New Quality Productive Forces in China's Prefecture-level Cities:Chronological Evolution,Group Characteristics and Development Strategies
New quality productive forces represent the direction of the new round of industrial change and scientific and technological revolution,and is the key focus point for reshaping the new advantages of global competition.By constructing a new quality productive forces measurement system,the entropy weight TOPSIS method is used to measure the development level of new productive forces in 270 cities in China,and the Dagum Gini coefficient decomposition method,the Kernel density estimation,the natural break method,the Moran's I index and the obstacle factor diagnostic model are used to analyze the temporal evolution and spatial differentiation of the new quality productive forces of the cities in the cluster analysis.The results show that:(1)The development level of new productive forces in Chinese cities generally shows an upward trend but the level needs to be improved,and the distribution of the layers shows a"pyramid"pattern with fewer heads,a comparable waist,and a pile up at the tail.(2)Chronologically,the overall gap in the development level of new quality productive forces shows signs of widening with the evolution of time,and the overall gap mainly comes from inter-regional differences;Spatially,the development of new quality productive forces shows positive spatial agglomeration characteristics.(3)The level of integration of industry and education,the number of high-tech enterprises,and the urban innovation index are the main obstacle factors restricting the development of new quality productive forces in cities.The conclusions of the study enriched the characteristic dimensions and spatial and temporal differentiation facts of the new quality productive forces,and provided the main direction and empirical evidence for the development of the new quality productive forces according to the local conditions.
new quality productive forcescharacteristic dimensionspatio-temporal differentiationobstacle factor
万方数据
维普
