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Abstract

Life expectancy in China has been influenced by several environmental, technological, and institutional factors over the past decades. Air pollution, particularly CO2 emissions from rapid industrialization and urbanization, has been a major concern, contributing to respiratory and cardiovascular diseases that reduce population health. Meanwhile, the expansion of information and communication technology (ICT) has improved access to health services and medical information, supporting better health outcomes. Increased health expenditure has enhanced health care infrastructure, service delivery, and preventive programs, further promoting longevity. Strong institutional quality ensures effective policy implementation, transparency, and equitable access to health care, which collectively support higher life expectancy in China. Therefore, this study examines the impact of air pollution, ICT, health expenditure, and institutional quality on life expectancy in China. This study first examined the stationarity properties of the variables using the Augmented Dickey–Fuller and Phillips–Perron unit root tests and then tested for cointegration to identify the existence of long-run relationships. After confirming cointegration, the autoregressive distributed lag approach was used to estimate both short-run adjustments and long-run effects. To ensure the robustness of the long-run results, Fully Modified Ordinary Least Squares was applied as an additional estimation technique to check the stability and consistency of the estimated coefficients. The finding shows that CO2 emissions has negative effect on life expectancy, while ICT, health expenditure and institutional quality have positive effect on life expectancy. To enhance life expectancy in China, the government should reduce CO₂ emissions through stricter environmental regulations and the promotion of renewable energy. Expanding ICT infrastructure and improving digital health literacy can increase access to health care services. Higher health expenditure and investment in preventive programs will strengthen health care quality and disease management. Strengthening institutional quality and ensuring transparent governance will improve the efficiency and equity of health service delivery.

Keywords

environmental degradation ICT health expenditure institutional quality life expectancy

Introduction

Carbon dioxide emissions (CO2E) are considered one of the most significant contributors to environmental degradation (Das & Debanth, 2023; Zaidi & Saidi, 2018). Carbon emissions resulting from the burning of fossil fuels generate significant negative externalities worldwide. Energy consumption, particularly from fossil fuels, along with manufacturing and construction activities, has adversely affected the environment, leading to environmental degradation, poor health outcomes, reduced life expectancy, and harm to aquatic life (Balan, 2016; Matthew et al., 2020; Osabohien et al., 2021). Rising concentrations of CO2E are not only responsible for global warming, sea-level rise, and other climate change effects but are also associated with increased morbidity and mortality resulting from air pollution (Das & Debanth, 2023; Jacobson, 2008). CO2E is a major greenhouse gas driving climate change, leading to extreme weather events that harm health and strain health care systems, ultimately affecting life expectancy. In addition, CO2E-induced climate changes alter disease patterns, increasing the spread of vector-borne illnesses such as malaria and dengue (Shaari et al., 2024). The emission of greenhouse gases and the rise in air pollution associated with economic and industrial growth can directly contribute to the development of various pollution-related diseases. These health consequences elevate disease and death rates, eventually shortening overall life expectancy (Altaee et al., 2025; Awewomom et al., 2024). Air pollution is also a major environmental factor that contributes to mortality across all segments of society (Hill et al., 2019). It poses significant planetary health risks, substantially driving premature deaths and facilitating the spread of pollution-related diseases worldwide (Balakrishnan et al., 2019; Mahalik et al., 2022). PM2.5 refers to particulate matter consisting of particles that are usually less than 2.5 micrometers in diameter, allowing them to penetrate deep into the lungs and enter the bloodstream. PM2.5 particles can enter the lungs through inhalation, where they deposit in the terminal bronchioles and alveoli. From there, these particles can penetrate the bloodstream and be transported to other tissues and organs, potentially causing damage across multiple physiological systems. Prolonged or high-level exposure to PM2.5 has been linked to respiratory, cardiovascular, and systemic health effects, highlighting its significant threat to human health (X. Li & Liu, 2021; Moller et al., 2008).

