Examining the Nexus Between Renewable Energy, CO₂ Emissions, and Economic Factors: Implications for Countries Marked by High Rates of Coronary Heart Disease

Abstract

This study investigates the complex interconnections between renewable energy adoption, carbon dioxide (CO₂) emissions, and economic factors in 50 countries marked by high rates of coronary heart disease (CHD) between 2000 and 2020. Utilizing a sophisticated two-step system generalized method of moments (GMM) estimator and panel data method, our analysis reveals a significant negative impact of renewable energy on greenhouse gas emissions, emphasizing the role of sustainable energy sources in mitigating environmental and quality of life degradation. Additionally, our findings support the existence of the environmental Kuznets curve (EKC), demonstrating an inverted U-shaped relationship between gross domestic product (GDP) per capita and CO₂ emissions. In line with nascent studies, the EKC for CO₂ emissions suggests that as a country’s GDP per capita increases, the CO₂ emissions initially rise but eventually decline after reaching a certain level of economic development, forming an inverted U-shaped relationship. Specifically, nations facing high cardiovascular heart disease mortality rates display an important link between renewable energy integration and improved environmental quality. These insights underscore the urgency for countries to transition rapidly to green energy, advocating for policies that promote renewable technologies through partnerships between the public, private sector, and government entities. Furthermore, the study emphasizes the necessity of innovative structural reforms in developing countries to stimulate economic growth beyond the EKC turning point, ensuring sustainable development while curbing their carbon footprint resulting from economic activities. Future research should explore the broader global contexts, considering variables like social capital and globalization to inform targeted policies aimed at safeguarding public health and the environment.

1. Introduction

The global CO₂ emissions level has increased by more than 50% kilotons since 1990. Cross-country research shows that an increase in CO₂ emissions has an adverse effect on a wide range of socio-economic and life quality outcomes [1], longevity [2], and infant mortality [3]. Numerous health impacts may arise from exposure to high CO₂ emissions, including symptoms such as headaches, dizziness, restlessness, sensations of tingling (or pins and needles), respiratory difficulties, perspiration, fatigue, heightened heart rate, elevated blood pressure, and prevalence of ischemic heart disease. The yearly increase in mortality due to ambient air pollution in Europe stands at 790,000, with the largest health effects observed through cardiovascular diseases (CVDs). While air pollution as a trigger for respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), and lung cancer is intuitive, its significant role in cardiovascular disease is now also well established. Some of the most relevant pathways linking air pollution to cardiovascular morbidity and mortality due to both long- and short-term exposure (even day-to-day fluctuations) are the induction of oxidative stress, systemic inflammation, endothelial dysfunction, atherothrombosis, and arrhythmogenesis [4]. The yearly increase in mortality due to ambient air pollution consequently also results in a reduction in the average life expectancy on the European continent by approximately 2.2 years annually [5]. For example, a recent study by Liu et al. [6] (p. 12) documents that “for a 1 mg/m³ increase in average carbon monoxide concentrations on the present day and previous day … observed significant increments in mortality of 1.12% from cardiovascular disease, 1.75% from coronary heart disease, and 0.88% from stroke”.

Hence, it is imperative to ascertain the efficacy of CO₂ emission-reduction variables in mitigating the impact of CO₂ emissions as it relates to heart disease mortality rates. One variable that has been explored in the context of the growth-emissions nexus is renewable energy consumption. While ample studies show that renewable energy improves economic growth and decreases CO₂ emissions across different geographical regions and income groups [7,8], renewable energy’s effect on CO₂ emissions in countries with high death rates from heart disease has specifically not been explored.

As a key facet of sustainable energy development strategies [9], ensuring universal and equitable access to energy resources can play an important role in elevating living, health, and education standards. Energy accessibility stands as a crucial determinant of overall well-being, often resulting in improved access to essential resources like food and clean water, job creation, diminished health-related burdens, and enhanced livelihoods. Consequently, a transition toward a green economy based on renewable energy sources is an imperative step in both enhancing the quality of life for all beings and safeguarding the planet for future generations. Renewable energy bolsters energy security by harnessing domestic energy resources, aids in combatting global warming by reducing pollution, and safeguards human health from the detrimental effects of air pollution. Considerable attention has been devoted to renewable energy’s influence on overall economic activity; however, the relationship to CO₂ emissions in the context of human development calls for more in-depth understanding. In this context, renewable energy holds the potential to advance the achievement of sustainable development objectives, particularly goal 3, which focuses on good health and well-being. This potential is underpinned by three key factors: Firstly, renewable energy stands out for its minimal CO₂ emissions, making it a formidable ally in the fight against climate change, which has adverse effects on human health [10]. Secondly, unlike certain traditional energy sources like coal and traditional biomass, renewable energy has a minimal local environmental footprint. This aspect aligns with the objective of promoting a healthy life by mitigating local air pollution and offering alternatives to indoor air pollution sources. Lastly, energy, when harnessed from renewable sources, has the capacity to enhance human health capital by fostering gender equality and advancing educational opportunities [11,12].

