Consequences of Information and Communication Technologies and Environmental Degradation on Health Expenditure

Abstract

This study aims to examine how the subcomponents and overall measurement of ecological footprint, as well as the use of information and communication technologies, affect health expenditures. For this study, the sample group consisted of the top 25 countries with the highest ecological footprint for the period 2000 to 2021. System GMM estimation results demonstrate that economic growth and ecological footprint have a positive impact on health expenditures. Covid-19 dummy variables, have a statistically significant and positive effect on health expenditures. On the other hand, information and communication technologies has a statistically significant but negative effect on health expenditures. The estimation results show that the Covid-19 pandemic increased health expenditures. Looking at the effect of subcomponents of environmental degradation on health expenditures, all subcomponents have a statistically significant and positive effect on health expenditures. It is seen that the most effective variable is forest products. The variable that has almost the same impact as the footprint of forest products is the carbon footprint. Carbon footprint has significant and positive impact on health expenditures, followed by fishing grounds cropland, grazing land, built-up land. The results of the study indicate which forms of pollution should be given priority by policymakers in order to prevent an increase in health expenditure resulting from environmental degradation.

Keywords

health expenditure environmental pollution technology economic growth

What do we already know about this topic?

Current understanding highlights a significant correlation between environmental degradation and increased health expenditures, particularly in the context of developing nations and rapidly advancing technological landscapes.

How does your research contribute to the field?

This research uniquely quantifies the impact of individual ecological footprint components and ICT on health expenditures, offering a more granular understanding vital for targeted policy interventions.

What are your research’s implications toward theory, practice, or policy?

The findings suggest that policies should integrate environmental and technological considerations with health expenditure strategies to enhance sustainable healthcare practices.

Introduction

Environmental pollution poses a persistent challenge in the contemporary world, presenting a substantial risk to the health and well-being of individuals. Nowadays, since environmental pollution has reached critical levels, the relationship between pollution and health is being discussed more.^1
-4 For example, a recent report from the World Health Organization indicates that air quality is under surveillance in 6000 cities spanning 117 countries and monitoring results show that that nearly 99% of the global population grapples with heightened levels of fine particulate matter and nitrogen dioxide, signifying suboptimal air quality; in fact, it is worth noting that individuals residing in low- and middle-income nations shoulder the most substantial burden of outdoor air pollution exposure.⁵

Environmental degradation manifests in various ways, such as air and water pollution, soil contamination, and the discharge of hazardous chemicals into the nature. These factors contribute to an escalation in the occurrence and intensity of severe climatic events, such as heat waves, storms, floods, droughts, and wildfires and this degradation threatens the delicate ecological balance and poses a substantial risk to human health.⁶ Epidemiological data indicates that environmental pollutants, including endocrine-disrupting chemicals (EDCs), fine particulate matter (PM), and heavy metals, contribute to the occurrence of numerous prevalent diseases.⁷ Moreover, environmental pollution also affects prominent health problems such as cardiovascular system^8,9 and respiratory system.^10,11 This effect is not limited to physical health; it can also be a determinant on mental health. For example, Ventriglio et al claim that exposure to contaminants, such as air pollutants, heavy metals, and environmental disasters lead to a variety of mental disorders, ranging from anxiety and mood disorders to psychotic syndromes.¹²

When considering the human health impacts of environmental pollution, it is inevitable that it would put pressure on healthcare systems and thereby lead to a raise in health expenditures.^13
-15 The health damages resulting from exposure to pollution, calculated globally by the World Bank, amount to $8.1 trillion in 2019, which is approximately 6.1% of the global Gross Domestic Product (GDP).¹⁶ Carleton et al have estimated that the cost of the mean global increase in mortality rate because of the climate change will be valued roughly 3.2% of global GDP in 2100.¹⁷

Technological developments can serve as a catalyst in the environmental pollution-health relationship. In this context, technological advancements are believed to mitigate the detrimental impact of economic activities and environmental degradation. This, in turn, would help stabilize pressure on expenditures for health, either by directly slowing pollution or by increasing the efficiency of technological tools in the health sector. For example, while Schmaltz et al have emphasized the importance of technology for cleaning and preventing marine plastic pollution,¹⁸ Adebayo and Kirikkaleli have pointed out how utilizing renewable energy technology can enhance air quality.¹⁹ Regarding the correlation between technology and health expenditures, although most studies indicate a negative association, a few studies have reported a positive correlation. For example, although Cutler and McClellan have stated that advances in medical technology and pharmaceutical innovation have been driving the growth of health expenditure,²⁰ recent studies such as Ullah et al and Shahzad et al claim that technological innovations in the energy, communication and economy sector have led to decrease in health expenditures.^21,22

On the other hand, technological developments are expected to increase per capita income, and the increased income is expected to increase the EK through the total demand channel. However, on the other hand, increasing technological infrastructure may lead to prioritization of technologies aimed at reducing environmental pollution and the reversal of the effect of technology on environmental pollution in the long term. This may lead to a decrease in health expenditures through channels that directly affect the environment, such as renewable energy.^23
-28

The main indirect transfer channel in the relationship between technology and health expenditures is the positive impact of technological advances on environmental quality. In this context, decreasing environmental pollution can reduce health expenditures by improving health outcomes. There is a general opinion in the literature that increasing technology reduces environmental pollution.^29
-31 On the other hand, decrease in energy intensity over time due to changes in technology and the integration of the economic structure with optimal energy policies will also reduce CO emissions.³²

In addition, according to Murthy and Ketenci, the impact of technological developments on health expenditures occurs through both supply and demand channels.³³ On the demand side, with the development of medical technologies, social awareness increases and the demand for health services increases. However, he argued that there would be no welfare cost since the marginal social benefit would be equal to the marginal social cost. However, on the supply side, the need for more qualified healthcare personnel is increasing in the context of adaptation to advanced technological infrastructure, which increases healthcare expenditures.

While CO₂ emissions are frequently used in studies discussing the link between pollution and health expenditures,^34,35 the use of EF is more limited, and in this studies, general ecological footprint is preferred. EF provides the area of productive land and water required to solve the waste problem caused by consumption as a result of individuals’ activities. In a sense, it is the mathematical measurement of the space used by individuals as a result of their consumption.

We also included EF sub-indicators in this study. These sub-indicators are: cropland, grazing land, fishing grounds, built-up land, and forest area have received limited attention or have been disregarded. In addition to EF and its sub-indicators, as Yang et al emphasizes, one of the driving parameters that should not be ignored in the relationship between EF and health expenditures is technology.³⁶ Thus, the aim of the study examines consequences of environmental degradation factors including, EF and its sub-components such as Built-up Land (BL), Carbon (CRB), Cropland (CL), Fishing Ground (FG), Forest Products (FP), Grazing Land (GL) as well as information and communication technologies (ICT) on health expenditures.

Previous studies in this area have primarily focused on the overall impact of CO2 emissions indicator. However, this study takes a novel approach by looking at each sub-element of EF individually and EF as a whole, providing a more detailed analysis. By examining these sub-elements of the EF and exploring the intricate connections between ICT and health expenditures, this study makes a distinctive scientific contribution that sheds new light on the complex interplay between environmental sustainability and health expenditures.

This paper is structured as follows. In section 2, a concise overview of the existing literature is presented. Section 3 delineates the data origins and performs a comprehensive statistical data analysis and interpretation the findings from the estimations. Finally, Section 4 encapsulates the study with its conclusion and the subsequent policy recommendations.

Literature

The Impact of Environmental Degradation on Health Expenditures

Environmental degradation has a direct impact on human health, resulting in an increase in government funding for healthcare. Nitrogen oxides, for instance, contribute to the formation of acid rain and ozone. High levels of nitrogen oxides can lead to respiratory issues in children, asthmatics, and the elderly. Sulfur dioxide, which is emitted during the combustion of fossil fuels, causes breathing difficulties by burning the nose and throat. Carbon-based emissions can affect the cardiovascular and central nervous systems of individuals. The decline in environmental quality leads to health problems that increase demand for health services.³⁷

The transmission mechanism between environmental degradation and health expenditures can be described as follows: environmental degradation has an adverse effect on human health, thus increasing the demand for health services within society. As a result, the demand for health services directly increase health expenditures. Empirical studies have been conducted on various pollution indicators and different country groups to explore this relationship in-depth.

Using more than one pollution indicator, Yahaya et al have analyzed the impact of environmental quality on per capita health expenditures in 125 developing countries. The results from Panel OLS and Panel DOLS estimations indicate that pollutants including NO, SO₂, CO, and CO₂ have a positive relationship with health expenditures in the long term.³⁸ Similarly, Khoshnevis Yazdi and Khanalizadeh used different pollution indicators and they found that in MENA countries income, CO₂, and PM₁₀ have a positive correlation with health expenditures.³⁹ Gündüz has conducted a research study on the correlation between carbon footprint and health expenditure in the USA. The study revealed a positive relationship between healthcare costs and carbon footprint.⁴⁰ Hao et al found that SO₂ and soot emissions increased public health expenditures in China.¹

In general, CO₂ emission is used as pollution indicator. In this context, Aydin and Bozatli, and Raihan et al found that carbon emissions increase health expenditures in Turkey and Bangladesh, respectively. Additionally, there is a bidirectional causal relationship between these variables.^41,42 Other study findings prioritizing CO₂ emissions are as follows: Apergis et al suggested that CO₂ emissions increase health expenditures in US states and that this effect is stronger for states with higher health expenditures.⁴³ In another study, Apergis et al reached the similar findings for 178 Countries.³² Atuahene et al investigated this relationship for both China and India.⁴⁴ According to the study, CO₂ emissions increase health expenditures in both countries. Yadav et al found that environmental degradation increases health expenditures in 22 developing countries. Increasing CO₂ emissions increase both noise and air pollution, which leads to heart diseases as well as chronic respiratory problems, increasing both private sector health expenditures and public health expenditures.⁴⁵ In addition, Xiu et al, and Mujtaba and Shahzad found that CO₂ increases health expenditure in China and OECD countries, respectively.^46,47 And, in India, which has high pollution like China, Vyas et al found that health expenditures depend on CO2 emissions in both the short and long term.⁴⁸ Recent studies by Ramos-Meza et al found that CO₂ emissions increase healthcare costs in 30 OECD countries.⁴⁹ Cheng et al investigated the relationship between CO₂ emissions and health expenditures for OECD countries.⁵⁰ According to the study, increasing CO₂ emissions can lead to increased diseases and malnutrition. Additionally, this causes governments to spend more on medical regulations to improve weather conditions. All these transmission channels lead to an increase in health expenditures through environmental degradation.

Except for CO₂, Using the ecological environment index, Ma et al revealed in their study that the decreasing ecological environmental quality in China significantly increased the health expenditures of the residents of the region.⁵¹ Chronic diseases such as lung diseases caused by environmental pollution play an important role in these findings. Xia et al found that industrial sulfur dioxide increases health expenditure in China.⁵²

Studies investigating the environment-health relationship in the context of ecological footprint are quite limited. For example, Yang et al emphasized that in the 10 countries with the highest health expenditures, the ecological footprint has an increasing effect on health expenditures.³⁶ Additionally, in the causality analysis, they found bidirectional causality between ecological footprint and health expenditures.

In contrast, Demir et al. focused on the impact of shocks in their study on Türkiye. Using a non-linear ARDL model, they found that environmental pollution shocks have a statistically significant effect on long-term health expenditures.⁵³ Conversely, adverse environmental pollution shocks do not manifest a significant effect on health expenditures. Furthermore, both affirmative and detrimental perturbations in natural resource availability exhibit a negative influence on long-term health expenditure trends. Intriguingly, positive economic growth shocks demonstrate a positive association with health expenditures. In the study, the main basis of environmental health expenditure is that increasing air pollution causes respiratory diseases, and increasing water pollution causes problems such as unhealthy food, thus increasing health expenditures. Evaluating environmental-health expenditure from a different perspective, Hussain et al found that carbon emissions from transportation increase health expenditures in OECD countries.⁵⁴ In addition to the effects of carbon emissions, transportation-related depression and anxiety are also effective in these findings.

Studies often emphasize that environmental pollution increases health expenditures. The literature suggesting that this effect is negative or statistically insignificant is quite limited. In this context, Zaidi and Saidi found that CO₂ and Nitrogen emissions have a negative effect on health expenditures in the long-term in sub-Saharan African economies.² And, according to Haseeb et al, who argues that the findings vary depending on whether they are short or long term, environmental pollution increases health expenditures in the long term, but is statistically insignificant in the short term.¹³

The Impact of Technology on Health Expenditures

The effects of information and communication technologies on health expenditures operate through different mechanisms. The first mechanism states that technological advancements lead to an improvement in environment quality, thereby positively impacting human health. In the final stage, a reduction of health risks to the public leads to a decreased demand for health services, resulting in lower health expenditures.²² In the second mechanism, technological advancements in the health industry lead to improvements in health systems, easier access to health services, and earlier disease detection. Overall, these developments decrease the strain on healthcare costs. Consequently, technological progress has a negative correlation with health expenditures. In addition, telemedicine, which has developed rapidly in recent years, has enabled poor or deprived individuals to find faster answers to their medical problems, and this brings efficiency in the healthcare sector through the cost channel.⁵⁵ In this context, developments in information and communication technologies toward e-health services are one of the determining factors in reducing health expenditures.⁵⁶ However, sometimes technological developments, especially in the health sector, can lead to increased health expenditures because they have high costs. In addition, new technologies can make it easier for patients who cannot start treatment before passing a certain stage, and increase the number of services offered, leading to positive pressure on health expenditures.⁵⁷

There are many studies discussed the links among technological developments and health outcomes,^58,59 however, studies on the direct effects of technological developments on health expenditures are quite limited. The overall findings of these studies indicate that technological advancements have a detrimental effect on expenditures for health.