ICT refers to a wide range of communication tools and software applications, including radio, television, mobile devices, computers, network hardware and software, and satellite technologies. It also includes associated services and applications such as videoconferencing, online platforms, and distance learning systems (Shao et al., 2022). ICT influences life expectancy both directly and indirectly by expanding access to health information, nutrition guidance, and epidemic updates, thereby improving public awareness and preventive behaviors. Access to online health resources also boosts health literacy, strengthens patient–doctor communication, and facilitates earlier detection and treatment of illnesses, helping individuals make better decisions about their quality of life (Coelho et al., 2015; Laing et al., 2004). Moreover, ICT enhances the efficiency of clinical time use, improving service delivery and patient outcomes (Bayar et al., 2024; Gerber & Eiser, 2001). The internet enables improved delivery of health care services, especially for pregnant and nursing mothers, and strengthens communication between patients and health care providers and systems. Information and communication technology supports health literacy by giving people access to vast amounts of information. Through the internet, individuals can actively search for health-related knowledge rather than passively receiving messages from pro-health advertisements (Bankole et al., 2013; Majeed & Khan, 2019). ICT also plays a major role in improving health systems worldwide through multiple channels. It enhances access to health care for geographically isolated communities, supports health care workers, and improves data sharing across institutions. ICT also provides data visualization tools that link population and environmental information to disease outbreaks, while strengthening data storage and management systems, ultimately contributing to better health care delivery and improved health outcomes (Majeed & Khan, 2019).

A nation’s health expenditure is a crucial measure of its investment in health and is regarded as an important factor, alongside practices like exercise and proper diet, in promoting better health outcomes (Grossman, 2000; Kiross et al., 2020). Health spending is commonly grouped into three broad categories: public, private, and out-of-pocket expenditures. Public and private expenditures represent spending by governments and private organizations, respectively, whereas out-of-pocket expenditures are paid directly by individuals or households. These expenditures finance a wide range of health-related activities, including curative and rehabilitative services, long-term and nursing care, the provision of medical facilities, training and education of health professionals, health-related research and development, and other supporting health services (Redzwan & Ramli, 2024). Health care expenditure is crucial for maintaining and enhancing human well-being, as it allows health systems to operate efficiently. Adequate funding ensures the employment of qualified medical professionals, access to advanced medical technologies, and the implementation of essential public health and disease prevention programs (World Health Organization [WHO], 2009). Without sufficient resources, health care services may be limited, medical technologies underutilized, and vital health initiatives neglected, resulting in poorer health outcomes. Health expenditure also reflects the population’s overall consumption of health-related goods and services, indicating how resources are allocated to meet medical needs worldwide (Nica et al., 2023; WHO, 2017).

Institutional quality has a major impact on population health, as lower-quality institutions are typically associated with higher infant mortality and decreased life expectancy (Rehmat et al., 2020). Kaufmann et al. (1999) identified six key indicators to assess institutional quality: Control of Corruption, Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Rule of Law, Regulatory Quality, and Government Effectiveness (Hadipour et al., 2023; Kaufmann et al., 2011). A strong rule of law ensures that citizens, communities, and institutions have access to justice while holding all stakeholders—such as health care providers, governments, and hospital authorities—accountable. This promotes transparency and fairness, supporting effective health care governance and improving overall health outcomes (Banik et al., 2023). Voice and civil accountability, reflecting political and individual rights of citizens, can influence the effectiveness of public health expenditure. By enabling better oversight of health programs and ensuring proper use of allocated funds, these mechanisms help improve the efficiency and impact of government spending in the health sector (Boundioa & Thiombiano, 2025).

China’s efforts to phase out coal-fired electricity generation would greatly improve public health by reducing exposure to harmful air pollutants, including fine particulate matter (PM2.5) and nitrogen dioxide (NO2), building on the progress of its ongoing “war on pollution.” Nationwide coal elimination could lower air pollutant concentrations by 30–50% and is estimated to prevent 41.7% of premature deaths and 54.5% of disability-adjusted life years (DALYs) caused by anthropogenic PM2.5 (Ge et al., 2023; Zhou et al., 2024). Despite continued investment in coal for energy security, this expansion is nearing its limit (Milner & Green, 2025).

