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Assessing Water Poverty in China Using Holistic and Dynamic Principal Component Analysis

Author

Listed:
  • Ane Pan

    (Wuhan University of Technology)

  • Darrell Bosch

    (Virginia Tech)

  • Huimin Ma

    (Wuhan University of Technology)

Abstract

The Water Poverty Index (WPI) expands the analysis of China’s water crises from hydrology to a broader focus on integrated water resources management including economic and social factors. This index was revised by principal component analysis (PCA) to avoid arbitrariness of weights and collinearity between variables. However, the traditional PCA is primarily oriented for static data, and it fails to reveal the evolutionary trend of data over time. Moreover, the conventional normalization methods are not adequate when the dimension of time is added to the data. In this study, the transformation of centralized logarithm of initial variable and holistic and dynamic principal component analysis are firstly proposed, then the improved methods are applied to assess water poverty in China using panel data from 2004 to 2012. The estimated WPI shows the growing scale and the clustering trend of regional water poverty. The analysis of influential factors reveals that aquatic environmental pollution is a vital driver of water poverty. Water resource endowment is the second important factor concerning regional water poverty. Inability to adapt to water scarcity, which leads to weak physical water access and low efficiency of water use, is still a critical driver of regional water poverty. Finally, the regional disparities and alleviation strategies of water poverty are discussed.

Suggested Citation

  • Ane Pan & Darrell Bosch & Huimin Ma, 2017. "Assessing Water Poverty in China Using Holistic and Dynamic Principal Component Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(2), pages 537-561, January.
    • Handle: RePEc:spr:soinre:v:130:y:2017:i:2:d:10.1007_s11205-015-1191-3
    DOI: 10.1007/s11205-015-1191-3
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    References listed on IDEAS

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    1. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Comparing aggregating methods for constructing the composite environmental index: An objective measure," Ecological Economics, Elsevier, vol. 59(3), pages 305-311, September.
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    3. Danny Cho & Tomson Ogwang & Christopher Opio, 2010. "Simplifying the Water Poverty Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 97(2), pages 257-267, June.
    4. Peter Lawrence & Jeremy Meigh & Caroline Sullivan, 2002. "The Water Poverty Index:an International Comparison," Keele Economics Research Papers KERP 2002/19, Centre for Economic Research, Keele University, revised Mar 2003.
    5. Agustí Pérez-Foguet & Ricard Giné Garriga, 2011. "Analyzing Water Poverty in Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3595-3612, November.
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    7. Hatem Jemmali & Caroline Sullivan, 2014. "Multidimensional Analysis of Water Poverty in MENA Region: An Empirical Comparison with Physical Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 115(1), pages 253-277, January.
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    Cited by:

    1. A. Majeed Nadeem & Roland Cheo & Huang Shaoan, 2018. "Multidimensional Analysis of Water Poverty and Subjective Well-Being: A Case Study on Local Household Variation in Faisalabad, Pakistan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(1), pages 207-224, July.

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