From their studies, Shahzad et al have examined the effects of ICT and renewable energy on the health expenditure in Pakistan. The results demonstrate that health expenditure is positively driven by GDP and CO2 emissions. However, it is negatively affected by ICT and renewable energy use. Bidirectional Granger causality has been found between health expenditure, GDP, CO2 emissions, and ICT. Additionally, unidirectional Granger causality has been identified from renewable energy usage to these variables.²² According to Okunade and Murthy, technological developments are one of the determinants of increasing health expenditures in the USA.⁶⁰ Demi̇rtaş and Ilikkan Özgür have analyzed the impact of ICT and air pollution on health expenditures in the provinces of Türkiye. Panel data analysis indicates a negative association between the quantity of ınternet users and health expenditures.⁶¹ Murthy and Ketenci found that technology increased healthcare expenditures in the USA.³³ Increasing demand for medical technologies is the main mechanism that stands out in these findings. Similarly, Rodriguez Llorian and Mann found a long-term relationship between medical technology and health expenditures in OECD countries.⁶² In some of these countries, technology increases health expenditures and in others it decreases them. In addition, Hosseinzadeh and Mozayani suggested that the implementation of ICT in the form of e-health in Iran will reduce health expenditures.⁵⁶ In this context, the use of ICT technologies in the field of e-health reduces healthcare costs for reasons such as facilitating communication between doctors and patients, improving health quality, removing time and space restrictions, and wider geographical coverage. Among recent studies, Ma et al found that high-tech procedures for patients with ischemic heart disease increased public healthcare expenditures in Portugal.⁵¹

Some studies have investigated the impact of technology on both health outcomes and health expenditures. Dutta et al directly investigated the impact of ICT on health and found that the impact of ICT on health outcomes was significant in 30 selected Asian countries.⁶³ ICT also has an impact on health expenditures. Zhang et al suggested that ICT reduced population deaths and improved health outcomes by increasing public health expenditures and increasing health literacy in China.⁵⁵ Because ICT provides efficiency in resource allocation in the health sector by reducing the problem of asymmetric information between stakeholders and the public sector. In addition, the main mechanism by which ICT increases health expenditures is the awareness channel, which causes positive pressure on health expenditures by increasing the demand for improvement of health-related infrastructure and resources.

There are many studies investigating the relationship between technology and health outcomes that lead to health expenditures rather than direct health expenditures. In this context, Omri et al investigated the relationship between environmental performance and human health in the presence of ICT.⁶⁴ According to the study, in the case of a good technological infrastructure, the healing power of the environment on health outcomes deepens. Byaro et al found that technology has an improving effect on health outcomes in sub-Saharan African countries.⁶⁵ According to Xu et al digital economy improves the quality of life in China.²⁹

Data, Model and Methodology

Data

This study examines the impact of information and communication technologies, ecological footprint and its sub-components on health expenditures by analyzing data from 2000 to 2021 in the 25 countries with the highest EF: Argentina, Australia, Brazil, Canada, China, Egypt, France, Germany, India, Indonesia, Italy, Japan, Mexico, Nigeria, Pakistan, Poland, Russian Federation, Saudi Arabia, South Africa, Spain, Thailand, Türkiye, the United Kingdom, the United States and Vietnam. In addition, the study tries to determine which of the sub-components of the EF is more impactful on health expenditures. In the main model of the study, health expenditures per capita of GDP per capita (%) (HE) is used as the dependent variable, while GDP per capita (LGDP) the number of internet users in the population representing information and communication technologies (ICT) and EF per capita representing environmental degradation are used as independent variables. Dummy variable for Covid-19 (DUMMY) were included in the models as independent variables. Since the Covid-19 pandemic has a global impact on the health system and health expenditures, the impact of this crisis is included in the model as a dummy variable. 2020 is used as a dummy variable to represent the pandemic that started at the end of 2019 and showed its effects on economies in 2020. In models other than the main model, 6 subcomponents of EF have been used instead of the total EF variable. EF data have been obtained from the Global Footprint Network (GFN) database, while other variables have been obtained from the World Bank database. Australia’s data for 2002 to 2004 and Pakistan’s data for 2000 were obtained from Internet live stats. L at the beginning of the variable name indicates that the natural logarithm of the variable is taken. Information on the variables is shown in Table 1.

Table 1.

The Descriptions of the Variables and Sources.

Variables	Add.	Unit of measurement	Source
Health expenditure	HE	Health expenditure per capita (% of GDP per capita)	World Bank-WDI
Economic growth	LGDP	GDP per capita (constant 2015 US$)	World Bank-WDI
Information and communication technologies	ICT	Individuals using the Internet (% of population)	World Bank-WDI
Dummy variable	DUMMY	Dummy variable for Covid-19
Ecological footprint (Total)	LEF	gha per person	Global Footprint Network (GFN)World Bank- Prosperity Data360
Built-up land	LBL	gha per person	GFN
Carbon	LCRB	gha per person	GFN
Cropland	LCL	gha per person	GFN
Fishing grounds	LFG	gha per person	GFN
Forest products	LFP	gha per person	GFN
Grazing land	LGL	gha per person	GFN

This study analyzes the effects of EF and technological innovation on health expenditures and constructs 7 different panel data models. All models in the study are derived from the main model in equation (1). Thus, the effects of both the total EF and the sub-components of the EF on health expenditures are revealed.

H E_{i t} = f (L G D P_{i t}, L I C T_{i t}, L E F_{i t}, D U M M Y_{i t})

(1)

Model and Methodology

In the recent period, panel data techniques have been widely used in empirical analyses. Panel data analyses allow us to examine research topics that cannot be analyzed with cross-section or time series. In panel data models, there are 2 dimensions, time (T) and cross-section (N); thus, the presence of 2 dimensions in the same model allows for more information, number of observations and degrees of freedom than other 1-dimensional analysis types.⁶⁶ A standard linear panel data model is as in equation (2).

y_{i t} = α_{0} + β_{1} X_{1 i t} + β_{2} X_{2 i t} + \dots + β_{k} X_{k i t} + μ_{i t}

(2)

Where, $y$ is variable the dependent, $α_{0}$ is the constant term, $X$ is the explanatory variables, $β$ is the long-run parameter coefficient and µ is the error term. In addition, i denotes the number of sections in the panel (i = 1, 2. . .N) and t denotes the time period (t = 1, 2. . .T).

In panel data analysis, static models employing fixed effects and random effects estimators are commonly utilized. However, because static models neglect dynamic relationships, dynamic panel data methods have become increasingly prevalent.

When discussing dynamic panel data models, autoregressive panel data models are typically considered. In these models, lagged values of the dependent variable are included as explanatory variables. In the context of this study, where the dependent variable is HE, a simple dynamic panel data model is represented by equation (3):

L H E_{i t} = α_{0} + β_{1} L H E_{i t - 1} + β_{2} X_{i t} + μ_{i t}

(3)

The lagged value of the dependent variable has been added to the model as an explanatory variable, which can be seen in equation (3). This inclusion creates a correlation between the lagged dependent variable and the error term, resulting in an endogeneity problem in the model. The difference generalized method of moments (GMM) estimator by Arellano and Bond and the system GMM estimators by Arellano and Bover as well as Blundell and Bond address this issue by using instrumental variables.^67
-69

It is also necessary to test 2 fundamental assumptions in order to determine whether the GMM estimator is consistent: the existence of autocorrelation and the validity of the instrumental variables. The AR (1) test, which tests the null hypothesis of no first-order autocorrelation, and the AR (2) test, which tests the null hypothesis of no second-order autocorrelation, have been proposed by Arellano and Bond as a means of checking for the presence of autocorrelation in a given model.⁶⁷ In order to achieve an optimal outcome in these tests, it is essential to ensure that there is no second-order autocorrelation, even in the presence of first-order autocorrelation. Moreover, the Hansen test is employed to evaluate the validity of the instrumental variables. If the null hypothesis, which is overidentifying restrictions are valid, cannot be rejected, it indicates that the selected instrumental variables in the model are appropriate.