Addressing emissions from residential sources offers additional opportunities to reduce air pollution exposure. Household air pollution, particularly PM2.5 from the use of solid fuels such as firewood and coal, is linked to increased mortality (N. Li et al., 2023). In China, indoor air pollution from cooking and heating is estimated to have caused 966,000 deaths between 2000 and 2022, accounting for 22% of total rural mortality (Fan et al., 2020; Yang et al., 2024). Reducing coal use for winter heating and improving housing energy efficiency can enhance air quality and health, especially in rural areas (Maidment et al., 2014; Milner & Green, 2025).

While China’s recent climate commitments are promising, they represent only an initial step. To achieve meaningful reductions in greenhouse gas emissions, the country will need to adopt more ambitious targets, implementing policies that promote health, well-being, and the reduction of health inequalities. Amid global setbacks, such as the United States retreating from previous climate commitments, China has the opportunity to position itself as a global climate leader and secure substantial health benefits for its population (Milner & Green, 2025).

Model

The current study examines the impact of environmental degradation, ICT, health expenditure, and institutional quality on life expectancy. The model used in this study emerged from previous studies such as (Azam et al., 2023; Shao et al., 2022; Uddin et al., 2024; Zhang et al., 2025):

L E_{t} = δ_{0} + δ_{1} C O 2 E_{t} + δ_{2} I C T_{t} + δ_{3} H E_{t} + δ_{4} I Q_{t} + u_{t}

(1)

Where in equation (1), the life expectancy, CO2 emissions, information and communication technology, health expenditure, and institutional quality are represented by life expectancy (LE), CO2E, ICT, HE, and IQ, respectively. The dependent variable is LE, while the explanatory variables are CO2E, ICT, HE, and IQ. The terms $δ_{0}$ , $δ_{1} t o δ_{4}$ , and $u_{t}$ signify the intercept, slope of the explanatory variable, and error term, respectively. The ICT index is shown in Table 1. The principal component analysis (PCA) results reveal that the first component (Comp1) has the largest eigenvalue of 2.944, explaining 73.6% of the total variance, making it the most significant component in capturing variation in ICT indicators. The second component (Comp2) explains 25.3% of the variance, while Comp3 and Comp4 together account for less than 2%, indicating that they contribute minimally to the overall variation. The variables in the PCA are: Individual internet Penetration (X1; % of population using the internet), Fixed Broadband Subscriptions (X2; per 100 people), Fixed Telephone Subscriptions (X3; per 100 people), and Mobile Cellular Subscriptions (X4; per 100 people). In Comp1, X1 (.580), X2 (.575), and X4 (.572) have strong positive loadings, suggesting that this component represents overall ICT usage in China. X3 loads heavily on Comp2 (.984), reflecting traditional telecommunication infrastructure rather than modern ICT access. Comp3 and Comp4 have small contributions from most variables, capturing minor variations in the dataset. Overall, Comp1 can be effectively used to construct a composite ICT index because it explains the largest share of variance and reflects the primary ICT-related variables.

Table 1.

PCA Output for ICT Index.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.944	1.931	.736	.736
Comp2	1.013	.978	.253	.989
Comp3	.035	.027	.009	.998
Comp4	.008		.002	1
Principal components (eigenvectors)
Variable	Comp1	Comp2	Comp3	Comp4
X1	.58	.056	−.192	.79
X2	.575	−.072	.783	−.227
X3	−.08	.984	.157	.028
X4	.572	.153	−.571	−.569

