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

Ane Pan (), Darrell Bosch and Huimin Ma
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Ane Pan: Wuhan University of Technology
Huimin Ma: Wuhan University of Technology

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2017, vol. 130, issue 2, No 6, 537-561

Abstract: 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.

Keywords: Aquatic ecosystems; Water Poverty Index; Water scarcity; Principal component analysis; China (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11205-015-1191-3

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