摘要
根据"坚持以人为本,树立全面、协调、可持续的发展观,促进经济社会和人的全面发展"的科学发展观内涵,以权威机构典型观点的高频指标为基础,从经济、生态、社会、人的全面发展、科学技术等五个方面构建了地级市科学发展评价指标体系。建立了基于聚类赋权的科学发展评价模型,并采用辽宁省地级市2007年的数据进行实证研究。本文的创新及特色一是根据有、无特定指标两种状态对评价对象聚类结果影响的大小来确定这个指标的权重。反映了对评价结果影响越大的指标、其权重应该越大的思路,改变了现有研究在确定权重时忽略指标对评价结果影响程度大小的问题。二是影响地级市科学发展的指标的重要性程度从大到小依次是:科技进步贡献率、人均公园绿地面积、万元GDP综合能耗、工业固体废物综合利用率、人均绿色GDP、恩格尔系数、工农业总产值比率、基尼系数、财政支出占GDP比重和全社会劳动生产率等10个指标。
According to the connotation of Scientific Outlook on Development,this paper constructs the comprehensive evaluation index system for Prefecture-level cities from economy,environment,society,the All-Round Human Development and the technological development on the basis of the authoritative organization indices.It paper contrasts the evaluation model of scientific development concept based on clustering,and then the model is applied to prefecture-level cities of Liaoning province.The special and contribution of this paper lies in two aspects.Firstly,it determines the weight of indicator by comparing the result of clustering between the fact that there's the indicator and the fact that the indicator is removed.The greater the impact on the results of the evaluation of indicators is,the bigger their weights should be.This changes the existing research in determining the weight of ignoring the impact of indicators on the results of evaluation.Secondly,the main indicators of scientific development are contribution rate of scientific and technological progress,park green area per capita,GDP million comprehensive energy consumption,the comprehensive utilization rate of industrial solid waste,green GDP per capita,Engel's coefficient,ratio of industrial and agricultural output value,Gini coefficient,the proportion of financial expenditure and GDP,society as a whole labor productivity.
出处
《运筹与管理》
CSCD
北大核心
2011年第5期94-102,共9页
Operations Research and Management Science
基金
国家自然科学基金项目(71171031)
国家社会科学基金重大项目(06&ZD039)
2009年大连市第二批科技计划项目(2009D11ZC102)
中央高校基本科研业务费专项资金资助(DUT10ZD107
DUT10RW107)
大连市社会科学院资助项目
关键词
科学发展观
科学发展评价
聚类赋权
实证研究
scientific development view
scientific development evaluation
clustering
empirical study