Environment International 173 (2023) 107831
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Environment International
journal homepage: www.elsevier.com/locate/envint
Full length article
Improved air quality from China’s clean air actions alleviates health
expenditure inequality
Zhixiong Weng a,1, Dan Tong b,1, Shaowei Wu c, Yang Xie d, e,*
a Institute of Circular Economy, Beijing University of Technology, Beijing 100124, People’s Republic of China
b Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua
University, Beijing, People’s Republic of China
c Department of Occupational and Environmental Health, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi,
People’s Republic of China
d School of Economics and Management, Beihang University, Beijing 100191, People’s Republic of China
e Laboratory for Low-carbon Intelligent Governance, Beihang University, Beijing 100191, People’s Republic of China
ARTICLE INFO
ABSTRACT
Handling Editor: Adrian Covaci
Clean air actions aimed at improving air quality in China have brought about significant health benefits, thereby
generating substantial savings in air-pollution-related healthcare spending. Yet, uneven regional air quality
improvements and economic developments may alter existing inequality in health expenditures in the context of
scarce healthcare resources. Here, we developed an econometric model that resolves individual characteristics at
the city level to examine the disparity of public health expenditures in air quality improvements across regions
differing in economic development and healthcare coverages and projected a range of future health expenditure
savings under different air quality targets. We find that of the estimation on four air-pollution-related diseases
(COPD, LRI, IHD, and stroke) in 98 cities over the year 2015–2017, a decline of 8.26 % in average hospitalization
days and 10.21 % in hospitalization expenses was achieved, leading to a reduction of 8.09 % in total health
expenditures as the implementation of clean air actions. Improved air quality has declined health expenditure
inequality in low-middle cities and cities with imbalanced healthcare coverage. For example, the total expenses for
the four diseases declined significantly in the low ( 11.31 %) and medium ( 7.34 %) per capita GDP groups, as well
as a remarkable decline in the fewer medical resources. Health savings in some future scenarios are significant,
showing substantial health expenditure savings under different air quality targets, but the savings will be greatly
offset by an aging society. For example, In the Low-Level Improvement Pathway of air quality targets with aging
(LLIPA scenario), health expenditure savings will be about 3537, 464, and 311 million CNY in the eastern, central,
and western regions in 2035, respectively. Our findings thus highlight the importance of strengthening air pollution
control policies and considering the equality of alleviating regional public health costs.
Keywords:
Air pollution
Health expenditure
Health impact
Regional inequality
Aging society
1. Introduction
al., 2022; Liao et al., 2021; Maji et al., 2018; Nie et al., 2021; Pandey et al.,
are often faced with heavy air pollution and huge improvement burdens
(Organization, 2018), which may further exacerbate the fair allocations of
scarce medical resources (i.e., “inequality”). Such a severe inequality,
which also exists within the country, directly affects individual health
benefits and reduces the affordability of health costs for poor pop-ulations
or less economically developed regions(Gheorghe et al., 2018; Niessen et
al., 2018; Yang and Liu, 2018).
2021; Xie et al., 2019; Zhang et al., 2018). More strikingly, low and middle-
On the positive side, China has experienced a continuous improve-
Health problems associated with exposure to severe air pollution have
become global and regional notable issues, whose costs increas-ingly
impose a heavy economic burden on individuals, families, com-munities,
and countries (Chanel et al., 2016; Chen and Chen, 2021; Haakenstad et
income countries with scarce medical sources
ment in air quality since the implementation of clean air actions in 2013
* Corresponding author at: School of Economics and Management, Beihang University, Beijing 100191, People’s Republic of China.
E-mail address: xieyangdaisy@buaa.edu.cn (Y. Xie).
1 These authors contributed equally to this work.
https://doi.org/10.1016/j.envint.2023.107831
Received 6 December 2022; Received in revised form 19 January 2023; Accepted 14 February 2023
Available online 15 February 2023
0160-4120/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).
Z. Weng et al.
Environment International 173 (2023) 107831
(Jiang et al., 2015; Li et al., 2019; Liu et al., 2022a; Liu et al., 2021;
Zhang et al., 2019; Zheng et al., 2017). Clean air actions are a series
of environmental policies to improve air quality. To tackle air pollution,
the Chinese government implemented the air pollution prevention and
control action plan in 2013. Ambitious targets were set to promote the
action plan that by 2017, the concentration of particulate matter in
cities at the prefecture level and above should be reduced by at least
10 percent from the 2012 level. Clean air actions are considered the
most stringent air pollution control policies and bring significant air
quality improvement. For example, PM2.5 in Beijing declined from 89.5
Supplementary Information).
