Exploring the Roles of Renewable Energy, Education Spending, and CO₂ Emissions towards Health Spending in South Asian Countries

Abstract

This research is mainly aimed at determining the effect of renewable energy (RE), education expenditures, and CO₂ emissions on health expenditures in selected South Asian countries. There is an insufficient number of studies that investigate the linkages between health expenditures (HE) and CO₂ emissions in South Asian countries. This study combined RE and gross domestic product (GDP) to identify their effect on health spending. We utilized the annual data of 1990–2018, and applied FMOLS and DOLS estimators over the panel data of five South Asian countries. According to the DOLS and FMOLS long-run results, GDP, RE, and education expenditures are negatively associated with health expenditures. This suggests that renewable energy puts less pressure on environmental quality, which leads to less health spending in the five South Asian countries studied. The empirical results also show that HE and CO₂ emissions are positively and significantly related, which implies that an increase in CO₂ emissions increases the financial burden on the various countries’ health sector. This study, therefore, recommends the usage of renewable sources to improve public health and to help lower health expenditures. To achieve sustainable development, it is also important to increase investment in the educational sector in the various countries.

1. Introduction

Today, breathing in clean air is a blessing, because air is continuously being contaminated by greenhouse gases (GHG) [1,2,3]. Air pollutants, such as CO₂ and SO₂, create respiratory diseases [4]. Therefore, an increase in air pollution has a negative effect on public health [5,6]. According to the World Health Organization (WHO), about three million people die globally due to air pollution every year [7]. Moreover, an increase in health expenditure (HE) negatively affects labor productivity, which affects economic growth [4]. The social cost of air pollution is of great concern, due to an uncertain estimation of air pollution levels, which, ultimately, affects economic growth [8]. CO₂ is the main pollutant in greenhouse gasses [9,10]. Governments all over the world are, therefore, trying to limit its emissions, due to its harmful effect on public health [4]. Many studies [9,11,12] that have investigated the linkages between HE and GDP have validated that not only does HE boost economic growth, but it also has a significant positive impact on public health.

The need to provide adequate healthcare services has led to rapid development in the health sector across the globe in recent times. There are two views regarding HE. The first view does not support excess HE, such as luxurious commodities [13]. On the other hand, the second point of view believes that HE is compulsory, and, as a result, governmental intervention is necessary [14,15]. Because HE is necessary for the development of a country’s economy, such expenditures put pressure on governments [4].

To increase HE, many other socio-economic factors need to be considered, such as population and the percentage of budget allocation to the health sector. Ross and Chia-Ling [16] pointed out that important factors of healthcare include economic growth and level of education. Health conditions can be estimated using HE [17]. Some underdeveloped countries have strong health systems, due to strong economic growth and efficient education systems. Therefore, it is important to probe the factors of HE, which may be environmental (air pollution), economic (GDP), or non-economic factors (education).

In addition to CO₂ emissions, the ecological footprint (EF) is another parameter that takes into consideration the extent of regenerative biological capacity human activities demand from the environment, which is occasioned by the production and consumption of goods and services. EF is defined as “how much area of biologically productive land and water an individual, population, or activity requires to produce all the resources it consumes and to absorb the waste (carbon dioxide) it generates, using prevailing technology and resource management practices” [18]. EF is a broad parameter taken for environmental degradation. The aggregation of EF includes grazing land, forest land, cropland, carbon footprint and built-up land [19]. According to Ozturk et al. [20], a positive linkage is found between environmental degradation and EF. The prime determinant of EF is GDP [21], while other determinants are renewable and non-renewable energy, the latter of which is the prime cause of GHG emissions [22,23,24,25].

In South Asian countries, the region’s vulnerability to many environmental issues has caused a rapid rise in HE [26,27]. HE has also increased considerably in the region as a result of the outbreak of the COVID-19 pandemic [28]. Compared to 2018, HE in 2020increased by 2.11%, 5.55% and 4.28% in India, Pakistan and Sri Lanka, while it decreased by 0.98% and 1.84% in Bangladesh and Nepal, respectively [29]. South Asian countries were faced with a shortage of healthcare supplies, such as ventilators and beds, during the pandemic, due to the region’s fragile healthcare systems. These countries had to allocate their development budgets on health expenditures during this period.

