ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
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"THE IMPACT OF PM2.5 POLLUTION ON LIFE EXPECTANCY IN
CHINA: EXPLORING THE ROLES OF URBANIZATION AND
HEALTHCARE SPENDING"
Nigina Shamsiddinova
WIUT
niginashamsiddinova19@gmail.com
Sherzod Abidov WIUT
sherzodabidov@gmail.com
Madinabonu Shamsiddinova
SamMU
madinabonu.shamsiddinova@gmail.com
Abstract
This study investigates the impact of fine particulate matter (PM2.5) on life
expectancy across 31 Chinese provinces from 2018 to 2022, using urbanization and
healthcare expenditure as control variables. Employing panel data regression with
ordinary least squares (OLS), the analysis reveals that higher PM2.5 levels are
significantly associated with lower life expectancy. Specifically, a 1 µg/m³ increase in
PM2.5 concentration leads to a 0.0342-year decrease in life expectancy. Urbanization
shows a positive and significant effect, with urban provinces experiencing an average
of 3.52 additional years in life expectancy compared to rural areas. In contrast,
healthcare expenditure has a statistically insignificant effect in the short run. The model
explains 39.5% of the variation in life expectancy, underscoring the critical role of air
quality and urban development in shaping public health outcomes.
Literature Review
Impact of PM2.5 Pollution on Life Expectancy in China
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PM2.5 are tiny particles with a diameter of less than 2.5 micrometers, allowing
them to pass through the respiratory system, reach the lungs, and enter the bloodstream
(WHO, 2024). According to Yang et al. (2020), PM2.5 has long been associated with
negative health impacts, contributing considerably to lower life expectancy in many
Chinese cities. Several studies have found that air pollution is linked to a wide range
of diseases, including cardiovascular and respiratory disorders, as well as higher
mortality (Hu et al., 2021). Air pollution can damage neurocognitive functions, causing
people to suffer from depression more easily, and decreasing life satisfaction levels
(Cao et al., 2017). Furthermore, Hu et al. (2021) state that a 10 mg/m3 increase in
PM2.5 resulted in a 0.3-year drop in adult life expectancy in China, demonstrating that
human life expectancy is strongly related to air pollution. Moreover, Cao et al. (2017)
state that air pollution has become an essential factor limiting China's economic
progress, resulting in a range of social issues.
Urbanization and Healthcare
Urbanization has a considerable impact on both PM2.5 levels and life expectancy.
A huge quantity of carbon dioxide emissions increases the greenhouse effect, and air
pollution such as PM2.5 grows more significant with the rise of urbanization, causing
harm to human health. (Shao et al., 2022). According to Cao et al. (2017), the eastern
part of China is the most developed and inhabited. It results in higher concentrations
of PM2.5, which leads to higher mortality of respiratory disease, whereas the western
part of China has lower concentrations of PM2.5 and lower mortality of respiratory
disease. Urbanization causes increased levels of PM2.5 pollution, which leads to poorer
health and shorter life expectancy (Diao et al., 2020). However, Miao and Wu (2016)
explain that urban populations may benefit from greater living conditions and health
services; thus, higher degrees of urbanization can minimize health risks. Shao et al.
(2022) note that some research indicated that, while urbanization in China causes
problems to residents' health, those who move to urban regions tend to have better
health conditions than those who stay in rural areas due to the availability of health
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services. Chen et al. (2019) discovered that, as compared to urban children, rural
children reported more anxiety and depression symptoms and poorer self-reported
mental health due to lower education levels and insufficient access to medical services
in rural areas. Nevertheless, according to Shao et al. (2022), most research concluded
that urbanization's negative consequences on people's health outweigh its positive
effects, resulting in a rise in medical and healthcare costs. According to Liu and Zhong
(2022), increased health investment in China might increase life expectancy in the long
run. Furthermore, Liu and Zhong (2022) claim that throughout the last ten years,
government investment in health per capita rose quickly, with an average yearly growth
of 22.9 percent, resulting in improved life expectancy during the same period.
However, Li and Zhang (2024) observe that, despite the importance of healthcare
expenditure, the quality and availability of medical treatments have an essential role in
increasing life expectancy.
Methodology and Data
This study investigates the relationship between PM2.5 levels and life expectancy
in China, using urbanization and healthcare spending as control variables. The research
is based on panel data from 31 Chinese provinces from 2018 to 2022. The data was
acquired from the National Bureau of Statistics of China to ensure accuracy and
reliability. The data is formatted as panel data, with observations made for each
province over a five-year period, for a total of 155. This enables the analysis to account
for both cross-sectional variations among provinces and temporal changes within each
province.
