Exploring the Roles of Education, Renewable Energy, and Global Warming on Health Expenditures

Abstract

Renewable energy sources—which are available in abundance all around us and are provided by the sun, wind, water, waste, and heat from the Earth—are replenished by nature and emit little to no greenhouse gases or pollutants into the air. This paper builds upon a preceding study that examined beliefs, perceptions, and attitudes regarding renewable energy technologies. In this study, we examine the implications renewable energy sources may have on science, technology policies, and education. This study embraced a sequential mixed-methods methodology to accomplish its objectives. The primary goal of this study was to ascertain the impact of global warming, education, and renewable energy on healthcare expenditure. In order to determine the impact of renewable energy on health care expenditure, the present research study coupled renewable energy with gross domestic product (GDP). Based on the long-term outcomes derived from our Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators, GDP, renewable energy, and education were found to be adversely correlated with healthcare expenditure. To collect data, we conducted interview sessions, which were subsequently complemented by a survey that was completed by 400 respondents (specifically chosen research participants). We then conducted thematic analyses. The findings of this study underscore a compelling inverse relationship linking GDP, renewable energy integration, and education with healthcare spending. Regions displaying lower healthcare outlays are seemingly less strained ecologically due to their judicious utilization of renewable energy sources. Furthermore, based on our findings, a noteworthy correlation between healthcare expenditure and global warming was observed, underscoring the potential escalation of financial burdens with intensifying climate shifts. In light of our findings, advocating for the amplification of renewable energy deployment emerges as a prudent strategy to fortify public health while mitigating healthcare expenses. Augmenting investments in education acts as a pivotal lever to steer sustainable growth. It is noteworthy that the survey participants’ level of familiarity with renewable energy technology was not found to be connected to their educational backgrounds, revealing a disparity in knowledge. The prevailing unfamiliarity with sustainability principles among the respondents underscores the need for widespread awareness initiatives. This study acts as a holistic exploration of the ramifications of renewable energy on healthcare expenditure; this is intertwined with the complex tapestry of global warming and education. The implications of renewable energy reverberate across policy and practice, accentuating the urgency of sustainable energy adoption for the betterment of public health and economic resilience. Future studies should focus on conducting more nuanced assessments of socio-economic aspects and generate strategies for bridging knowledge gaps among diverse stakeholders.

1. Introduction

The idea of meeting the demands of today’s society without negatively impacting the capacity of future generations to satisfy their demands is used to describe the concept of sustainable growth [1]. The proactive involvement of enterprises in environmentally responsible technology innovation is becoming increasingly important due to the acceleration of ecological deterioration. Due to the world’s steadily worsening ecological condition, businesses are increasingly required to prioritize the development of green technologies. Renewable energies are an innovative technical approach that have the ability to reduce pollutants in the environment, save energy, and make it easier to recycle waste and substances. The essential demand for sustainable methods and the alignment of business performance with protecting the environment are both addressed by these creative ideas [2]. Fresh air is beneficial, especially in industrialized/developed countries [3]. Lung diseases are caused by CO₂ and SO₂ [4]. Thus, air pollution worsens health issues [5]. The World Health Organization (WHO) estimates that air pollution kills three million people each year [6]. The financial impact of air pollution is unknown [7,8]. Since global warming harms the health of the population, mainly through air pollution (predominantly increased CO₂ emissions), administrations worldwide are working to reduce carbon emissions [9,10,11].

