主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院

中国管理科学 ›› 2018, Vol. 26 ›› Issue (3): 160-168.doi: 10.16381/j.cnki.issn1003-207x.2018.03.017

• 论文 • 上一篇    下一篇

水环境安全评价方法及其在京津冀地区的应用

刘秀丽1,2,3,4, 涂卓卓1,3   

  1. 1. 中国科学院数学与系统科学研究院, 北京 100190;
    2. 中国科学院预测科学研究中心, 北京 100190;
    3. 中国科学院大学, 北京 100049;
    4. 中国科学院管理、决策与信息系统重点实验室, 北京 100190
  • 收稿日期:2016-09-28 修回日期:2017-05-26 出版日期:2018-03-20 发布日期:2018-05-24
  • 通讯作者: 刘秀丽(1975-),女(汉族),山东成武人,中国科学院数学与系统科学研究院研究员,研究方向:资源环境管理、投入产出分析、经济分析与预测,E-mail:xiuli.liu@amss.ac.cn. E-mail:xiuli.liu@amss.ac.cn
  • 基金资助:

    国家自然科学基金资助项目(71173210);中国科学院重点部署资助项目

Assessment Method on Water Environment Security and Its Application in Jing-Jin-Ji Region

LIU Xiu-li1,2,3,4, TU Zhuo-zhuo1,3   

  1. 1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;
    2. Center for Forecasting Science, Chinese Academy of Sciences, Beijing 100190, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China;
    4. Key Laboratory of Management, Decision and Information Systems, CAS, Beijing 100190, China
  • Received:2016-09-28 Revised:2017-05-26 Online:2018-03-20 Published:2018-05-24

摘要: 为了克服模糊综合评价方法中的权重选取以及最大最小算法导致的信息缺失问题,本文提出一种改进的模糊综合评价方法。首先以熵权法、灰色关联分析法以及主成分分析法为基础,建立组合权重模型;其次,在加权平均原则下引入综合评分方法进行评价得到最终评价结果。从经济社会、水质状况和资源条件三个方面出发,初步建立了包含17个指标的水环境安全评价体系。利用主成分分析法对初步建立的指标体系进行了筛选,最终确定了13个指标作为评价指标。在筛选之后的指标体系下,用改进的模糊综合评价法对北京、天津、河北以及京津冀地区总体的水环境安全进行了评价,给出了2006-2014年间各地区水环境安全变化情况,结果显示京津冀地区水环境安全基本呈现北京优于河北优于天津的趋势;京津冀地区总体的水环境安全呈现先变好再变差的趋势。森林覆盖率是水环境安全最主要的影响因素,其次是第三产业占GDP的比重、人均水资源量以及Ⅰ-Ⅲ类水质占比。弹性系数分析表明,这四个指标的改善也是对京津冀地区水环境安全提高最有效的措施。最后,根据对评价结果的分析,提出了提高京津冀水环境安全的一些建议。

关键词: 京津冀地区, 水环境安全, 改进的模糊综合评价方法, 弹性分析

Abstract: To overcome the disadvantages of weight selection and information missing in the evaluation, an improved fuzzy comprehensive evaluation method is proposed in this paper. First, the combined weight model which is based on entropy method, grey relational analysis method and principal component analysis is established. Then, with the weighted average principle, a technique of synthetic grade is introduced to evaluate the result. Our improved fuzzy comprehensive evaluation method can avoid the impact of subjective factors on the determination of weights of the indexes and reflect the contribution of every index to the evaluation result more objectively. Jing-Jin-Ji region is now threatened by the increasingly serious water environmental security problems which adversely affects human health and imposes a limit on social and economic development. Based on this background, it is of practical and theoretical significance to study the water environment security in Jing-Jin-Ji region and explore effective control measures, which is a major task of realizing Jing-Jin-Ji integration. In this paper, an evaluation index system of water environment security is initially established from three respects:economic society, water quality and resource conditions, which included 17 indicators. Then the principal component analysis method is applied to screen the index system, and 13 indexes are selected as final assessment indexes. Based on the screened index system, we apply this improved method to the water environment security assessment in Jing-Jin-Ji region with data from Beijing, Tianjin and Hebei Annual Water Resources Bulletin during 2006-2014. The results show that the water environment security in Jing-Jin-Ji region became better from 2006 to 2012, then got worse. The order of water environment security in three regions was Beijing, Hebei and Tianjin. Among the 13 indicators, the principal influence factors of water environment security are forest coverage rate, the proportion of tertiary industry in GDP, per capital water resources and the proportion of class Ⅰ-Ⅲ water quality. The elasticity coefficient analysis shows that to improve these four factors was also the most effective way in improving water environment security in Jing-Jin-Ji region.

Key words: Jing-Jin-Ji region, water environment security, improved fuzzy comprehensive evaluation method, elasticity coefficient analysis

中图分类号: