Calculation and Spatial Correlation Effects of Water Poverty in Various Provinces of China

Deng Guangyao; Zhang Xin

doi:10.13961/j.cnki.stbctb.2024.05.022

您当前的位置：

首页 >

文章列表页 >

Calculation and Spatial Correlation Effects of Water Poverty in Various Provinces of China

- Calculation and Spatial Correlation Effects of Water Poverty in Various Provinces of China
- Bulletin of Soiland Water Conservation Vol. 43, Issue 5, Pages: 204-213(2024)
- 作者机构：
  
  1. 兰州财经大学一带一路经济研究院,甘肃,兰州,730020
  2. 兰州财经大学统计与数据科学学院,甘肃,兰州,730020
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2024.05.022
  CLC： F205
- Published：2024
- 稿件说明：
移动端阅览
Deng Guangyao, Zhang Xin. Calculation and Spatial Correlation Effects of Water Poverty in Various Provinces of China[J]. Bulletin of Soiland Water Conservation, 2024, 43(5): 204-213.
DOI：

Deng Guangyao, Zhang Xin. Calculation and Spatial Correlation Effects of Water Poverty in Various Provinces of China[J]. Bulletin of Soiland Water Conservation, 2024, 43(5): 204-213. DOI： 10.13961/j.cnki.stbctb.2024.05.022.

摘要

[目的] 测度中国各省(市)的水贫困情况，并进行社会网络分析，为缓解中国水贫困窘境提供决策依据。[方法] 采用熵权法和社会网络分析方法，对2010—2021年中国各个省(市)的水贫困程度进行测算并分析其空间关联效应。[结果] ①中国省级水贫困指数在考察期内整体呈上升趋势，水贫困程度逐步下降，但空间非均衡特征也比较明显。②省级水贫困网络整体呈现出显著的空间关联性和复杂的结构形态，所有地区都关联其中，但这种关联性的紧密程度不高。③根据块模型分析结果，北京、天津等5个省(市)被归类为“净受益”板块，湖南、海南等13个省(市)则被归类为“净溢出”板块，广东、重庆等4个城市则被归类为“双向溢出”板块；而内蒙古、黑龙江等9个省(市)则被归类为“经纪人板块”。此外，板块内关系稀疏，板块间联系紧密。④核心边缘密度分析表明，核心地区的数目频繁波动，边缘地区的数目则先增加后减少。[结论] 国家应全面认识水贫困指数的空间关联关系和网络结构特征，制定并推行区域差异化的政策和策略，以协同促发展，充分发挥政府和市场作用，有效改善水贫困窘境，提升水安全保障。

Abstract

[Objective] The water poverty in China’s provinces (cities) was measured and social network analysis was conducted in order to provide a decision-making basis for alleviating China’s water poverty dilemma. [Methods] The entropy weight method and social network analysis were employed to measure and analyze the spatial correlation effects of water poverty in various Chinese provinces (cities) from 2010 to 2021. [Results] ① The water poverty index of Chinese provinces has shown an overall upward trend during the inspection period

and the degree of water poverty has gradually decreased. However

the spatial non-equilibrium characteristics remain quite evident. ② The provincial water poverty network exhibits significant spatial correlation and complex structural forms as a whole

with all regions interconnected; however

the degree of closeness of this correlation is not high. ③ According to the block model analysis results

5 provinces (cities) including Beijing and Tianjin are classified as the “net beneficiary” group

13 provinces (cities) including Hunan and Hainan as the “net spillover” group

4 provinces (cities) including Guangdong and Chongqing as the “two-way spillover” group

and 9 provinces (cities) including Inner Mongolia and Heilongjiang as the “broker” group. In addition

the relationships within the plates are sparse

whereas the connections between the plates are strong. ④ Analysis of the core-edge density shows that the number of core areas fluctuates frequently

while the number of edge areas initially increases and then decreases. [Conclusion] The State should develop a comprehensive understanding of the spatial correlation and network structure characteristics of the Water Poverty Index

formulate and implement regionally differentiated policies and strategies

promote coordinated development

give full play to the roles of the government and market

effectively address the challenges of water poverty

and enhance water security.