The ability of people, societies, organizations, and governments to take action to advance sustainable development is nurtured and improved via education. To achieve sustainable development, it is imperative that governments initiate the implementation of cutting-edge renewable energy technology [12]. Effective education regarding sustainable energy will play a crucial role in fostering widespread acceptance and the successful integration of renewable energy technology, leading to profitability [13]. The present study aims to fill a gap in the literature which has arisen due to the relative scarcity of studies examining the relationships among non-economic variables and healthcare expenditure. The authors of [14] examined the link between healthcare expenditure and schooling. They discovered that while healthcare expenditure is unaffected by education, it is favorably impacted by education opportunities. However, some studies have discovered that schooling places a detrimental strain on healthcare expenditure [15]. Several studies [16,17,18,19] have shown that healthcare expenditure boosts gross domestic product (GDP) and affects the health of the public. Global medical business development has accelerated due to the demand for efficient healthcare facilities. Such expenses place a strain on governments because HE is essential for economic growth [7]. Population and health spending must be evaluated in order to increase healthcare expenditure [20]. Ross and Chia-Ling [21] noted that growth in the economy and educational opportunities affect healthcare. Healthcare expenditure is profoundly influenced by a confluence of determinants, as elucidated by the authors of [22]. The advancement of the economies of developing nations, as well as the dissemination of quality education in these nations, has synergistically fortified their healthcare systems. Consequently, the need to comprehensively investigate the factors that shape healthcare expenditure has risen. This necessitates a diligent investigation into variables such as air pollution, GDP, and education, among others [23,24,25].

Global warming is a major environmental concern, since it harms the health of human society [26]. The authors of [27] employed the Middle East and North Africa (MENA) method to discuss healthcare expenditure factors using datasets from 1995 to 2014. The MENA method is a research approach/framework that is commonly employed to analyze and explore various economic and social factors within the context of the Middle East and North Africa regions. The MENA method often involves collecting and analyzing data from different sources, including statistical agencies, international organizations, academic research, and publications, to gain insights into a wide array of topics, such as economic development, healthcare, education, trade, and more. Pollution and related factors (e.g., CO₂ emissions, healthcare expenditure, and PM10) have been shown to have a strong correlation using the autoregressive distributed lag (ARDL) approach [26,28]. Chaabouni et al. [29] conducted a study on CO₂ emissions, healthcare expenditure, and GDP in 51 countries from 1995 to 2013. The GMM and dynamic simultaneous equations were used for that analysis. Researchers found that there was a dual causation relationship among CO₂ emissions, GDP, and healthcare expenditure. In the majority of economies, global warming was found to cause healthcare expenditure to increase in a unidirectional manner. The release of CO₂ affects public health and ecosystems [30]. GDP is positively correlated with healthcare expenditure, but CO₂ exhibited a negative correlation. Other research projects have confirmed that polluting the atmosphere increases death rates. GDP and global warming are directly linked [31]. Healthcare expenditure is influenced by the ecological footprint [32] and pollutants like SO₂ and CO₂ [33]. Industries use energy from fossil fuels but could switch to renewable/sustainable sources, thereby enhancing the condition of the environment [34]. Renewable energy can boost GDP without harming the ecosystem [35]. Energy derived from renewable sources can also stimulate employment opportunities [36].

Numerous studies have examined the relationship between healthcare expenditure and air pollution [33,37,38,39]. Research studies have indicated that a significant obstacle to the development and application of renewable energy sources is the insufficient level of widespread acceptability [30,40]. Our research examines CO₂ emissions, economic issues, and healthcare expenditure as a primary focus [29]. In addition, this study establishes a connection between renewable energy, healthcare expenditure, and economics. Wang et al. [41] used ARDL analysis to examine the relationships between GDP, healthcare expenditure, and CO₂ emission. The ARDL method provides reliable outcomes while considering the number of participants and includes an error correction term (ECT). In their research, Khan et al. [42] employed canonical co-integration regression (CCP) to analyze the relationships between expenditure, medical care, and pollution. The study utilized the vector error correction model (VECM) and fully modified ordinary least squares (FMOLS) panels to assess these connections.

This study examines the relationships between healthcare expenditure, education, renewable energy, and global warming. This hybrid technique investigation also explores public perception of renewable energy technologies. The subjective portion examined individuals, while the numerical component analyzed factors to uncover relationships among the aforementioned variables. Research using quantitative methods can be applied to large numbers of people and can be generalized. This study was based on a participant cohort of 870,234 individuals [43], augmented by a survey of 400 respondents. Efficient renewable energy education fosters sustainable development [44].