Table 2 shows the PCA output for the IQ index. The PCA results indicate that the first component (Comp1) has an eigenvalue of 2.564, explaining 42.7% of the total variance, making it the most significant component in capturing variation among governance indicators. The second component (Comp2) explains 32.5% of the variance, while Comp3 through Comp6 together account for about 27.3%, indicating that they contribute smaller proportions to the overall variation. The PCA includes Control of Corruption (G1), Government Effectiveness (G2), Political Stability (G3), Regulatory Quality (G4), Rule of Law (G5), and Voice and Accountability (G6). Comp1 has an eigenvalue of 2.564 and explains 42.7% of the total variance, with strong positive loadings for Government Effectiveness (.595), Rule of Law (.576), and Control of Corruption (.432). Comp2 has an eigenvalue of 1.950 and accounts for 32.5 percent of the variance, showing high loadings for Political Stability (.596), Voice and Accountability (.606), and Control of Corruption (.462). Comp3 records an eigenvalue of 1.005 and explains 16.8% of the variance, being dominated by Regulatory Quality (.984). Comp4, Comp5, and Comp6 have eigenvalues of .294, .145, and .042, explaining 4.9%, 2.4%, and 0.7% of the variance, respectively, indicating relatively smaller contributions. Finally, Comp1 can be effectively used to construct a composite governance index, as it explains the largest share of variance and represents the main aspects of governance quality.

Table 2.

PCA Output for Institutional Quality Index.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.564	.613	.427	.427
Comp2	1.95	.945	.325	.752
Comp3	1.005	.712	.168	.92
Comp4	.294	.149	.049	.969
Comp5	.145	.103	.024	.993
Comp6	.042	.	.007	1
Principal components (eigenvectors)
Variable	Comp1	Comp2	Comp3	Comp4	Comp5	Comp6
G1	.432	.462	−.06	−.359	−.662	.172
G2	.595	−.065	.154	.29	.339	.647
G3	−.23	.596	.033	.754	−.146	.002
G4	−.082	.046	.984	−.117	−.065	−.068
G5	.576	.24	.016	.051	.331	−.706
G6	−.261	.606	−.052	−.448	.558	.222

Methods

The first step of any time series analysis is testing the unit root test. The Augmented Dickey–Fuller (ADF) test is widely used to examine whether a time series is stationary or contains a unit root. It extends the basic Dickey–Fuller test by including lagged differences of the dependent variable to control for serial correlation in the error term. The general ADF regression is specified as:

Δ G_{t} = α + β t + γ G_{t - 1} + \sum_{i = 1}^{p} δ_{i} Δ G_{t - i} + ε_{t}

(2)

where $G_{t}$ represents the series under consideration, $Δ$ denotes the first-difference operator, $t$ is a time trend, and $p$ is the optimal lag length. The null hypothesis $H_{0} : γ = 0$ indicates the presence of a unit root, while rejection of the null suggests that the series is stationary. The ADF test allows for different model specifications, including with intercept, with trend, or without both, depending on the characteristics of the data. The Phillips–Perron (PP) test is a non-parametric alternative to the ADF test that also examines the existence of a unit root in a time series. Unlike the ADF test, the PP test does not include lagged difference terms but instead corrects the test statistics for serial correlation and heteroskedasticity in the error process using Newey–West–type corrections. The basic PP regression model is given as:

Δ G_{t} = α + β t + γ G_{t - 1} + u_{t}

(3)

where $u_{t}$ may be serially correlated and heteroskedastic. Similar to the ADF test, the null hypothesis is that the series has a unit root $(γ = 0)$ . The PP test is particularly useful when the error structure of the series is unknown or complex, as it provides robust test statistics without explicitly modeling the short-run dynamics.

The autoregressive distributed lag (ARDL) approach is used to examine both short-run and long-run connections among variables that may be integrated of different orders, I(0) or I(1). It is particularly suitable for small sample sizes and avoids the pre-testing problems associated with traditional cointegration techniques. The ARDL framework allows separate estimation of long-run coefficients and short-run dynamics through an error correction model (ECM). Cointegration is tested using the bounds testing procedure (Pesaran et al., 2001). The general ARDL $(p, q_{1}, q_{2}, q_{3}, q_{4})$ specification can be written as:

\begin{matrix} {LE}_{t} = α_{0} + \sum_{i = 1}^{p} α_{i} {LE}_{t - i} + \sum_{j = 0}^{q_{1}} β_{j} {CO}_{2} E_{t - j} + \\ \sum_{k = 0}^{q_{2}} γ_{k} {ICT}_{t - k} + \sum_{l = 0}^{q_{3}} δ_{l} {HE}_{t - l} + \sum_{m = 0}^{q_{4}} ϕ_{m} {IQ}_{t - m} + ε_{t} \end{matrix}

(4)

The long-run relationship between life expectancy and its determinants is expressed as:

{LE}_{t} = θ_{0} + θ_{1} {CO}_{2} E_{t} + θ_{2} {ICT}_{t} + θ_{3} {HE}_{t} + θ_{4} {IQ}_{t} + u_{t}

(5)

where $θ_{1} - θ_{4}$ represent the long-run coefficients. The short-run dynamics, along with the speed of adjustment toward long-run equilibrium, are captured by the ECM as follows:

\begin{matrix} Δ {LE}_{t} = α_{0} + \sum_{i = 1}^{p - 1} α_{i} Δ {LE}_{t - i} + \sum_{j = 0}^{q_{1} - 1} β_{j} Δ {CO}_{2} E_{t - j} + \\ \sum_{k = 0}^{q_{2} - 1} γ_{k} Δ {ICT}_{t - k} + \sum_{l = 0}^{q_{3} - 1} δ_{l} Δ {HE}_{t - l} + \\ \sum_{m = 0}^{q_{4} - 1} ϕ_{m} Δ {IQ}_{t - m} + λ {ECM}_{t - 1} + ε_{t} \end{matrix}

(6)

Here, ${ECM}_{t - 1}$ represents the lagged error correction term, while $λ$ is anticipated to be negative and statistically significant, reflecting the speed at which deviations from the long-run equilibrium are corrected over time. For robustness analysis, this study employed the Fully Modified Ordinary Least Squares (FMOLS) estimator to validate the long-run association among the variables. FMOLS corrects for endogeneity and serial correlation through non-parametric adjustments, producing unbiased long-run estimates (Phillips & Hansen, 1990). The use of both estimators strengthens the reliability and consistency of the long-run coefficients. These techniques are commonly used as robustness checks for ARDL-based findings.

Results and Discussions

Table 3 shows the descriptive statistics, which indicate that LE has a mean of 4.3191 and a very low standard deviation of .0315, suggesting that it is relatively stable across the sample. CO2E has a mean of 1.7165 with moderate variation (Std. Dev. .4446), while ICT and IQ have means close to zero but higher standard deviations (1.7158 and 1.6012), reflecting significant dispersion. HE shows a mean of 4.6184 and a standard deviation of .5038, indicating moderate variability. The skewness values show that LE and CO2E are slightly negatively skewed (−.3531 and −.5362), while ICT, HE, and IQ are positively skewed (.454, .320, and .769), indicating longer right tails for these variables. All variables have kurtosis below 3, suggesting flatter (platykurtic) distributions, with CO2E at 1.6837. The Jarque–Bera test shows p-values above .05 for all variables (e.g., LE = .3457, CO2E = .1861), indicating that none significantly deviate from normality.

Table 3.

Descriptive Statistics.

Statistics	LE	CO2E	ICT	HE	IQ
Mean	4.3191	1.7165	2.14E-08	4.6184	−8.57E-08
Median	4.3240	1.8786	−.3156	4.4171	−.2355
Maximum	4.3593	2.2406	3.3406	5.5936	3.7111
Minimum	4.2602	1.0092	−1.8379	3.6749	−2.0199
Std. Dev.	.0315	.4446	1.7158	.5038	1.6012
Skewness	−.3531	−.5362	.4539	.3195	.7694
Kurtosis	1.8502	1.6837	1.8059	2.1973	2.7242
Jarque–Bera	2.1244	3.3630	2.6249	1.2281	2.8516
Probability	.3457	.1861	.2692	.5412	.2403

Table 4 presents the results of the unit root tests, including ADF and PP. Both tests indicate that LE is stationary at a level, whereas CO2E, ICT, HE, and IQ are non-stationary in their level form. However, after taking the first difference, all variables become stationary, suggesting that they are integrated of order one. These results confirm that the dataset exhibits a mixed order of integration, with some variables stationary at the level and others requiring differencing to achieve stationarity. This mixed order supports the suitability of ARDL for analyzing the long-run and short-run relationships among the variables.