The aim of this study is to contribute to existing research on energy emissions research by focusing on a specific sample that has been selected based on human health outcomes. We do so as emissions are shown to have a direct negative impact on human health capital. Indeed, we extend this research by using data from 50 countries with the highest death rates from coronary heart disease or ischemic heart disease. By focusing on countries with high CHD rates, we sought to examine the potential role of renewable energy adoption in reducing CO₂ emissions as it relates to improving environmental quality—a factor closely tied to public health outcomes. In addition, this analysis allows us to test the environmental Kuznets curve (EKC) hypothesis in a setting where environmental degradation may be closely tied to health outcomes, thus adding a novel dimension to the current understanding of the EKC hypothesis.

Globally, approximately 1 in 12 individuals is affected by a heart or circulatory disease. The number of people living with these conditions has risen significantly over the decades, from an estimated 305 million in 1990 to 640 million by 2024. CHD is a major global health concern, accounting for nearly one in three deaths worldwide. Thus, an improved understanding of the relationship between renewable energy and CO₂ emissions in countries with the highest mortality rates from CVDs may offer novel policy implications. Using a two-step generalized method of moments (GMM) estimator, we find that renewable energy significantly negatively impacts CO₂ emissions levels. The rest of the research is structured as follows: Section 2 reviews related empirical studies, Section 3 presents data and empirical strategy, and Section 4 presents the main results. In Section 5, the results of this study are summarized.

2. Literature Review

2.1. Cardiovascular Disease Mortality and CO₂ Emissions

The body of empirical research exploring the potential effects of CO₂ exposure on human health is growing [13,14,15]. Since cardiovascular diseases (CDVs) are estimated to be the leading cause of death globally [16], particular attention has been directed toward modeling the dynamic nexus of fossil fuel dependence and cardiovascular mortality [17,18,19,20]. The existing literature on this topic has employed multiple approaches to capture the effects of greenhouse gas emissions on cardiovascular health.

Over the last two decades, a group of epidemiological studies examining the link between greenhouse gas emissions and heart attack death rates across different country groups collectively confirmed the positive association between the two. In broad terms, these studies can be classified into those that have tested the effects of short-term [21,22,23] and long-term [24,25,26] pollution exposure on cardiovascular health. Numerous studies investigating the impact of acute air pollution exposure on heart failure employ population-based time-series analysis (temporal variations), while chronic exposure has been studied through the use of data taken from multiple geographic regions (e.g., fixed-site monitors). Both types of studies primarily rely on studying the composition and size of gaseous pollutants and their spatial variations [27,28]. Clearly, fossil fuels are the greatest source of air pollution in urban areas and, thus, tend to represent more severe problems associated with cardiovascular health in these regions. In contrast, the consumption of energy from renewable sources significantly reduces CO₂ emissions, improving air quality and mitigating the adverse health impacts associated with pollution. Although epidemiological studies yield robust findings, their limitations are extensive. One major limitation is that evidence from epidemiological studies is not always representative of individual exposure levels; in other words, each subject’s exposure to air pollution has been averaged based on citywide pollution measurements.

In their review of the literature, Lee et al. (2014) [29] focused on exploring the association between greenhouse gas concentrations and CVD mortality. They state that these two variables are generally strongly correlated, and the effects tend to be more pronounced among certain groups of the population, for example, people with preexisting heart conditions, children, and older adults. Nevertheless, the lack of research on different types of pollution contributing to cardiovascular mortality, as pointed out by the authors, prevents scholars from establishing clear causal linkages between the two. In the same vein, the adverse cardiovascular effects of air pollution exposure are confirmed by the systematic review by Shah et al. [30] (2013) of almost 200 studies. Since much of the existing literature encompasses developed countries (major US and European cities), the authors conclude that the magnitude of the impact of greenhouse gas emissions on CVD occurrence in developing countries remains understudied.

