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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

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Front. Environ. Sci. Eng.    2023, Vol. 17 Issue (7) : 90    https://doi.org/10.1007/s11783-023-1690-4
RESEARCH ARTICLE
PM2.5 concentration declining saves health expenditure in China
Yang Xie1,2, Hua Zhong1,2, Zhixiong Weng3(), Xinbiao Guo4, Satbyul Estella Kim5, Shaowei Wu6
1. School of Economics and Management, Beihang University, Beijing 100191, China
2. Laboratory for Low-carbon Intelligent Governance, Beihang University, Beijing 100191, China
3. Institute of Circular Economy, Beijing University of Technology, Beijing 100124, China
4. Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100871, China
5. Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba 305-8577, Japan
6. Department of Occupational and Environmental Health, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710049, China
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Abstract

● Monthly hospitalization expenses are sensitive to increases in PM2.5 exposure.

● The increased PM2.5 causes patients with CHD and LRI to stay longer in the hospital.

● The impact of PM2.5 on total expenses for stroke is greater in southern China.

● Males may be more sensitive to air pollution than females.

Air pollution has been a severe issue in China. Exposure to PM2.5 has adverse health effects and causes economic losses. This study investigated the economic impact of exposure to PM2.5 pollution using monthly city-level data covering 88.5 million urban employees in 2016 and 2017. This study mainly focused on three expenditure indicators to measure the economic impact considering lower respiratory infections (LRIs), coronary heart disease (CHD), and stroke. The results show that a 10 µg/m3 increase in PM2.5 would cause total monthly expenses of LRIs, CHD, and stroke to increase by 0.226%, 0.237%, and 0.374%, respectively. We also found that LRI, CHD, and stroke hospital admissions increased significantly by 10%, 8.42%, and 5.64%, respectively. Furthermore, the total hospital stays of LRIs, CHDs, and strokes increased by 2.49%, 2. 51%, and 1.64%, respectively. Our findings also suggest heterogeneous impacts of PM2.5 exposures by sex and across regions, but no statistical evidence shows significant differences between the older and younger adult subgroups. Our results provide several policy implications for reducing unequal public health expenditures in overpolluted countries.