A direct relationship is observed between GDP and CO₂ emissions, according to a study by [26]; thus, the negative effect of pollution on health increases [2,3]. Therefore, pollution (i.e., CO₂) and EF are directly related to HE. However, apart from the usage of fossil fuels in industries, renewable sources are also being utilized, which improves air quality [30,31]. Renewable sources have the potential to increase the GDP of a country, without hurting the quality of the environment. The renewable energy sector also holds the capacity to create more jobs to reduce unemployment [32].

Despite the availability of many research studies that investigated the connection between HE and air pollution, there are few studies that probed this association in South Asian countries [14,33,34]. Therefore, the current study fills the research gap by investigating the linkages between HE, renewable energy (RE), and air pollutants in South Asian countries, i.e., Bangladesh, India, Nepal, Pakistan, and Sri Lanka.

This study makes the following contributions: Firstly, this study investigates the association between CO₂, GDP, and HE. Secondly, this research incorporates renewable energy with HE and GDP. Wang et al. [35] explored the linkages among GDP, HE, and CO₂ by applying the autoregressive distributed lag (ARDL) technique. The ARDL approach provides reliable results, without considering sample size, and provides an error correction term (ECT). Khan et al. [36] investigated the linkages between investment, health, and environmental degradation by utilizing canonical co-integration regression (CCP). They employed the VECM and FMOLS panels to assess the linkages. The study shows that HE, air pollution, and renewable energy move together. It also indicates that non-economic factors play a role in health spending.

This paper is organized into five sections. The first section deals with a brief introduction to the social, economic, and environmental factors affecting HE, and the factors responsible for the increasing HE in South Asian countries, followed by a literature review in the second section. The third section focuses on the methodology, while the fourth section provides the results and discussion. Finally, the last section deals with the conclusion and policy implications for sustainable development in South Asian countries.

2. Literature Review

2.1. CO₂ Emissions on Health Expenditures

CO₂ emissions are considered an important factor in the environment, since their increase negatively affects public health. Several studies have investigated the healthcare air pollution nexus [5,12]. Yazdi and Khanalizadeh [37] explored the determinants of HE, and examined MENA countries using data from 1995 to 2014. The ARDL method revealed an association between CO₂, PM10, and HE. They found a positive correlation between the increase in pollutants and HE. The studies of [38,39] also found similar results. Chaabouni et al. [14] examined the linkages between CO₂, HE, and GDP from 1995 to 2013, over the panel of 51 countries. The scholars applied the Gaussian mixture model (GMM) and the dynamic simultaneous equation for the analysis. They found two-way causality between CO₂ and GDP, GDP, and HE. Moreover, one-way causality was found between CO₂ and HE in most economies. According to [40], environmental problems are due to CO₂ emissions that affect public health. They analyzed data from 1990 to 2015 for the African region by applying the ARDL approach. A positive association was found between GDP and HE, but negative linkages were found between CO₂ and HE. Chaabouni and Saidi [41], and Usman et al. [42], also confirmed that air pollution increases the mortality rate.

2.2. Health Expenditures and GDP

HE is increasing rapidly in the world today, and these expenditures are increasing the GDP of different economies. A school of thought proposes that if a country establishes a sound healthcare system, then the life expectancy and social welfare of its people will also increase [42,43]. It, therefore, suggests that an improvement in the health conditions of the people affects their work efficiency positively, which improves a country’s economy. Atilgan et al. [44] found that HE improves GDP; if a country spends more on health, it will grow economically. However, the inability of most developing countries to invest in the health sector results in a lower GDP for such economies.