Table 1. Definition of variables
Variable Name
Definition
Life_Expectancy
Average years of life (years), dependent
variable
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
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PM2.5
Fine particulate matter levels (µg/m³),
independent variable
Urbanization
Urban vs. rural (1 = urban, 0 = rural),
control variable 1
Healthcare_Expenditure
Spending on healthcare per capita (local
currency yuan CNY), control variable 2
Table 2. Descriptive statistics
Variable Name
O
bs
Mean
Std.
dev
Mi
n
Ma
x
Life_Expectancy
1
55
77.806
26
2.2638
74
71.
5
82.
95
PM2.5
1
55
37.377
55
12.635
29
7.1
74.
1
Urbanization
1
55
0.7096
774
0.4553
826
0
1
Healthcare_Expe
nditure
1
55
1519.6
81
718.42
81
117
.36
528
5.9
In the case of life expectancy, we can observe a small standard deviation of 2.26,
indicating that life expectancy does not vary significantly among regions or time
periods in the dataset. In addition, the range between the lowest and maximum shows
an 11-year difference between the shortest and longest life expectancies, which could
be influenced by regional variances in healthcare, pollution, or urbanization. China's
average PM2.5 level is 37.38 µg/m³, substantially greater than the recommended range
of 5 µg/m³ set by the World Health Organization. Furthermore, the high standard
deviation of 12.64 shows significant variance in air quality among the regions or time
periods analyzed. The urbanization has a mean of 0.71, which suggests that 71% of
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provinces can be categorized as urban. Because of the high standard deviation of
718.43 yuan, we may conclude that there is considerable variance in healthcare
spending between regions and that certain areas have higher expenditure on healthcare
than others. This is also demonstrated by the fact that the minimum healthcare spending
is 117.36 yuan and the maximum is 5285.9 yuan.
Econometric model
The study uses the OLS method to determine the relationship between life
expectancy and PM2.5 as independent variable, and urbanization and healthcare
spending as control variables.
The econometric model would be:
𝐿𝑖𝑓𝑒 𝑒𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦
𝑖,𝑡
= 𝛽
0
+ 𝛽
1
𝑃𝑀2.5
𝑖,𝑡
+ 𝛽
2
𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛
𝑖,𝑡
+ 𝛽
3
𝐻𝑒𝑎𝑙𝑡ℎ𝑐𝑎𝑟𝑒𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒
𝑖,𝑡
i
: Province index
t
: Year index
Life Expectancy is a dependent variable that shows life expectancy at region
i
at
time
t
. PM2.5 is an independent variable thar demonstrates PM2.5 levels in region
i
at
time
t
. Urbanization is a dummy control variable that indicates whether the region
i
is
urban (1) or rural (0). Healthcare Expenditure is continuous control variable that shows
healthcare expenditure per capita in region i at time t.
Results
In Model 2, the PM2.5 coefficient is -0.0342, indicating that a one-unit increase
in PM2.5 concentration leads to 0.0342 years fall in life expectancy, while other
variables remain constant. This is entirely consistent with the research conducted by
Hu et al. (2021), who found that a 10 mg/m3 rise in PM2.5 resulted in a 0.3-year decline
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
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in adult life expectancy in China, suggesting that human life expectancy is highly tied
to air pollution. It also confirms the observation made by Yang et al. (2020) that PM2.5
has long been associated with poor health impacts, contributing significantly to
decreased life expectancy in many Chinese cities. The standard error is 0.0141, which
makes this coefficient statistically significant. In the instance of urbanization, which is
a dummy control variable, the urbanization coefficient is 3.5181, indicating that urban
districts have a mean life expectancy that is 3.52 years greater than that of rural regions,
holding PM2.5 concentration and healthcare spending constant. This supports Shao et
al.'s (2022) conclusion that, while urbanization in China has a negative impact on
inhabitants' health, those who relocate to urban areas have better health outcomes than
those who remain in rural areas due to the availability of health services. It further
endorses Miao and Wu's (2016) claim that increasing levels of urbanization can reduce
health hazards. The standard error is 0.3685, indicating a very strong effect. In the
example of healthcare expenditure, which is a continuous control variable, the
healthcare expenditure coefficient is 0.0000629, implying that a one-unit increase in
healthcare expenditure per capita is related to a very small increase of 0.0000629 years
in life expectancy. In addition, the standard error is 0.000265, which is much higher
than the coefficient and contributes to an insignificant result. The insignificance of
healthcare expenditure as a control variable can be attributed to the fact that the model
captured the short-term relationship between healthcare expenditure and life
expectancy from 2018 to 2022, whereas Liu and Zhong's (2022) research found that
increased health investment in China may increase life expectancy in the long-term.
