Impact of Renewable and Non-Renewable Energy on EKC in SAARC Countries: Augmented Mean Group Approach

Abstract

This study looks at the short- and long-term effects of fossil fuels, renewable energy, and nuclear energy on CO₂ emissions in the South Asian Association for Regional Cooperation (SAARC) countries from 1982 to 2021. We assess the impacts of SAARC’s current and anticipated use of nuclear, fossil, and alternative energies by testing the environmental Kuznets curve (EKC) hypothesis. The study applied the second-generation unit root test, cointegration test, and the newly introduced AMG technique to handle the presence of cross-sectional dependence. The results indicate that EKC does not hold in SAARC, and a U-shaped quadratic link exists between GDP and environmental pollution. The findings also reveal that the environmental pollution in the SAARC is caused by fossil fuel, whereas using renewable (REN) and nuclear energy can cut long-term pollution. While renewable energy is critical to minimizing environmental deterioration in SAARC, empirical findings also show that more than rising national wealth is needed to meet environmental demands. According to the results of this study, SAARC countries should take the lead in achieving sustainable growth and the efficient use of clean energy.

1. Introduction

The debate over the consequences of global climate change has increase in importance in recent years. Every day, the temperature of the entire global environment is rising. Environmentalists are therefore trying to pinpoint the mechanisms by which environmental quality could be improved. Because of rapid industrialization and urbanization, coal, oil, and natural gas combustion are emitting more carbon dioxide (CO₂), which spreads throughout the environment. For example, as a region becomes more urbanized, new barriers to sustainable development emerge, such as the need for their economies to rapidly expand, the number of people employed, and the need to burn fuel. Urbanization is significantly increasing, which is generally associated with environmental degradation.

Desiring to be seen as “developed countries”, South Asian nations have focused on growing their GDP, including in the production and consumption sectors. This is identical to what has been attempted by other emerging nations. Economic expansion and greater agricultural productivity in emerging countries could potentially have significant environmental effects on society, claim Cetin et al. [1]. However, the challenge is that industrialized countries have earned their current status by placing greater value on environmental quality, rather than focusing on GDP alone. However, in developing countries, environmental deterioration has a considerable impact on climate instability, mainly brought on by the use of fossil fuels in production [2].

The South Asian Association for Regional Cooperation (SAARC) was established in Dhaka in 1985, with the goal of maintaining socioeconomic integration, achieving sustainable development goals, and enhancing the quality of life among the eight member nations: Bangladesh, Bhutan, Afghanistan, the Maldives, India, Nepal, Sri Lanka, and Pakistan. Due to population growth and economic development, the CO₂ emissions among SAARC countries and Asian neighbors are rising. India is Asia’s largest emitter of carbon dioxide, with China and Pakistan’s primary contributors being energy usage and urbanization [3]. The Sustainable Development Goals (UN 2030), which address extreme weather events (it is No. 13 on the agenda), present a critical challenge in developing countries associated with SAARC. South Asian nations are highly vulnerable in a world in which carbon emissions are continuously increasing and impacting the environment. By showing an inefficient association between GDP and CO₂ emissions, Pandey and Mishra [4] contend that the applicability of the environmental Kuznets curve (EKC) is flawed in South Asian countries. Such findings suggest that SAARC countries need to consider a shift in their energy mix towards a more sustainable path, following empirical evidence supporting the adoption of cleaner energy sources in other regions. Mbarek et al. [5] and Weimin et al. [6] examined the nexus between economic prosperity and CO₂ pollution, finding that the extensive use of coal, oil, and natural gas has a negative impact on the environment in Nepal and raises CO₂ levels by 0.67%. The use of renewable energy may generate a 3.65 percent reduction in CO₂ emissions over time. Evidence in Brazil suggests that alternative energies can effectively curb CO₂ emissions [7,8]. Empirical findings from Peru also suggest that adopting renewable energy (a 1% increase) contributes to a decrease in CO₂ emissions of 0.52% [9].

On the other hand, SAARC countries are experiencing rapid economic growth rates, suggesting the need to examine their economic development path, which may be linked to environmental degradation. Tenaw and Beyene [10] exemplify how, in the short term, economic growth in the SSARC region is positively coupled with ecological erosion. Still, in the long term, adopting renewable energies and protecting forest areas can help prevent environmental degradation. Similarly, the case of India suggests that urbanization, economic growth, industrialization, and tourism are likely to increase India’s carbon dioxide emissions; however, an increase in the use of renewable energy (REN), higher agricultural productivity and greater total area of forest land has helped to reduce emissions and maintain India’s climate [11]. OECD and global high-income countries are actively seeking to decrease their dependence on non-renewable energy, boost urban design, and increase use of renewable sources by promoting financial access and prioritizing sustainable economic growth [12,13].

Failing to embrace a more sustainable economic development path for the SAARC will likely have severe environmental consequences. Energy use contributes considerably to the growth in ecological contaminants in SAARC members, claim Akhmat et al. [2]. By 2030, urbanization will have increased emissions in middle- and low-income countries by about 76% [14]. The rapid global migration of SAARC residents also threatens to enhance SAARC nations’ carbon dioxide emissions, as remittance inflows are linked to economic growth and a more considerable demand for energy [15]. The trade-off between present and future financial growth is a primary source of hindrance for SAARC countries pursuing sustainable economic development. The primary causes of the connection between lower ecological quality and faster economic development include the underdevelopment of resources, free trade in an open economy, and loss of sight of the ways in which nature is deformed. In the short term, it is unlikely that developing nations (e.g., SAARC), which are dependent on fossil fuels as the primary energy source, can achieve increasing GDP without sacrificing the environment [16,17]. Regulatory efforts, adoption of green energy, improvements in energy efficiency, and more effective implementation of environmental regulations, among other measures, are needed to mitigate the consequences of climate change [18].

South Asian countries have been struggling to achieve high environmental quality amid rapid growth in population, urbanization, exacerbated increase in energy use, inflows of FDI, and the economic tights with China and India [19]. Nguyen and Kanaka [20] noted significant emissions from various energy-intensive industries and a spike in power generation in the region. Due to the excessive costs associated with clean energies and the need for improved technological capabilities, most SAARC countries must embrace renewable energy sources more rapidly. Countries within South Asia continue to rely on fossil fuels (i.e., Bangladesh, Pakistan, and India), and are slow to adopt greener power-generating technologies [21,22]. While depending on non-renewable energy may “lower” the cost of producing items, it can also have adverse long-term repercussions on the ecosystem [23]. By contrast, shifting to renewable sources promises to lower CO₂ emissions, as noted by Menyah and Rufael [24]. Using renewable energy sources and natural resources aids the environment over time [25,26].

Using the EKC assumption, the main objective of this article is to explore how GDP impacts emerging nations such as SAARC countries (see Table 1). However, this presents a different interpretation of the EKC, as it is thought to be U-shaped, showing that while an increase in GDP initially reduces emissions, this will be followed by an improvement in trade or other factors related to higher GDP and an increase in carbon dioxide emissions. This could either be because of a weak concern for environmental quality in developing countries or because there is a chance that these countries are being used as “pollution havens”, except that the use of fossil fuels in SAARC nations indicates an even poorer perception of the environment than is already present in this research. Nuclear energy and renewable energies (REN) are employed in this paper to cut CO₂ emissions and confirm that the EKC is genuine.

However, the use of REN is essential to limiting environmental deterioration in SAARC nations; hence, the EKC is no longer viable, and new policy implementations are required to uphold long-term SDG objectives. As SAARC countries face considerable climate change challenges, reducing CO₂ emissions is crucial for maintaining an environmentally friendly climate, relieving pressure on the healthcare system, boosting the global economy, and preserving biodiversity. Environmental protection is a vital concern for governments in developing countries, since they are often more exposed and more susceptible to higher rates of carbon emissions than developed countries, as they are used for production [26]. To make the EKC relevant in the field of the pollution haven hypothesis, it is necessary to understand how these emissions can be reduced by employing other elements, because developing countries use traditional production methods without considering the nature of the environment.