Wang et al. [35] applied the ARDL approach and the Granger causality application to annual data of 1995–2017, and found that HE, CO₂, and GDP move together in the long run. Bidirectional causality was found between HE and CO₂. Omri et al. [45] also conducted research on 13 MENA economies for the annual data of 1995–2005. They found that HE and GDP move together. Few scholars used foreign direct investment (FDI) as a proxy for economic growth, and investigated its association with HE. Usman et al. [42] investigated the association between air pollution, HE, and socio-economic factors. They used CO₂ as a proxy for environmental pollution, FDI and GDP as economic factors, and education and population as non-economic factors. The researchers evaluated the data of 13 countries for 1994–2017. The panel co-integration model found that CO₂, GDP, education, FDI, and HE correlated in the long run. The non-economic factor, i.e., population, was found to be positively associated with public and private HE, while negative linkages were found between education and HE. The studies of [46,47] found a positive association between HE and GDP. Most of the early studies found inconclusive results about the association between HE and GDP. They mostly used GDP as a proxy for economic growth.

2.3. Non-Economic Variables and Health Expenditures

Past studies rarely probed the linkages between non-economic factors and HE; thus, this study attempts to fill this research gap. Yao et al. [47] investigated the association between education and HE. They used the GMM approach over the data of 2001–2016. They found that while education does not affect HE, HE affects education positively. However, [48] found that education puts negative pressures on HE. There are very few studies [42,49] that probed the associations between non-economic factors and HE.

2.4. Health Expenditures and Renewable Energy

Today, the development and use of RE are increasing, in order to meet the world’s energy requirements. Khan et al. [36] probed the HE and RE associations with some other factors in ASEAN countries, using 2007–2017 data. They found that RE reduces HE and improves labor production. Therefore, environmental improvement leads to more economic growth. Apergis et al. [50] probed the linkages between healthcare, RE, and GDP in 42 African economies, over the data of 1995–2011. A two-way association was found between RE and CO₂ emissions, and one-way causality was found between RE and healthcare. Apergis and Payne [51], and Alola et al. [52], also made similar conclusions. Furthermore, Jebli [53] found two-way causality between health and RE. Air pollution puts pressure on public health, which negatively affects a country’s economic performance. Therefore, RE is a solution for better public health and good economic growth [54].

3. Material and Methods

Annual data of 1995–2019 for 5 South Asian countries were employed for the analysis. All data were obtained from the World Bank Data Indicators (WDI) [29]. HE is taken as current health expenditure, i.e., % of GDP as a dependent variable, GDP as an economic factor, and CO₂ as an air pollutant. RE and education as educational attainment, at least completed lower secondary education, population 25+, total (%) (cumulative) are also independent variables. The general equation of this study is as follows:

$$lnH{E}_t={\beta }_0+{\beta }_{1}R{E}_t+{\beta }_2{G}_t+{\beta }_{3}E{D}_t+{\beta }_{4}C{O}_t+{\epsilon }_t$$

(1)

A series of steps were used for the long-run estimations. Initially, we tested the cross-section dependence (CD) within the used data. It provides valuable insights and prevents bias in the outcomes. This study employed the LM test [55] and CD tests [56]. Mathematically, it can be written as follows:

$$LM=T{\displaystyle \sum }_{i=1}^{n-1}{\displaystyle \sum }_{j=i+1}^n{\partial }_{ij}^t$$

(2)

$$CD=\sqrt{\frac{2T}{N\left(N-1\right)}}\left({\displaystyle \sum }_{i=1}^{n-1}{\displaystyle \sum }_{j=i+1}^n{\partial }_{ij}^t\right)$$

(3)

where $T$ and $N$ stand for the period and the number of cross-sections, respectively. Errors’ pairwise correlation between $i$ and $j$ is denoted by ${\partial }_{ij}^t$. After the CD test, in order to test the slope phenomenon, we performed a slope homogeneity test. The homogeneity existence among the panel can cause unreliable and misleading outcomes [57]. The mathematical expressions for slope homogeneity tests of $\stackrel{\mathsf{\sim }}{\Delta }$ and ${\stackrel{\sim }{\Delta }}_{\mathrm{adj}}$ are as follows:are as follows:

$$\stackrel{\sim }{\Delta }=\sqrt{N}\left(\frac{N^{-1}\stackrel{\sim }{S}-K}{\sqrt{2K}}\right)$$