Finally, we project the future health expenditure savings under a
range of air quality targets that span three levels of air quality targets
for the period 2020–2035. To distinguish the influences under different
scenarios, we define scenarios without the consideration of future
population aging (scenarios of HLIP, MLIP, and LLIP), as well as the
corresponding air quality targets considering the aging trend. Our pre­
diction shows detailed future impacts with selective air quality
improvement and social population change scenarios, which provide
valuable policy implications for policymakers.
The remainder of the paper is organized as follows: Section 2 de­
μg/m3 in 2013 to 58 μg/m3 in 2017.
Significant air quality improvement can undoubtedly bring consid­
erable health benefits and alleviate public hospitalization expenditures
(Liang et al., 2019; Liao et al., 2021; Tainio et al., 2021; Yang and Zhang,
2018). These substantial savings in healthcare spending from China’s
improved air quality have been extensively investigated (Barber et al.,
2017; Huang et al., 2018; Wang et al., 2020). However, regional dis­
parities in the magnitudes of air quality improvement, economic
development, and healthcare resources may affect this inequality of
reduced health expenditures. Because these regional disparities are
specifically significant in China. The PM2.5 in the Beijing-Tianjin-Hebei
3
scribes the materials and methods. Section 3 provides the results.
Sec­ tion 4 discusses and provides policy implications.
2. Materials and methods
2.1. Data sources and summary statistics
Daily air pollutants data, including the fine particulate matter with
diameters of 2.5 or 10 µm and smaller (PM2.5 and PM10, respectively),
and a measured air quality index (AQI) was obtained from China Na­
tional Urban Air Quality Real-time Publishing Platform. The AQI is a
comprehensive indicator using measured concentrations and estab­
lished breakpoints of air pollutants (PM 2.5, PM10, SO2, NO2, CO, O 3),
with the highest value applied. The most dominant value is reported in
locations where multiple pollutants are measured. In general, the
higher the AQI, the worse the air quality represents. Here, we also
choose PM2.5 and PM10 as air quality indicators mainly because they
have been regarded as the most important measurement of air quality
than other air pollutants in China. In the past decades, the Chinese
government has made a great effort to curb SO2 and NO2 in coal-fired
power plants. In the recent ten years, particulate matter has been the
primary source of air pollution in many Chinese cities. Furthermore, we
used a series of social and economic control variables to identify other
confounders that may affect health expenditure. The data of these
control variables were derived from the China National Bureau of
Statistics and the City Sta­ tistical Yearbook.
3
region was 64 μg/m in 2017, higher than the national average (47 μg/ m ).
Concerning economic development, the eastern region is signifi­ cantly
higher than the central and western regions, accounting for 52.56
% of the national GDP in 2017. Furthermore, the economic development
level also directly affects the allocation of medical resources, leading to the
shortage of medical resources in the central and western regions. Despite
the potential health expenditure inequality brought by the dif­ ferences
mentioned above, there has been no comprehensive accounting of the
sensitivity and inequality of public health expenditures to dif­ ferences in
city-level air quality improvements.
Therefore, the research questions are: What directly influences
health expenditure under the unprecedented strict clean air actions?
Are there any heterogeneous effects of the clean air actions
concerning the regional unfair medical resources? What are the
potential impacts on regional health expenditure variation as an aging
society has come? To resolve these questions, we employ a fixedeffect empirical model to investigate the effect of China’s clean air
actions on disease-specific health expenditures, characterize the
declined inequality of health ex­ penditures among cities with different
economic development levels and healthcare coverage over the year
2015–2017, and highlight the possible health expenditure savings for
the period 2020–2035 for meeting the different air quality improvement
targets. The Methods provide our data sources, models, scenario
design, and analytic methods.
In summary, our academic contributions can be summarized as
three aspects:
First, we construct a measurement to characterize the level of air
quality improvements from the clean air actions. Based on a unique
daily Urban Employee Basic Medical Insurance Database covering 98
Chinese cities, we assess the impact of clean air actions on four airpollution-related diseases (see yearly urban medical and pension
insurance and per capita wages in Figures S1-S3 in the
Supplementary Information). The disease data include Chronic
Obstructive Pulmonary Disease (COPD), Ischemic Heart Disease
(IHD), Lower Respiratory Infection (LRI), and stroke, which
comprehensively describe residential health expenditures.
