摘要
文章从创新、协调、绿色、开放、共享发展以及提质增效六个方面选取了 32 个二级指标,并运用多层次因子分析法对在中国式现代化战略目标下安徽省 16 个地级市的经济高质量发展水平进行测度,最后使用系统聚类分析方法将测度结果分为 5 个层次进行深入分析.研究表明:安徽省各地经济高质量发展水平差距较大,合肥市作为安徽省的省会,发展基础较好.在创新发展、开放发展以及提质增效三个方面均排名第一,遥遥领先于其他城市.亳州市、黄山市以及芜湖市在协调发展、绿色发展以及共享发展方面分别排名第一.安徽省经济高质量发展整体上呈现"皖南>皖中>皖北"的不均衡分布格局,安徽省 16 个地级市各层次之间存在明显的区域内相似性和区域间差异性.
Abstract
High-quality development is the fundamental requirement of economic,social and ecological development.Under the goal of"double-carbon",the connotation of high-quality economic development is relatively rich,and it is deepened and expanded in practice.This paper selects 32 secondary indicators from the six aspects of innovative development,coordinated development,green development,open development,shared development and quality and efficiency improvement,and uses the multi-level factor analysis method to measure the high-quality economic development level of 16 prefecture-level cities in Anhui Province under the strategic goal of Chinese-style modernization.Finally,the system cluster analysis method is used to divide the measurement results into five levels for in-depth analysis.The research shows that there is a large gap in the high-quality economic development level in Anhui Province.Hefei city,as the provincial capital of Anhui Province,ranks the first in the three aspects of innovation development,open development and quality and efficiency improvement,far ahead of other cities.Bozhou,Huangshan,and Wuhu ranked first in coordinated development,green development and shared development,respectively.The analysis of the comprehensive evaluation score shows that the high-quality economic development in Anhui province presents an unbalanced distribution pattern of"southern Anhui>central Anhui>northern Anhui".Based on the systematic cluster analysis,the 16 prefecture-level cities in Anhui province were divided into 5 levels,and there were obvious regional similarities and regional differences between each level.
基金项目
安徽省优秀科研创新团队项目(2022AH010069)
安徽省高等学校优秀青年人才支持计划(gxyq2022095)
滁州学院博士科研基金(2022qd027)
NETL
NSTL
万方数据