摘要
选取新能源汽车产业发展和美丽中国建设综合评价指标体系,采用改进的熵值法,构建耦合协调度模型和BP神经网络预测模型,对动态耦合问题进行定量研究。研究发现:(1)新能源汽车产业和美丽中国建设的综合发展水平均呈现稳定上升态势,整体而言新能源汽车产业发展相对滞后;(2)新能源汽车产业-美丽中国系统耦合度始终处于由拮抗向磨合过渡的阶段,但两大系统的相互联系和相互促进作用逐渐加强,耦合协调效益正在逐步提升;(3)预测结果表明未来5年新能源汽车产业系统与美丽中国系统协同发展功效持续增强,耦合协调度将从中级协调跨入良好协调阶段。
Selecting the comprehensive evaluation index system for the development of new energy automobile industry and the construction of beautiful China,using the improved entropy method,the coupling coordination degree model and BP neural network prediction model are constructed to quantitatively study the dynamic coupling problem.The results show that:(1)the comprehensive development level of the new energy automobile industry system and the beautiful China system shows a steady upward trend,and the development of the new energy automobile industry relatively lags behind on the whole;(2)the coupling degree between the new energy automobile industry and the beautiful China system has always been in the transition from antagonism to running-in,but the interaction and mutual promotion between the two systems are gradually strengthened,and the coupling and coordination benefits are gradually improving.(3)The forecast results indicate that the coordinated development effect of the new energy vehicle industry system and the beautiful China system will continue to enhance in the next five years,and the coupling coordination degree will step from the intermediate coordination to a good coordination stage.
作者
潘苏楠
李北伟
聂洪光
Pan Sunan;Li Beiwei;Nie Hongguang(School of Management,Jilin University,Changchun 130022,China;School of Economics and Management,Changchun University of Science and Technology,Changchun 130022,China;Institute of Science and Technology Strategic Consulting,Chinese Academy of Sciences,Beijing 100190,China)
出处
《科技管理研究》
CSSCI
北大核心
2019年第18期123-129,共7页
Science and Technology Management Research
基金
国家自然科学基金青年项目“城市居民能源消费影响因素及低碳转型引导机制研究”(71503026)
关键词
新能源汽车
美丽中国
耦合度
耦合协调度
BP神经网络预测
new energy vehicle
beautiful China
coupling degree
coupling coordination degree
BP neural network prediction