Potential serious health consequences of CO₂ emissions exposure have resulted in the emergence of studies assessing the health risks, particularly cardiopulmonary, of different sources of air pollution. For example, a number of panel studies suggest that motorized or vehicular emissions are significant triggers of heart attack mortality [31,32,33,34]. Similarly, another strand of literature attempts to assess how industrial emissions affect cardiovascular health outcomes [35,36]. These studies and numerous evidence from global assessments report that the rise in heart attack death rates in recent years can also be explained by extreme weather events caused by increased fossil fuel combustion.

2.2. Renewable Energy and CO₂ Emissions

In recent years, the role of renewable energy in reducing greenhouse gas emissions has been of particular interest among the scientific community. Previous studies on this topic generally reveal that the deployment of renewable energy tends to reduce carbon dioxide emissions. For example, an empirical study using sample data from 128 countries for the period from 1990 to 2014 and employing a common correlated effects mean group (CCEMG) estimator suggests that a high penetration of renewable energy can curb carbon emissions [37]. Although they vary across regions due to different energy consumption patterns, the results of the study apply to all countries studied, regardless of their economic standing. These findings are in line with studies employing the GMM estimator [38,39,40], FMOLS panel technique [41], and PMG-ARDL [42].

In the same way, the existing literature examining the energy emissions nexus across different country groups has pointed out the prevalence of a statically negative link between the two variables. Using FMOLS and DOLS estimators covering the annual data for G20 nations over the period 1990–2019, Jamil et al. [43] (2022) report that while economic growth, financial development, and remittances are significantly positively correlated with CO₂ emissions, renewable energy and emissions were negatively linked. Similarly, investment in renewable and nuclear energy has been found to reduce CO₂ emissions in the case of 13 out of 15 OECD economies [44]. In a sample of 25 African economies, Zoundi (2017) [45] finds that renewable energy adoption tends to reduce CO₂ emissions with an increasing effect over time. Mirziyoyeva and Salahodjaev [39] (2023), using a two-step system GMM estimator for the top 50 highly globalized economies, documented a negative nexus between carbon emissions and renewables. Further, the study tested the EKC hypothesis, confirming its existence.

Employing the ARDL cointegration technique and VECM model for a sample of BRICS countries over the period from 1970 to 2010, Sebri and Ben-Salha [46] (2014) suggest that an increase in greenhouse gases encourages policymakers to move away from fossil fuels toward renewable energy sources. The study also reveals the presence of two-way causality between economic growth and renewable energy consumption. Moreover, the positive effect of GDP and CO₂ per capita was confirmed for the G7 countries in a study by Sadorsky (2009) [47]. This study employing FMOLS estimation for annual data from 1980 to 2005 indicates that the rise in oil prices is negatively related to per capita renewable energy use.

Another strand of literature focused on examining the energy emissions nexus in the context of single countries. For example, the negative link between renewables and gaseous pollutants was verified for China [48], Pakistan [49], Thailand [50], and Ecuador [51].

The review of studies discussed above suggests that renewable energy is instrumental in mitigating CO₂ emissions across various samples of countries and regions. At the same time, CO₂ emissions are a significant predictor of quality of life and health outcomes (including CVDs). The major gap that exists in nascent research is whether renewable energy can be used as an important policy tool to reduce CO₂ emissions in countries with the highest levels of CHD rates.

3. Data

3.1. Sample

We used data from 50 countries with the highest levels of coronary disease death rates for the year 2020. The data on all variables in this study were obtained from the World Bank Development Indicators. The dependent variable is CO₂ emissions (CE), which is measured in metric tons per person. The main independent variable is renewable energy consumption (REC). REC is measured in % of the total final energy consumption. The role of renewable energy in CO₂ emissions is examined in the context of the EKC framework. Therefore, we include GDP per capita and its squared term to account for a non-linear relationship between economic growth and environmental degradation [52]. GDP per capita (GDP) is measured in constant 2017 international dollars. In addition, several studies include trade openness, urbanization, and government size to model the CO₂ emissions across regions [53,54]. Trade openness (TO) is measured as bilateral trade flows as a share of GDP. Urbanization is measured by the growth rate in urban population (%), while government size is measured in the final government consumption expenditure as a share of the GDP.

Table 1. Summary statistics.

