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Influencing Factors Analysis of Water Footprint Based on the Extended STIRPAT Model

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Application of the Water Footprint: Water Stress Analysis and Allocation

Abstract

In this chapter, using the panel data covering 37 cities in China for the year 2007 and 2012, the extended STIRPAT model was applied to analyze the key impact factors of city’s total blue WFs and their blue WFs in agricultural, industrial and residential sectors via the Pearson’s Correlation Analysis and the Principal Component Analysis. From the regression results, the dominant contributors to the increase of the blue WFs in various sectors were quantified respectively. The overall STIRPAT analysis results indicated that population, per capita GDP, secondary sector’s share of GDP and the urbanization rate were four key driving factors for all the blue WFs. Among them, population, per capita GDP and urbanization rate played a positive role in the increase of the blue WFs, whereas the increase of the secondary share in the total GDP had significant effects on the reducing of the regional blue WFs. In addition, the city’s water self-sufficiency and per capita water supply had no correlations to the blue WFs. Thus, the reduction of the regional blue WFs cannot be accomplished by the increase in its water self-sufficiency or per capita water supply. In addition, the most dominant contributors on each economic sectors were further analyzed. The conclusion from the STIRPAT model analysis can be an important reference for the authorities to stipulate the relevant water policies or strategies.

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Author information

Authors and Affiliations

  1. School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou, China

    Meng Xu

  2. School of Environment, Beijing Normal University, Beijing, China

    Chunhui Li

Authors
  1. Meng Xu
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  2. Chunhui Li
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Correspondence to Meng Xu .

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Xu, M., Li, C. (2020). Influencing Factors Analysis of Water Footprint Based on the Extended STIRPAT Model. In: Application of the Water Footprint: Water Stress Analysis and Allocation. Springer, Singapore. https://doi.org/10.1007/978-981-15-0234-7_10

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