Second, we identify the clean air action’s impacts on lessening
regional inequality of health expenditures. The impact measurement is
achieved from two aspects: one that compares the declined health ex­
penditures caused by the clean air actions among low, middle, and
high-income cities; the other investigates the policy effect among cities
with imbalanced healthcare coverage. In addition to the AQI-related
mea­ surement of clean air actions, we also provide robustness tests
by defining indicators correlated with the fine particulate matter with an
aerodynamic diameter of 2.5/10 μm or less (Tables S1-S10 in the
Four kinds of diseases, namely chronic obstructive pulmonary dis­ ease
(COPD), ischemic heart disease (IHD), lower respiratory infection (LRI),
and stroke are used in this study. In general, they are common diseases
that rank one of the most infectious diseases in China. Based on the strong
representation of these disease data, we use them to describe the health
status of urban residents. Three disease-related health ex­ penditures,
including the total expenses, hospitalization days, and hos­ pitalization
expenses of 98 cities, were obtained from the urban employee-based basic
medical insurance scheme database (UEBMI). For each disease, the
hospitalization expenses and hospitalization stays for a given day during
the study were defined as the average expenses and days per person
multiplied by the number of patients admitted to the hospital because of the
disease on that day (Xie et al., 2021).
The air pollutant and disease expenditure data mentioned above
are used monthly in our study. In general, the daily data can better
capture the changing characteristics, especially the daily-level air
pollutants. However, the influence of changes in daily pollutant on
health expen­ ditures are not immediate but have a lag effect.
Therefore, the main regressions are constructed with monthly data
from three available years (2015–2017), which covers most of the
clean air actions period. Based on three years of disease-related data,
our monthly analysis can effectively investigate the main influences of
clean air actions. Overall, the health expenditure data is advantaged in
its broad coverage in urban regions and full inclusion of four major
diseases, which provides a comprehensive characteristic of healthrelated expenditure characteristics.
Table 1 shows the summary statistics of the total expenses for the
four diseases. The total expenses for the 98 cities increased from
2,698,438 (10,000 CNY) in 2015 to 5,799,672 (10,000 CNY) in 2017.
2
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Environment International 173 (2023) 107831
GDP (psecind), the local financial expenditure in the general budget of
financial revenue (pfininco), per capita wage (perslry), number of persons
employed in urban units (urb eply), number of registered unemployment
persons in urban areas (urb uneply), number of hospitals (hosp nums),
number of hospital beds (hosp beds), number of doctors (doct nums),
average number of employees (employee), total wages of employees
(eplyee totsalry), average wages of employees (eplyee avgsalry), number
of urban basic pension insurance participants (urb oldinsu), number of
urban employees covered by basic medical insurance (urb medcinsu),
number of participants in unemployment insurance (uneplyinsu), average
relative humidity (avg humid), average temperature (avg temp), total hours
of sunshine (hour sunshine).
Table 1
Summary of total expenses for different diseases.
Year
Total expenses for diseases (10,000 CNY)
COPD
IHD
LRI
Sum
STROKE
2015
2016
2017
Year
2015
2016
2017
Year
2015
2016
298,070
968,065
593,406
838,897
453,956
1,379,590
868,332
1,187,526
702,126
1,862,088
1,374,891
1,860,567
Hospitalization days for diseases (10,000 days)
226
718
649
741
249
842
844
906
349
1,018
959
1,088
Hospitalization expenses for diseases (10,000 CNY)
25,088
119,731
58,954
75,872
34,963
174,179
85,644
108,043
2,698,438
3,889,403
5,799,672
Sum
2,334
2,841
3,414
Sum
279,645
402,830
2017
43,832
467,440
192,562
109,323
121,724
The city-specific fixed effect λc controls for all observed and unobserved time-invariant determinants of health expenditures across
cities, such as geographical location and inherent medical resource
endow-ments. While the time-fixed effect μmy is used to control for city
common-trends in health expenditures during different periods to capture factors such as medical-related stimulus policies, and εit
represents a stochastic error term. To reduce the effects of
heteroscedasticity, we adapt the logarithmic form of all variables
except for those in percentage.