Our paper contributes to the literature related to the environmental Kuznets curve (EKC) hypothesis in several different ways. First, we provide new evidence on the EKC, which is thought to be invalid in the SAARC region. Employing renewable energy sources, nuclear energy sources, and alternative econometric approaches can provide new insights into the EKC in the SAARC region. Accounting for renewable sources and introducing nuclear energy as a green source has been overlooked in studies in the SAARC region. Second, we apply the Augmented Mean Group (AMG) model using data from 1982 to 2021 to the eight member states of the SAARC region (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka). Only a few studies have employed the AMG to examine the EKC in some SAARC countries. Several studies have examined the EKC in the South Asia region using diverse approaches. However, only a few have employed AMG and approaches for handling cross-sectional dependency and slope endogeneity issues. We test for slope homogeneity and cross-section dependence to validate our data before performing cointegration and unit root tests. We performed a robustness test by applying the Mean Group (MG) and the Common Correlated Effects Mean Group (CCEMG) to determine the AMG test’s accuracy.

This paradigm guides how the remainder of the article is organized. The literature review is presented in the next section. In Section 3, the methods, the variables, and the regression models are presented. In Section 4, the results and analysis of the experiment were looked at. The last section has the discussion and conclusion concerning policy recommendations. In total, this research paper has three Appendices. The list of counties is in Appendix A, the correlation of variables is in Appendix B, and the VIF findings are in Appendix C.

2. Literature Review

Russian economist Simon Kuznets described an inverted U-shaped curve representing income discrimination and growth in the 1950s, known as the “Kuznets curve”. Grossman and Krueger [27] admire his theory and used a similar formula in 1995 with a variable of economic growth and CO₂ emissions based on environmental quality. Since then, many other researchers have described relationships among many variables based on the EKC hypothesis with many econometric methods. To clarify this topic, a literature evaluation is needed to be compare papers in order to see how various factors affect the overall environmental condition by increasing CO₂ emissions.

According to many studies, EKC occurs in SAARC countries in U, inverted U, or N shapes. Xue et al. [28] agreed on the soundness of the EKC hypothesis by applying the AMG regression analysis for four South Asian countries. He represented renewable energy as a highlighting factor of an independent variable that helps economies to reduce their pollution and ecological footprints, as a dependent variable. Jadoon et al. [29] represent the EKC hypothesis for SAARC members from 1980 to 2018, employing industrial production as a regressor to determine how it affects the environment. They show a two-sided view of EKC, where a U-shaped curve is found when calculating the integration between CO₂ and economic growth. In contrast, an inverted U is found when measuring the integrity between CO₂ and industrial growth. Applying the panel ARDL and FMOLS methods, Waqih et al. [30] validated the “EKC hypothesis” for long- and short-run dynamics, including the assumption of the “pollution haven” hypothesis, by focusing on the fact that FDI, energy, and economic growth resulted in increases in CO₂ emissions in the SAARC states from 1986 to 2014. By demonstrating the connection of urbanization, income per capita, energy use, trade openness, and carbon dioxide emissions using panel data techniques from 1980 to 2016, Afridi et al. [31] postulate an N-curve EKC hypothesis in SAARC countries that provides a cubic function.

In another string of studies including global macroeconomy variables, Apergis and Ozturk [32] used the “Pedroni Panel Cointegration technique” and the FMOLS for SAARC countries from 1972 to 2015 to discuss the EKC based on a closed and open economy model. The closed economy model provides an invalid EKC by showing a positive effect between per capita GDP and GDP2 and emissions. The open economy model shows that FDI helps produce fewer emissions products. In another study, Jayasooriya and Sujith [33] concluded that trade liberalization is environmentally harmful to developing countries, as producers become more intensively involved in global production. Rehman and Rashid [23] examined the effect of carbon dioxide emissions on energy utilization in Asian markets, where EKC was identified by applying a fully modified OLS (FMOLS) as well as dynamic OLS (DOLS), confirming the pollution haven hypothesis. Latief et al. [19] presumed a relationship between carbon footprint and FDI, growth (EG), and various economic indicators for the SAARC countries, finding evidence of a one-way relationship between EG and CO₂ emissions. It is worth noting that most studies about environmental conditions in SAARC countries have pointed out that to improve environmental quality, South Asian members must ensure optimal trade openness, more effective trade regulation, and broader access to green finance.

Among studies looking into fossil and renewable energy sources, Ali et al. [34] confirmed the EKC, highlighting the positive role of green energy sources in maintaining environmental quality in four South Asian nations. Using 1990–2017, Khalid et al. [35] tried to find the association between primary and REN financial development using AMG, ECM, and D-H to test the EKC for SAARC members. Countries like Bangladesh and Sri Lanka have increased pollution levels in order to enhance financial development and accommodate increasing energy demand. Using renewable energy was found to aid a decrease in pollution. Zhang and Liu [36] represent the invalidity of the EKC for 10 Asian countries using the FMOLS and AMG estimation method, where non-renewable energy is the foremost cause of emissions production. Shifting to renewable energy was found to help balance environmental quality. As coal, oil, and gas negatively affect the environment, nations should promote alternative clean energy sources that reduce pollution, such as wind and solar power [37]. Similar findings were supported by Ramzan et al. [38], who found that non-renewable energies impose a sizeable environmental cost for countries (i.e., Pakistan), signaling that cleaner energy should be a priority in energy policy to achieve sustainable economic growth [38]. Almas et al. [39] argued that the EKC hypothesis of an inverted U-shaped shape holds in the SAARC region, as presented by the transportation system, which relies entirely on fossil fuel and is heavily responsible for CO₂ in SAARC countries. Wealthier economies have expanded their dependence on alternative energies, among them nuclear energy, to keep carbon footprints at a manageable level. Usman et al. [40] used CS-ARDL to explore how human capital and nuclear energy boosted ecological integrity in 12 advanced economies between 1980 and 2015, finding that nuclear sources effectively lower pollution.

Another string of studies looked at alternative variables to examine environmental quality in developing countries. Using the Kao cointegration test, Mehmood [41] reviewed the sustainable transportation sector and urban communities to determine how they affected CO₂ emissions from 1996 to 2015 in SAARC countries. The results showed that urbanization can slow down environmental deterioration. Khan et al. [25] look at the effect of the authority, funds, and natural resources on environmental filth and economic growth in seven SAARC nations, pointing out that while natural resources have a detrimental effect, finance, and governance have a significant benefit. For 54 countries within the African Union from 1996 to 2019, Hussain et al. [42] used PQR and FMOLS to demonstrate that EKC was relevant for African Union member nations and that non-renewable energy usage was exerting significant pressure on CO₂ emissions. A link between dirty energy sources in economic growth and CO₂ emissions among African economies is generally supported [43]. Usman et al. [44] identified that while the potential of clean energy and the taxation of nuclear energy can allow Pakistan to create a carbon-free environment in the future, short- and long-term studies show a negligible positive and negative connection between electric power use and carbon emissions. Carbon-reducing infrastructure needs to be included in manufacturing operation. In a study of 12 neighbors to SAARC (i.e., developing East Asian and Pacific countries), Hanif [45] illustrated the association among energy, economic growth, and urbanization that significantly increases CO₂.