Variable	Description	Mean	Std. Dev.	Min	Max
CO2	CO2 emissions per capita Source: World Bank Development Indicators	4.541	6.660	0.025	47.657
RE	Renewable energy consumption, % Source: World Bank Development Indicators	23.867	24.625	0.000	95.080
GDP	GDP per capita Source: World Bank Development Indicators	13.981	15.760	0.823	99.147
TO	Trade as % of GDP Source: World Bank Development Indicators	86.404	33.888	0.785	206.767
URB	Urban population growth, % Source: World Bank Development Indicators	1.810	2.099	−6.512	17.764
GS	Government final consumption expenditure as % of GDP Source: World Bank Development Indicators	16.583	8.097	2.047	74.067

reports the descriptive statistics for the main variables used in this study. Regarding CO₂ emissions, the data range from 0.03 Central African Republic to 46.42 metric tons per capita in Qatar (see Table 1). Renewable energy consumption is, on average, 24% of the total energy use in our sample, with the Central African Republic reaching 95.08%, followed by Haiti and Zimbabwe.

3.2. Econometric Model and Estimation Strategy

We assess the impact of REC on CE in a sample of 50 countries according to the following empirical model (CE is a function of REC and other control variables) (See Figure 1):

$${CE}_{i,t}={a_0+a_1\ast REC}_{i,t}+a_2\ast {GDP}_{i,t}+{a_3\ast TO}_{i,t}+a_4\ast {URB}_{i,t}+a_5\ast {GS}_{i,t}+{\epsilon }_{i,t}$$

(1)

In Equation (1), CE, REC, GDP, TO, URB, and GS denote, respectively, CO₂ emissions, renewable energy consumption, GDP per capita, trade openness, urbanization, and government size, while i represents countries and t stands for period. We use data covering the years 2000–2020. See

Table A1. Renewable energy consumption and CO₂ emissions in the top 50 CHD countries for the year 2020.

Country	CO2 Emission	Renewable Energy Consumption	Country	CO2 Emission	Renewable Energy Consumption
Afghanistan	0.22	17.58	Latvia	3.65	43.75
Albania	1.54	44.58	Morocco	1.82	10.92
Armenia	2.40	8.38	Moldova	3.27	23.66
Azerbaijan	3.40	1.23	Montenegro	4.07	39.56
Bulgaria	4.92	21.08	Mongolia	6.43	3.98
Bahrain	21.98	0	Oman	15.64	0.05
Belarus	5.84	8.39	Pakistan	0.81	46.6
Central African Republic	0.04	90.88	Philippines	1.19	29.06
Dominican Republic	2.08	16.69	Papua New Guinea	0.56	54.28
Algeria	3.72	0.15	Qatar	31.73	0.06
Egypt, Arab Rep.	1.96	6.51	Romania	3.56	24.06
Fiji	1.12	31.79	Russian Federation	11.23	3.72
Micronesia, Fed. Sts.	0.96	2.03	Saudi Arabia	14.27	0.06
Georgia	2.75	23.45	Sudan	0.47	62.13
Guyana	3.47	12.04	Solomon Islands	0.32	49.01
Haiti	0.28	76.31	Syrian Arab Republic	1.21	1.08
Hungary	4.59	14.76	Tajikistan	0.98	38.75
Iraq	3.84	1.08	Turkmenistan	10.18	0.06
Kazakhstan	11.30	1.78	Tunisia	2.41	12.88
Kyrgyz Republic	1.38	30.04	Ukraine	3.75	8.72
Kiribati	0.45	42.75	Uzbekistan	3.38	1.03
Lebanon	3.79	6.71	Vanuatu	0.39	26.03
Libya	6.68	3.12	Samoa	0.96	37.54
Lesotho	1.03	41.42	Yemen, Rep.	0.31	3.53
Lithuania	4.18	31.7	Zimbabwe	0.53	84.36

for the list of countries. We estimate Equation (1) using several empirical methods. Our baseline results are obtained with the aid of an ordinary least squares (OLS) estimator to assess the association between renewable energy consumption and environmental degradation. Next, we used fixed effects and random effects methods. Employing fixed and random effects methods in our empirical analysis offers several distinct advantages over the traditional OLS estimator. Firstly, these panel data techniques account for unobserved heterogeneity among countries, capturing factors that remain constant over time and vary between countries, such as differences in policy regimes, cultural attributes, or geographical characteristics. Fixed effects models are particularly valuable in this regard, as they control for time-invariant variables specific to each country, effectively reducing omitted variable bias. Finally, we use a two-step system GMM estimator, which presents a notable advancement beyond fixed effects regression in our analysis. One key advantage lies in its ability to address endogeneity and omitted variable bias more effectively. While fixed effects models control for time-invariant unobserved heterogeneity, GMM goes a step further by allowing for the inclusion of lagged dependent variables and other instruments to deal with potential endogeneity issues. This is especially relevant in our study, where the relationship between renewable energy consumption and CO₂ emissions may be subject to reverse causality or omitted variables that evolve over time. In this vein, numerous studies have used a two-step GMM estimator to model the relationship between renewable energy and CO₂ emissions across countries. Moreover, the two-step GMM estimator is particularly efficient in studies where the number of countries (panels) exceeds the number of years. Additionally, the two-step GMM estimator is robust in the face of weak instruments, making it suitable for addressing potential instrument relevance concerns that may arise in panel data settings.