Before estimating the GMM, it is essential to check the stationarity of the variables. In order to determine the type of unit root test to be used for the stationarity test, it is necessary to check whether the series contain cross-section dependence. If the series contain cross-section dependence, stationarity should be tested with second generation unit root tests. For this purpose, the series are tested for cross-section dependence using the bias-corrected LM test.

The power of the Breusch and Pagan LM test is weakened when the group mean is zero and the individual mean is different from zero.⁷⁰ To overcome this problem, Pesaran et al suggest using the bias-corrected LM test for the detection of inter-unit correlation⁷¹:

\begin{array}{l} L M_{a d j} = \sqrt{(\frac{2 T}{N (N - 1)})} \sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} {\hat{ρ}}_{i j} \\ \frac{(T - k) {\hat{ρ}}_{i j}^{2} - μ_{T i j}}{\sqrt{υ_{T i j}^{2}}} ~ N (0, 1) \end{array}

(4)

Where µ $T_{i j}$ and $υ_{T i j}^{2}$ are the mean and variance of $(T - k) {\hat{ρ}}_{i j}^{2}$ proposed by Pesaran et al^.71 The null hypothesis of this test is that there is no cross-section dependence. As T and N approach infinity, this hypothesis asymptotically follows a standard normal distribution.

On the other hand, in panel data analyses, if cross-section dependence is detected in the series, second generation unit root tests should be used. The Karavias and Tzavalis⁷² panel unit root test has been used in the study.

This panel unit root test can be applied to panels with small or large time series sizes and to both balanced and unbalanced panels, offering numerous advantages over other panel unit root tests. This test can consider unknown structural breaks, non-normal errors, cross-sectional variance, cross-sectional dependence, and homogeneity or heterogeneity across different cross-sectional units.⁷³ In this test, the null hypothesis states that all panel time series exhibit unit roots, whereas the alternative hypothesis suggests that some or all of the panel time series display stationary processes. Given that this test considers cross-section dependence and structural breaks, it possesses greater power than other tests in distinguishing between the null and alternative hypotheses.⁷⁴

This study used dynamic panel data models based on standard panel data models. These models include total EF and its subcomponents are as follows:

Model (1)

\begin{array}{l} H E_{i t} = α_{0} + β_{1} H E_{i t - 1} + β_{2} L G D P_{i t} + β_{3} L I C T_{i t} \\ + β_{4} L E F_{i t} + β_{5} D U M M Y + μ_{i t} \end{array}

(5)

Model (2)

\begin{array}{l} H E_{i t} = α_{0} + β_{1} H E_{i t - 1} + β_{2} L G D P_{i t} + β_{3} L I C T_{i t} \\ + β_{4} L B L_{i t} + β_{5} D U M M Y + μ_{i t} \end{array}

(6)

Model (3)

\begin{array}{l} H E_{i t} = α_{0} + β_{1} H E_{i t - 1} + β_{2} L G D P_{i t} + β_{3} L I C T_{i t} \\ + β_{4} L C R B_{i t} + β_{5} D U M M Y + μ_{i t} \end{array}

(7)

Model (4)

\begin{array}{l} H E_{i t} = α_{0} + β_{1} H E_{i t - 1} + β_{2} L G D P_{i t} + β_{3} L I C T_{i t} \\ + β_{4} L C L_{i t} + β_{5} D U M M Y + μ_{i t} \end{array}

(8)

Model (5)

\begin{array}{l} H E_{i t} = α_{0} + β_{1} H E_{i t - 1} + β_{2} L G D P_{i t} + β_{3} L I C T_{i t} \\ + β_{4} L F G_{i t} + β_{5} D U M M Y + μ_{i t} \end{array}

Model (6)

\begin{array}{l} H E_{i t} = α_{0} + β_{1} H E_{i t - 1} + β_{2} L G D P_{i t} + β_{3} L I C T_{i t} \\ + β_{4} L F P_{i t} + β_{5} D U M M Y + μ_{i t} \end{array}

(9)

Model (7)

\begin{array}{l} H E_{i t} = α_{0} + β_{1} H E_{i t - 1} + β_{2} L G D P_{i t} + β_{3} L I C T_{i t} \\ + β_{4} L G L_{i t} + β_{5} D U M M Y + μ_{i t} \end{array}

(10)

Empirical Results

In consideration of the methodology employed in the study, the following tests have been conducted: cross-sectional dependence tests for the variables, second generation unit root tests that take into account cross-sectional dependence, and finally, System GMM estimation.

Before estimating the models utilized in the study, it is necessary to apply cross-section dependence and stationarity tests first and the results are presented in Table 2.

Table 2.

Cross-Section Dependence and Panel Stationary Test Results.

Variables	Panel unknown break unit root test		Cross-section dependence test
Variables	Zmin-stat	5%bootstrap critical value	LM_adj
HE	−11.0686***	4.6146	138.449***
LGDP	−14.6038***	7.2047	165.141***
LICT	−21.0074***	12.4567	115.540***
LEF	−4.0663***	−0.6363	137.547***
LBL	−9.1318***	5.8641	138.584***
LCRB	−4.9172***	1.1999	161.847***
LCL	−23.9292***	12.9494	139.790***
LFG	−5.3676***	2.3139	135.181 ***
LFP	−4.5588***	−0.0136	134.881***
LGL	−4.8535***	0.6934	133.924***

***

Indicate the rejection of null hypothesis at 1% significance levels.

Based on the calculation derived from Table 2, the null hypothesis positing the absence of cross-section dependence in the series is decisively rejected at a 1% significance level. Since all the series have cross-section dependence, Karavias and Tzavalis⁷² panel unit root test, which takes into account cross-section dependence and structural breaks, has been used. The results obtained from the panel unit root test indicate that the null hypothesis is rejected for all variables. This indicates that all series in the study are stationary.

After determining that the series are stationary, the effects of EF and its subcomponents on health expenditures are estimated using a System GMM approach and the results are presented in Table 3.

Table 3.

System GMM Estimation Results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
HE_t-1	0.9882*** (0.0055)	0.998*** (0.0055)	0.9907*** (0.0051)	0.9964*** (0.0041)	0.9939*** (0.0054)	0.9837*** (0.0064)	0.9883*** (0.0068)
LGDP	0.0096* (0.0054)	0.0254** (0.0106)	0.021*** (0.0072)	0.0185** (0.0071)	0.0356*** (0.0066)	0.0401*** (0.0076)	0.0317*** (0.0069)
LICT	−0.0306** (0.0152)	−0.0188 (0.0156)	−0.0339** (0.0158)	−0.0172 (0.0154)	−0.0248* (0.0137)	−0.0267* (0.0151)	−0.0197 (0.0152)
DUMMY	0.7319*** (0.1505)	0.6976*** (0.147)	0.729*** (0.1506)	0.7036*** (0.1495)	0.7132*** (0.147)	0.7221*** (0.1502)	0.7098*** (0.1495)
LEF	0.1322*** (0.0264)
LBL		0.0355* (0.0194)
LCRB			0.0822*** (0.0188)
LCL				0.0552* (0.0316)
LFG					0.0629*** (0.0086)
LFP						0.0978*** (0.0210)
LGL							0.0401*** (0.0075)
Observations	525	525	525	525	525	525	525
Groups	25	25	25	25	25	25	25
AR(1)	−3.22***	−3.19***	−3.21***	−3.22***	−3.20***	−3.21***	−3.20***
AR(2)	−0.07	−0.05	−0.06	−0.17	−0.06	−0.11	−0.06
Hansen Test	24.10	21.81	24.09	23.48	23.87	21.97	22.31

Note. Standard errors in parentheses.

