Is Carbon Dioxide (CO2) Emission an Important Factor Affecting Healthcare Expenditure? Evidence from China, 2005–2016

Dados Bibliográficos

ID15499577
AutoresLinhong Chen (0009-0009-7134-6619, Chongqing Technology and Business University), Yue Zhuo (0000-0002-7096-1793, Sichuan University, autor correspondente), Zhiming Xu (0000-0001-9881-9820, ESCP Business School), Xiaocang Xu (0000-0002-6784-9717, Chongqing Technology and Business University, autor correspondente), Xin Gao (0000-0002-0937-6288, Hohai University)
Ano2019
Volume16
Fascículo20
Páginas3995-3995
Data de publicação2019-10-18
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores do periódicoISSN: 1661-7827 • E-ISSN: 1660-4601
EditoraMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph16203995
PMID31635413
OpenAlexW2981296693
IdiomaEN
Citações recebidas4
Referências citadas38

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

Air pollution · Carbon dioxide · China · Econometrics · Economic growth · Economics · Geography · Health care · Ordinary least squares · Quantile · Quantile regression · Air Quality and Health Impacts · Chemistry · Climate Change and Health Impacts · Environmental Science · Global Health Care Issues