4. Empirical Findings and Discussion

Table 2. CO₂ emissions and renewable energy consumption.

	CO2 Emissions Per Capita	Renewable Energy Consumption, %
Top 50 CHD countries	4.34	22.8
Rest of the world	3.61	31.9

Source: Authors’ estimates for the year 2020 based on the World Bank Development Indicators dataset.

presents the data trends for renewable energy consumption and CO₂ emissions in 50 countries marked by the highest rates of coronary heart disease and the rest of the world (ROW). Evidently, CO₂ emissions rates are considerably higher in CHD countries compared to ROW. At the same time, the renewable energy consumption levels in CHD countries are lagging behind those in ROW, suggesting that renewable energy consumption may be an important variable in mitigating CO₂ emissions.

Next,

Table 3. Baseline results.

	I	II	III	IV	V
RE	−0.041	−0.001	−0.001	−0.011	−0.002
	(37.25) ***	(3.53) ***	(4.15) ***	(4.55) ***	(4.82) ***
CO2, lag		0.964	0.961	0.681	0.950
		(105.24) ***	(100.02) ***	(13.68) ***	(79.19) ***
GDP		0.003	0.002	0.010	0.002
		(3.63) ***	(2.65) ***	(3.20) ***	(2.19) **
GDP * GDP		−0.000	−0.000	−0.000	−0.000
		(3.34) ***	(2.00) **	(2.12) **	(1.71) *
TO			0.000	0.000	0.000
			(2.01) **	(0.90)	(1.91) *
URB			−0.001	0.003	−0.001
			(0.49)	(1.21)	(0.89)
GS			−0.001	−0.000	−0.001
			(1.32)	(0.11)	(1.98) **
Constant	1.731	0.036	0.048	0.405	0.059
	(43.26) ***	(3.01) ***	(3.45) ***	(5.40) ***	(3.42) ***
R2	0.59	0.99	1.00	0.79	0.74
N	1045	954	823	823	823

* p < 0.1; ** p < 0.05; *** p < 0.01.

reports the preliminary results using OLS regression fixed and random effects estimators. Column 1 displays the bivariate association between RE and CE in our sample. The coefficient is negative and significant, suggesting that carbon emissions are lower in countries with higher rates of renewable energy consumption. For example, a 10 percentage points increase in RE is associated with a 4% decrease in CE. A number of studies document that renewable energy is instrumental in reducing CO₂ emissions and improving health [55] as “carbon dioxide can cause cardiovascular disease and other diseases caused by the greenhouse effect” [56] (p. 9). Moreover, this effect is stronger than that documented globally [45] and similar to those reported for OIC member states [57].

Column II now includes lagged CO₂ emissions and GDP per capita to account for the presence of the EKC framework. The results suggest that GDP per capita has an inverted U-shaped relationship with CO₂ emissions, while renewable energy retains its negative link with CO₂ emissions. The turning point is approximately 67,000 international dollars. The turning point falls within the range reported by existing studies: USD 35,480 for the global sample [58] and USD 115,000 for Europe and Central Asia [59]. We introduce the remaining control variables in column III. Of these variables, only trade openness is positively related to CO₂ emissions. Trade openness can lead to higher CO₂ emissions by increasing industrial production and transportation activities as countries engage in the exchange of goods, often relying on energy-intensive processes and fossil-fuel-powered logistics. Additionally, trade can drive the relocation of polluting industries to countries with less stringent environmental regulations, amplifying emissions in those regions. These results are in line with other studies for developing countries [60,61]. The results for random and fixed effects estimators are reported in columns IV and V, respectively. Again, the findings suggest that renewable energy consumption mitigates CO₂ emissions in countries with high levels of coronary disease death rates. We tested for the presence of multicollinearity in our data using variance inflation factors (VIFs) for the independent variables specified in a linear regression model. At 6, the VIF results are well below the threshold level of 10, suggesting that multicollinearity is not a concern in our study.