Several studies have focused on the impacts on environmental quality due to shifting towards renewable sources. By using the MM-QR approach to analyze the effects of the transition to renewable energy, ecological innovation, and environmental policy rigor on the ecological footprint of OECD economies from 1990 to 2017, Afshan et al. [46] noted that green energy has to be encouraged in order to achieve sustainable development since it has a bad reputation. Saidi and Mbarek [47] create an association between nuclear and clean energy use, emissions, and real GDP for nine industrialized nations from 1990 to 2013, adding labor and capital. In the short term, the unidirectional causality between REN and real income per capita (GDPP), the bidirectional causality between renewable energy and real GDPP in the long term, and the unidirectional causality between GDP and emissions all showed the significance of REN for economic growth. Nuclear energy has been shown by Mahmood et al. [48], Hassan et al. [49], Dietz and Rosa [50], Majumder et al. [51], and Voumik et al. [52] to contribute to lessening the environmental pollution. Bekun’s [53] findings revealed a negative relation between renewable energy and CO₂ emissions and a positive relation among non-renewable energy, economic growth, and CO₂ emissions in India, pointing out the effectiveness of green energy at curbing CO₂ emissions. Bekun et al. [54] showed the existence of the EKC hypothesis and tourism-initiated CO₂ emissions in E7 economies. Zhao et al. [55] ascertained the validity of the EKC hypothesis, showing that consumption of renewable energy sources significantly alleviates climate change. In contrast, using non-renewable energy deteriorates the environment in emerging Asian countries.

Moreover, most EKC papers are based on either a production-based or a consumption-based factor. This paper considers both factors in order to demonstrate the method’s robustness for the estimation of AMG, which has been identified as superior to many other approaches [28]. We include a new panel analysis in all SAARC countries, which constitutes a great addition to the existing literature. Furthermore, the robustness of the paper’s insights into the variables is evaluated using MG and CCEMG. We contribute to the current literature on the EKC within South Asia by highlighting the relationship between fossil fuels, clean energy, and nuclear energy—all of which are hypothesized to be connected to the environmental quality of the SAARC countries. This study intends to close these gaps. By applying a unique AMG estimate approach and a brand-new panel analysis, this research’s main contribution is to experimentally evaluate the influence of emission through the U-shaped EKC hypothesis on long-run elasticity.

3. Methodology

3.1. Data

Data were obtained from the World Development Indicator (WDI). To make things simpler for the stakeholders, we included the details of the variables as well as a descriptive analysis and we have included the details of the variables as well as a descriptive analysis. Table 1 presents the variable lists for this article, while the descriptive statistics for every attribute are shown in Table 2.

3.2. Theoretical Context and Model Framework

A model linking the impacts of urbanization, industrialization, electricity usage, and renewable energy on carbon dioxide emissions is required to meet the aims of this study. The traditional EKC (Environmental Kuznets Curve) model, devised by Dietz and Rosa [50], is given by our dependent and independent variables. Our baseline equation is:

GHG_i,t = α + βGDP_i,t + γX_i,t + μ_I + δ_t + ε_i,t(1)

Equation (2) consists of the details from of Equation (1).

LGHG_i,t = β₁+ β₂GDP_it + β₃GDP²_it + β₄REN_it+ β₅FOS_it+ β₆NUC_it + e_it (2)

where GHG = GHG emission_, GDP = gross domestic product, REN = renewable energy, FOS = fossil fuels, and NUC = nuclear energy.

Equation (3) is the log form:

LGHG_i,t = β₁+ β₂LGDP_it + β₃LGDP²_it + β₄LREN_it + β₅ LFOS_it+ β₆LNUC_it + e_it(3)

where ${\mathsf{\beta }}_0$ is the intercept term, ${\mathsf{\beta }}_1, {\mathsf{\beta }}_2, {\mathsf{\beta }}_3, {\mathsf{\beta }}_4, {\mathsf{\beta }}_5,\mathrm{and} {\mathsf{\beta }}_6 \mathrm{are}\mathrm{the}\mathrm{slope}\mathrm{coefficient},$ $\mathsf{\epsilon }$ represents the residual, i represents the cross-section of the individual country, and t represents the period.

3.3. Econometrics Procedure

The outcomes of this study were estimated using panel unit root tests, MG, AMG, and CCEMG. Additionally, the association of the variables with cause and effect is provided by the Granger causality test. The Bruesch–Pagan LM and Pesaran CD tests were also used to examine cross-sectional dependence in the model’s error component, and were also utilized to determine whether slopes are homogeneous or heterogeneous. Due to the presence of heterogeneous slopes and CSD issues, this research employed second-generation panel unit root tests, including the CIPS test established by Pesaran [56] and the CADF test, to observe the unit root in the data. The CIPS test has been demonstrated to be an effective alternative to the first-generation unit root tests (in our case, the LLC and IPS tests), since it reduces the cross-sectional reliance found in those tests. After confirming the data’s unit roots, the investigation also employed a cointegration test. Empirical researchers can best estimate the linear connection between the model’s variables by using the most appropriate mean estimator technique and the best unit root tests [57]. Step-by-step, our study's structure is depicted in Figure 1.

3.3.1. Slope Homogeneity Test and Cross-Sectional Dependency (CSD) Test

When dealing with panel data, it is critical to keep slope heterogeneity in mind. Therefore, slope homogeneity tests were performed according to Pesaran and Yamagata [58]. The exam results were calculated using the weighted slope of each participant. The findings of the testing were:

(4)$\check{\Delta }=\sqrt{N}(\frac{N^{-1}S\%-k}{\sqrt{2k}})\mathrm{and}{\check{\Delta }}_{adj}=\sqrt{N}(\frac{N^{-1}S\%-k}{\sqrt{\frac{2k\left(T-k-1\right)}{T+1}}})$

However, the CSD in the panel data illustrates the impact of a shock in one nation on another. A problem with CSD was observed to produce inaccurate findings if model considerations were ignored [59]. An increase in CSD in panel data econometrics would probably result from more economic incorporation and the exclusion of other impediments [60]. If we choose to ignore the problem and pretend that cross-sections are autonomous with respect to one another, cross-section reliance might result in information that is skewed, deceptive, and inconsistent [61]. We use a variety of CSD tests, including the Pesaran CD test [62], the Breusch–Pagan LM test [63], and the bias-corrected scale LM test [62]. Baltagi et al. [64] presented the SH and CSD as prerequisites for the use of panel data econometric unit root tests. The following calculation was used to administer the CSD test:

(5)$CSD=\sqrt{\frac{2T}{N(N-1)N}({\displaystyle \sum }_{i=1}^{N-1}{\displaystyle \sum }_{K=i+1}^N{\widehat{Corr}}_{i,t})}$

where ${\widehat{Corr}}_{i,t}$ is equal to the pairwise correlation produced by the equation. Each unit is independent of the others; this is the null hypothesis of the CSD test.

3.3.2. Unit Root Test and Cointegration Test

Standard unit root tests may yield erroneous findings because of slope fluctuation and CSD [62]. Pesaran’s [56] CIPS, a unit root test, was used to determine whether CSDs and slope heterogeneity were present. In accordance with the alternate theory, all panel series are stationary. The preceding image demonstrates how a CIPS estimate must be derived from a cross-sectional average of t_i:

(6)$CIPS=\frac{1}{N}{\displaystyle \sum }_{i=1}^N{t}_i\left(N,T\right)$

Due to its capacity to govern CSD and heterogeneity, CIPS has become more popular in recent publications. The absence of a unit root in the sequence under examination is the test’s null hypothesis. This reveals that “a cointegration test should be run before parameter estimation if the variable is stationary at the first difference”. The CADF technique must be used to obtain CIPS information. Moreover, cross-sectional Augmented Dicky Fuller (CADF) can be computed as follows:

(7)$\Delta Y_{it}={\phi }_i+{\zeta }_i{Y}_{i,t-1}+{\delta }_i{\overline{Y}}_{t-1}+{\displaystyle \sum }_{j=0}^P{\delta }_{ij}{\overline{Y}}_{t-1}+{\displaystyle \sum }_{j=1}^P{\lambda }_{ij}\Delta Y_{i, t-1}+{\epsilon }_{it}$

where ${\overline{Y}}_{t-1}$ and $\Delta Y_{i, t-1}$ represent the mean of the lagged and first difference of each cross-sectional series. However, the first generation of cointegration tests [65,66,67] does not account for CSD, and therefore it is impossible to forecast panel data distortion using them. The Westerlund [68] technique was used to analyze the cointegration due to the presence of CSD. The measurement of cointegration between the variables was performed. Error correction was used in the four-panel non-cointegration tests to determine the statistics. The following is a basic statement of this assessment:

(8)$C_{\alpha }=\frac{1}{n}{{\displaystyle \sum }}_{i=1}^N\frac{{\acute{\theta }}_i}{SE({\acute{\theta }}_i)}$

(9)$D_t=\frac{1}{n}{{\displaystyle \sum }}_{i=1}^N\frac{T{\acute{\theta }}_i}{{\acute{\theta }}_i\left(1\right)}$

(10)$P_t=\frac{\acute{\alpha }}{SE(\acute{\theta })}$

(11)$P_{\alpha }=T\acute{\theta }$

In statistical analysis, $P_t$ and $P_{\alpha }$ stand for cointegration, whereas $D_t$ and $C_{\alpha }$ indicate group mean statistics. When a model’s variables are said to exhibit cointegration under the alternative hypothesis, but not under the null hypothesis, the test statistics are estimated.

3.3.3. Augmented Mean Group (AMG) Test

For empirical research, the AMG, MG, and CCEMG estimation methods are the most frequently used, because of their flexibility in dealing with cross-sectional dependence, heterogeneity, endogeneity, and serial-correlation issues. These three estimators are heterogeneous panel data estimators that take into account issues of cross-sectional dependence and endogeneity due to the existence of common factors; they are also robust to missing data, imperfect measurements, abrupt changes in design, serial correlation, and other potential sources of non-stationarity [69]. After establishing that the study’s cointegrated variables are the variables of interest, the final step is to estimate the long-run coefficients in Equation (12). The AMG estimator, introduced by Eberhardt and Teal [70], was used for this purpose in the current investigation. Compared to first-generation estimators, the AMG estimator produces more trustworthy findings, since it considers the CSD, mixed-order stationarity, and heterogeneity within the panel data. This was a two-step process in AMG estimation. In the first step, a pooled regression model was used, which was supplemented by year dummies calculated using the first difference OLS:

(12)${\Delta }_{yit}={\propto }_i+{\beta }_i\Delta x_{it}+{{\displaystyle \sum }}_{t=2}^T{c}_t\Delta D_t+e_{it}$

The second stage of AMG was as follows:

(13)${\widehat{\beta }}_{AMG}=N^{-1}{\displaystyle \sum }_i\widehat{{\beta }_i}$

where the first difference operator is Δ, D is the time variable (presented as a dummy variable) and c_t is its coefficient, and ${\widehat{\beta }}_{AMG}$ is the coefficient of AMG estimator.

4. Findings and Discussion

The outcomes of the cross-section dependency test Pesaran [59], and the slope homogeneity test [58] are shown in Table 3 and Table 4, respectively. Table 3 displays the results of the slope homogeneity test performed by Pesaran and Yamagata [58]. It demonstrates that the model has a problem with heterogeneity. There is a wide variation in the slope of the model’s coefficients among countries, which suggests that the model is not homogeneous. Panel causality analysis may lead to incorrect conclusions if it restricts the variable of interest to only being homogeneous when slope homogeneity is ignored.

A panel data econometric analysis must first determine whether or not there is a cross-sectional dependency before proceeding. Table 4 summarizes their findings, showing evidence of cross-sectional dependency in each data panel. Due to their shared economic, social, and political traits, GHG, GDP, REN, FOS, and NUC are interrelated, according to Pesaran [59]. This is to be expected, given the size of the South Asian region in comparison to the rest of the world. These nations also have comparable macroeconomic and trade policies. Numerous cross-sectional dependency tests have been found to be valid for CSD. According to the results, the estimations of the Pesaran scaled LM test, Pesaran CD test, Friedman test, and Breausch- Pagan LM test are significant at a significance level of 1%. It was found that no estimates were more precise than that obtained using the conventional mean group estimator, which ignores cross-sectional dependency. Every variable was demonstrated to exhibit a heterogeneous order of amalgamation. The variables were not heading in the same direction as the rest of the economy, which is true for GHG and REN as well as GDP, FOS, and NUC.

The panel data must be examined to see whether the variables are steady. According to Table 5 and Table 6, the outcomes of the second-generation unit root tests indicate that “some variables remain non-stationary the at level, or I(0), while others become stationary after only 1st difference, or I(1) and we can conclude that all of the variables in the research are either I(0) or I(1)”. After establishing that the variables are stationary, it is necessary to investigate the cointegration of the long-run variables.

However, Table 7 presents the findings of the cointegration tests conducted by Westerlund [68], which were utilized to reach these conclusions. At a significance level of 1%, the p-values indicate that “the null hypothesis of Westerlund [68] can be rejected and our long-term variables are therefore co-integrated as a result”. In addition, it is arguable that in the context of South Asian states, GHG, GDP, REN, FOS, and NUC are interconnected in a way that is sustained over time. In order to forecast long-term coefficients using the panel regression approach, long-term cointegration correlations must be confirmed.

Table 8 highlights the long-term consequences of AMG by using a log–log model to capture the elasticity of the coefficients and assess the impact of EKC in SAARC countries. Recent years have seen substantial research on the association between economic growth and environmental corrosion [71]. The findings of this study, which are similar to those reported in [72] for SAARC and China, show that GDP has a significant influence on and is inversely related to emission levels [33]. Positive production shock, according to Pata [73], Mikayilov et al. [74], and Bozkurt and Akan [75], is mostly to blame for environmental deterioration. Conversely, the square of GDP has a minor and inverse value over time. In line with the estimates of GDP and GDPSQ, EKC does not hold true for the SAARC area, since it posits a U-shaped linkage with pollution. While taking into account an invalid EKC for the SAARC area [76,77]. Likewise, Zhang and Liu [41] additionally disproved the EKC theory for Asian nations between 1995 and 2014. In all of those papers, it was reported that more pollution was emitted to the environment. The calculated coefficient of renewable energy, which translates to a 0.548 percent reduction in carbon emissions, is statistically significant, but negatively correlated. As per Lu [78] for 24 Asian countries and Saad and Taleb [79] for Europe, renewable energy has an equivalent influence. Furthermore, Ozgur et al. [77] and Al Mulali et al. [80] reached the same conclusions for a different region. This indicates that a 1% increment in each coefficient causes deterioration to shrink by 0.1128 and 0.1422 percent, respectively.

Additionally, evidence presented by Iwata et al. [81] demonstrates that depletion and renewable energy are mutually exclusive. The use of renewable energy sources is essential in the fight against climate change and the reduction of carbon dioxide pollution. By making the switch to renewable energy, we can make the future safer and more secure for ourselves and future generations [82,83]. Coal, oil, and natural gas are all examples of fossil fuels, formed from the decaying organic matter of long-dead plants and creatures. Carbon dioxide and other greenhouse gases are released into the environment when these fuels are extracted and burned to produce electricity. Since fossil fuels are related to emissions in SAARC nations, they signify a high likelihood of increased emissions in those nations—by 0.0519 percent at a 1% level of significance. Use of coal, oil, and natural gas all increase CO₂ emissions, which in turn add to global warming and other unfavorable environmental effects [84,85]. The vast majority of studies on EKC and fossil fuels arrive at the conclusion that a nation’s usage of non-renewable energy sources like fossil fuels must have a negative environmental impact. Fossil fuels exert a wider influence on ecological sustainability [86,87].

Rehman and Rashid [23], on the other hand, showed that 0.6426% of CO₂ emissions were due to fossil fuel. Ramzan et al. [38] stated that 1% of fossil fuel usage produced 0.108% of the carbon footprint. In contrast to the claim made by Ramzan et al. [38] that using 1% more nuclear energy would result in a 0.9% reduction in carbon emissions, we discovered that using 1% more nuclear energy would have a negative influence on the environment and would enhance the environment’s coefficient value by 0.00243%. Usman et al. [37] described the same adverse outcome in their scenario, as well. Even if South Asia’s economy is flourishing and its GDP is continually increasing, this growth does not necessarily translate into fewer environmental risks in the absence of cutting-edge technologies and stringent environmental regulations. Due to South Asia’s negative GDP and positive GDPSQ coefficients, the EKC curve has a U-shape.

The EKC hypothesis was therefore proven to be untrue throughout the SAARC study area. However, we investigated the interactions between the variables and assessed the coefficients in the long term using the CCEMG, AMG, and MG estimators to provide thorough guidance for policy recommendations. According to Table 9, the robustness is shared by the “mean group (MG), the augmented mean group (AMG), and the commonly correlated mean group (CCEMG)”. MG, AMG, and CCEMG verified a U-shaped EKC curve in South Asia. Mekhzoumi et al. [88] discovered an N-shaped EKC assumption, whereby negative GDP and positive GDPSQ were predicted in the MG estimation, while the reverse was predicted by AMG and CCEMG; Murshed et al. [89] discovered an inverted U-based connection that was contrary to our findings. In addition, with coefficient values of 0.0519, 0.00864, and 0.332%, each model shows a positive connection between the use of fossil fuels and greenhouse gas emissions. The same conclusion was reached by Sahoo and Sethi [90]. The MG, CCEMG, and AMG models all show a negative nexus between renewable energy and GHG emissions.

5. Conclusions and Policy Recommendations

A crystal association between the economic, social, and environmental components of sustainable development and GDP growth and REN is argued by Vasylieva et al. [91]. Utilizing data from the SAARC region from 1982 to 2021, the current research investigated the measurement of environmental quality using variables such as fossil fuel, REN, and nuclear energy, in accordance with the EKC hypothesis. The study empirically applied the “slope homogeneity test”, the “CSD test”, the “second-generation unit root test”, the “cointegration test”, and the newly developed AMG, which helps explain the nexus of the various energies with CO₂ emission. The outcomes of the AMG, applied in both long-term and short-term evaluation, are superior to those of many other approaches when establishing the right association between variables.

AMG shows that both the long-term and short-term effects of GDP on CO₂ emissions can be asserted to be significant, and possess a negative coefficient, where CO₂ emissions, GDP, and the GDP square relationship provide an invalid EKC by providing a U-shaped assumption for SAARC countries. The robustness of these results was determined using MG and CCEMG, validating a U-shaped EKC curve in southern Asia. The AMG methodology also demonstrated that while the use of fossil fuels has a significant negative impact on the environment, renewable energy and nuclear energy are the most environmentally safe forms of energy. This paper demonstrates that, while the GDP of SAARC countries is projected to steadily increase in both the long and short term, this will not translate into a reduction in carbon emissions for a given economy because, as developing countries, it is nearly impossible to improve environmental quality and balance distortions occurring as a result of emissions. Therefore, it is essential to focus on environmentally friendly energy in order to enhance the environment while not emitting damaging greenhouse gases.

In addition, this research highlights the necessity of technical advancements and strict environmental laws in improving the environment’s overall quality. Educating the current generation to create a safe and healthy world for the next generation is another aspect that needs to be mentioned in the promotion of sustainable development in South Asia. Governments must enact policies that encourage SAARC countries to use energies that are environmentally friendly. In this modern era of higher trade, globalization, and upgraded industries, it has become a critical challenge to balance growth and degradation in order to provide sustainable development in SAARC countries. Aside from this, there are some limitations to this paper. Various concepts, such as deforestation, population growth, FDI, and transportation, could not be overlooked in this paper, because they are burning issues related to the rising levels of CO₂ in SAARC countries. Researchers can provide more informative and valuable results in the future by including other variables in this paper.

5.1. Policy Recommendations

This study recommends that, firstly, the South Asian countries should move toward more sustainable energy technologies. However, due to their limited capital accumulation, the majority of SAARC nations are unable to implement sustainable energy technologies. Transitioning to a low-carbon economy and reducing the effects of climate change require nations to develop sustainable energy technologies. If SAARC countries wish to be leaders in sustainable energy, they must fund R&D, foster ingenuity and entrepreneurship, and implement policies and regulations to promote the use of clean energy sources. Incentives for renewable energy output, energy efficiency standards for buildings and appliances, and financial support for sustainable energy initiatives are all ways of achieving this goal.

In addition, collaborations between public agencies, businesses, and academic organizations can speed up the creation and dissemination of renewable energy solutions. To combat climate change and boost environmental sustainability, nations would do well to invest in the research and development of sustainable energy technologies that will allow them to use less fossil fuels while increasing energy security and opening up new economic possibilities. Renewable energy decreases carbon dioxide emissions, and the results also demonstrate that fossil fuels are bad for the environment. The SAARC nations need to abandon fossil fuels in favor of sustainable energy.

According to Li et al. [92], the Indian state should boost the production of electricity using non-fossil fuels and renewable energy sources, like wind, solar, and hydropower, as well as continuing to plant trees and expanding the amount of forest cover to absorb CO₂. Secondly, countries must shift toward the service sector in order to achieve financial and economic growth, which emits fewer greenhouse gases into the atmosphere. Thirdly, governments must conduct research on hydro, photovoltaic solar, geothermal, and wind energy if they are to be utilized effectively. Siddiqi [93], Adewuyi, and Awodumi [94] discussed the manner in which biomass benefits the ecosystem. Pakistan is unable to develop water energy plants due to a lack of water routes, for example. Additionally, the seashore regions in SAARC countries have a promising future for wind energy. According to Sebitosi and Pillay [95], nations must shift away from non-renewable energy sources.

Finally, in order to safeguard environmental quality, the governments of South Asian nations should implement appropriate policies and programs to lessen their dependence on fossil fuels by transitioning to biomass energy [96,97,98,99]. It has been established that developing countries experience higher population growth than developed nations. As the population propagates, there is a corresponding increase in the basic necessity for everyday items, which drives up production to meet demand. Higher carbon emissions result from the production technologies used in conventional production methods. Therefore, it is essential to employ cutting-edge scientific technology in order to simultaneously improve both GDP and the environment. In addition to implementing clean technology for proper waste management, promoting green urbanization, putting in place an efficient transportation system, planting trees to lessen the impact of CO₂, and holding various seminars and programs can all be done to raise awareness among the populace regarding the value of a clean environment.

5.2. Limitation of the Study and Future Research

An important question concerning the EKC and the role of energy sources in driving economic development and environmental degradation in SAARC nations was investigated. However, future studies could address several issues and explore additional avenues. This research may have flaws due to important variables being left out of the analysis. While factors like trade, population growth, and technological advancement may also contribute to driving the EKC, they were not taken into account in this study. To better comprehend the factors that contribute to the EKC in SAARC countries, future research could include additional variables. Data constraints are another issue. A few numbers are absent. In the end, this will be solved by future scientists. This study made extensive use of data related to both renewable and fossil energy sources. Data from both renewable and non-renewable sources of energy should be considered by future scholars. Finally, this research is limited in its applicability because it only looks at SAARC countries. To obtain a fuller picture of how both renewable and non-renewable energy sources contribute to economic development and environmental degradation, future studies could broaden the scope of the analysis to include other nations or regions. Overall, this study sheds light on the connection between energy sources and the EKC in SAARC nations, but there is room for additional investigation to deepen our understanding and address the study’s limitations.

Author Contributions

Conceptualization, L.C.V. and M.H.R.; methodology, R.S. and R.D.; validation, L.C.V., M.H.R. and M.I.H.; formal analysis, L.C.V. and M.H.R.; investigation, M.I.H.; data curation, M.I.H. and R.D.; writing—original draft preparation, M.I.H. and R.D.; writing—review and editing, R.S. and M.A.E.; supervision, M.A.E.; project administration, M.A.E.; funding acquisition, M.A.E. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

World Development Indicator (WDI, 2022) accessed on 15 May 2022.

