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. 2019 Aug 1;16(15):2748.
doi: 10.3390/ijerph16152748.

How Does Industrial Waste Gas Emission Affect Health Care Expenditure in Different Regions of China: An Application of Bayesian Quantile Regression

Xiaocang Xu  1 , Zhiming Xu  2 , Linhong Chen  3   4 , Chang Li  5
Affiliations

How Does Industrial Waste Gas Emission Affect Health Care Expenditure in Different Regions of China: An Application of Bayesian Quantile Regression

Xiaocang Xu et al. Int J Environ Res Public Health. .

Abstract

Industrial development has brought about not only rapid economic growth, but also serious environmental pollution in China, which has led to serious health problems and heavy economic burdens on healthcare. Therefore, the relationship between the industrial air pollution and health care expenditure (HCE) has attracted the attention of researchers, most of which used the traditional empirical methods, such as ordinary least squares (OLS), logistic and so on. By collecting the panel data of 30 provinces of China during 2005-2016, this paper attempts to use the Bayesian quantile regression (BQR) to reveal the impact of industrial air pollution represented by industrial waste gas emission (IWGE) on HCE in high-, middle-, low-income regions. It was found that double heterogeneity in the influence of IWGE on HCE was obvious, which revealed that people in high-, middle-, low-income regions have significantly different understandings of environmental pollution and health problems. In addition, the BQR method provided more information than the traditional empirical methods, which verified that the BQR method, as a new empirical method for previous studies, was applicable in this topic and expanded the discussion space of this research field.

Keywords: Bayesian quantile regression (BQR); double heterogeneity; health care expenditure (HCE); industrial waste gas emission (IWGE); regional difference.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The Markov Chain Monte Carlo (MCMC) chains for the intercept (the upper left), health care expenditure (HCE) (the upper right) and industrial waste gas emission (IWGE) (the lower left), INCOME (the lower right) for the Bayesian quantile regression (BQR). Only the whole country sample as an example were listed here due to space constraints.
Figure 2
Figure 2
The quantile plots for the Bayes quantile regression and the dotted lines represent the ordinary least squares (OLS) estimate (Variable = IWGE).
See this image and copyright information in PMC

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