To estimate the causal effect of renewable energy on CO₂ emissions, we used a two-step system GMM estimator (

Table 4. Two-step system GMM results.

	I	II
CO2, lag	0.553	0.639
	(10.21) ***	(11.65) ***
RE	−0.020	−0.009
	(8.32) ***	(1.41)
GDP	0.013	0.017
	(3.02) ***	(3.34) ***
GDP*GDP	−0.000	−0.000
	(1.13)	(1.80) *
TO	0.000	−0.001
	(0.02)	(1.02)
URB	0.002	−0.001
	(0.73)	(0.50)
GS	−0.007	−0.007
	(3.23) ***	(3.74) ***
RE*RE		−0.000
		(1.53)
Constant	0.808	0.599
	(9.35) ***	(4.59) ***
AR(1)	0.001	0.003
AR(2)	0.589	0.705
Hansen’s p-value	0.108	0.159
N	823	823

* p < 0.1; *** p < 0.01.

). Column I reports the results from estimating Equation (1). We find that lagged CO₂ emissions are positively related to the current levels, suggesting inertia in environmental degradation across countries. However, we fail to confirm the presence of the EKC framework. It is evident from our findings that increased adoption of renewable energy is linked to a decrease in the levels of CO₂ emissions, and a 1 percentage point increase in renewable energy consumption leads to a 0.2% decrease in CO₂ emissions. The AR(2) coefficient in a two-step GMM estimator is a diagnostic tool used to assess whether the errors in the model exhibit second-order autocorrelation. The AR(2) z-statistics suggest that there is no second-order autocorrelation in our model. In addition, Hansen’s p-value shows that our two-step GMM estimators are reliable and efficient. In column II, following nascent studies, we tested the non-linear effects of renewable energy deployment on CO₂ emissions [62]. To achieve this goal, we used a quadratic transformation of renewable energy variables. However, the results suggest that renewable energy does not exert a non-linear impact on environmental degradation, proxied by CO₂ emissions.

5. Conclusions

In this study, we explored the interplay between renewable energy adoption, CO₂ emissions, and economic factors in countries with high rates of coronary heart disease (CHD), emphasizing the broader implications for public health and environmental sustainability. Our findings highlight the significant role of renewable energy in reducing greenhouse gas emissions and improving environmental quality, factors closely linked to CHD prevalence. By demonstrating the presence of the environmental Kuznets curve, we further underline how economic development can influence environmental outcomes. These insights not only contribute to understanding the environmental and economic drivers of emissions but also underscore the potential co-benefits of renewable energy policies in alleviating CHD risks, thereby aligning sustainable energy transitions with public health objectives.

These discoveries bear substantial policy implications. Primarily, there is an urgent need for countries to reassess their energy portfolios, prioritizing a swift transition to green energy solutions. Practical tools such as grants, low-interest loans, and tax incentives can incentivize both private entities and households to embrace renewable technologies. Fostering a tripartite partnership involving the public, private sector, and government entities is pivotal. This collaboration ensures widespread awareness of the benefits associated with clean energy and empowers individuals to transition from non-renewable to renewable energy sources.

Furthermore, developing nations must undertake structural transformation reforms to stimulate innovation and increase the GDP per capita beyond the EKC turning point, ensuring sustainable development. Fostering investments in research and development, coupled with the establishment of educational institutions, can increase the number of skilled researchers, fostering innovation and economic growth. Additionally, healthcare policies aimed at improving public health must be coupled with carefully designed strategies to reduce the carbon footprint resulting from economic activities.

Looking forward, prospective studies should broaden the scope to encompass diverse countries where health indicators are affected by air pollution. Furthermore, exploring the influence of variables such as social capital and globalization on the relationship between renewable energy adoption and CO₂ emissions can deepen our understanding of this complex interplay. In addition, endogeneity remains a crucial challenge in estimating the relationship between renewable energy and CO₂ emissions across countries due to the potential for reverse causality or omitted variable bias. To mitigate this issue, our study employed a two-step system GMM estimator, which is well-suited for panel data with potential endogeneity concerns [63]. This method leverages lagged variables as instruments to account for unobserved heterogeneity and dynamic relationships. While these techniques help address endogeneity, we acknowledge the inherent limitations and suggest that future research explore alternative identification strategies, such as natural experiments or policy interventions, to further validate the causal inference. If continuing this line of inquiry, then it will be possible to inform decisions about more targeted, effective policies to safeguard both public health and the environment on a global scale.