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Examining the U-shaped relationship between non-agricultural sources water pollution and the urban-rural income gap

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  • Dong He
  • Zhongyuan Sheng
  • Chunxiao Tian

Abstract

Determining how the economy and society interact with the environment of water quality is essential to determining the financial impact of green development. Based on China’s provincial panel data from 2010 to 2021, this research considers non-agricultural sources of water pollution (NASWP) as a negative factor of production, investigates its influence on the urban-rural divide, and explains the mechanism of action. The empirical results show that there is a significant correlation between NASWP and the urban-rural gap, with a "U-shaped" relationship between the two. Water pollution first reduces and then increases the urban-rural income gap, and the results are robust after considering endogeneity. Mechanistic research demonstrates that NASWP cause a loss in food output, which in combination with changes in food prices and food subsidy programs impacts the incomes of rural dwellers, thereby having an influence on the urban-rural income gap. Using the threshold effect model, it is discovered that under the combined influence of agricultural mechanization and food subsidy policy, the relationship between NASWP and urban-rural income divide exhibits an U-shape in areas with high agricultural mechanization and an "inverted U" shape in areas with low agricultural mechanization.

Suggested Citation

  • Dong He & Zhongyuan Sheng & Chunxiao Tian, 2024. "Examining the U-shaped relationship between non-agricultural sources water pollution and the urban-rural income gap," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-21, July.
    • Handle: RePEc:plo:pone00:0305530
    DOI: 10.1371/journal.pone.0305530
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    References listed on IDEAS

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    1. Peng Zhou & Siwei Sun & Tao Chen & Yue Pan & Wanqing Xu & Hailu Zhang, 2022. "Impacts of Social Inequality, Air Pollution, Rural–Urban Divides, and Insufficient Green Space on Residents’ Health in China: Insight from Chinese General Social Survey Data Analysis," IJERPH, MDPI, vol. 19(21), pages 1-17, October.
    2. Nam, Kyung-Min & Selin, Noelle E. & Reilly, John M. & Paltsev, Sergey, 2010. "Measuring welfare loss caused by air pollution in Europe: A CGE analysis," Energy Policy, Elsevier, vol. 38(9), pages 5059-5071, September.
    3. Sun, Tao & Zhang, Hongwei & Wang, Yuan, 2013. "The application of information entropy in basin level water waste permits allocation in China," Resources, Conservation & Recycling, Elsevier, vol. 70(C), pages 50-54.
    4. Moslener, Ulf & Requate, Till, 2007. "Optimal abatement in dynamic multi-pollutant problems when pollutants can be complements or substitutes," Journal of Economic Dynamics and Control, Elsevier, vol. 31(7), pages 2293-2316, July.
    5. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
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