关键词

Keywords

references

Falkenmark M, Lundqvist J, Widstrand C. Macro-scale water scarcity requires micro-scale approaches [J]. Natural Resources Forum, 1989,13(4):258-267.

Sullivan C. Calculating a water poverty index [J]. World Development, 2002,30(7):1195-1210.

Kini J. Inclusive water poverty index: A holistic approach for helping local water and sanitation services planning [J]. Water Policy, 2017,19(4):758-772.

Pérez-Foguet A, Giné Garriga R. Analyzing water poverty in basins [J]. Water Resources Management, 2011,25(14):3595-3612.

Wilk J, Jonsson A C. From water poverty to water prosperity: A more participatory approach to studying local water resources management [J]. Water Resources Management, 2013,27(3):695-713.

Juran Luke, MacDonald M C, Basu N, et al. Development and application of a multi-scalar, participant-driven water poverty index in post-tsunami India [J]. International Journal of Water Resources Development, 2017,33:955-975.

Yuan Liang, Yang Dongquan, Wu Xia, et al. Development of multidimensional water poverty in the Yangtze River Economic Belt, China [J]. Journal of Environmental Management, 2023,325:116608.

Kallio M, Guillaume J H A, Kummu M, et al. Spatial variation in seasonal water poverty index for Laos: An application of geographically weighted principal component analysis [J]. Social Indicators Research, 2018,140(3):1131-1157.

Korc M E, Ford P B. Application of the Water Poverty Index in border colonias of West Texas [J]. Water Policy, 2013,15(1):79-97.

杨玉蓉,张青山,邹君.基于村级尺度的湖南农村水贫困比较研究[J].长江流域资源与环境,2014,23(7):1027-1034. Yang Yurong, Zhang Qingshan, Zou Jun. A comparative study of Hunan rural water poverty based on village scale [J]. Resources and Environment in the Yangtze Basin, 2014,23(7):1027-1034.

Manandhar S, Pandey V P, Kazama F. Application of water poverty index (WPI) in Nepalese context: A case study of Kali Gandaki River basin (KGRB) [J]. Water Resources Management, 2012,26(1):89-107.

Thakur J K, Neupane M, Mohanan A A. Water poverty in upper Bagmati River basin in Nepal [J]. Water Science, 2017,31(1):93-108.

邵薇薇,杨大文.水贫乏指数的概念及其在中国主要流域的初步应用[J].水利学报,2007,38(7):866-872. Shao Weiwei, Yang Dawen. Water poverty index and its application to main river basins in China [J]. Journal of Hydraulic Engineering, 2007,38(7):866-872.

曹茜,刘锐.基于WPI模型的赣江流域水资源贫困评价[J].资源科学,2012,34(7):1306-1311. Cao Qian, Liu Rui. Assessment of water poverty in Ganjiang Basin based on WPI model [J]. Resources Science, 2012,34(7):1306-1311.

陈莉,石培基,魏伟,等.干旱区内陆河流域水贫困时空分异研究: 以石羊河为例[J].资源科学,2013,35(7):1373-1379. Chen Li, Shi Peiji, Wei Wei, et al. Water poverty temporal-spatial differentiation in the Shi Yang River basin [J]. Resources Science, 2013,35(7):1373-1379.

王克龙.大凌河流域水贫困时空分异特征及影响因素分析[J].黑龙江水利科技,2023,51(8):70-74. Wang Kelong. Spatial and temporal differentiation of water poverty and its influencing factors in Daling River Basin [J]. Heilongjiang Hydraulic Science and Technology, 2023,51(8):70-74.

Wurtz M, Angeliaume A, Herrera M T A, et al. A spatial application of the water poverty index (WPI) in the state of Chihuahua, Mexico [J]. Water Policy, 2019,21(1):147-161.