Table 4.

Unit Root Test.

Variables	ADF				PP
	I(0)		I(1)		I(0)		I(1)
	t-statistic	p-value	t-statistic	p-value	t-statistic	p-value	t-statistic	p-value
LE	−4.3062	.0023	−5.1633	.0016	−4.5498	.0013	−5.1504	.0017
CO2E	−1.4503	.5428	−6.9732	.0000	−1.1680	.6731	−6.8653	.0000
ICT	1.5534	.9990	−7.9624	.0000	3.2929	1.0000	−7.8656	.0000
HE	−.2724	.9168	−4.3481	.0022	−.2724	.9168	−4.3248	.0023
IQ	2.2826	.9999	−4.3815	.0021	.0869	.9586	−6.1017	.0000

Table 5 presents the results of the Johansen cointegration test using both the Trace and Maximum Eigenvalue statistics. Both statistics confirm the existence of one cointegrating equation among the variables, indicating a stable long-run relationship linking LE, CO2E, ICT, HE, and IQ. This suggests that despite some variables being non-stationary at the level, they move together in the long run, maintaining an equilibrium relationship. Table 6 reports the results of the ARDL bounds test for cointegration. The calculated F-statistic of 9.8269 exceeds the upper critical bound of 4.37 at the 1% significance level, providing strong evidence of a long-run relationship among the variables. This result complements the Johansen test and confirms that an ARDL model is appropriate to estimate both short-run dynamics and long-run coefficients for the model.

Table 5.

Cointegration Test.

Hypothesized	Trace				Max-Eigen
No. of CE(s)	Eigen value	Statistic	Critical value	Prob.	Statistic	Prob.
None	.7992	111.9094	69.8189	.0000	41.7394	.0047
At most 1	.7653	70.1700	47.8561	.0001	37.6864	.0018
At most 2	.5766	32.4836	29.7971	.0240	22.3481	.0336
At most 3	.2774	10.1355	15.4947	.2705	8.4467	.3351
At most 4	.0629	1.6888	3.8415	.1938	1.6888	.1938

Table 6.

ARDL Bound Test.

F-bounds test		Null hypothesis: no level relationship
Test statistic	Value	Signif.	I(0)	I(1)
F-statistic	9.826935	10%	2.2	3.09
k	4	5%	2.56	3.49
		2.50%	2.88	3.87
		1%	3.29	4.37

Table 7 shows the long-run and short-run results of the ARDL. The findings show that, in both the long run and short run, CO2E has a negative effect on LE, while ICT, HE and IQ have a positive effect on life expectancy. The estimated coefficients show the impact of each explanatory variable on LE in both the long and short run.

Table 7.

ARDL Estimates.

Variable	Long run			Short run
Variable	Coefficient	Std. error	Prob.	Coefficient	Std. error	Prob.
CO2E	−.0610	.0199	.0025	−.0019	.0074	.8021
ICT	.4934	.1887	.0176	.0043	.0019	.0357
HE	.7180	.1140	.0000	.0262	.0019	.0000
IQ	.0860	.0148	.0000	.0043	.0005	.0000
ECM(-1)	-	-	-	−.0502	.0058	.0000
ARDL Diagnostic test
Test		F—stats	P-value	Remarks
Jarque—Bera		1.0853	.4803	Residual is normal distributed
ARCH		2.0787	.1623	No hetrocedasticity
LM		2.2013	.1430	No serial correlation
Ramsey RESET		.4331	.5192	Stable

