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干旱区地理 ›› 2017, Vol. 40 ›› Issue (5): 1079-1088.

• 区域发展 • 上一篇    下一篇

基于DPSIR-PLS模型的中国水贫困评价

孙才志1,2, 吴永杰1, 刘文新2   

  1. 1 辽宁师范大学城市与环境学院, 辽宁 大连 116029;
    2 辽宁师范大学海洋经济与可持续发展研究中心, 辽宁 大连 116029
  • 收稿日期:2017-05-09 修回日期:2017-07-12 出版日期:2017-09-25
  • 作者简介:孙才志(1970-),男,山东烟台市人,教授,博士生导师,主要从事水资源经济与海洋经济地理研究.Email:suncaizhi@lnnu.edu.cn
  • 基金资助:

    国家社会科学重点基金项目(16AJY009)

Application of DPSIR-PLS model to analyze water poverty in China

SUN Cai-zhi1,2, WU Yong-jie1, LIU Wen-xin2   

  1. 1 College of Urban and Environmental Science Liaoning Normal University, Dalian 116029, Liaoning, China;
    2 Center for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, Dalian 116029, Liaoning, China
  • Received:2017-05-09 Revised:2017-07-12 Online:2017-09-25

摘要: 针对现有水贫困评价中系统间缺乏关联性和指标体系未验证的情况,在借鉴原有研究成果的基础上,将基于因果关系的DPSIR模型和PLS结构方程模型相结合,构建中国水贫困评价指标体系和框架模型,并进行验证,最后测算了2003-2014年我国31个省(市)区的水贫困现状。采用ISODATA聚类方法,划分各省区水贫困驱动系统类型,进一步通过核密度估计和马尔科夫链分析水贫困分布的整体形态和演进趋势。结果显示:我国水贫困状况逐渐向"俱乐部收敛"转化,整体呈现良好的发展态势;水贫困类型出现跨越式发展,但流动性较低;不同省区出现严重水贫困、较重水贫困、中度水贫困和微水贫困集聚的现象。评价结果基本可以反映各省区水贫困的实际情况,具有一定的现实意义。

关键词: DPSIR-PLS模型, 水贫困评价, 结构方程, 核密度估计, 马尔科夫链

Abstract: With economic development and accelerating urbanization, water shortage situation is becoming more and more serious in China. Water resources assessment has been of concern for many researchers and policy makers. This paper details an application of the Driving forces-Pressure-State-Impact-Response(DPSIR)model, a holistic tool concerning with multiple systems such as water resources, economy, society and environment, to develop indicators of water poverty in China. This is different from traditional Water Poverty Index(WPI)theory and integrates various indicators by structuring the cause-effect relationships. Furthermore, we chose the partial least squares approach to structural equation modeling(PLS-SEM)to calculate data and test model. This PLS-SEM give the weights based on the model validation, which has advantages compared with the traditional methods such as the analytic hierarchy process, principal component analysis, and entropy value method. While using the DPSIR-PLS, all hypotheses of this study passed the test, indicating that there was causality among the components of water poverty assessment model, and the index system was suitable for evaluating water poverty in China. The results show that holistic scores of water poverty presented an increase trend, indicating the situation of water poverty in China was improving gradually. The transformation of nuclear density distribution curve was changing obviously from double peak to single peak. At the same time, the distribution curve gave certain "club convergence" characteristic, and nuclear density values corresponding to severe water poverty decreased. In addition, ISOQATA clustering method and Markov chains were used to analyze types of water poverty driving system and dynamic evolution of water poverty. Results show as follows:(1)Areas with multiple types to drive water poverty were mainly distributed in the southern and eastern coastal regions, and areas with double types to drive water poverty were mainly distributed in the northern inland and western arid or semi-arid regions.(2)The type of water poverty in China had strong stability. Markov chains analysis demonstrates that a region of micro water poverty or serious water poverty had the probability of 62.5% and 71.43% respectively to maintain the same type of water poverty; while a region of both heavy and moderate water poverty had the probability of 50%. (3)The adjacent areas usually had the similar water poverty types with the region,and the clustering phenomenon of severe water poverty, heavy water poverty, moderate water and micro water poverty always emerged. By introducing the DPSIR-PLS model, This paper developed an indicator system and measured water poverty in China, which provided a new approach to evaluate water poverty in the future. The results can well reflect the actual situation and are helpful for developing countermeasures to alleviate water poverty in China.

Key words: DPSIR-PLS model, water poverty assessment, structural equation, Kernel density estimation, Markov chains

中图分类号: 

  • TV213.4