(4)

$${\stackrel{\sim }{\Delta }}_{\mathrm{adj}}=\sqrt{N}\left(\frac{N^{-1}\stackrel{\sim }{S}-E\left({\stackrel{\sim }{Z}}_{iT}\right)}{\sqrt{var\left({\stackrel{\sim }{Z}}_{iT}\right)}}\right)$$

(5)

where $var\left({\stackrel{\sim }{Z}}_{iT}\right)$ = 2K (T − K − 1)/(T + 1) and $E\left({\stackrel{\sim }{Z}}_{iT}\right)$ = K. The modified test of $\stackrel{\sim }{S}$ is as follows:

$$\stackrel{\sim }{S}={\displaystyle \sum }_{i=1}^n\left({\stackrel{\sim }{\gamma }}_i-{\stackrel{\sim }{\gamma }}_{WFE}\right)\frac{Y_i^{\prime }{M}_T{X}_i}{{\stackrel{\sim }{\varnothing }}_i^2}\left({\stackrel{\sim }{\gamma }}_i-{\stackrel{\sim }{\gamma }}_{WFE}\right)$$

(6)

For an individual unit, the pooled OLS test value is represented by ${\stackrel{\sim }{\gamma }}_i$. Similarly, ${\stackrel{\sim }{\gamma }}_{WFE}$ and $M_T$ denote the weighted pooled estimator and identity matrix, respectively.

Subsequently, the stationarity among the variables of interest can be checked by unit root tests. The current study utilized two types of unit root tests, i.e., a set of first-generation [58,59] unit root test, as well as second-generation unit root tests. In the case of the order of integration at level I(0), simple regression of ordinary least square can be employed. However, it becomes compulsory to check the cointegration between variables in the case of cointegration at the first difference. Instead of the cointegration test [60,61,62], the current study incorporated the [63] test that incorporates CD among the data.

After co-integration confirmation, we examined the long-run relationship among variables of interest. To do this, the current study utilized dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS) approaches. Its mathematical expression is as follows:

$${\stackrel{\sim }{\gamma }}_{FMOLS}={\left[N^{-1}{\displaystyle \sum }_{i=1}^N{\left({\displaystyle \sum }_{t=1}^t{\cup }_{it}-{\stackrel{-}{\cup }}_i\right)}^2\right]}^{-1}\times \left[\left({\displaystyle \sum }_{t=1}^T{\cup }_{it}-{\stackrel{-}{\cup }}_i\right){\hat{S}}_{it}-T{\hat{\Delta }}_{ϵ\mu }\right]$$

(7)

$${\stackrel{\sim }{\gamma }}_{DOLS}=\left[N^{-1}{\displaystyle \sum }_{i=1}^N{\left({\displaystyle \sum }_{t=1}^T{\mathrm{C}}_{it}{\mathrm{C}}_{it}^{\prime }\right)}^{-1}\left({\displaystyle \sum }_{t=1}^T{\mathrm{C}}_{it}{\mathrm{C}}_{it}^{\prime }\right)\right]$$

(8)

4. Results and Discussion

This section provides the econometric test results. To obtain robust results, this work used widely used methodologies. For this purpose, step-by-step tests of descriptive statistics, a set of unit root tests, a cross-section dependence test, and FMOLS and DOLS tests were applied. The current study implemented the FMOL and DOLS approaches for long-run estimations. Both techniques are capable of eliminating the endogeneity and autocorrelation issues. To uncover the causality among the selected variables, the current study incorporated a test that is efficient enough to eliminate the CS issues in panel data. A discussion on how health expenditure, renewable energy, education expenditures, and GDP are correlated in the long run is conducted. The descriptive statistics are followed by panel unit root tests, panel co-integration, and panel FMLOS and DOLS tests.

Table 1. Descriptive statistics.