Acknowledgments

We would like to thank Universitas Airlangga for the support in the APC.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AMG	Augmented Mean Group
CCEMG	Common Correlated Effects Mean Group
CIPS	Cross-sectionally augmented Im, Pesaran and Shin (IPS) test
CO2	Carbon dioxide
CS-ARDL	Cross-sectional-autoregressive-distributed lag
CSD	Cross-Sectional Dependence
GDP	Gross Domestic Product
GHG	Green House Gas
GMM	generalized method of moments
MG	Mean Group
REN	Renewable Energy Consumption
SAARC	South Asian Association for Regional Cooperation
SH	Slope Homogeneity

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figure and Tables

Figure 1. The analysis flow of the methodology.

Table 1

Variable names and meanings.

Variable Name	Log Form	Indicators’ Names
Greenhouse Gas	LGHG	“Total greenhouse gas emissions (kt of CO2 equivalent)”
GDP	LGDP	“GDP (constant 2015 USD)”
GDP2	LGDP2	“GDP (constant 2015 USD) square”
Renewable energy	LREN	“Renewable energy consumption (% of total final energy consumption)”
Fossil fuel	LFOS	“Fossil fuel energy consumption (% of total)”
Nuclear energy	LNUC	“Alternative and nuclear energy (% of total energy use)”

Source: World Development Indicators (WDI, 2022).

Table 2

Descriptive statistics of the selected variables.

Variables	Mean	Sd	Min	Max
LGHG	10.46	2.624	5.347	15.03
LGDP	24.12	2.216	18.91	28.62
LGDP2	586.8	105.9	357.7	819.3
LREN	3.637	1.250	0.112	4.564
LFOS	3.411	0.901	0.652	4.301
LNUC	0.387	0.976	−2.246	1.686

Table 3

Slope homogeneity test.

Slope Homogeneity Tests	Δ Statistic	p-Value
$\check{\Delta }$ test	7.466 ***	0.012
${\check{\Delta }}_{adj}$ test	8.798 ***	0.001

A test for slope heterogeneity is based on the assumption that all slope coefficients are identical. Less than one percent of the population is shown with the *** symbol.

Table 4

Estimated outcomes of CSD analysis.

Test	Stat.
Pesaran CD test	10.288 ***
Pesaran scaled LM	21.42 ***
Friedman test	141.288 ***
Breusch–Pagan LM test	208.37 ***

Key: *** shows 1% significant. Source: Author estimation.

Table 5

Second-generation CIPS unit root test.

Variables	Level	First Difference	Order
	without Trend	with Trend	without Trend	with Trend
Cross-Sectionally Augmented IPS (CIPS)
LGHG	−2.170	−3.197 ***	−3.639 ***	−3.636 ***	I(1)
LREN	−3.837 ***	−3.604 ***	−5.730 ***	−5.900 ***	I(0)
LFOS	−2.886 ***	−2.774 *	−4.839 ***	−5.124 ***	I(1)
LNUC	−2.381 **	−2.438	−4.148 ***	−5.232 ***	I(1)
LGDP	−2.727 ***	−2.248 *	−3.426 ***	−3.706 ***	I(1)
LGDP2	−1.524	−2.624 **	−2.066	−2.293	I(0)

Note: Significance at the 10%, 5%, and 1% levels are indicated by *, **, and ***, respectively, whereas the values in parentheses are p-values.

Table 6

Second-generation CADF unit root test.

CADF Test
Variable	at Level			1st Differences
	T-Bar	Z-t-Tilde-Bar	p Value	T-Bar	Z-t-Tilde-Bar	p Value
LGHG	−2.48	−1.67	0.47
LGDP	−2.66	−1.96	0.02
LGDP2	−2.04	−0.62	0.26	−3.05	−3.02	0.001
LREN	−2.34	−1.32	0.09
LFOS	−1.17	1.44	0.92	−4.74	−7.05	0.000
LNUC	−1.79	−0.03	0.48	−4.98	−7.62	0.000

Table 7

Cointegration tests.

Statistic	Value	Z-Value	p-Value
$D_t$	−3.431	−1.842	0.010
$C_{\alpha }$	−6.539	2.291	0.050
$P_t$	−4.404	2.685	0.550
$P_{\alpha }$	−3.886	2.799	0.750

Table 8

Outcomes of AMG.

AMG Estimator
LGDP	−2.809 *** (2.346)
LGDP2	0.0566 (0.0448)
LREN	−0.548 *** (0.0231)
LFOS	0.0519 * (0.187)
LNUC	0.00243 (0.0272)
Constant	46.00 (37.81)
R-squared	0.544

*** p < 0.01 and * p < 0.1.

Table 9

Robustness results.

VARIABLES	MG	CCEMG
LGDP	−4.374 * (3.853)	−4.607 ** (8.800)
LGDP2	0.0895 (0.0790)	0.0967 (0.177)
LREN	−0.562 ** (0.224)	−0.264 (0.395)
LFOS	0.0864 ** (0.177)	0.332 ** (0.319)
LNUC	0.0167 (0.0256)	0.00123 (0.0310)
Constant	66.12 (46.60)	5.278 (92.63)

Standard errors in parentheses. ** p < 0.05 and * p < 0.1.

Appendix A. List of SAARC Countries

Afghanistan, Bangladesh, India, Nepal, Pakistan, Sri Lanka, Maldives, and Bhutan.

Appendix B. Correlation of All Variables

Variables	(LGHG)	(LGDP)	(LGDP2)	(LREN)	(LFOS)	(LNUC)
LGHG	1.000
LGDP	0.768 ***	1.000
LGDP2	0.770 ***	0.998 ***	1.000
LREN	0.422 ***	0.202 ***	0.199 ***	1.000
LFOS	0.635 ***	0.797 ***	0.780 ***	−0.807 ***	1.000
LNUC	−0.012	0.214 ***	0.211 ***	0.159 *	0.237 ***	1.000

*** p < 0.01 and * p < 0.1.

Appendix C. VIF Results

Variable	VIF	1/VIF
LGDP	9.64	0.1037
GDP2	8.43	0.1186
LFOS	5.30	0.1886
LREN	7.88	0.1269
LNUC	1.13	0.8814
Mean VIF	6.484

References

1. Cetin, M.A.; Bakirtas, I.; Yildiz, N. Does agriculture-induced environmental Kuznets curve exist in developing countries?. Environ. Sci. Pollut. Res.; 2022; 29, pp. 34019-34037. [DOI: https://dx.doi.org/10.1007/s11356-021-18065-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35031988]

2. Akhmat, G.; Zaman, K.; Shukui, T.; Irfan, D.; Khan, M.M. Does energy consumption contribute to environmental pollutants? Evidence from SAARC countries. Environ. Sci. Pollut. Res.; 2014; 21, pp. 5940-5951. [DOI: https://dx.doi.org/10.1007/s11356-014-2528-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24453017]

3. Rehman, E.; Rehman, S. Modeling the nexus between carbon emissions, urbanization, population growth, energy consumption, and economic development in Asia: Evidence from grey relational analysis. Energy Rep.; 2022; 8, pp. 5430-5442. [DOI: https://dx.doi.org/10.1016/j.egyr.2022.03.179]

4. Pandey, S.; Mishra, M. CO₂ emissions and economic growth of SAARC countries: Evidence from a panel VAR analysis. World J. Appl. Econ.; 2015; 1, pp. 23-33. [DOI: https://dx.doi.org/10.22440/EconWorld.J.2015.1.2.SP.0009]

5. Ben Mbarek, M.; Saidi, K.; Rahman, M.M. Renewable and non-renewable energy consumption, environmental degradation and economic growth in Tunisia. Qual. Quant.; 2018; 52, pp. 1105-1119. [DOI: https://dx.doi.org/10.1007/s11135-017-0506-7]

6. Weimin, Z.; Sibt-e-Ali, M.; Tariq, M.; Dagar, V.; Khan, M.K. Globalization toward environmental sustainability and electricity consumption to environmental degradation: Does EKC inverted U-shaped hypothesis exist between squared economic growth and CO₂ emissions in top globalized economies. Environ. Sci. Pollut. Res.; 2022; 29, pp. 59974-59984. [DOI: https://dx.doi.org/10.1007/s11356-022-20192-3]