***

P < .01. **P < .05. *P < .01.

According to Table 3, AR (1) and AR (2) tests have been conducted to determine whether there is an autocorrelation problem in all models. The results obtained from the AR (1) test show that all models are significant at the 1% level, while the results obtained from the AR (2) test show that all models are not significant at the 1% level. There is a first order autocorrelation problem in the models, while there is no second order autocorrelation problem. The absence of second order autocorrelation indicates that the model is valid. Furthermore, Hansen test has been conducted for the validity of the instrumental variables used in the models. The findings obtained from Hansen test, which the null hypothesis is instrumental variables are appropriate, show that the null hypothesis cannot be rejected at 1% significance level for all models. These results reveal that the instrumental variables used in the estimation of the models are valid.

The effects of GDP per capita (GDP), information and communication technologies (ICT), Covid-19 effect with dummy variable (DUMMY), ecological footprint (EF) and sub-components of EF on health expenditures (HE) are analyzed with the System GMM, which is the most appropriate model as a result of the preliminary tests and the results are shown in Table 3. While there are 7 models in the study, model 1 shows the effect of our main variables GDP, ICT, DUMMY and EF on HE. The other models show the effects of the subcomponents of EF on HE. In the models, the Covid-19 pandemic, which started in 2019 and had an impact on the health system and expenditures all over the world, was included as a dummy variable in order to see its impact on health expenditures.

Model 1, which shows the effects of the main variables in the study on health expenditures indicates that GDP, DUMMY and EF have a positive effect on health expenditure, while ICT has a negative effect on health expenditure. Our econometric model is a level-log semi-elasticity model. This should be taken into account when interpreting the model estimation results. A 1% increase in the GDP per capita will increase health expenditures’ share in GDP per capita by 0.000096 units. A 1% increase in the ecological footprint per capita (gha) increases health expenditures’ share in GDP per capita by 0.0013 units. As for the other control variables, a % 1 increase in the share of Individuals using the Internet (% of population), which is used to represent information and communication technologies, decreases health expenditures’ share in GDP per capita by 0.0003 units. A 1 percentage point increase in the share of the population aged 65 and over in the total population, which is another control variable, increases health expenditures by 0.3%. Finally, the variable used in the main model is DUMMY, which shows the impact of the Covid-19 pandemic on health expenditures. The coefficient obtained is statistically significant and positive. Therefore, it shows that the Covid-19 pandemic increased health expenditures. All variables in our main model have a statistically significant effect on health expenditures.

Upon examination of the primary findings of the study, it becomes evident that EF is significant factor influencing health expenditures. By comparing the statistical significance and coefficient values of the variables in the model, this situation is clearly observable. However, specific environmental factors that impact health expenditures can be identified by estimating the subcomponents of the EF in addition to the overall EF, as illustrated in Models 2 to 7 in Table 3.

All subcomponent indicators have a statistically significant and positive effect on health expenditures. The estimation results are given in Table 3. Based on the findings regarding the effects of EF subcomponents on HE, it has been determined in Model 6 that Forest Product (FP) variable is the most influential one on HE. A 1% increase in forest products footprint per capita increases health expenditures’ share in GDP per capita by 0.00097 units. The country group is very important in the effect of this variable on health expenditures. Because 25 countries with high ecological footprint are considered in this study. The main factor in the increase in forest footprint is deforestation. When this country group is analyzed by taking into account the deforestation statistics published by Global Forest Watch, the most deforested countries in 2021 are Russia and Brazil. Considering the 2021 data, there are 6 countries (Russia, Brazil, Canada, United States, Indonesia, China) in our data set among the top 10 countries with the highest deforestation. These 6 countries account for 8% of global deforestation in 2021. The other most influential sub-component on health expenditures is the carbon footprint. A 1% increase in per capita carbon footprint increases health expenditures’ share in GDP per capita by 0.00082 units. The per capita carbon footprint semi-elasticity of per capita health expenditures is 0.082. The third most influential variable on HE is found to be Fishing Grounds (FG) variable in Model 5. For the country group considered, the sub-components by degree of influence are Cropland (CL), Grazing Land (GL), Built-up Land (BL).

Discussion

Our analysis concluded that economic growth, ecological footprint and its components including carbon, cropland, fishing ground and grazing land, forest products and built-up land lead to increase in health expenditure in 25 countries with the highest EF: Argentina, Australia, Brazil, Canada, China, Egypt, France, Germany, India, Indonesia, Italy, Japan, Mexico, Nigeria, Pakistan, Poland, Russian Federation, Saudi Arabia, South Africa, Spain, Thailand, Türkiye, the United Kingdom, the United States and Vietnam. As economic growth is increasing, the general economic conditions of the economies increase as well. Thus, people spend more on health with their increased income. At the same time, since it is relatively easy for governments to finance major health works at such times, economic growth is directly proportional to government expenditure on health. In addition, energy use increases during periods of economic growth, which can lead to higher health costs for people through pollution, especially air pollution and governments are eagerly to increase health expenditure to protect human capital. Hence, findings of the study coincide with the previous literature findings such as Zaidi and Saidi² and Demir et al.⁵³ An increase in the ecological footprint, which is an indicator of environmental pollution, is an expected result that increases health expenditures. Increases in the ecological footprint cause air, water and soil pollution, causing an increase in viruses, bacteria or infectious diseases, such as COVID-19. In such cases, it is natural for people to spend more on health in order to maintain their health or avoid illness. The existing literature generally has findings in this direction and our findings are consistent with these studies such as Yang et al.³⁶ This is despite the fact that it is known that ecological footprint affects health expenditure. To the best of our knowledge, there is no study in the literature that shows which ecological footprint components, except carbon or carbon dioxide, have an effect on health expenditure. Carbon dioxide is a pollution proxy that is frequently used in environmental studies to show the impact of economic activities on environment. It especially shows air pollution and increasing amount of carbon dioxide adversely affects human health. For these reasons, it is an expected result that increase in the amount of carbon dioxide increase health expenditures and the general opinion in the literature is in this direction. The results of our study are consistent with the studies in the literature such as Yahaya et al³⁸ and Khoshnevis Yazdi and Khanalizadeh.³⁹ The effects of cropland and grazing land on health expenditure have not been investigated so far. However, increase in size of cropland and grazing land causes to shrink forest areas and reduces water resources. Given the environmental and human health impacts of deforestation and water shortages, our results are consistent and in line with expectations. Similarly, forest product implies the forest areas destroyed due to the impact of consumption. Thus, the increase in forest product is directly related to the decrease in forested areas. The decrease in forest areas due to forest product negatively affects human health, just like cropland and grazing land, and this increases health expenditures. Fishing ground also positively affects health expenditure. An increase in fishing grounds can cause both to consume more energy for excessive fishing and drive some species to extinction. Both damage the balance of environment and thus human health. The findings show that an increase in build-up land leads to increase in health expenditure. Build-up land causes environmental degradation such as deforestation and habitat loss which is leading health expenditure. Furthermore, an increase in built-up land can contribute to elevated pollution levels in urban areas. Pollution of the air, water, and soil can result in the outbreak of diseases, leading to increased healthcare expenditures.