Furthermore, we examine the impact of clean air actions on health
expenditure inequality, primarily regarding regional differences in
economic development and healthcare resources. Firstly, we apply a
dummy variable to portray differences in the level of economic development across regions. Based on the level of per capita GDP, we
classify the 98 cities into three levels of economic development: high,
medium, and low. Secondly, we regress a proxy variable for clean air
actions on the dummy variable, which measures the level of economic
develop-ment. Therefore, the regression coefficients obtained can thus
represent the impact of clean air actions on health expenditures for
different economic development groups.
Similarly, we measure healthcare resources using two indicators:
the number of hospitals and per capita beds. The dummy variables
were constructed to classify cities into three and four classes
according to the number of hospitals and per capita beds, respectively.
Further, we interact with each of the two healthcare resource dummy
variables with the clean air actions proxy variable and applied the
regression co-efficients on interaction terms to explain the impacts of
clean air actions on cities with different medical resources. The main
model specifica-tions were set as follows:
Among different diseases, CHD disease bears a large total expenses
burden. In 2017, the total expenses for CHD were 1,862,088 (10,000 CNY),
which is higher than that of other diseases. Similarly, hospitali-zation days
and expenses show an increasing trend over the years. The hospitalization
days increased from 2,334 (10,000 days) in 2015 to 3,414 (10,000 days) in
2017, while the hospitalization expenses grew from 279,645 (10,000 CNY)
to 467,440 (10,000 CNY). Concerning different diseases, the hospitalization
days and expenses are evident for each disease. For example, the
hospitalization days for COPD was 349 (10,000 days) in 2017, while 1,088
(10,000 days) for Stroke. In contrast, the hospitalization expenses for IHD
and LRI were 192,562 (10,000 CNY) and 109,323 (10,000 CNY) in 2017,
respectively.
2.2. Methods
We use a two-way fixed effect model to estimate the causal relationship between clean air actions and health expenditures. The fixed
effect model has been widely used in other studies. Such a model contains city and time-fixed effects and can be applied effectively to
capture the causal relationship (Weng et al., 2022a; Weng et al.,
2022b). In general, this model adjusts for unobserved unit-specific and
time-specific confounders simultaneously. The model specifications
were set as follows:
Ycdmy = α0 + βCleanActcmy + γXcmy + λc + μmy + εit
(1)
Where Y cdmy represents the d th disease-related expenditures in city c
in month m of year y. Based on detailed hospital admissions data, we
choose hospitalization days, hospitalization expenses, and total expenditures to characterize the health costs of four diseases (COPD, IHD, LRI,
and stroke). To better reflect the impact of overall spending, we also sum
the data for the four diseases to examine the overall impact.
CleanAct is the variable measuring the clean air actions, which is
described as three indicators, including Daqi cln rat, Dpm25 cln rat, and
Dpm10 cln rat. These indicators were calculated based on the changing
yearly rate of the number of days meeting the requirements of different air
quality standards (Air quality index (AQI), PM2.5, and PM10). Based on
China’s grade II air quality standards, we determine the number of days a
city meets the air quality standards based on the AQI, PM2.5, and PM10
concentrations, respectively. The advantage of the inter-year changing rate
indicators is that they effectively reduce the effects of model endogeneity to
better identify the impact of clean air actions. In the main regression, we
present the rate of change in the number of AQI attainment days to
characterize the clean air actions. At the same time,
Ycdmy = α0 + ηDummycmy *CleanActcmy + γXcmy + λc + μmy + εit
(2)
Where Dummy is a dummy variable used to characterize different
levels of economic development, as well as different numbers of hospitals and per capita hospitalization beds. Here, we interact Dummy with
the policy indicator variable CleanAct to obtain the regression coefficient η, which captures the different impacts of clean air actions on
health expenditures when comparing cities differ in economic development levels and healthcare coverage. Other variables and
coefficients in equation (2) are the same as in equation (1).
2.3. Scenarios design
Table 2 shows the summary of scenarios designed in this study. Based
on the results of the clean air actions’ impact on health expenditures, three
levels of air quality targets during 2020–2035 in the 98 cities have been
considered: high, medium, and low. In the High-Level Improve-ment
Pathway of air quality improvement (HLIP), all 98 cities will significantly
improve their air quality in a high rate (i.e., the average growth rate for all
98 cities from 2020 to 2035 is 24 %), aiming to meet the objectives of the
Beautiful China programme, with a steady increase in the number of days
meeting the air quality standards of Grade II (i.e., AQI less than 50) or
above, and ensure daily compliance throughout the year by 2035. In the
Medium-Level Improvement Pathway scenario
we also report the results for PM2.5 and PM10 attainment days in the
Supplementary Information. Therefore, the coefficient β estimates the
causal effect of clean air actions on health expenditures.
