摘要
采用三阶段Malmquist指数构建了排除外部环境与随机干扰因素的八大综合经济区绿色创新效率测度模型,并结合概率神经网络对绿色创新效率进行了智能诊断。研究发现:①绿色创新效率总体呈“下降-上升-下降-上升”波动趋势,其中,西南地区属于共同推动型,其余地区为技术进步型。绿色创新效率主要由技术进步决定,技术效率起抑制作用,而技术效率低下是由规模效率下降所致;②在剔除外部环境与随机因素干扰后,各区域Malmquist指数均有所下降,在第一阶段中绿色创新效率被高估,究其原因是技术效率被高估。其中,西北和黄河中游地区排名与第一阶段结果差别较大,其余地区排名保持不变;③加快发展技术市场和优化产业结构有助于绿色创新效率提升,而经济发展水平、经济开放程度和环境规制对绿色创新效率的影响不显著;④根据智能诊断结果,可将区域分为全部效率有效地区、纯技术无效地区和规模无效地区三类。
This paper applies three-stage Malmquist index to measure the green innovation efficiency of eight comprehensive economic zones excluding external environment and random interference factors.The probability neural network(PNN)is used to conduct intelligent diagnosis of green innovation efficiency.The results show that:①The green innovation efficiency of the eight comprehensive economic zones shows the fluctuation trend of"decline-rise-decline-rise".It is mainly determined by technological progress,while the low technology efficiency is caused by the decline of scale efficiency.②After eliminating the interference of external environment and random factors,Malmquist index of each region decreases.The green innovation efficiency in the first stage is overestimated,which is due to the overestimation of technical efficiency.Among them,the rankings of northwest and middle reaches of the Yellow River are quite different from that of the first stage,while the rankings of other regions remain unchanged.③Accelerating the development of technology market and optimizing industrial structure contribute to the growth of green innovation efficiency,while the level of economic development,economic openness and environmental regulation have no significant impact on green innovation efficiency.④According to the results of intelligent diagnosis,the eight comprehensive economic zones are divided into three categories:the areas of total efficiency,the areas of pure technology inefficiency and the areas of scale inefficiency.
作者
许学国
周燕妃
Xu Xueguo;Zhou Yanfei(School of Management, Shanghai University, Shanghai 200444,China)
出处
《科技进步与对策》
CSSCI
北大核心
2020年第24期54-62,共9页
Science & Technology Progress and Policy
基金
上海市软科学研究重点项目(19692108000)
沪苏浙皖“长三角高质量一体化发展重大问题研究”专项项目(201908)。