Technological developments have impacted people’s lives in many ways, and health is one of them. In particular, information and communication technologies play an important role in the treatment and prevention of diseases, in the management process of chronic diseases, especially, in the effective use of the health system, in reducing congestion and medical errors by using digital technologies and database. In this way, a decrease in health expenditures can be observed. The finding of our study has been confirm these expectations. Based on our finding, development in information and communication technologies leads to reduce in health expenditure and this result is in line with Shahzad et al²² and Demi̇rtaş and Ilikkan Özgür.⁶¹

Conclusion

The present study explores the complex nexus between environmental aspects, economic progress, technological progress, and health expenditure. The results show that economic growth and the ecological footprint have considerable and favorable impacts on health expenditure. Therefore, the study provides valuable insights into the interplay of different factors affecting health expenditure. An expected consequence of economic growth in developing nations is an increase in ecological footprint, which in turn raises and demand for health services and ultimately results in higher healthcare expenditures. Moreover, after analyzing the subcomponents of ecological footprint, it is identified that forest products as having the most pronounced positive effect on health expenditure, followed by carbon emissions, fishing grounds, cropland, grazing land, built-up land. The overuse of forest products has many effects such as the reduction of forested areas, which leads to a decrease in water quality, increased erosion and flooding, and a decrease in living species. since these are effects that disrupt human natural life and thus health, it is inevitable that there will be an increase in health expenditures. Similar negative effects can be observed with increases in build-up area. Furthermore, urbanization can result in the excessive consumption of energy, which in turn leads to an increase in carbon dioxide emissions and a simultaneous increase in the expenditure required to maintain human health. The expansion of cropland and grazing areas could lead to greater utilization of agriculture, livestock, and mechanization, resulting in increased environmental pollution, particularly carbon emissions. This, in turn, could lead to increased health expenditures. Similarly, an escalation in fishing grounds can be resulted with energy consumption for overfishing, reduced biodiversity and environmental pollution. Those factors can also lead to health problems and increased health expenditures.

Furthermore, the research has determined that the use of Information and Communication Technology (ICT) has a significant adverse effect on health expenses. This can be interpreted in 3 ways. Firstly, the impact of ICT on improving the environment and reducing the ecological footprint leads to a decrease in the demand for health services. Secondly, ICT’s online system results in cost reduction, such as transportation expenses. Lastly, ICT improves health services, such as early diagnosis, which leads to a decrease in costs. These factors can result in a reduction in health expenditure.

Based on the study’s findings, policymakers should strive to balance economic growth, environmental sustainability, and health expenditures. To achieve this, authorities should promote sustainable economic development that considers the environmental impact of growth, encourages technological innovation to minimize ecological footprints, and invests in healthcare infrastructure to alleviate the negative effects on health expenditure. Efforts must be directed toward improving the efficacy of healthcare systems to ensure efficient utilization of available resources.

In particular, ICT use should be increased to improve efficiency. To this end, policymakers should promote policies to encourage investment in ICT technology and its use in the health system, such as tax incentives, training for health workers and patients on ICT use. During the transition to ICT use, small mobile centers can be established to help patients use the system more easily and reduce congestion in the hospital. ICT use will also reduce carbon emissions, one of the largest components of the ecological footprint. In summary, with ICT, there will be no need for the large physical construction required by the health system and also no pollution of the environment, especially since there is no need for physical transportation. This will lead to a reduction in health expenditures. Among the ecological footprint components, forest products and carbon emission cause the highest increase in health expenditures. The ranking of forest products as the most significant contributor to rising healthcare expenditures highlights the urgent need for policymakers to prioritize this issue. One potential solution to this challenge is the accelerated expansion of reforestation. Policymakers should prioritize policies to reduce carbon emissions, such as increasing the use of renewable energy, reducing the use of fossil energy and increasing energy efficiency. The growth of built-up areas can damage natural habitats and species, as well as trigger the emergence and rapid spread of epidemics that can increase health expenditures. Policymakers should therefore closely monitor zoning laws and their implementation. Policymakers should control the increase in cropland and grazing land areas and develop policies for efficient utilization, not for increasing the area to control the increasing demand. These policies could include preventing land from being left fallow and replacing it with crops that can be intercropped. Policymakers should prevent overfishing, which will reduce water pollution, energy use and related health problems, and reduce health expenditures.

Due to data limitations, this study focused solely on information and communication technologies as a technological innovation variable. Therefore, it is suggested that future investigations examine the specific mechanisms through which technological innovations have a greater impact on health expenditure.

Footnotes

Acknowledgements

As this manuscript was solely authored by the co-authors, there are no acknowledgments to include.

Author Note

This study did not directly involve human participants, animals, or biological materials; instead, it comprised a secondary analysis of publicly available data and literature. As such, the nature of this research exempted it from the requirement for ethical review and consent. The study was conducted in strict accordance with the ethical guidelines and principles of scientific research, ensuring confidentiality and integrity of the data analyzed.

Data Availability Statement

Due to the nature of the research and data used, there is no data availability statement to provide.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

Given that this study does not involve human subjects or ethical considerations, an ethical statement is not applicable.

Trial Registration Number/Date

Since there are no clinical trials reported in this manuscript, no trial registration number or date is provided.

ORCID iDs

Serdar Aydin

Mehmet ali Çakır

References

Hao

Liu

Huang

Zhao

The impact of environmental pollution on public health expenditure: dynamic panel analysis based on Chinese provincial data. Environ Sci Pollut Res. 2018;25(19):18853-18865. doi:10.1007/s11356-018-2095-y

Zaidi

Saidi

Environmental pollution, health expenditure and economic growth in the Sub-Saharan Africa countries: panel ARDL approach. Sustain Cities Soc. 2018;41:833-840. doi:10.1016/j.scs.2018.04.034

Chen

Cost of economic growth: Air pollution and health expenditure. Sci Total Environ. 2021;755(Pt 1):1-14.doi:10.1016/j.scitotenv.2020.142543

Chang

Wang

Zhou

The relationship between health expenditure, CO₂ emissions, and economic growth in the BRICS countries-based on the Fourier ARDL model. Environ Sci Pollut Res. 2022;29(8):10908-10927. doi:10.1007/s11356-021-17900-w

World Health Organization. World Health Statistics 2022: Monitoring Health for the SDGs, Sustainable Development Goals. World Health Organization; 2022. Accessed November 3, 2023. https://iris.who.int/handle/10665/356584

World Health Organization. World health statistics 2023: monitoring health for the SDGs, sustainable development goals. Published 2023. Accessed November 3, 2023. https://www.who.int/publications-detail-redirect/9789240074323

Jia

Sun

, et al. Environmental pollution, a hidden culprit for health issues. Eco-Env Health. 2022;1(1):31-45. doi:10.1016/j.eehl.2022.04.003

Xie

Zhao

, et al. Relationship between fine particulate air pollution and ischaemic heart disease morbidity and mortality. Heart. 2015;101(4):257-263. doi:10.1136/heartjnl-2014-306165

Hamanaka

Mutlu

GM.

Particulate matter air pollution: effects on the cardiovascular system. Front Endocrinol. 2018;9:680. doi:10.3389/fendo.2018.00680

10.