2. Materials and Methods

All the information provided can be traced back to its origin in the World Bank Data Indicators (WBDI). An in-depth analysis of the WBDI in relation to the education sector presents a comprehensive overview of performance and major challenges. This analysis aims to synthesize a nuanced understanding of the dynamics of the education sector, drawing upon the data and insights provided by the WBDI [45]. Current healthcare spending (as defined by percentage unit) is the dependent variable in the current research, with GDP acting as a financial indicator. Furthermore, as separate elements, the population aged 25 years and older, having completed at least lower secondary education, is included. The following represents the fundamental equation of this research [46].

$$lnH{E}_t={\beta }_0+{\beta }_1 R{E}_t+{\beta }_2 G_t+{\beta }_3 E{D}_t+{\beta }_4 C{O}_t+{\epsilon }_t$$

(1)

The provided formula represents a linear regression model used to examine the relationship between the natural logarithm of a healthcare-related variable at time $t$ ($lnH{E}_t$) and several independent variables. Coefficients ${\beta }_1$, ${\beta }_2$, ${\beta }_3$, and ${\beta }_4$ correspond to the impact of renewable energy ($R{E}_t$), GDP ($G_t$), education expenditure ($E{D}_t$), and an external context factor ($C{O}_t$), respectively. The intercept term ${\beta }_0$ represents the predicted value of $lnH{E}_t$ when all independent variables are zero. The model aims to explain the variation in $lnH{E}_t$ and the error term ${\epsilon }_t$ accounts for unexplained differences between predicted and observed values. This equation provides a quantitative framework for understanding how changes in these variables influence healthcare-related outcomes.

In this study, several phases are included in the final calculations. First, experiments were conducted to determine if the information being analyzed exhibited any cross-section dependency (CD). These tests are essential to obtain trustworthy and objective results. CD tests encompass a variety of statistical tests designed to examine the presence of CD in panel data [47]. These tests provide a comprehensive assessment of the overall CD and help determine the appropriate modeling approach for a panel data analysis. CD tests evaluate different aspects of cross-section dependence, such as spatial or temporal dependence, and consider various dependence structures [48]. They help researchers understand the nature and extent of cross-section dependence in the data, enabling them to choose appropriate estimation techniques to account for this dependence. By accounting for CD, the estimation results can be more reliable, and the inferences can be more accurate [49].

The Lagrange multiplier (LM) test is a statistical test used to assess the presence of CD in panel data analyses. It examines whether the error terms in a panel model are correlated across different units or entities (e.g., countries, regions, individuals). The test is based on the idea that if there is CD, the error terms are not independently distributed, which can lead to biased and inefficient estimates [50]. The LM test involves estimating the panel model and then calculating the LM test statistic, which follows a chi-square distribution under the null hypothesis of no CD. If the test statistic exceeds the critical value, it is evidence of CD, suggesting that the error terms are correlated across the entities [51]. In our study, to determine if CD existed, both the LM [52] and CD [50] tests were applied. The following mathematical formulation was applied:

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

(2)

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

(3)

In this context, T stands for the overall quantity of time periods, whereas N stands for the total range of cross-sections or objects analyzed. The pairwise correlation between the errors of cross-sections i and j is expressed by the symbol ${\partial }_{ij}^t$, which also indicates the strength of the correlation between the error terms. The researchers first conducted the CD test and then performed a slope homogeneity test. This test aims to determine if the panel’s slopes for the examined variables are homogeneous or uniform across the board. Slope heterogeneity can lead to erroneous or misleading findings [53]. To ensure the accuracy of the outcomes of estimation, it is crucial to determine if slope homogeneity exists. The mathematical formula for the slope homogeneity tests of ${\widetilde{∆}}_{adj}$ is as follows [54].

$$\left\{\begin{array}{c}{\widetilde{∆}}_{adj}=\sqrt{N \left(\frac{N^{-1}\tilde{S}-E \left({\tilde{Z}}_{iT}\right)}{\sqrt{var \left({\tilde{Z}}_{iT}\right) }}\right)}\\ \left({\tilde{Z}}_{iT}\right)=\frac{2K\left(T-K-1\right)}{T+1}\\ E \left({\tilde{Z}}_{iT}\right)=K\end{array}\right.$$

(4)

Below, $\tilde{S}$ is used in the modified test:

$$\tilde{S}={\sum }_{i=1}^n\left(\widetilde{{\gamma }_i}- {\tilde{\gamma }}_{WFE}\right)\frac{Y_i^{\prime } M_T X_i}{{\tilde{\varnothing }}_i^2}\left(\widetilde{{\gamma }_i}- {\tilde{\gamma }}_{WFE}\right)$$