The coefficient of CO2E is −.0610 in the long run, indicating that a 1% increase in CO2E reduces LE by 0.0610% in the long run. CO2E negatively affects life expectancy in China by contributing to air pollution, which increases respiratory and cardiovascular diseases. Higher emissions degrade environmental quality, reducing overall public health. Long-term exposure to pollutants accelerates mortality, lowering average life expectancy. This explains why increases in CO2E are associated with a decline in life expectancy over time. According to the WHO (2023), air pollution is responsible for nearly seven million deaths each year. Furthermore, carbon emissions and air pollution contribute to the rise of chronic health conditions such as asthma, lung cancer, and heart disease, leading to premature mortality (Segbefia et al., 2023). Widespread environmental pollution and degradation can accelerate the spread of numerous diseases, resulting in higher mortality rates and lower life expectancy for both the affected country and neighboring regions. In severe cases, the harmful effects of pollution persist over generations, causing long-lasting health problems and reducing overall quality of life for local communities (Altaee et al., 2025; Ebhota et al., 2023). Although the short-run coefficient of CO₂E is negative (−.0019), it is statistically insignificant (p > .8021). This may be due to China’s rapid industrial expansion and energy-intensive growth, which dilutes the immediate measurable effects of CO2E. Also, policy interventions and technological adjustments can delay the short-run environmental impacts, rendering them statistically insignificant.

For ICT, the coefficients are .4934 (long run) and .0043 (short run), suggesting that a 1 unit increase in ICT improves LE by .4934% over the long run and .0043% in the short run. Thus, ICT positively affects life expectancy by improving access to health information and services, enabling better disease prevention and management. Digital technologies facilitate telemedicine, health monitoring, and awareness campaigns, especially in remote areas. Enhanced communication and data sharing allow for more efficient health care delivery. Consequently, greater ICT adoption contributes to healthier populations and longer life expectancy. ICT provides a cost-efficient approach to health promotion, facilitating the sharing of information and experiences among individuals dealing with common health concerns (Dutta et al., 2019; Gibbons et al., 2011). ICT-supported health care services are increasingly vital for efficiently managing health sector costs, which constitute a significant portion of government budgets. They also enhance access to health care across all segments of society, contributing to longer and healthier lives for individuals (Lorcu & Erduran, 2015).

HE has coefficients of .7180 (long run) and .0262 (short run), showing that a 1% increase in HE increases LE by 0.7180% in the long run and 0.0262% in the short run. Health expenditure positively impacts life expectancy by increasing the availability and quality of health care services. Higher spending improves access to hospitals, medical treatments, and preventive care. It also supports public health programs and disease-control initiatives. Consequently, increased health expenditure enhances overall population health and longevity. Increased health expenditure can significantly enhance life expectancy by improving the quality of health care services. Investments in modern medical facilities and better-trained physicians ensure more effective diagnosis and treatment, leading to improved patient outcomes (Anwar et al., 2023). Moreover, allocating funds to preventive measures, curative care, proper nutrition, and vaccination programs plays a crucial role in reducing disease prevalence and mortality rates. Together, these expenditures not only strengthen the overall health care system but also provide long-term benefits by promoting healthier, longer-living populations (Bein, 2020; Köroğlu et al., 2025).

IQ shows coefficients of .0860 (long run) and .0043 (short run), indicating that a 1-unit improvement in IQ raises LE by 0.0860% in the long run and 0.0043% in the short run. Institutional quality positively influences life expectancy by ensuring effective governance and implementation of health policies. Strong institutions promote better health care systems, sanitation, and regulatory frameworks. They also reduce corruption, improving the efficiency of public health spending. As a result, higher institutional quality supports healthier populations and longer life expectancy. Political instability can severely undermine social infrastructure, particularly hospitals and other health care facilities, while also restricting the availability of resources for the health sector. This disruption not only limits access to essential medical services but also negatively affects the overall health outcomes of the population. Prolonged instability may lead to the displacement of health care professionals and interruptions in ongoing health programs, further exacerbating the challenges in delivering adequate care to communities in need (Boundioa & Thiombiano, 2025; Datta et al., 2020).

The value −.0502 represents the coefficient of the ECM in the ARDL model. Its negative sign indicates that any short-run deviation from the long-run equilibrium is corrected over time. The magnitude, .0502, suggests that approximately 5.02% of the disequilibrium adjusts back toward the long-run equilibrium in each period. The associated p-value of .0000 confirms that this adjustment is highly statistically significant.