7. Raihan, A.; Tuspekova, A. Dynamic impacts of economic growth, energy use, urbanization, tourism, agricultural value-added, and forested area on carbon dioxide emissions in Brazil. J. Environ. Stud. Sci.; 2022; 12, pp. 794-814. [DOI: https://dx.doi.org/10.1007/s13412-022-00782-w]

8. Raihan, A.; Tuspekova, A. Nexus between economic growth, energy use, agricultural productivity, and carbon dioxide emissions: New evidence from Nepal. Energy Nexus; 2022; 7, 100113. [DOI: https://dx.doi.org/10.1016/j.nexus.2022.100113]

9. Raihan, A.; Tuspekova, A. The nexus between economic growth, renewable energy use, agricultural land expansion, and carbon emissions: New insights from Peru. Energy Nexus; 2022; 6, 100067. [DOI: https://dx.doi.org/10.1016/j.nexus.2022.100067]

10. Tenaw, D.; Beyene, A.D. Environmental sustainability and economic development in sub-Saharan Africa: A modified EKC hypothesis. Renew. Sustain. Energy Rev.; 2021; 143, 110897. [DOI: https://dx.doi.org/10.1016/j.rser.2021.110897]

11. Raihan, A.; Tuspekova, A. Nexus between emission reduction factors and anthropogenic carbon emissions in India. Anthr. Sci.; 2022; 1, pp. 295-310. [DOI: https://dx.doi.org/10.1007/s44177-022-00028-y]

12. Shafiei, S.; Salim, R.A. Non-renewable and renewable energy consumption and CO₂ emissions in OECD countries: A comparative analysis. Energy Policy; 2014; 66, pp. 547-556. [DOI: https://dx.doi.org/10.1016/j.enpol.2013.10.064]

13. Khan, I.; Han, L.; Khan, H. Renewable energy consumption and local environmental effects for economic growth and carbon emission: Evidence from global income countries. Environ. Sci. Pollut. Res.; 2022; 29, pp. 13071-13088. [DOI: https://dx.doi.org/10.1007/s11356-021-16651-y]

14. Poumanyvong, P.; Kaneko, S.; Dhakal, S. Impacts of Urbanization on National Residential Energy Use and CO₂ Emissions: Evidence from Low-, Middle-and High-Income Countries; No. 2–5 Hiroshima University, Graduate School for International Development and Cooperation (IDEC): Hiroshima, Japan, 2012.

15. Rani, T.; Wang, F.; Rauf, F.; Ali, H. Linking personal remittance and fossil fuels energy consumption to environmental degradation: Evidence from all SAARC countries. Environ. Dev. Sustain.; 2022; 19, pp. 1-22. [DOI: https://dx.doi.org/10.1007/s10668-022-02407-2]

16. Abdallah, K.B.; Belloumi, M.; De Wolf, D. Indicators for sustainable energy development: A multivariate cointegration and causality analysis from Tunisian road transport sector. Renew. Sustain. Energy Rev.; 2013; 25, pp. 34-43. [DOI: https://dx.doi.org/10.1016/j.rser.2013.03.066]

17. Abu-Madi, M. Estimation of main greenhouse gases emission from household energy consumption in the West Bank, Palestine. Environ. Pollut.; 2013; 179, pp. 250-257. [DOI: https://dx.doi.org/10.1016/j.envpol.2013.04.022]

18. Allard, A.; Takman, J.; Uddin, G.S.; Ahmed, A. The N-shaped environmental Kuznets curve: An empirical evaluation using a panel quantile regression approach. Environ. Sci. Pollut. Res.; 2018; 25, pp. 5848-5861. [DOI: https://dx.doi.org/10.1007/s11356-017-0907-0]

19. Latief, R.; Kong, Y.; Javeed, S.A.; Sattar, U. Carbon emissions in the SAARC countries with causal effects of FDI, economic growth and other economic factors: Evidence from dynamic simultaneous equation models. Int. J. Environ. Res. Public Health; 2021; 18, 4605. [DOI: https://dx.doi.org/10.3390/ijerph18094605]

20. Nguyen, K.H.; Kakinaka, M. Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis. Renew. Energy; 2019; 132, pp. 1049-1057. [DOI: https://dx.doi.org/10.1016/j.renene.2018.08.069]

21. Ahmad, M.; Jiang, P.; Murshed, M.; Shehzad, K.; Akram, R.; Cui, L.; Khan, Z. Modelling the dynamic linkages between eco-innovation, urbanization, economic growth and ecological footprints for G7 countries: Does financial globalization matter?. Sustain. Cities Soc.; 2021; 70, 102881. [DOI: https://dx.doi.org/10.1016/j.scs.2021.102881]

22. Voumik, L.C.; Rahman, M.H.; Hossain, M.S. Investigating the subsistence of Environmental Kuznets Curve in the midst of economic development, population, and energy consumption in Bangladesh: Imminent of ARDL model. Heliyon; 2022; 8, e10357. [DOI: https://dx.doi.org/10.1016/j.heliyon.2022.e10357] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36090220]

23. Rehman, M.U.; Rashid, M. Energy consumption to environmental degradation, the growth appetite in SAARC nations. Renew. Energy; 2017; 111, 284294. [DOI: https://dx.doi.org/10.1016/j.renene.2017.03.100]

24. Menyah, K.; Wolde-Rufael, Y. CO₂ emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy; 2010; 38, pp. 2911-2915. [DOI: https://dx.doi.org/10.1016/j.enpol.2010.01.024]

25. Khan, I.; Hou, F.; Le, H.P. The impact of natural resources, energy consumption, and population growth on environmental quality: Fresh evidence from the United States of America. Sci. Total Environ.; 2021; 754, 142222. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.142222] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32920417]

26. Shaari, M.S.; Esquivias, M.A.; Ridzuan, A.R.; Fadzilah Zainal, N.; Sugiharti, L. The impacts of corruption and environmental degradation on foreign direct investment: New evidence from the ASEAN + 3 countries. Cogent Econ. Financ.; 2022; 10, 2124734. [DOI: https://dx.doi.org/10.1080/23322039.2022.2124734]

27. Grossman, G.M.; Krueger, A.B. Environmental Impacts of a North American Free Trade Agreement; National Bureau of Economic Research, Inc.: Cambridge, MA, USA, 1991; Available online: https://www.nber.org/papers/w3914 (accessed on 25 July 2022).

28. Xue, L.; Haseeb, M.; Mahmood, H.; Alkhateeb, T.T.Y.; Murshed, M. Renewable energy use and ecological footprints mitigation: Evidence from selected South Asian economies. Sustainability; 2021; 13, 1613. [DOI: https://dx.doi.org/10.3390/su13041613]

29. Jadoon, A.K.; Akhtar, S.; Sarwar, A.; Batool, S.A.; Chatrath, S.K.; Liaqat, S. Is economic growth and industrial growth the reason for environmental degradation in SAARC countries. Int. J. Energy Econ. Policy; 2021; 11, pp. 418-426. [DOI: https://dx.doi.org/10.32479/ijeep.11769]

30. Waqih, M.A.U.; Bhutto, N.A.; Ghumro, N.H.; Kumar, S.; Salam, M.A. Rising environmental degradation and impact of foreign direct investment: Empirical evidence from SAARC region. J. Environ. Manag.; 2019; 243, pp. 472-480. [DOI: https://dx.doi.org/10.1016/j.jenvman.2019.05.001]

31. Afridi, M.A.; Kehelwalatenna, S.; Naseem, I.; Tahir, M. Per capita income, trade openness, urbanization, energy consumption, and CO₂ emissions: An empirical study on the SAARC Region. Environ. Sci. Pollut. Res.; 2019; 26, pp. 29978-29990. [DOI: https://dx.doi.org/10.1007/s11356-019-06154-2]

32. Apergis, N.; Ozturk, I. Testing environmental Kuznets curve hypothesis in Asian countries. Ecol. Indic.; 2015; 52, pp. 16-22. [DOI: https://dx.doi.org/10.1016/j.ecolind.2014.11.026]

33. Jayasooriya, S. Experiential EKC: Trade Openness for Optimal CO₂ Emission in SAARC Region. 2019; Available online: https://mpra.ub.uni-muenchen.de/93203/ (accessed on 24 July 2022).