(5)

Pooled OLS test values are represented by ${\tilde{\gamma }}_i$ for each unit. ${\tilde{\gamma }}_{WFE}$ and $M_T$ are the weighted pooled estimator and identity matrix, respectively. Unit root tests can then be used to verify stationarity among the variables. Unit root tests are statistical tests used to determine whether a time series variable has a unit root or not. A unit root indicates that the variable is non-stationary, meaning it has a stochastic trend and does not exhibit a constant mean and variance over time [55]. This study used first- and second-generation unit root testing. Ordinary least square regression can be used for level I(0) implementation. The combination of the initial difference requires checking factor cointegration [56]. The current research used the CD-based cointegration test [57]. Following cointegration confirmation, the long-term association between parameters that are relevant is addressed. This study used dynamic ordinary least squares (DOLS) and FMOLS estimators. The theoretical explanation is as follows [58]:

$${\tilde{\gamma }}_{FMOLS}={\left[N^{-1} {\sum }_{i=1}^N{\left({\sum }_{t=1}^T{U}_{it}-{\overline{U}}_i\right)}^2\right]}^{-1}. \left[\left({\sum }_{t=1}^T{U}_{it}-{\overline{U}}_i\right){\widehat{S}}_{it}-T{\widehat{∆}}_{\epsilon \mu }\right]$$

(6)

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

(7)

This study used categorized allocation selection. Population statistics were used to categorize male and female participants. The total number of participants in the investigation was 870,234 [43]; additionally, 400 people were surveyed. The study assessment included 14 open-ended questionnaires. The questionnaire inquired about individuals’ awareness of renewable energy terms, technology, sources of information, and the reason that they thought renewable energy should replace fossil fuels. The remaining inquiries assessed the opinions of participants regarding renewable energy technology and their commitment to sustainable energy use.

Cronbach’s alpha reliability test was used to assess the internal coherence of the questionnaire. Cronbach’s alpha measures the internal consistency of an index or examination; it is frequently employed to examine several Likert scale declarations to check for idea consistency. The formula is shown below [59]:

$$\alpha =\left(k \frac{k}{k-1 }\right)\left(1-\frac{{\sum }_{i=1}^k{\sigma }_{yi}^2}{{\sigma }_x^2}\right)$$

(8)

where k indicates the number of samples, $\sigma$ is coefficient reliability, which is in the range [0,1], and $\alpha$ is the efficiency of coefficients below 0.70, which are typically considered to be inappropriate. Higher coefficient values indicate that items measure the same concept [60]. In this investigation, the Cronbach’s alpha coefficient was 0.82, indicating the reliability of the tool. The statistical information was reviewed employing SPSS Version 23, while information from qualitative sources was thematically assessed. As mentioned, assisting in GDP computation (Equation (9)) is a fundamental way to measure the economic output of a country. It quantifies the total value of all goods and services produced within a nation’s borders during a specific time frame, usually a year or a quarter.

GDP = C + I + G + (X − M)

(9)

In this equation, $C$ represents spending by households on goods and services like food, housing, and entertainment; $G$ is equal to government spending; $I$ encompasses business spending on capital goods, such as machinery and equipment, as well as residential and commercial construction and the difference between a country’s exports ($X$) and imports ($M$), reflecting international trade [61].

3. Results

In this section, statistical procedures and tests such as unit root, CD, FMOLS, and DOLS tests are used. In this investigation, long-term calculations employed FMOL and DOLS. Both methods aimed to eliminate heterogeneity and unpredictability. The current work used an effective strategy to reduce CS problems in data collected in panels in order to find coherence across the parameters in question. The discussion focuses on long-term correlations between medical care, energy from renewable sources, education, and GDP. The statistical descriptions used include panel unit root, co-integration, FMLOS, and DOLS tests.