Keywords Air pollution      Health expenditure      PM2.5 concentration      Economic impact      Heterogeneous effect     
Corresponding Author(s): Zhixiong Weng   
Issue Date: 16 February 2023
 Cite this article:   
Yang Xie,Hua Zhong,Zhixiong Weng, et al. PM2.5 concentration declining saves health expenditure in China[J]. Front. Environ. Sci. Eng., 2023, 17(7): 90.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1690-4
https://academic.hep.com.cn/fese/EN/Y2023/V17/I7/90
Regions City Regions City Regions City Regions City
Hebei Handan Zhejiang Quzhou Shandong Laiwu Guangdong Guangzhou
Hebei Chengde Zhejiang Taizhou Shandong Dezhou Guangdong Shantou
Shanxi Datong Zhejiang Lishui Shandong Liaocheng Chongqing Chongqing
Shanxi Shuozhou Anhui Wuhu Shandong Binzhou Sichuan Dazhou
Shanxi Jinzhong Anhui Huaibei Shandong Heze Yunnan Yuxi
Shanxi Yuncheng Anhui Tongling Henan Anyang Yunnan Baoshan
Shanxi Xinzhou Anhui Chuzhou Hubei Wuhan Yunnan Zhaotong
Inner Mongolia Wuhai Anhui Suzhou Hubei Shiyan Yunnan Puer
Liaoning Shenyang Anhui Bozhou Hubei Yichang Yunnan Lincang
Liaoning Anshan Anhui Xuancheng Hubei Xiangyang Yunnan Honghe
Liaoning Jinzhou Shandong Jinan Hubei Xiaogan Yunnan Xishuangbanna
Liaoning Yingkou Shandong Zibo Hunan Zhuzhou Yunnan Dali
Liaoning Tieling Shandong Zaozhuang Hunan Shaoyang Shaanxi Baoji
Jilin Yanbian Shandong Dongying Hunan Yueyang Gansu Lanzhou
Jiangsu Wuxi Shandong Yantai Hunan Zhangjiajie Gansu Baiyin
Jiangsu Changzhou Shandong Weifang Hunan Yiyang Gansu Qingyang
Zhejiang Hangzhou Shandong Jining Hunan Chenzhou Xinjiang Urumqi
Zhejiang Jiaxing Shandong Taian Hunan Huaihua
Zhejiang Jinhua Shandong Weihai Hunan Xiangxi
Tab.1  City samples in 18 provincial regions
Variable Total LRI expenses (1) Total CHD expenses (2) Total stroke expenses (3)
PM2.5 0.00226*** 0.00237* 0.00374***
−0.00084 −0.00132 −0.00099
Geographical location (north side of the Huai River) Yes Yes Yes
Weather (monthly average relative humidity,  standard deviation of daily temperature within a month) Yes Yes Yes
Monthly average concentrations of O3, SO2, and CO Yes Yes Yes
Square terms of O3, SO2, and CO Yes Yes Yes
Average temperature Yes Yes Yes
Constant 9.399*** 9.841*** 9.678***
−0.307 −0.386 −0.305
Observations 1541 1541 1541
Tab.2  Regression of each 10 µg/m3 increase in PM2.5 on total monthly expenses of LRIs during 2016–2017
Disease Health variable Linear regression without covariates (1) Linear regression model with covariates (2) Linear regression with fixed-effects (3) Fully adjusted model with additional covariates (4)
LRI Ln(HA) 0.0888*** 0.1000*** 0.0125 0.0066
(0.0190) (0.0307) (0.0076) (0.0194)
Ln(TE) −0.0074 0.0041 0.0089** 0.0220**
(0.0133) (0.0185) (0.0034) (0.0083)
Ln(THS) 0.0865*** 0.1140*** 0.0249*** 0.0012
(0.0186) (0.0298) (0.0075) (0.0202)
CHD Ln(HA) 0.0765*** 0.0842*** 0.0156*** 0.0192
(0.0200) (0.0298) (0.0058) (0.0198)
Ln(TE) −0.0081 0.0049 0.0135*** 0.0230*
(0.0130) (0.0191) (0.0043) (0.0132)
Ln(THS) 0.0660*** 0.0957*** 0.0251*** 0.0036
(0.0202) (0.0328) (0.0075) (0.0284)
Stroke Ln(HA) 0.0480** 0.0564* 0.0083 0.0224
(0.0183) (0.0284) (0.0056) (0.0168)
Ln(TE) −0.0028 0.0111 0.0130*** 0.0367***
(0.0094) (0.0150) (0.0046) (0.0099)
Ln(THS) 0.0496** 0.0798** 0.0164** 0.0222
(0.0197) (0.0322) (0.0074) (0.0202)
Tab.3  Robustness check of per 10 µg/m3 increase in ambient PM2.5 on health care and health expenditures
Disease Health variable North (1) South (2)
LRI Ln(HA) −0.0110 0.0082
(0.0136) (0.0080)
Ln(TE) 0.0103* 0.0073
(0.0054) (0.0048)
Ln(THS) 0.0076 0.0126*
(0.0255) (0.0086)
CHD Ln(HA) 0.0093 0.0068
(0.0150) (0.0086)
Ln(TE) 0.0142 0.0135**
(0.0084) (0.0056)
Ln(THS) 0.0211 0.0105
(0.0255) (0.0086)
Stroke Ln(HA) 0.0083 0.0069
(0.0120) (0.0075)
Ln(TE) 0.0074 0.0230***
(0.0068) (0.0050)
Ln(THS) 0.0147 0.0153*
(0.0198) (0.0089)
Tab.4  Regression of per 10 µg/m3 increase in ambient PM2.5 on hospitalisation expenditures in different regions
Disease Health variable Males (1) Females (2)
LRI Ln(HA) 0.0227* 0.0124
(0.0123) (0.0117)
Ln(TE) 0.0088** 0.0112***
(0.0039) (0.0041)
Ln(THS) 0.0308*** 0.0200*
(0.0119) (0.0123)
CHD Ln(HA) 0.0369*** 0.0246**
(0.0122) (0.0112)
Ln(TE) 0.0120** 0.0147***
(0.0046) (0.0055)
Ln(THS) 0.0449*** 0.0340***
(0.0119) (0.0123)
Stroke Ln(HA) 0.0363*** 0.0250**
(0.0117) (0.0107)
Ln(TE) 0.0132*** 0.0169***
(0.0044) (0.0059)
Ln(THS) 0.0432*** 0.0300**
(0.0110) (0.0120)
Tab.5  Regression of per 10 µg/m3 increase in ambient PM2.5 on health care by gender
Disease Health variable Age 15–64 years (1) Age ≥ 65 years (2)
LRI Ln(HA) 0.0185 0.0185*
(0.0132) (0.0108)
Ln(TE) 0.0103** 0.0097**
(0.0044) (0.0037)
Ln(THS) 0.0307** 0.0215*
(0.0114) (0.0118)
CHD Ln(HA) 0.0305*** 0.0319***
(0.0113) (0.0116)
Ln(TE) 0.0129** 0.0150***
(0.0050) (0.0047)
Ln(THS) 0.0380*** 0.0410***
(0.0114) (0.0118)
Stroke Ln(HA) 0.0303*** 0.0301***
(0.0109) (0.0108)
Ln(TE) 0.0138** 0.0128***
(0.0053) (0.0045)
Ln(THS) 0.0387*** 0.0315***
(0.0104) (0.0116)
Tab.6  Regression of per 10 µg/m3 increase in ambient PM2.5 on health care by age
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