D’Amato

Chong-Neto

Monge Ortega

, et al. The effects of climate change on respiratory allergy and asthma induced by pollen and mold allergens. Allergy. 2020;75(9):2219-2228. doi:10.1111/all.14476

11.

Good

Fedak

Goble

, et al. Respiratory aerosol emissions from vocalization: age and sex differences are explained by volume and exhaled CO2. Environ Sci Technol Lett. 2021;8(12):1071-1076. doi:10.1021/acs.estlett.1c00760

12.

Ventriglio

Bellomo

di Gioia

, et al. Environmental pollution and mental health: a narrative review of literature. CNS Spectr. 2021;26(1):51-61. doi:10.1017/S1092852920001303

13.

Haseeb

Kot

Hussain

Jermsittiparsert

Impact of economic growth, environmental pollution, and energy consumption on health expenditure and R&D expenditure of ASEAN countries. Energies. 2019;12(19):3598. doi:10.3390/en12193598

14.

Nasreen

Association between health expenditures, economic growth and environmental pollution: long-run and causality analysis from Asian economies. Int J Health Plann Manage. 2021;36(3):925-944. doi:10.1002/hpm.3132

15.

Yang

Theoretical model and actual characteristics of air pollution affecting health cost: a review. Int J Environ Res Public Health. 2022;19(6):3532. doi:10.3390/ijerph19063532

16.

World Bank. The Global Health Cost of PM2.5 Air Pollution: A Case for Action Beyond 2021. The World Bank; 2022.

17.

Carleton

Jina

Delgado

, et al. Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits. Q J Econ. 2022;137(4):2037-2105. doi:10.1093/qje/qjac020

18.

Schmaltz

Melvin

Diana

, et al. Plastic pollution solutions: emerging technologies to prevent and collect marine plastic pollution. Environ Int. 2020;144:106067. doi:10.1016/j.envint.2020.106067

19.

Adebayo

Kirikkaleli

Impact of renewable energy consumption, globalization, and technological innovation on environmental degradation in Japan: application of wavelet tools. Environ Dev Sustain. 2021;23(11):16057-16082. doi:10.1007/s10668-021-01322-2

20.

Cutler

McClellan

Is technological change in medicine worth it?

Health Aff. 2001;20(5):11-29. doi:10.1377/hlthaff.20.5.11

21.

Ullah

Rehman

Khan

Shah

Khan

Nexus between trade, CO₂ emissions, renewable energy, and health expenditure in Pakistan. Int J Health Plann Manage. 2020;35(4):818-831. doi:10.1002/hpm.2912

22.

Shahzad

Jianqiu

Hashim

Nazam

Wang

Impact of using information and communication technology and renewable energy on health expenditure: A case study from Pakistan. Energy. 2020;204:1-9. doi:10.1016/j.energy.2020.117956

23.

Kahia

Jarraya

Kahouli

Omri

Do environmental innovation and green energy matter for environmental sustainability? Evidence from Saudi Arabia (1990–2018). Energies. 2023;16(3):1376. doi:10.3390/en16031376

24.

Khan

Saleem

Ozkan

Do geopolitical oil price risk influence stock market returns and volatility of Pakistan: evidence from novel non-parametric quantile causality approach. Resour Policy. 2023;81:103355. doi:10.1016/j.resourpol.2023.103355

25.

Kahia

Moulahi

Mahfoudhi

Boubaker

Omri

A machine learning process for examining the linkage among disaggregated energy consumption, economic growth, and environmental degradation. Resour Policy. 2022;79:1-11. doi:10.1016/j.resourpol.2022.103104

26.

Kahia

Jarraya

kahouli

Omri

The role of environmental innovation and green energy deployment in environmental protection: evidence from Saudi Arabia. J Knowledge Econ. 2024;15(1):337-363. doi:10.1007/s13132-022-01093-9

27.

Kahia

Ben Jebli

Industrial growth, clean energy generation, and pollution: evidence from top ten industrial countries. Environ Sci Pollut Res. 2021;28(48):68407-68416. doi:10.1007/s11356-021-15311-5

28.

Kahia

Omri

Jarraya

Does green energy complement economic growth for achieving environmental sustainability? Evidence from Saudi Arabia. Sustainability. 2020;13(1):180. doi:10.3390/su13010180

29.

Zhao

Cheng

Zhang

Quality of life and carbon emissions reduction: does digital economy play an influential role?

Clim Policy. 2024;24(3):346-361. doi:10.1080/14693062.2023.2197862

30.

Rehman

Gill

Ali

Information and communication technology, institutional quality, and environmental sustainability in ASEAN countries. Environ Sci Pollut Res. Published online April 29, 2023. doi:10.1007/s11356-023-27219-3

31.

Kazemzadeh

Fuinhas

Salehnia

Osmani

The effect of economic complexity, fertility rate, and information and communication technology on ecological footprint in the emerging economies: a two-step stirpat model and panel quantile regression. Qual Quant. 2023;57(1):737-763. doi:10.1007/s11135-022-01373-1

32.

Apergis

Bhattacharya

Hadhri

Health care expenditure and environmental pollution: a cross-country comparison across different income groups. Environ Sci Pollut Res. 2020;27(8):8142-8156. doi:10.1007/s11356-019-07457-0

33.

Murthy

VNR

Ketenci

Is technology still a major driver of health expenditure in the United States? Evidence from cointegration analysis with multiple structural breaks. Int J Health Econ Manag. 2017;17(1):29-50. doi:10.1007/s10754-016-9196-2

34.

Chaabouni

Zghidi

Ben Mbarek

On the causal dynamics between CO₂ emissions, health expenditures and economic growth. Sustain Cities Soc. 2016;22:184-191. doi:10.1016/j.scs.2016.02.001

35.

Zeeshan

Han

Rehman

Ullah

Afridi

FEA

. Exploring asymmetric nexus between CO2 emissions, environmental pollution, and household health expenditure in China. Risk Manag Healthc Policy. 2021;14:527-539. doi:10.2147/RMHP.S281729

36.

Yang

Usman

jahanger

Do industrialization, economic growth and globalization processes influence the ecological footprint and healthcare expenditures? Fresh insights based on the STIRPAT model for countries with the highest healthcare expenditures. Sustain Prod Consumption. 2021;28:893-910. doi:10.1016/j.spc.2021.07.020

37.

Narayan

Does environmental quality influence health expenditures? Empirical evidence from a panel of selected OECD countries. Ecol Econ. 2008;65(2):367-374. doi:10.1016/j.ecolecon.2007.07.005

38.

Yahaya

Nor

Habibullah

Ghani

Noor

ZM.

How relevant is environmental quality to per capita health expenditures? Empirical evidence from panel of developing countries. Springerplus. 2016;5(1):925. doi:10.1186/s40064-016-2505-x

39.

Khoshnevis Yazdi

Khanalizadeh

. Air pollution, economic growth and health care expenditure. Econ Res Istraz. 2017;30(1):1181-1190. doi:10.1080/1331677X.2017.1314823

40.

Gündüz

Healthcare expenditure and carbon footprint in the USA: evidence from hidden cointegration approach. Eur J Health Econ. 2020;21(5):801-811. doi:10.1007/s10198-020-01174-z

41.

Aydin

Bozatli

The impacts of the refugee population, renewable energy consumption, carbon emissions, and economic growth on health expenditure in Turkey: new evidence from Fourier-based analyses. Environ Sci Pollut Res. 2023;30(14):41286-41298. doi:10.1007/s11356-023-25181-8

42.