The table in the results section provides a comprehensive statistical overview of key variables, including healthcare expenditure, renewable energy, GDP, education, and CO₂ emissions. Notably, the medians of GDP and CO₂ stand out, indicating their contrasting impacts on the examined framework. The interquartile ranges reflect the dispersion of the distribution, while standard deviations highlight the variability of each variable. Skewness and kurtosis values offer insights into the distribution shape and the presence of outliers. Collectively, the table enhances our understanding of the dataset’s statistical characteristics, revealing distinct patterns and tendencies among the variables, thereby contributing to a more comprehensive analysis of their relationships and implications (

Table 1. Descriptive statistics.

	Health Expenditure	Renewable Energy	GDP	Education	CO2
Mean	1.362	3.981	6.525	0.963	−0.880
Median	1.245	3.729	6.417	0.909	−0.515
Maximum	1.808	4.306	8.001	1.604	0.493
Minimum	0.723	3.440	5.919	0.438	−2.630
Std. Dev.	0.240	0.272	0.533	0.310	0.284
Skewness	0.634	0.255	0.147	0.102	− 0.477
Kurtosis	2.290	1.997	2.558	2.175	2.011

). GDP emerges as the most significant factor, whereas education and atmospheric carbon are largely insignificant.

Table 2. CIPS unit root tests.

	Level	1st Difference
ln(Health Expenditure)	− 1.02	− 5.17
ln(Renewable Energy)	0.23	− 3.12
ln(GDP)	5.95	− 4.09
ln(Education)	− 0.61	− 7.04
ln(CO2)	0.46	−3.85

displays CIPS outcomes. GDP and health expenditure are unit roots at level I(0). At initial variance, health expenditure and GDP are fixed. At the initial distinction, energy production from global warming, renewable sources, and education remain unchanged. Thus, this work was conducted in two sets of unit root tests [62], and second-generation tests were used to confirm the findings. GDP, global warming, renewable energy, and schooling have unit roots at level I(0). Every other factor is stationary at the fundamental discrepancy level.

Table 3. Cross-section dependence test.

	ln(Health Expenditure)	ln(Renewable Energy)	ln(GDP)	ln(Education)	ln(CO2)
CD test	16.95	17.12	16.87	17.08	16.62
LM test	279.01	281.33	280.49	280.71	273.25

exhibits the CD test findings. It is thought that societies are culturally, environmentally, and economically interdependent.

The Westerlund [63] test findings in

Table 4. Westerlund test.

	Gt	Ga	Pt	Pa
value	−1.09	−0.65	−1.71	−0.83
Z-value	4.18	4.51	4.09	4.03
Prob	1.00	1.00	1.00	1.00
Robust prob	0.78	0.60	0.59	0.45

validate the long-term connection among the predicted factors. Co-integration analysis allows for both long- and short-term studies. According to the DOLS and FMOLS long-run data (see

Table 5. FMOLS and DOLS test results with CO₂.

	FMOLS	Prob	DOLS	Prob
ln(Renewable Energy)	−0.43	0.00	− 0.51	0.00
ln(GDP)	− 0.79	0.00	− 0.70	0.00
ln(Education)	− 0.16	0.00	− 0.14	0.00
ln(CO2)	0.86	0.00	0.88	0.00

), GDP, renewable energy, and expenditure on education have an adverse influence on healthcare.

4. Discussion

The results of the Westerlund test affirm the lasting links between GDP, renewable energy, education spending, and healthcare, showcasing their unfavorable effects, in line with insights from the DOLS and FMOLS analyses. This highlights the intricate intermingling of economic, environmental, and social factors. These findings suggest possible trade-offs between economic advancement and public well-being, underscoring the importance of cohesive policymaking that takes into account the diverse consequences of choices across various domains. This approach ultimately nurtures a robust and sustainable path of development.

The implications extracted from these findings highlight a noteworthy relationship between supporting the renewable energy sector and the mitigation of healthcare costs through the reduction of atmospheric pollution. Moreover, when a nation invests in advancing its educational framework, a tendency emerges among citizens to be more proactive about their health, resulting in diminished healthcare expenses on a national scale. The nexus between GDP growth and reduced healthcare expenditure underscores an inverse correlation, suggesting that certain nations might be achieving economic prosperity at the expense of their citizens’ well-being. Evidently, there is a deficiency in terms of allocating adequate resources to educational institutions, hindering meaningful impacts on individual health trajectories. As economies progress, it becomes imperative for governments to elevate their healthcare expenditure correspondingly. A salient correlation emerges between the escalating effects of global warming and heightened healthcare costs, hinting at the potential financial implications of mounting environmental concerns.

