本文以2012~2021年我国30个省/市为研究对象,基于超效率SBM模型结合DEA-Malmquist指数模型对我国30个省/市新质生产力水平进行测算,从静态、动态两个维度进行分析。结果显示,我国新质生产力发展效率呈现波动上升趋势,从东西中部地区按...本文以2012~2021年我国30个省/市为研究对象,基于超效率SBM模型结合DEA-Malmquist指数模型对我国30个省/市新质生产力水平进行测算,从静态、动态两个维度进行分析。结果显示,我国新质生产力发展效率呈现波动上升趋势,从东西中部地区按从高到低排序为东部地区、西部地区、中部地区;同时,我国新质生产力总体水平受技术进步的影响更大。其次,利用Tobit回归模型分析数字经济影响新质生产力发展水平的因素,发现数字经济对发展新质生产力有着显著的正向促进作用。因此,为推进我国整体新质生产力的发展,重点关注未来产业和新兴产业,加强高技术人才、数字技术研发、环境保护、数字经济、数字基础设施等要素的投入。This study focuses on the 30 provinces/cities in China from 2012 to 2021, employing the super-efficiency SBM model in conjunction with the DEA-Malmquist index model to measure the level of new quality productivity in these provinces/cities, and analyzes it from both static and dynamic perspectives. The findings indicate that the development efficiency of China’s new quality productivity has shown a fluctuating upward trend, with the eastern, central, and western regions ranked from high to low as follows: the eastern region, the western region, and the central region. Moreover, the overall level of China’s new quality productivity is more significantly influenced by technological progress. Furthermore, using the Tobit regression model to analyze the factors influencing the development level of new quality productivity, it is discovered that the digital economy has a significant positive effect on the development of new quality productivity. Therefore, to promote the overall development of China’s new quality productivity, it is essential to focus on future and emerging industries and to strengthen the investment in high-tech talent, digital technology research and development, environmental protection, and digital infrastructure.展开更多
文摘本文以2012~2021年我国30个省/市为研究对象,基于超效率SBM模型结合DEA-Malmquist指数模型对我国30个省/市新质生产力水平进行测算,从静态、动态两个维度进行分析。结果显示,我国新质生产力发展效率呈现波动上升趋势,从东西中部地区按从高到低排序为东部地区、西部地区、中部地区;同时,我国新质生产力总体水平受技术进步的影响更大。其次,利用Tobit回归模型分析数字经济影响新质生产力发展水平的因素,发现数字经济对发展新质生产力有着显著的正向促进作用。因此,为推进我国整体新质生产力的发展,重点关注未来产业和新兴产业,加强高技术人才、数字技术研发、环境保护、数字经济、数字基础设施等要素的投入。This study focuses on the 30 provinces/cities in China from 2012 to 2021, employing the super-efficiency SBM model in conjunction with the DEA-Malmquist index model to measure the level of new quality productivity in these provinces/cities, and analyzes it from both static and dynamic perspectives. The findings indicate that the development efficiency of China’s new quality productivity has shown a fluctuating upward trend, with the eastern, central, and western regions ranked from high to low as follows: the eastern region, the western region, and the central region. Moreover, the overall level of China’s new quality productivity is more significantly influenced by technological progress. Furthermore, using the Tobit regression model to analyze the factors influencing the development level of new quality productivity, it is discovered that the digital economy has a significant positive effect on the development of new quality productivity. Therefore, to promote the overall development of China’s new quality productivity, it is essential to focus on future and emerging industries and to strengthen the investment in high-tech talent, digital technology research and development, environmental protection, and digital infrastructure.