Raihan

Farhana

Muhtasim

, et al. The nexus between carbon emission, energy use, and health expenditure: empirical evidence from Bangladesh. Carbon Res. 2022;1(1):30. doi:10.1007/s44246-022-00030-4

43.

Apergis

Gupta

Lau

CKM

Mukherjee

Z. U.S.

State-level carbon dioxide emissions: does it affect health care expenditure?

Renew Sustain Energ Rev. 2018;91:521-530. doi:10.1016/j.rser.2018.03.035

44.

Atuahene

Yusheng

Bentum-Micah

Health Expenditure, CO2 Emissions, and Economic Growth: China vs. India. Published online September 17, 2020. doi:10.20944/preprints202009.0384.v1

45.

Yadav

Aneja

Ahmed

Do clean energy transition, environment degradation, and energy efficiency influence health expenditure: Empirical evidence from emerging countries. J Clean Prod. 2023;428:139355. doi:10.1016/j.jclepro.2023.139355

46.

Xiu

Ameer

Abbas

Altuntaş

Does carbon emissions, and economic expansion induce health expenditure in China: evidence for sustainability perspective. Front Environ Sci. 2022;9:838734. doi:10.3389/fenvs.2021.838734

47.

Mujtaba

Shahzad

SJH

. Air pollutants, economic growth and public health: implications for sustainable development in OECD countries. Environ Sci Pollut Res. 2021;28(10):12686-12698. doi:10.1007/s11356-020-11212-1

48.

Vyas

Mehta

Sharma

The nexus between toxic-air pollution, health expenditure, and economic growth: an empirical study using ARDL. Int Rev Econ Finance. 2023;84:154-166. doi:10.1016/j.iref.2022.11.017

49.

Ramos-Meza

Zhanbayev

Bilal

, et al. Does digitalization matter in green preferences in nexus of output volatility and environmental quality? Environ Sci Pollut Res. 2021;28(47):66957-66967. doi:10.1007/s11356-021-15095-8

50.

Cheng

Ren

Zhang

Wang

The nexus among CO₂ emission, health expenditure and economic development in the OECD countries: New insights from a cross-sectional ARDL model. Environ Sci Pollut Res. 2024;31(11):16746-16769. doi:10.1007/s11356-024-32081-y

51.

Timóteo

Ribeiro

Mateus

Perelman

Contribution of high-technology procedures to public healthcare expenditures: the case of ischemic heart disease in Portugal, 2002-2015. Int J Health Econ Manag. Published online March 29, 2024. doi:10.1007/s10754-024-09372-5

52.

Xia

Zhang

, et al. The superposition effects of air pollution on government health expenditure in China— spatial evidence from GeoDetector. BMC Public Health. 2022;22(1):1411. doi:10.1186/s12889-022-13702-y

53.

Demir

Karaduman

Cetin

Environmental quality and health expenditures efficiency in Türkiye: the role of natural resources. Environ Sci Pollut Res. 2023;30(6):15170-15185. doi:10.1007/s11356-022-23187-2

54.

Hussain

Marcel

Majeed

Tsimisaraka

RSM

. Effects of transport-carbon intensity, transportation, and economic complexity on environmental and health expenditures. Environ Dev Sustain. 2023;26:16523-16553. doi:10.1007/s10668-023-03297-8

55.

Zhang

Gong

Zhang

ICT diffusion and health outcome: Effects and transmission channels. Telematics Inform. 2022;67:101755. doi:10.1016/j.tele.2021.101755

56.

Hosseinzadeh

Mozayani

Effect of Information and Communication Technology Deployment on Health Expenditures of Iranian Households: A Provincial Approach. Int J Bus Dev Stud. 2021;13(1):33-45. doi:10.22111/ijbds.2021.6579

57.

Sorenson

Bhuiyan-Khan

. Medical technology as a key driver of rising health expenditure: disentangling the relationship. Clinicoecon Outcomes Res. 2013;5:223. doi:10.2147/CEOR.S39634

58.

Iverson

Howard

Penney

BK.

Impact of internet use on health-related behaviors and the patient-physician relationship: a survey-based study and review. J Am Osteopath Assoc. 2008;108(12):699-711.

59.

Yuan

Internet use and mental health problems among older people in Shanghai, China: the moderating roles of chronic diseases and household income. Aging Ment Health. 2021;25(4):657-663. doi:10.1080/13607863.2020.1711858

60.

Okunade

Murthy

VNR

. Technology as a ‘major driver’ of health care costs: a cointegration analysis of the Newhouse conjecture. J Health Econ. 2002;21(1):147-159. doi:10.1016/S0167-6296(01)00122-9

61.

Demi̇rtaş

Ilikkan Özgür

The effect of information-communication technologies (ICT) and air pollution on health expenditures. Sosyoekonomi. 2022;30(51):199-214. doi:10.17233/sosyoekonomi.2022.01.10

62.

Rodriguez Llorian

Mann

. Exploring the technology–healthcare expenditure nexus: a panel error correction approach. Empir Econ. 2022;62(6):3061-3086. doi:10.1007/s00181-021-02125-0

63.

Dutta

Gupta

Sengupta

PP.

ICT and health outcome nexus in 30 selected Asian countries: fresh evidence from panel data analysis. Technol Soc. 2019;59:101184. doi:10.1016/j.techsoc.2019.101184

64.

Omri

Kahouli

Kahia

Environmental sustainability and health outcomes: do ICT diffusion and technological innovation matter?

Int Rev Econ Finance. 2024;89:1-11. doi:10.1016/j.iref.2023.09.007

65.

Byaro

Rwezaula

Ngowi

Does internet use and adoption matter for better health outcomes in sub-Saharan African countries? New evidence from panel quantile regression. Technol Forecast Soc Change. 2023;191:122445. doi:10.1016/j.techfore.2023.122445

66.

Greene

WH.

Econometric Analysis. Prentice Hall; 2003.

67.

Arellano

Bond

Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud. 1991;58(2):277. doi:10.2307/2297968

68.

Arellano

Bover

Another look at the instrumental variable estimation of error-components models. J Econom. 1995;68(1):29-51. doi:10.1016/0304-4076(94)01642-D

69.

Blundell

Bond

Initial conditions and moment restrictions in dynamic panel data models. J Econom. 1998;87(1):115-143. doi:10.1016/S0304-4076(98)00009-8

70.

Breusch

Pagan

AR.

The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud. 1980;47(1):239-253. doi:10.2307/2297111

71.

Pesaran

Ullah

Yamagata

A bias-adjusted LM test of error cross-section independence. Econ J. 2008;11(1):105-127.

72.

Karavias

Tzavalis

Testing for unit roots in short panels allowing for a structural break. Comput Stat Data Anal. 2014;76:391-407. doi:10.1016/j.csda.2012.10.014

73.

Chen

Karavias

Tzavalis

Panel unit-root tests with structural breaks. Stata J Promot Commun Stat Stata. 2022;22(3):664-678. doi:10.1177/1536867X221124541

74.

Guliyev

Yerdelen Tatoğlu

The relationship between renewable energy and economic growth in European countries: evidence from panel data model with sharp and smooth changes. Renew Energy Focus. 2023;46:185-196. doi:10.1016/j.ref.2023.06.005