The ARDL diagnostic tests indicate that the estimated model is well-specified and reliable. The Jarque–Bera test (F = 1.0853, p = .4803) shows that the residuals are normally distributed. The ARCH test (F = 2.0787, p = .1623) indicates no evidence of heteroskedasticity, while the LM test (F = 2.2013, p = .1430) confirms the absence of serial correlation in the residuals. In addition, the Ramsey RESET test (F = .4331, p = .5192) suggests that the model is correctly specified and stable. Overall, these results support the validity of the ARDL estimates. Figure 1 presents the CUSUM test, and Figure 2 shows the CUSUMSQ test for the ARDL model. Both plots indicate that the lines remain within the critical bounds, suggesting that the model is stable over the sample period. This confirms that the estimated coefficients are reliable and there are no structural breaks affecting the long-run relationship among the variables. Table 8 shows the FMOLS estimates. The finding shows that CO2E has a negative effect on LE, while ICT, HE, and IQ have a positive effect on life expectancy.

Figure 1.

CUSUM Test.

Figure 2.

CUSUM Squares test.

Table 8.

Robustness Analysis (FMOLS Estimators).

Variable	Coefficient	Std. error	t-statistic	Prob.
CO2E	−.7829	.0773	−10.1294	.0000
ICT	.3857	.0396	9.7462	.0000
HE	.6441	.0291	22.1106	.0000
IQ	.0598	.0197	3.0289	.0000

Conclusions

This study investigated the impact of air pollution (CO2 emissions), ICT, health expenditure, and institutional quality on life expectancy in China over the period 2000 to 2023. To ensure the reliability of the analysis, the study applied Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests to examine the stationarity of the variables, followed by Johansen cointegration and ARDL bounds tests to determine the existence of a long-run relationship. The unit root tests revealed a mixed order of integration, with some variables stationary at the level and others becoming stationary after first differencing. Both the cointegration and ARDL bounds tests confirmed the presence of a stable long-run relationship among the variables. The ARDL results further showed that CO2 emissions negatively affect life expectancy in both the long and short run, while ICT, health expenditure, and institutional quality positively contribute to improving life expectancy in China.

This study offers several policy recommendations to enhance life expectancy in China: first, the Chinese government should strengthen environmental regulations to limit industrial emissions and promote cleaner production technologies, reducing the population’s exposure to harmful pollutants. Second, policies promoting renewable energy and low-emission transportation, such as electric vehicles and public transit expansion, can help decrease CO2 emissions and improve overall air quality, supporting longer life expectancy. Third, the government should expand digital health infrastructure, including telemedicine platforms and health information systems, to increase access to medical services across urban and rural areas. Fourth, ICT literacy programs should be implemented to help citizens utilize digital health tools effectively, improving disease prevention, early diagnosis, and health management. Fifth, increasing public spending on health care facilities, medical equipment, and training of health professionals can directly enhance the quality and accessibility of health care services. Sixth, the government should invest in preventive health programs, such as vaccination campaigns and health education, to reduce disease prevalence and improve population longevity. Seventh, strengthening governance and regulatory frameworks can ensure efficient allocation and utilization of health resources, reducing corruption and improving service delivery. Eighth, transparent policy implementation and accountability mechanisms should be enforced to maintain consistent and equitable access to health care services, promoting better health outcomes and life expectancy.

Footnotes

ORCID iDs

Ijaz Uddin

Xiaolan Zhang

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Humanities and Social Sciences Research Project for Universities in Jiangxi Province (Grant No. GL24218) and the 2025 Ji’an Social Science Planning Project (Grant No. 25GHB020).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Do Environmental Degradation,ICT,Health Expenditure,and Institutional Quality Affect Life Expectancy?

Abstract

Keywords

Introduction

Model

Methods

Results and Discussions

Conclusions

Footnotes

ORCID iDs

Funding

Declaration of Conflicting Interests

References