	Health Expenditure	Renewable Energy	GDP	Education	CO2
Mean	1.263758	4.079114	6.755095	1.029647	−0.864372
Median	1.217133	4.053435	6.684298	1.060974	−0.541043
Maximum	1.906449	4.555136	8.200345	1.544385	0.548122
Minimum	0.811057	3.598303	5.878314	0.354242	−3.386252
Std. Dev.	0.281230	0.268093	0.568801	0.307137	0.872248
Skewness	0.698690	0.250900	0.635160	−0.124949	−0.576190
Kurtosis	2.522287	2.092878	2.714132	2.046960	2.419577

shows the results of the descriptive statistics, which show that GDP and CO₂ have the highest and the lowest mean values, respectively. GDP has the highest maximum value, while education and CO₂ are negatively skewed.

Table 2. LLC and IPS unit root tests.

Variable	Im, Pesaran, Shin		Levin-Lin-Chu Unit Root Test
	Level	1st Difference	Level	1st Difference
$lnH{E}_t$	−1.47 *	−5.34 ***	−1.24	−6.85 ***
$ln{G}_t$	6.44	−3.18 ***	4.16	−2.61 **
$lnR{E}_t$	0.26	−2.99 **	−1.34**	−0.97
$lnE{D}_t$	−0.63	−6.47 ***	−1.70**	−5.11 ***
$lnC{O}_{2t}$	0.39	−4.04 ***	−1.57**	−2.12 **

***, ** and * represent significant levels of 1% and 5%, respectively.

shows the empirical outcomes of the unit root tests. It is evident that HE and GDP have a unit root at level I(0). However, HE and GDP are stationary at the first difference. The data of renewable energy, education expenditures, and CO₂ emissions are stationary at the first difference. We, therefore, performed two sets of unit root tests, i.e., [58], [64], and second-generation tests to validate the robustness of the results. The Im, Pesaran, and Shin test results show that GDP, renewable energy, carbon dioxide emissions, and education expenditures have a unit root at level I(0). However, the stationarity of all the other variables is found at the first difference. The CIPS and CADF test results in

Table 3. CIPS and CADF unit root tests.

Variable	CIPS test		CADF test
	Level	First Difference	Level	First Difference
$lnC{O}_{2t}$	0.47	−2.53 **	−1.45	−4.22 **
$lnH{E}_t$	−2.50	−5.40 ***	−1.87	−2.53 **
$lnR{E}_t$	−3.62 **	−5.40 ***	−3.51 ***	−4.84 ***
$ln{G}_t$	−3.31 ***	−5.72 ***	−2.76	−4.52 ***
$lnE{D}_t$	−1.41	−2.46 **	−3.12 **	−2.35 *

***, ** and * represent significant levels of 1%, 5% and 10%, respectively.

also show similar patterns of findings.

Table 4. Cross-section dependence test.

	lnHE	lnG	lnRE	lnED	lnCO2
CD test	17.02 *** (0.00)	17.04 *** (0.00)	17.01 *** (0.00)	17.02 *** (0.00)	15.81 *** (0.00)
LM test	289.91 *** (0.00)	289.88 *** (0.00)	290.84 *** (0.00)	289.91 *** (0.00)	251.04 *** (0.00)

*** represents significant level of 1%.

shows the results of the CD test, with cross-sectional dependence. This is due to the cultural, environmental, and economic dependence among the estimated nations.

Table 5. Westerlund test.

Stats	${\mathit{G}}_{\mathit{t}}$	${\mathit{G}}_{\mathit{a}}$	${\mathit{P}}_{\mathit{t}}$	${\mathit{P}}_{\mathit{a}}$
value	−1.22	−0.61 ***	−1.7	−0.78 ***
Z-value	4.04	4.33	4.17	3.40
Prob	1.00	1.00	1.00	1.00
Robust prob	0.80	0.00	0.55	0.00

*** represents significant level of 1%.

is the Westerlund [63] test results, which confirm the long-run association among the estimated variables. After confirming the co-integration, the long- and short-run analyses can then be conducted. According to the DOLS and FMOLS long-run results, GDP, RE, and education expenditures are negatively associated with health expenditures, as shown in

Table 6. FMOLS and DOLS test results with CO₂.