Another explanation could be that, according to Li and Zhang (2024), additional major
factors that impact life expectancy include the quality and availability of medical
services. In Model 2 the R squared value is 0.3946, demonstrating that the model
explains about 39.46% of the variation in life expectancy. Compared to Model 1, which
had a R squared of 0.0255, the addition of control variables in Model 2 significantly
improves the model's ability to explain the variance in life expectancy. In Model 1,
PM2.5 had a positive coefficient of 0.0286, while in Model 2, it had a negative value
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
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of -0.0342 after adding control variables urbanization and healthcare expenditure.
Model 1 lacked an essential control variable, urbanization, which is associated with
higher pollution and life expectancy, resulting in a bias in the coefficient of PM2.5.
After including urbanization as a control variable in Model 2, an actual negative
connection between PM2.5 and life expectancy emerges. Additionally, the F-statistic
in Model 1 has a value of 4.00, indicating low overall significance and demonstrating
that with only one independent variable, the model barely estimates life expectancy.
At the same time, the F-statistic in Model 2 is 32.81, revealing that the model is
significant and that adding urbanization and even healthcare spending as control
variables improves the model's ability to explain life expectancy.
Table 3. The estimated models
Variables
Model 1
Model 2
PM2.5
0.0285929
-0.0341549
(0.0142995)
(0.0141014)
Urbanization
3.518136
(0.3684704)
Healthcare_Expenditure
0.0000629
(0.0002265)
constant
76.73753
76.4905
(0.5640049)
(0.7218794)
R
squared
0.0255
0.3946
F-statistic
4.00
32.81
N
155
155
Limitations
Influence of External Shocks
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The COVID-19 pandemic, which caused the Chinese government to irrationally
increase its healthcare expenditures, falls within the analysis period of 2018 to 2022.
According to the National Bureau of Statistics of China, the pandemic caused a
significant increase in healthcare expenditure in most regions, which may not convert
into significant increases in life expectancy. Furthermore, pandemic-related mortality
may disproportionately affect provinces, distorting life expectancy estimations.
Overall, the influence of Covid-19 could impact the relationships in the model,
especially for healthcare spending.
Short-term nature of the data
The study covers only five years, from 2018 to 2022, and does not completely
represent the long-term effects of healthcare spending on life expectancy in China. Liu
and Zhong's (2022) research indicated that increasing health spending in China may
increase life expectancy in the long term. The short-term nature of the study may
explain why the healthcare expenditure control variable is insignificant in the model.
Missing relevant variables
The model may leave out crucial factors that have a substantial impact on life
expectancy, such as education levels, wealth disparity, or the quality and accessibility
of healthcare services. According to Li and Zhang (2024), other key factors that
influence life expectancy include the quality and availability of medical services, which
are not directly reflected in the healthcare spending control variable. The exclusion of
these elements could result in inaccurate estimates. Moreover, urbanized provinces
may have different healthcare and environmental conditions than rural areas, as
evidenced by research conducted by Shao et al. (2022), who explains that those who
migrate to urban areas tend to have better health conditions than those who remain in
rural areas due to the availability of health services. This may have an effect on the
coefficients, especially for urbanization.
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Conclusions
This study provides empirical evidence that higher PM2.5 concentrations are
significantly associated with lower life expectancy across Chinese provinces, while
urbanization positively influences health outcomes. However, healthcare expenditure
shows no significant short-term effect, likely due to the limited time frame and the
confounding impact of the COVID-19 pandemic, which inflated spending without
immediate improvements in life expectancy. The study’s five-year scope restricts its
ability to capture long-term dynamics, particularly in the case of healthcare
investments. Additionally, the exclusion of relevant socio-economic variables such as
education, income inequality, and healthcare quality may lead to omitted variable bias.
Despite these limitations, the findings underscore the urgent need for environmental
and public health policy coordination to enhance life expectancy in China. Future
research should adopt a longer time horizon and incorporate broader determinants of
health to obtain more comprehensive insights.
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