摘要
为了实现《山东省“十三五”科技创新规划》目标,从全局最优角度出发运用多目标优化思想建立了在R&D投入强度约束下山东省企业创新投入规模优化模型和经济发展优化模型,综合分析了依据“十三五”规划对山东省R&D投入强度加强至2.6%要求下对山东省企业创新投入规模与经济增长的影响,得出了在此强度约束下2020年山东省各市创新资金投入的最优路径.结果表明,若2020年山东省能实现“十三五”规划要求则其R&D投入强度将达到2.8256%,经济年均增长率将达到7.696%.并以R&D投入强度、R&D投入规模及生产总值作为合并变量通过层次聚类方法将各市分为“三高”、“三中”及“三低”发展区域进行研究分析,为改善山东省创新发展提供具有建设性的意见.需要通过注资或市区间合作,降低企业创新成本,提高R&D资本投放利用率,来实现“三高”发展区带动“三中”及“三低”发展区的发展,从而促进山东省企业创新发展.
In order to realizing the goal about“13th Five-Year for Scientific and Techno-logical Innovation of Shandong Province.The multi-objective optimization idea to establish a scale optimization model and economic development optimization model for enterprises in Shandong Province under the constraints of R&D input intensity were constructed.Ac-cording to the“13th Five-Year Plan”,the impact of the R&D input intensity of Shandong Province to 2.6%on the scale of investment innovation and economic growth of Shandong Province,and the innovation of cities in Shandong Province in 2020 under this intensity con-straint The optimal path for capital investment.The results show that if Shandong Province can meet the requirements of the 13th Five-Year Plan in 2020,its R&D input intensity will reach 2.8256%,and the average annual economic growth rate will reach 7.696%.Based on the R&D input intensity,R&D input scale and total production value as the combined vari-ables,the cities are divided into“three high",“three middle"and“three low""development areas through the hierarchical clustering method.Through using capital injection or inter-city cooperation,reducing the cost of enterprise innovation and increasing the utilization rate of R&D capital input,the enterprise innovation and development would be promoted rapidly in Shandong Province.
作者
王传会
朱梦湘
公维凤
WANG Chuan-hui;ZHU Meng-xiang;GONG Wei-feng(School of Economics Qufu Normal University,Rizhao 276826,China)
出处
《数学的实践与认识》
北大核心
2020年第21期15-24,共10页
Mathematics in Practice and Theory
基金
山东社科基金项目"山东省企业创新路径与企业管理协同研究"(16CGLJ09)。
关键词
企业创新
多目标优化
R&D投入强度
enterprise innovation
multi-objective optimization
R&D input intensity