Variables	FMOLS	Prob	DOLS	Prob
lnRE	−0.41 ***	0.00	−0.49 ***	0.03
lnG	−0.86 ***	0.00	−0.78 ***	0.00
lnED	−0.08 ***	0.00	−0.07 ***	0.00
lnCO2	0.96 ***	0.00	0.96 ***	0.00

*** represents significant level of 1%.

and

Table 7. FMOLS and DOLS test results with EF.

Variables	FMOLS	Prob	DOLS	Prob
lnRE	−0.41 ***	0.00	−0.97 ***	0.03
lnG	−0.19 ***	0.01	−1.69 ***	0.01
lnED	−0.07 ***	0.05	−0.84 ***	0.05
lnEF	0.21 ***	0.00	0.67 ***	0.00

*** represents significant level of 1%.

. These results are in line with [4]. These results imply that investing in the renewable energy sector improves air quality, which puts less pressure on health expenditures. At the same time, when the government spends more money on education, people tend to pay more attention to their health and improve their health status, which reduces a country’s health expenditure significantly. GDP is also negatively associated with health expenditure, which means that a rise in GDP reduces health expenditure in South Asian countries. These results suggest that these countries are experiencing economic growth by compromising on the health of the people. Their spending on education is not high enough to improve the health of the people. The governments of these five countries must increase their health expenditure as economic growth increases. CO₂ emissions are positively related to HE. This is an indication that a rise in CO₂ emissions will also increase health expenditure in South Asian countries. This finding is consistent with that reported in [4].

5. Conclusion and Recommendations

It has been widely discussed that both developed and developing countries are facing the problems of drastic environmental changes. Considering the vulnerabilities of South Asian countries, this work attempts to highlight some important factors of health expenditure. Therefore, this study mainly aims to find associations between health expenditure, CO₂ emissions, education expenditure, and renewable energy in selected South Asian countries. This work utilized the annual data of 1990–2018, and applied FMOLS and DOLS estimators over the panel data of five South Asian countries. The panel unit root tests were employed to check the stationarity of the data. After the order of integration confirmation in time series, we applied the Westerlund co-integration test to determine the integration between the time series. FMOLS and DOLS tests were applied to check the association among the data, to show how the dependent and independent variables are inter-related. The empirical results show that HE and CO₂ emissions are positively and significantly related, which implies that an increase in CO₂ emissions increases the financial burden on the health sector in the various countries. Moreover, the FMOLS and DOLS tests revealed that the use of renewable energy reduces health expenditure in the five South Asian countries studied. GDP is also negatively associated with health expenditure, which means that a rise in GDP reduces health expenditure in the five South Asian countries. Renewable energy puts less pressure on environmental quality, which leads to less spending on health. The policy implication of this study is that South Asian countries need to invest in clean energy sources, such as geothermal, solar, wind, waste-to-energy, etc. It is also important for the countries to implement measures that promote efficiency in their energy sector. In doing so, these governments should consider the implementation of financial policies, such as tax waivers, and the provision of incentives to individuals and companies to use RE, since the initial cost of RE technologies is generally high. Subsidies on fossil fuel products can also be removed if such a policy exists in any of the countries, and the proceeds would be channeled into RE development. Various industries should be encouraged to use efficient and advanced technologies during production, to help cut down on their emissions and enhance economic growth. Taxes can, therefore, be imposed on firms whose activities result in heavy air pollution.

The various countries must also increase their investment into their education sector, particularly in the area of innovation in the RE sector. These countries should also consider amending aspects of their educational curriculum to include energy efficiency, environmental protection, and RE studies beginning from the basic level of education. Educating the populace is one of the possible ways that government policies on the environment can be institutionalized. These steps will ultimately lower the health expenditure of these developing countries.

Apart from the contribution of this work, future research can analyze the data for some gulf and other groups of countries to form effective environmental policies. Future work can use country-specific data by adopting the econometric techniques in this study.