冉启智,廖和平.西南地区水贫困测度和空间格局分析: 以重庆市为例[J].中国农业资源与区划,2021,42(11):109-120. Ran Qizhi, Liao Heping. Measurement and spatial pattern analysis of water poverty in South West China: A case study of Chongqing Municipality [J]. Chinese Journal of Agricultural Resources and Regional Planning, 2021,42(11):109-120.

孙才志,吴永杰,刘文新.基于熵权TOPSIS法的大连市水贫困评价及障碍因子分析[J].水资源保护,2017,33(4):1-8. Sun Caizhi, Wu Yongjie, Liu Wenxin. Application of TOPSIS method based on entropy weight to water poverty evaluation and obstacle indicator diagnoses for Dalian City [J]. Water Resources Protection, 2017,33(4):1-8.

孙才志,王雪妮.基于WPI-ESDA模型的中国水贫困评价及空间关联格局分析[J].资源科学,2011,33(6):1072-1082. Sun Caizhi, Wang Xueni. Research on the assessment and spatial correlation pattern of water poverty in China based on WPI-ESDA model [J]. Resources Science, 2011,33(6):1072-1082.

苟凯歌,蒋辉,刘兆阳.2000—2017年中国农村水资源贫困与经济贫困的耦合协调状态及其影响因素[J].水土保持通报,2021,41(5):255-263. Gou Kaige, Jiang Hui, Liu Zhaoyang. Coupling coordination and influencing factors of rural water resource poverty and economic poverty in China during 2000—2017[J]. Bulletin of Soil and Water Conservation, 2021,41(5):255-263.

王淑贺,王利军.黄河流域水贫困与经济高质量发展的耦合协调关系[J].水土保持通报,2022,42(3):199-207. Wang Shuhe, Wang Lijun. Coupling and coordination between water poverty and high-quality economic development in Yellow River basin [J]. Bulletin of Soil and Water Conservation, 2022,42(3):199-207.

刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J].中国工业经济,2015(5):83-95. Liu Huajun, Liu Chuanming, Sun Yanan. Spatial correlation network structure of energy consumption and its effect in China [J]. China Industrial Economics, 2015(5):83-95.

黄杰.中国能源环境效率的空间关联网络结构及其影响因素[J].资源科学,2018,40(4):759-772. Huang Jie. The spatial network structure of energy-environmental efficiency and its determinants in China [J]. Resources Science, 2018,40(4):759-772.

孙才志,马奇飞.中国省际水资源绿色效率空间关联网络研究[J].地理研究,2020,39(1):53-63. Sun Caizhi, Ma Qifei. Spatial correlation network of water resources green efficiency between provinces of China [J]. Geographical Research, 2020,39(1):53-63.

刘军.整体网分析: UCINET软件实用指南[M].3版.上海: 格致出版社,2019. Liu Jun. Whole Network Approach: A Practical Guide to UCINET [M]. 3rd. Shanghai: Gezhi Publishing House, 2019.

戴向前,周飞,杨彦明,等.关于水利领域发挥市场机制作用的思考[J].水利发展研究,2023,23(7):3-6. Dai Xiangqian, Zhou Fei, Yang Yanming, et al. Thoughts on the role of market mechanism in the field of water conservancy [J]. Water Resources Development Research, 2023,23(7):3-6.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Spatial Network Characteristics and Driving Forces of Net Carbon Sequestration in Yangtze River Economic Belt

Remote sensing monitoring and soil and water conservation effects of abandoned farmland in core black soil protection area of Jilin Province from 2000 to 2023

Causes and mitigation measures of group-occurring slope debris flows in Mentougou District， Beijing City

Spatiotemporal evolution and multi-scenario simulation of carbon storage in Pingxiang City， Jiangxi Province based on PLUS-InVEST model

Impacts of climate， land use and ecological governance on water ecosystem services in Haihe River basin

Related Author

Tan Deming

Ouyang Fanglan

Zhang Caiping

Related Institution

University of South China, School of Economic Management and Law

⁰