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

Abstract

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.

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Notes

  1. 1.

    These values are in 2010 constant prices. The rate of exchange in 2010 was approximately US$ 1 = 6.77 yuan. The index of GDP per capita for 2013 is 1,837.5 if using 1978 as the base year.

  2. 2.

    The “new toxics” scenario argues that new pollutants, for example, CO2, may not show the inverted U-shape curve. The revised EKC scenario claims that technological change may accelerate to achieve the turning point; thus, the EKC graph shifting downward and left. The race to bottom scenario insist the greatest increase of environmental regulations and policies happen from low to middle economic levels (Dasgupta et al. 2002).

  3. 3.

    Bandwidth defines how each data point is weighted by the distance from the regression point. This is determined by a spatial weighting function that affects the distance between regression and data points (Fotheringham et al. 2002). Therefore, in the adaptive spatial kernels, we can observe larger bandwidths of kernels, where data are scarce, and smaller bandwidths, where data are dense, while all regression points have the same bandwidth in the fixed kernel function.

  4. 4.

    In this function, if a locally weighted regression parameter is similar to a global OLS model, w ij would be close to 1 regardless of d ij . In other words, a value of w ij close to 0 indicates that the estimated parameter would vary across space. This function allows us to use the bandwidth with the same number of data points with non-zero weights (Fotheringham et al. 2002; Fischer and Getis 2009).

  5. 5.

    AIC is a model selection technique based on information theory, providing the information loss of models between the goodness-of-fit and degrees of freedom. In this analysis, AIC evaluates an optimal bandwidth between the global OLS and GWR models. The bandwidth with minimized AIC value is utilized in the GWR estimation (Zhen et al. 2013).

  6. 6.

    Tibet is not included in our analysis, because of data limitations from statistical data sources. Chongqing is also not included, because it was split from Sichuan during the estimation period (in 1997). To maintain data consistency, we merge Chongqing and Sichuan data, and treat them as a single province in this study.

  7. 7.

    Coastal provinces refer to Beijing, Fujian, Guangdong, Guangxi, Hainan, Hebei, Jiangsu, Liaoning, Shanghai, Shandong, Tianjin, and Zhejiang.

  8. 8.

    GRP per capita and statistics of three pollutants in 2012 are used to identify the trend of sustainable development after 2010 in Table 7.

  9. 9.

    To conduct GeogDetector, the numerical dependent variables—GRP per capita and population density—were transformed to the categorical variables based on the ranking among the provinces, since the precondition for this program is “Y is numerical and X MUST be categorical” (Wang et al. 2010).

  10. 10.

    A positive value of the diff-criterion (AICc, AIC, Bayesian inference criterion/minimum description length, or coefficient of variation) suggests no spatial variability in terms of model selection criteria.

  11. 11.

    Adjusted GRP per capita by CPI (1993 = 100) of each province in 2012 is as follows: Beijing (1.223), Shanghai (1.112), Liaoning (0.794), Tianjin (0.759), Jiangsu (0.708), Zhejiang (0.706), Guangdong (0.609), Xinjiang (0.566), Shandong (0.553), Heilongjiang (0.543), Fujian (0.530), Inner Mongolia (0.530), Hainan (0.513), Jilin (0.512), Ningxia (0.489), Shaanxi (0.482), Shanxi (0.475), Qinghai (0.457), Hebei (0.457), Hubei (0.443), Hunan (0.439), Sichuan (0.399), Henan (0.385), Yunnan (0.343), Guangxi (0.343), Jiangxi (0.338), Gansu (0.324), Anhui (0.324), and Guizhou (0.298).

  12. 12.

    In their analyses, the city type of Beijing shows significant improvement between 1990 and 2000, but is categorized with other cities (Wang et al. 2012a).

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Acknowledgements

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

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Correspondence to Yoomi Kim.

Appendix: Distributions of the GRP per capita and the pollutants in 1994 and 2010

Appendix: Distributions of the GRP per capita and the pollutants in 1994 and 2010

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Kim, Y., Tanaka, K. & Ge, C. Estimating the provincial environmental Kuznets curve in China: a geographically weighted regression approach. Stoch Environ Res Risk Assess 32, 2147–2163 (2018). https://doi.org/10.1007/s00477-017-1503-z

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Keywords

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