evaluation index system and evaluation methods,and technology and innovation efficiency influencing factors;secondly,it adopts the global super-efficiency EBM model to measure the technology and innovation efficiency of national major strategic regions,and analyses its evolutionary characteristics from the perspective of time,and using coefficient of variation,spatial Gini coefficient and Thiel index are used to explore its spatial differentiation characteristics;the spatio-temporal geographically weighted regression(GTWR)model is used to scientifically measure the regression coefficients of each influencing factor on the efficiency of science and technology innovation,and then to analyse the spatio-temporal evolution of the effect of the influence.The research results show that:(1)In the matter of the technology and innovation efficiency in regions of national major strategies,it shows a trend of decreasing and then increasing from the perspective of time evolution,,the Yangtze River Delta region and the Yangtze River Economic Belt show a balanced distribution with low overall divergence and insignificant clustering characteristics from the perspective of spatial characteristics,while the Beijing-Tianjin-Hebei region and the Yellow River Ecological Protection and High Quality Development region show a decreasing trend of divergence and clustering year by year;(2)In the matter of the influencing factors effect on technology and innovation efficiency,the spatial distribution pattern of the effect of government support shows a decreasing trend year by year and is high in the east and low in the west;the spatial distribution pattern of the effect of economic development level shows a decreasing and then increasing trend and increases from east to west;the spatial distribution pattern of the effect of higher education level shows an increasing trend and increases in the circle from inside to outside;the effect of openness to the outside world is positive during the study period and shows an increasing trend.The spatial distribution pattern of the effect of the degree of openness to the outside world is positive and shows an upward trend,with the spatial distribution pattern of positive promotion in the eastern coast and negative suppression in the inland regions;the spatial distribution pattern of the effect of digital economy shows a decreasing trend from east to west and from south to north.Based on the findings,this paper gives policy recommendations at the levels of optimising the allocation of science and technology innovation resources,improving talent training models and introduction policies,promoting regional collaborative development and accelerating the construction of a digital economy. |
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