Estimating the provincial environmental Kuznets curve in China: a geographically weighted regression approach

Yoomi Kim, Katsuya Tanaka, Chazhong Ge

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35 Scopus citations


This study estimates the environmental Kuznets curve (EKC) relationship at the province level in China. We apply empirical methods to test three industrial pollutants—SO2 emission, wastewater discharge, and solid waste production—in 29 Chinese provinces in 1994–2010. We use the geographically weighted regression (GWR) approach, wherein the model can be fitted at each spatial location in the data, weighting all observations by a function of distance from the regression point. Hence, considering spatial heterogeneity, the EKC relationship can be analyzed region-specifically through this approach, rather than describing the average relationship over the entire area examined. We also investigate the spatial stratified heterogeneity to verify and compare risk factors that affect regional pollution with statistical models. This study finds that the GWR model, aimed at considering spatial heterogeneity, outperforms the OLS model; it is more effective at explaining the relationships between environmental performance and economic growth in China. The results indicate a significant variation in the existence of the EKC relationship. Such spatial patterns suggest province-specific policymaking to achieve balanced growth in those provinces.

Original languageEnglish
Pages (from-to)2147-2163
Number of pages17
JournalStochastic Environmental Research and Risk Assessment
Issue number7
StatePublished - 1 Jul 2018

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2013S1A5B8A01054955).

Publisher Copyright:
© 2017, Springer-Verlag GmbH Germany, part of Springer Nature.


  • China
  • Economic growth
  • Environmental Kuznets curve
  • Environmental performance
  • Geographically weighted regression


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