X is a variety of controls that may affect the health expenditures,
including the per capita GDP (perGDP), urbanization rate (urbrate), education expenditure in the general budgets of local governments (peduexp),
the proportion of the added value of the secondary industry in
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Environment International 173 (2023) 107831
Table 2
Summary of scenarios designed in this study.
Aging
Scenario
Description
Without
aging
HLIP
Air quality will be improved at a high rate (i.e., the
average growth rate for all 98 cities from 2020 to 2035 is
24 %), and aim to meet the objectives of the Beautiful
China programme, with a steady increase in the number
of days meeting the air quality standards of Grade II (i.e.,
AQI less than 50) or above, and ensure daily compliance
throughout the year by 2035.
Air quality will be improved at a medium rate (i.e., the
average growth rate for all 98 cities from 2020 to 2035 is
13 %), and aim to gradually increase the number of days
to meet the air quality standards of Grade II (i.e., AQI
less than 50) or above, and ensure more than 60 % of
cities have 95 % or more of their days meeting the
standards throughout the year by 2035.
Air quality is at the same level as in 2017 as in the
reference scenario.
Air quality remains the same as in the HLIP scenario, but
the aging populations are steadily increasing (i.e., the
average growth rate for all 98 cities from 2020 to 2035 is
75 %), which will offset the health expenditure savings
by the air quality improvement.
Air quality remains the same as in the MLIP scenario,
and the aging trend remains the same as in the HLPA
scenario.
Air quality remains the same as in the LLIP scenario, and
the aging trend remains the same as in the HLPA
MLIP
LLIP
With aging
HLIPA
MLIPA
LLIPA
scenario.
(MLIP), air quality improves in a medium rate (i.e., the average growth
rate for all 98 cities from 2020 to 2035 is 13 %), aiming to gradually
increase the number of days meeting the air quality standards of
Grade II (i.e., AQI less than 50) or above, and ensure more than 60 %
of cities have 95 % or more of their days meeting the standards
throughout the year by 2035. By contrast, we set a Low-Level
Improvement Pathway scenario (LLIP) as a reference, assuming that
the same air quality attainment status is maintained throughout the
period 2020–2035 as 2017, i.e., the air quality situation remains
unchanged. Based on the scenarios of air quality targets, we further
define the other three sce-narios considering future aging populations,
namely scenarios of HLIPA, MLIPA, and LLIPA, respectively. The
aging population in these 98 cities grows at a specific rate.
Fig. 1. Estimated effects on average days of hospitalization, hospitalization
expenses, and total expenses for four diseases. a, Reduction effect of the clean
air actions on average days of hospitalization for diseases of COPD, IHD, LRI,
and stroke, as well as the aggregated results of the four diseases. b, Reduction
effect of the clean air actions on hospitalization expenses. c, Reduction effect of
the clean air actions on total expenses. The estimated effects on average days
of hospitalization, hospitalization expenses, and total expenses for each disease
were obtained by controlling for the city-fixed effects, year-fixed effects, and
month-fixed effects.
3. Results
3.1. Effects of the clean air actions on health expenditures
Fig. 1 shows the effect of clean air actions on health expenditures
for four diseases. The average hospitalization days and expenses
were reduced by 8.26 % and 10.21 % when a set of unobserved timeinvariant and city-specific fixed effects and a series of economicenvironment controls were included. Consequently, a remarkable
decrease in hospi-talization days and expenses led to a significant
reduction in total ex-penses, which decreased by 8.09 % when
implementing the clean air actions among 98 cities.
disease.
3.2. Health expenditure inequality declining effect in low-middleincome cities
Fig. 2 shows the heterogeneous effect of clean air actions on cities with
different groups of per capita GDP. Health expenditure disparity among
cities with different economic levels declines as clean air actions is
implemented, and the declining health expenditures are considerable for
the less developed cities among 98 cities. For example, the aggre-gated
total expenses for the four diseases in the low and medium per capita GDP
groups declined by 11.31 % and 7.34 %, respectively. But the estimated
coefficients are not statistically significant in the high per capita GDP group.
