IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i20p3995-d278176.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/20/3995/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/20/3995/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Kyriaki Remoundou & Phoebe Koundouri, 2009. "Environmental Effects on Public Health: An Economic Perspective," IJERPH, MDPI, vol. 6(8), pages 1-19, July.
    5. 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.
    6. 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.
    7. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Minhas Akbar & Ammar Hussain & Ahsan Akbar & Irfan Ullah, 2021. "The dynamic association between healthcare spending, CO2 emissions, and human development index in OECD countries: evidence from panel VAR model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10470-10489, July.
    3. Miguel Ángel Aguilar-Jurado & Pedro Gil-Madrona & Juan Francisco Ortega-Dato & David Zamorano-García, 2019. "Effects of an Educational Glass Recycling Program against Environmental Pollution in Spain," IJERPH, MDPI, vol. 16(24), pages 1-16, December.
    4. Rihem Zeiri & Aida Bouzir & Mohamed H di Benhadj Mbarek & Saloua Benammou, 2023. "The Link between Economic Growth, Air Pollution and Health Expenditure in the G7 Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 156-168, July.
    5. Mahmut ZORTUK & Sinan ÇEKEN, 2015. "The Relationship between Health Care Expenditures and Income in the Selected Transition Economies: A Panel Smooth Transition Regression Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 105-118, June.
    6. Vyas, Vishal & Mehta, Kiran & Sharma, Renuka, 2023. "The nexus between toxic-air pollution, health expenditure, and economic growth: An empirical study using ARDL," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 154-166.
    7. Xinpeng Xing & Jianhua Wang & Tiansen Liu & He Liu & Yue Zhu, 2019. "How Energy Consumption and Pollutant Emissions Affect the Disparity of Public Health in Countries with High Fossil Energy Consumption," IJERPH, MDPI, vol. 16(23), pages 1-18, November.
    8. Ramesh Chandra Das & Enrico Ivaldi, 2021. "Is Pollution a Cost to Health? Theoretical and Empirical Inquiry for the World’s Leading Polluting Economies," IJERPH, MDPI, vol. 18(12), pages 1-17, June.
    9. Tsionas, Mike G., 2020. "Quantile Stochastic Frontiers," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1177-1184.
    10. Murat Gündüz, 2020. "Healthcare expenditure and carbon footprint in the USA: evidence from hidden cointegration approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 801-811, July.
    11. Zijian Zeng & Meng Li, 2020. "Bayesian Median Autoregression for Robust Time Series Forecasting," Papers 2001.01116, arXiv.org, revised Dec 2020.
    12. Yingying Hu & Huixia Judy Wang & Xuming He & Jianhua Guo, 2021. "Bayesian joint-quantile regression," Computational Statistics, Springer, vol. 36(3), pages 2033-2053, September.
    13. Mujaheed Shaikh & Afschin Gandjour, 2019. "Pharmaceutical expenditure and gross domestic product: Evidence of simultaneous effects using a two‐step instrumental variables strategy," Health Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 101-122, January.
    14. Li, Dan & Clements, Adam & Drovandi, Christopher, 2023. "A Bayesian approach for more reliable tail risk forecasts," Journal of Financial Stability, Elsevier, vol. 64(C).
    15. Samia Nasreen, 2021. "Association between health expenditures, economic growth and environmental pollution: Long‐run and causality analysis from Asian economies," International Journal of Health Planning and Management, Wiley Blackwell, vol. 36(3), pages 925-944, May.
    16. Zeng, Zijian & Li, Meng, 2021. "Bayesian median autoregression for robust time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1000-1010.
    17. Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022. "High-frequency monitoring of growth at risk," International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
    18. Ayhan Kuloglu & Ebru Topcu, 2016. "The Relationship Between Health And Growth In Eurasian Economic Union," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 4(4), pages 42-48.
    19. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
    20. Muhammad Arshad Khan & Muhammad Iftikhar Ul Husnain, 2019. "Is health care a luxury or necessity good? Evidence from Asian countries," International Journal of Health Economics and Management, Springer, vol. 19(2), pages 213-233, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:20:p:3995-:d:278176. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.