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Is Carbon Dioxide (CO 2 ) Emission an Important Factor Affecting Healthcare Expenditure? Evidence from China, 2005–2016

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  • Linhong Chen

    (School of Public Administration, Sichuan University, Chengdu 610065, China
    School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China)

  • Yue Zhuo

    (School of Public Administration, Sichuan University, Chengdu 610065, China
    Finance office, Sichuan University, Chengdu 610065, China)

  • Zhiming Xu

    (Department of Business, ESCP Europe Business School, 75011 Paris, France)

  • Xiaocang Xu

    (School of Economics, Chongqing Technology and Business University, Chongqing 400067, China)

  • Xin Gao

    (Business School, Hohai University, Nanjing 211100, China)

Abstract

As a result of China’s economic growth, air pollution, including carbon dioxide (CO 2 ) emission, has caused serious health problems and accompanying heavy economic burdens on healthcare. Therefore, the effect of carbon dioxide emission on healthcare expenditure (HCE) has attracted the interest of many researchers, most of which have adopted traditional empirical methods, such as ordinary least squares (OLS) or quantile regression (QR), to analyze the issue. This paper, however, attempts to introduce Bayesian quantile regression (BQR) to discuss the relationship between carbon dioxide emission and HCE, based on the longitudinal data of 30 provinces in China (2005–2016). It was found that carbon dioxide emission is, indeed, an important factor affecting healthcare expenditure in China, although its influence is not as great as the income variable. It was also revealed that the effect of carbon dioxide emission on HCE at a higher quantile was much smaller, which indicates that most people are not paying sufficient attention to the correlation between air pollution and healthcare. This study also proves the applicability of Bayesian quantile regression and its ability to offer more valuable information, as compared to traditional empirical tools, thus expanding and deepening research capabilities on the topic.

Suggested Citation

  • Linhong Chen & Yue Zhuo & Zhiming Xu & Xiaocang Xu & Xin Gao, 2019. "Is Carbon Dioxide (CO 2 ) Emission an Important Factor Affecting Healthcare Expenditure? Evidence from China, 2005–2016," IJERPH, MDPI, vol. 16(20), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:20:p:3995-:d:278176
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    References listed on IDEAS

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    1. Hao Zhang & Yue Niu & Yili Yao & Renjie Chen & Xianghong Zhou & Haidong Kan, 2018. "The Impact of Ambient Air Pollution on Daily Hospital Visits for Various Respiratory Diseases and the Relevant Medical Expenditures in Shanghai, China," IJERPH, MDPI, vol. 15(3), pages 1-10, February.
    2. Wang, Kuan-Min, 2011. "Health care expenditure and economic growth: Quantile panel-type analysis," Economic Modelling, Elsevier, vol. 28(4), pages 1536-1549, July.
    3. Baltagi, Badi H. & Moscone, Francesco, 2010. "Health care expenditure and income in the OECD reconsidered: Evidence from panel data," Economic Modelling, Elsevier, vol. 27(4), pages 804-811, July.
    4. Apergis, Nicholas & Ben Jebli, Mehdi & Ben Youssef, Slim, 2018. "Does renewable energy consumption and health expenditures decrease carbon dioxide emissions? Evidence for sub-Saharan Africa countries," Renewable Energy, Elsevier, vol. 127(C), pages 1011-1016.
    5. Muhammad Usman & Zhiqiang Ma & Muhammad Wasif Zafar & Abdul Haseeb & Rana Umair Ashraf, 2019. "Are Air Pollution, Economic and Non-Economic Factors Associated with Per Capita Health Expenditures? Evidence from Emerging Economies," IJERPH, MDPI, vol. 16(11), pages 1-22, June.
    6. Fengping Tian & Jiti Gao & Ke Yang, 2016. "A Quantile Regression Approach to Panel Data Analysis of Health Care Expenditure in OECD Countries," Monash Econometrics and Business Statistics Working Papers 20/16, Monash University, Department of Econometrics and Business Statistics.
    7. Kyriaki Remoundou & Phoebe Koundouri, 2009. "Environmental Effects on Public Health: An Economic Perspective," IJERPH, MDPI, vol. 6(8), pages 1-19, July.
    8. Xiaocang Xu & Zhiming Xu & Linhong Chen & Chang Li, 2019. "How Does Industrial Waste Gas Emission Affect Health Care Expenditure in Different Regions of China: An Application of Bayesian Quantile Regression," IJERPH, MDPI, vol. 16(15), pages 1-12, August.
    9. Narayan, Paresh Kumar & Narayan, Seema, 2008. "Does environmental quality influence health expenditures? Empirical evidence from a panel of selected OECD countries," Ecological Economics, Elsevier, vol. 65(2), pages 367-374, April.
    10. Mead, Robert W. & Brajer, Victor, 2005. "Protecting China's children: valuing the health impacts of reduced air pollution in Chinese cities," Environment and Development Economics, Cambridge University Press, vol. 10(6), pages 745-768, December.
    11. Xiaocang Xu & Linhong Chen, 2019. "Projection of Long-Term Care Costs in China, 2020–2050: Based on the Bayesian Quantile Regression Method," Sustainability, MDPI, vol. 11(13), pages 1-13, June.
    12. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    13. Yunwen Yang & Huixia Judy Wang & Xuming He, 2016. "Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood," International Statistical Review, International Statistical Institute, vol. 84(3), pages 327-344, December.
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