Further, the most significant decline in average hospitalization days and
hospitalization expenses was observed in the low-income group, with
decreases of 12.88 % and 13.76 %, respectively. The results across
geographical regions are similar, as the central-western region is less
developed than the eastern region. For example, the aggregated total
expenses for the four diseases in the central-western region declined by
13.41 %, but no significant effect in the eastern
Our measurement of identifying the effect of clean air actions on
hospital expenditures compares different diseases. The average number of
hospitalization days declined more significantly for LRI ( 15.39 %) than for
other diseases. By contrast, clean air actions have the least impact on the
average hospitalization days for stroke ( 10.12 %). Similarly, the reduction
effects of clean air actions are considerable in hospitalization expenses for
IHD and LRI. For example, we have noticed a significant reduction of 15.52
% and 15.51 % in hospitalization ex-penses for IHD and LRI, respectively.
As a result, high reductions in hospitalization days and expenses lead to a
substantial decline in total expenses for IHD ( 15 %). These differences are
probably from the sensitivity to air pollutants and health service price
variation of each
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Environment International 173 (2023) 107831
Fig. 2. Heterogeneous effect of the clean air actions on total hospital expenditures across cities with different economic levels. We provide the reduction effects of clean
air actions on total hospital expenditures for each disease (COPD, IHD, LRI, and stroke) and an aggregated estimation of the four diseases. The three top-down
subfigures were divided into city groups of high, low, and medium per capita GDPs. Each regression for a specific disease in the three per capita GDP groups implements the econometric model by controlling for the city fixed effects, year fixed effects, and month fixed effects.
More specifically, the impacts of clean air actions on total expenses for
different diseases are slightly different. IHD has the most significant decline
in total expenses, showing a 16.12 % reduction in the low number of
hospitals group. For the low-level per capita bed groups (i.e., groups of
[1,4) and [4,5)), expenditure declined by 16.16 and 10.93 %, respectively.
By comparison, the total expenses for stroke also decreased in cities with
fewer medical resources. The declining magnitude is relatively small
compared with other diseases. For example, total expense reduction for
stroke in the low number of hospital group de-clines by 10.67 %. These
reduction effects can also be found between COPD and LRI. Our results
indicate that cities with fewer medical re-sources are more sensitive to air
quality change because of health service demand competition wither other
causes. This is because when air pollution continues to worsen, scarce
medical resources cannot support the increasing demand of medical care
demand, leading to significant changes in people’s demand for medical
resources in the short term. Nevertheless, there is no evidence supporting
the health expenditures decline in cities with rich medical resources.
Conversely, the improved air quality may increase health expenditures in
these regions because of better access to healthcare resources and higher
individual incomes.
region (Table S11). These results suggest that clean air actions can
decline health expenditure inequality in low-middle income cities.
The health expenditure inequality declines differently for the four
diseases. For IHD, both the low and high per capita GDP groups benefited
from the clean air actions, with a 15.53 and 9.77 % decrease in the total
expenses, respectively. The total expenses for LRI decreased by 14.45 %
in the low per capita GDP group but not significantly in the medium per
capita GDP group. However, we do not find any significant reductions in the
low per capita GDP group for COPD, possibly because COPD is a chronic
disease and not sensitive to air pollution improvement in the short term. By
contrast, the reduction effect for stroke is only significant in the medium per
capita GDP group, with total expenses decreasing by 6.26 %. Similarly, the
four diseases in the central-western region, where the economic
development is lower than that in the eastern region, all show significant
reductions in total expenses. For example, the total expenses for LRI and
IHD in the central-western re-gion declined significantly by 20.1 % and
19.73 %, respectively, with the implementation of clean air actions (Tables
S12-S15).
3.3. Declined health inequality among cities with imbalanced
healthcare coverage
3.4. Air quality target and future health expenditure savings
Fig. 3 shows the declined health inequality among cities with
imbalanced healthcare coverage. The impact of clean air actions on
reducing health expenditure inequality can effectively alleviate the
innate imbalance of medical resource endowment across regions. We
find that cities with fewer medical resources declined in hospital expenditures significantly under better air quality. More specifically, the
aggregated total expenses for the four diseases decreased by 12.03 %
in the low number of hospital group. However, there is a 6.27 %
increase in the median number of hospital groups but no statistical
change in the high number of hospitals group in the aggregated total
expenses. The results with different measures of medical resources
are similar to the number of hospitals. In the groups with per capita
hospital beds between [1,4) and [4,5), the aggregated total expenses
for the four diseases declined by 11.86 and 8.56 %. Furthermore, the
total expenses in cities with plenty of per capita beds have not reduced
significantly but are likely to increase.
