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Spatial correlation model of economy-energy-pollution interactions: The role of river water as a link between production sites and urban areas

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  • Liu, Gengyuan
  • Yang, Zhifeng
  • Tang, Yuchen
  • Ulgiati, Sergio

Abstract

This paper applies a spatial autocorrelation method to empirical data about the Huaihe River and provides a reference for research concerning other rivers. It uses the method of spatial econometrics to determine the relationship between the existing “Cancer Villages” on the Huaihe River and the economic development and polluting emissions of nearby urban areas, using combined pollution frequency of BOD, COD, Ammonia and industrial wastewater as indices of pollution levels, through the establishment of spatial regression and cross section analysis models. The study aims to analyze cancer villages’ spillover effect on big cities and the importance of the different model parameters. Results show that the ordinary OLS (Ordinary Least Squares) goodness-of-fit is lower than the goodness-of-fit of the SAR (Spatial Auto-Regressive) model on average, further confirming the disadvantage of the OLS model, failing to consider the spatial correlations. Parameter estimation results show the change rate of GDP per capita size is 0.11%, 0.20%, 0.28%, and 0.22% when industrial wastewater emissions increase by 1% in Jiangsu Province, Anhui Province, Henan Province, and Shandong Province, respectively. This method could be used in future research works to highlight some facets of the relationship that are completely hidden in regional relationships and allow the investigator to raise questions about the origin and dynamics of cancer villages, an irreversible damage to the environment and society.

Suggested Citation

  • Liu, Gengyuan & Yang, Zhifeng & Tang, Yuchen & Ulgiati, Sergio, 2017. "Spatial correlation model of economy-energy-pollution interactions: The role of river water as a link between production sites and urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1018-1028.
    • Handle: RePEc:eee:rensus:v:69:y:2017:i:c:p:1018-1028
    DOI: 10.1016/j.rser.2016.09.068
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    References listed on IDEAS

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    1. Luc Anselin & Raymond J. G. M. Florax, 1995. "Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax (ed.), New Directions in Spatial Econometrics, chapter 2, pages 21-74, Springer.
    2. Cletus C. Coughlin & Eran Segev, 2000. "Foreign Direct Investment in China: A Spatial Econometric Study," The World Economy, Wiley Blackwell, vol. 23(1), pages 1-23, January.
    3. Kelejian, Harry H. & Robinson, Dennis P., 1992. "Spatial autocorrelation : A new computationally simple test with an application to per capita county police expenditures," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 317-331, September.
    4. Maddison, David, 2006. "Environmental Kuznets curves: A spatial econometric approach," Journal of Environmental Economics and Management, Elsevier, vol. 51(2), pages 218-230, March.
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    Cited by:

    1. Xiaoye Jin & Meiying Li & Fansheng Meng, 2019. "Comprehensive Evaluation of the New Energy Power Generation Development at the Regional Level: An Empirical Analysis from China," Energies, MDPI, vol. 12(23), pages 1-15, December.
    2. Chen, Zhongfei & Chen, Fanglin & Zhou, Mengling, 2021. "Does social trust affect corporate environmental performance in China?," Energy Economics, Elsevier, vol. 102(C).
    3. Su, Yi & Fan, Qi-ming, 2022. "Renewable energy technology innovation, industrial structure upgrading and green development from the perspective of China's provinces," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

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