In this study, respondents had limited knowledge of renewable energy technologies, as demonstrated in both types of information analysis. Numerous individuals noted that they observe renewable energy technologies in operation but lack an understanding of how they produce energy. While some have knowledge about climate change, fewer are aware of the emission of greenhouse gases. Despite widespread recognition of and worry about climate change, investigations have demonstrated that a lack of fundamental understanding of its causes, consequences, and remedies prevents individual involvement [64,65,66]. Thus, recognizing the impact of global warming can help increase public support for solutions. Our statistical analysis indicated that only about 20% of respondents were aware of the importance of sustainable growth. This indicates that people need more knowledge of comprehensive growth, i.e., information related to renewable energy technologies.

This variable was shown to be unrelated to educational level. As indicated in Table 5, individuals with various types of schooling had significantly lower comprehension ratings. These findings have significant implications for national scientific and technological education programs. The education system lacks alignment with culture and the adaptability required to meet national and global requirements, notably in technological and scientific subjects [67]. According to the authors of [68], most students at polytechnic institutions favor graduate degrees in management because they offer better employment prospects.

5. Conclusions

This study focuses on vulnerable healthcare expenditure variables; it seeks to establish a connection between health care expenditure, global warming, education, and alternative energy sources. The present inquiry employed FMOLS and DOLS estimators. Panel unit root tests were used to check the stationary behavior of our obtained data. The Westerlund co-integration test was used to determine periods of integration after the sequence had undergone integration verification. FMOLS and DOLS tests showed how the dependent and independent variables were interconnected. Health expenditure and global warming are extremely closely correlated, implying that CO₂ pollution raises the cost of healthcare. The findings derived from the application of FMOLS and DOLS methodologies conspicuously demonstrated the ameliorative impact of renewable energy on the reduction of healthcare expenditure. Concurrently, it was determined that GDP possesses a correlative influence on diminishing healthcare outlays. Remarkably, the adoption of renewable energy engenders the mitigation of ecological stress and, by extension, a curbing of medical costs. Considering these insights, this report strongly advocates for the widespread adoption of renewable energy sources, encompassing geothermal, wind, solar, waste-to-energy conversion, and comparable alternatives. Furthermore, it is recommended that governmental bodies proactively encourage energy usage efficiency measures as a complementary endeavor. This collective approach could engender substantial healthcare savings while representing a major stride toward the realization of sustainable energy. Given that renewable energy technologies can be expensive, administrations should explore financing strategies like tax exemptions and subsidies for people and enterprises that employ renewable energy. To reduce pollution and boost GDP growth, enterprises should embrace effective and advanced manufacturing technology. Thus, air pollution-causing enterprises should be subject to taxation. Governments must spend more on learning, in particular, regarding renewable technologies innovation. Education may institutionalize government environmental policies. It is impossible to overstate the importance of education in matters related to energy. Public attitudes toward renewable energy technology deployment depend on their level of understanding and perceptions about these technologies. Due to the complexity of renewable energy technology, people’s knowledge needs to extend beyond what is espoused by the mainstream media. Education must be adjusted to meet societal needs. Additionally, there should be a greater focus on environmental sustainability in science and technology instruction and governmental administration. To enhance our general understanding of renewable technologies beyond the classroom and what is offered by the media, public discussions, seminars, and technological and scientific exhibitions on renewable energy products need to be employed. In future research endeavors, it would be valuable to conduct more intricate evaluations of the socio-economic dimensions surrounding renewable energy adoption, delving into its multifaceted impacts on various strata of society. Furthermore, exploring comprehensive strategies to effectively bridge the knowledge gap among diverse stakeholders, ranging from policymakers and industry leaders to the public, would foster a more informed and collaborative approach toward sustainable energy integration. This could involve tailored educational initiatives, targeted awareness campaigns, and inclusive forums for knowledge exchange, all of which have the potential to contribute to the advancement of renewable energy implementation and its associated benefits.