Fig. 4 provides the predicted regional health expenditure savings
considering air quality targets and aging, showing that the health
expenditure savings significantly vary among regions. Total health
expenditure savings in the eastern region are higher than in the central and
western regions. In the Low-Level Improvement Pathway of air quality
targets with aging (LLIPA scenario), health expenditure savings will be
about 3,537, 464, and 311 million CNY in the eastern, central, and western
regions in 2035, respectively. The aging trend has been a crucial factor that
will directly influence health expenditure savings (the orange group vs. the
blue one in Fig. 4). Aging people are more vulnerable to air pollution,
leading to higher health expenses. Therefore, an aging society will
substantially offset the effects of health expenditure savings brought by
clean air actions. Under the High-Level Improvement Pathway of air quality
targets without aging (HLIP scenario), the health expenditure savings for
the eastern, central, and western regions in 2035
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Environment International 173 (2023) 107831
Fig. 3. Heterogeneous effect of the clean air actions on hospital expenditures across cities with different medical resources. We divide cities into groups with different
levels of medical resources. a, the level of medical resources is divided into three groups (low, medium, and high) according to the number of hospitals. b, the level of
medical resources is divided into four groups on the basic of the number of per capita hospital beds, including the intervals of [1,4), [4,5), [5,6), and greater than 6. Each
regression for a specific disease in different medical resource groups implements the econometric model by controlling for the city fixed effects, year fixed effects, and
month fixed effects.
improvement in 2035 will be 3,956 million CNY, much higher than that in
the Medium-Level Improvement Pathway of air quality improvement
(MLIPA) (3,842 million CNY) and Low-Level Improvement Pathway (LLIPA)
(3,537) scenarios. These findings can also be found in the cen-tral and
western regions, showing a sharper increase in health expen-diture savings
after 2025. More specifically, under the HLIPA scenario, the health
expenditure savings in the central region will increase from 554 million CNY
in 2025 to 642 million CNY in 2035. In the western regions, expenditure
savings will increase from 371 million CNY in 2025
are predicted to be 4,264, 742, and 482 million CNY, respectively.
Similarly, the expenditure savings under the Low-Level Improvement
Pathways without considering aging (LLIP scenario) are 3,791, 536,
and 365 million CNY for the eastern, central, and western regions in
2035, respectively, higher than that under the aging scenarios.
Furthermore, the more stringent the air quality target will bring
higher health expenditure savings. In the High-Level Improvement
Pathway of air quality targets aging (HLIPA scenario), for the eastern
region, the health expenditure savings caused by air quality
6
Z. Weng et al.
Environment International 173 (2023) 107831
to 409 million CNY in 2035.
4. Discussion and policy implications
Our econometric analysis of health expenditure impacts from air
pollution control at the city level reveals that China’s large-scale clean
air actions effectively alleviate regional inequality of health expenditures. The declining effects are incredibly considerable for cities with
less economically developed and few healthcare coverages. Further,
we evaluate the potential impacts on health expenditure savings and
target different future air quality improvements, indicating that the
health expenditure benefits from improved air quality will gradually
increase and be considerable by 2035. Under the goal of implementing
the long-term Beautiful China Initiative, health expenditure inequality
between regions will be alleviated, delivering more health benefits to
bridge regional disparities in economics and healthcare resources.
Neverthe-less, the future intensifying aging trend will simultaneously
offset the health expenditure benefits from improved air quality.
Our analysis of health-related benefits inequality implies different
and targeted policy implications compared to recent studies(Almond et
al., 2009; Chen et al., 2013; Ebenstein et al., 2017; Wang et al., 2017).
Previous studies have revealed substantial health benefits after implementing clean air actions in China. For example, some scholars found
that the national PM2.5-attributable mortality decreased from 1.22
million (95 % CI: 1.05, 1.37) in 2013 to 1.10 million (95 % CI: 0.95,
1.25) in 2015 (Zheng et al., 2017). As a result of air quality policies,
some findings indicate that annual deaths attributable to PM2.5
pollution in 2019 decreased by 177 thousand compared with the
deaths in 2000 (Liu et al., 2022a). In addition, some scholars predicted
the health benefits in the scenario that tracks China’s carbon mitigation
target and uses the best available pollution control technologies (Liu et
al., 2022b). Their findings suggest that PM2.5-related deaths in China
will decrease slightly by 2030 to 1.23 million per year.
By evaluating the air quality improvement and individual health
expenditures at the city level, our estimations are important for policyrelevant discussions of regional health expenditure inequality alleviation by showing that such benefits depend on the implementation of
stringent and effective air pollution control policies with incredible
determination and strength. Considering the inadequate and poor supply capability of medical resources in some regions (Dieleman et al.,
2018; Jerrett, 2015), governments should strengthen fair allocations of
scarce medical resources between regions by expanding the
healthcare coverages and investing more in medical infrastructure in
the less developed regions. As aging has become a common trend in
the future, governments should comprehensively consider the
characteristics of the elderly group and provide sufficient subsidies for
improving healthcare to elderly households. Furthermore, our findings
also have positive implications for other countries, especially
developing ones. They should make strong measures to control air
pollution and raise much attention to reduce the potential expenditure
inequality. Since the imbalanced regional healthcare resources are
evident in developing countries, they need to consider reducing the
inequality through policies.
Fig. 4. Predictions for the health expenditure savings in the eastern, central, and
western regions under different strictness levels of the clean air actions and
aging scenarios for 2020–2035. For each region, we provide the results of
predicted health expenditure savings under different levels of air quality
improvement pathways, including the High-Level Improvement Pathway of air
quality improvement (HLIP scenario), Medium-Level Improvement Pathway
(MLIP scenario), and Low-Level Improvement Pathway (LLIP scenario). Based
on air quality improvement pathways, we further consider the other three
scenarios with the aging trend for checking the impact on health expenditures
(HLIPA, MLIPA, and LLIPA scenarios). a, The health expenditure savings in the
eastern region; b, The health expenditure savings in the central region; c, The
health expenditure savings in the western region. Due to limited data, all the
results under different scenarios are based on the 98 cities and can only
represent their predicted outcomes in the future.
However, there are several limitations to our study. First, despite the
representative analysis of the clean air actions on healthcare expendi-tures
for four air-pollution-related diseases (COPD, IHD, LRI, and stroke), our
limited datasets of research sample may still bias some qualifications.
Second, our estimations at the city level are based on detailed individual
expenditures on medical treatment. However, the currently available data
does not support analysis for identifying the individual behavioral choices
(e.g., income levels, occupational char-acteristics, family, and educational
background). Third, the scenarios designed in this study on different
targeted air quality improvements have various projections on future elderly
population growth, which would affect the estimates of healthcare savings.
In our future study, we will focus on two directions of the health7
Z. Weng et al.
Environment International 173 (2023) 107831
related topic. First, we will investigate residential disease-related behavioral
characteristics if data is available. Analyzing these charac­ teristics
promotes further understanding of whether residents vary their
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expenses with the heterogeneous features of clean air actions. Finally, we
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CRediT authorship contribution statement
To what extent can China’s near-term air pollution control policy protect air quality
and human health? A case study of the Pearl River Delta region. Environ. Res.
Lett. 10, 104006.
Li, K., Jacob, D.J., Liao, H., Zhu, J., Shah, V., Shen, L., Bates, K.H., Zhang, Q., Zhai,
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China. Nat. Geosci. 12, 906–910.
Zhixiong Weng: Conceptualization, Methodology, Software, Data
curation, Visualization, Writing – original draft, Writing – review &
editing. Dan Tong: Conceptualization, Methodology, Software, Data
curation, Visualization, Writing – original draft, Writing – review &
editing. Shaowei Wu: Supervision. Yang Xie: Conceptualization,
Methodology, Software, Data curation, Visualization, Writing – original
draft, Writing – review & editing.
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exacerbation of chronic obstructive pulmonary disease in Beijing, 2013–17: an ecological
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reducing future air pollution deaths in China: a modelling study. The Lancet
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Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Maji, K.J., Ye, W.-F., Arora, M., Nagendra, S.S., 2018. PM2. 5-related health and
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The authors do not have permission to share data.
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Acknowledgements
The study was supported by the National Natural Science
Foundation of China (72134006, 71903010) and the Collaborative
Research Fund 2021/22 (Project title: “Turning 2060 Carbon Neutrality
into Reality: a cross-disciplinary study of air pollution and health cobenefits of climate change mitigation of the Guangdong-Hong KongMacau Greater Bay Area (GBA)”, Project No. C7041-21GF) of the
Hong Kong Research Grant Council.
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