34. Ali, S.; Anwar, S.; Nasreen, S. Renewable and Non-Renewable Energy and its Impact on Environmental Quality in South Asian Countries. Forman J. Econ. Stud.; 2017; 13, pp. 177-194. [DOI: https://dx.doi.org/10.32368/FJES.20170009]

35. Khalid, K.; Usman, M.; Mehdi, M.A. The determinants of environmental quality in the SAARC region: A spatial heterogeneous panel data approach. Environ. Sci. Pollut. Res.; 2021; 28, pp. 6422-6436. [DOI: https://dx.doi.org/10.1007/s11356-020-10896-9]

36. Zhang, S.; Liu, X. The roles of international tourism and renewable energy in environment: New evidence from Asian countries. Renew. Energy; 2019; 139, pp. 385-394. [DOI: https://dx.doi.org/10.1016/j.renene.2019.02.046]

37. Usman, A.; Ozturk, I.; Naqvi, S.M.M.A.; Ullah, S.; Javed, M.I. Revealing the nexus between nuclear energy and ecological footprint in STIRPAT model of advanced economies: Fresh evidence from novel CS-ARDL model. Prog. Nucl. Energy; 2022; 148, 104220. [DOI: https://dx.doi.org/10.1016/j.pnucene.2022.104220]

38. Ramzan, M.; Raza, S.A.; Usman, M.; Sharma, G.D.; Iqbal, H.A. Environmental cost of non-renewable energy and economic progress: Do ICT and financial development mitigate some burden?. J. Clean. Prod.; 2022; 333, 130066. [DOI: https://dx.doi.org/10.1016/j.jclepro.2021.130066]

39. Almas, L.K. Transport Energy Consumption and Climatic Challenges in SAARC Countries. Indian J. Econ. Bus.; 2021; 20, Available online: http://www.ashwinanokha.com/IJEB.php (accessed on 15 July 2022).

40. Usman, M.; Jahanger, A.; Radulescu, M.; Balsalobre-Lorente, D. Do Nuclear Energy, Renewable Energy, and Environmental-Related Technologies Asymmetrically Reduce Ecological Footprint? Evidence from Pakistan. Energies; 2022; 15, 3448. [DOI: https://dx.doi.org/10.3390/en15093448]

41. Mehmood, U. Transport energy consumption and carbon emissions: The role of urbanization towards environment in SAARC region. Integr. Environ. Assess. Manag.; 2021; 17, pp. 1286-1292. [DOI: https://dx.doi.org/10.1002/ieam.4463]

42. Hussain, M.N.; Li, Z.; Sattar, A. Effects of urbanization and non-renewable energy on carbon emission in Africa. Environ. Sci. Pollut. Res.; 2022; 29, pp. 25078-25092. [DOI: https://dx.doi.org/10.1007/s11356-021-17738-2]

43. Awodumi, O.B.; Adewuyi, A.O. The role of non-renewable energy consumption in economic growth and carbon emission: Evidence from oil producing economies in Africa. Energy Strategy Rev.; 2020; 27, 100434. [DOI: https://dx.doi.org/10.1016/j.esr.2019.100434]

44. Usman, A.; Ullah, S.; Ozturk, I.; Chishti, M.Z.; Zafar, S.M. Analysis of asymmetries in the nexus among clean energy and environmental quality in Pakistan. Environ. Sci. Pollut. Res.; 2020; 27, pp. 20736-20747. [DOI: https://dx.doi.org/10.1007/s11356-020-08372-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32246426]

45. Hanif, I. Impact of fossil fuels energy consumption, energy policies, and urban sprawl on carbon emissions in East Asia and the Pacific: A panel investigation. Energy Strategy Rev.; 2018; 21, pp. 16-24. [DOI: https://dx.doi.org/10.1016/j.esr.2018.04.006]

46. Afshan, S.; Ozturk, I.; Yaqoob, T. Facilitating renewable energy transition, ecological innovations and stringent environmental policies to improve ecological sustainability: Evidence from MM-QR method. Renew. Energy; 2022; 196, pp. 151-160. [DOI: https://dx.doi.org/10.1016/j.renene.2022.06.125]

47. Saidi, K.; Mbarek, M.B. Nuclear energy, renewable energy, CO₂ emissions, and economic growth for nine developed countries: Evidence from panel Granger causality tests. Prog. Nucl. Energy; 2016; 88, pp. 364-374. [DOI: https://dx.doi.org/10.1016/j.pnucene.2016.01.018]

48. Mahmood, N.; Wang, Z.; Zhang, B. The role of nuclear energy in the correction of environmental pollution: Evidence from Pakistan. Nucl. Eng. Technol.; 2020; 52, pp. 1327-1333. [DOI: https://dx.doi.org/10.1016/j.net.2019.11.027]

49. Hassan, S.T.; Baloch, M.A.; Tarar, Z.H. Is nuclear energy a better alternative for mitigating CO₂ emissions in BRICS countries? An empirical analysis. Nucl. Eng. Technol.; 2020; 52, pp. 2969-2974. [DOI: https://dx.doi.org/10.1016/j.net.2020.05.016]

50. Dietz, T.; Rosa, E.A. Effects of population and affluence on CO₂ emissions. Proc. Natl. Acad. Sci. USA; 1997; 94, pp. 175-179. [DOI: https://dx.doi.org/10.1073/pnas.94.1.175]

51. Majumder, S.C.; Voumik, L.C.; Rahman, M.H.; Rahman, M.M.; Hossain, M.N. A Quantile Regression Analysis of the Impact of Electricity Production Sources on CO₂ Emission in South Asian Countries. Strateg. Plan. Energy Environ.; 2023; 31, pp. 307-330. [DOI: https://dx.doi.org/10.13052/spee1048-5236.4223]

52. Voumik, L.C.; Islam, M.; Rahaman, A.; Rahman, M. Emissions of carbon dioxide from electricity production in ASEAN countries: GMM and quantile regression analysis. SN Bus. Econ.; 2022; 2, 133. [DOI: https://dx.doi.org/10.1007/s43546-022-00318-y]

53. Bekun, F.V. Mitigatung emissions in India: Accounting for the role of real income, renewable energy consumption and investment in energy. Int. J. Energy Econ. Policy; 2021; 12, pp. 188-192. Available online: https://econjournals.com/index.php/ijeep/article/download/12652/6209 (accessed on 26 July 2022). [DOI: https://dx.doi.org/10.32479/ijeep.12652]

54. Bekun, F.V.; Adedoyin, F.F.; Etokakpan, M.U.; Gyamfi, B.A. Exploring the tourism-CO₂ Emissions-real income nexus in E7 countries: Accounting for the role of institutional quality. J. Policy Res. Tour. Leis. Events; 2022; 14, pp. 1-19. [DOI: https://dx.doi.org/10.1080/19407963.2021.2017725]

55. Zhao, J.; Zhang, T.; Ali, A.; Chen, J.; Ji, H.; Wang, T. An empirical investigation of the impact of renewable and non-renewable energy consumption and economic growth on climate change, evidence from emerging Asian countries. Front. Environ. Sci.; 2023; 10, [DOI: https://dx.doi.org/10.3389/fenvs.2022.1085372]

56. Pesaran, M.H. A simple panel unit root test in the presence of cross-section dependence. J. Appl. Econom.; 2007; 22, pp. 265-312. [DOI: https://dx.doi.org/10.1002/jae.951]

57. Mensah, I.A.; Sun, M.; Gao, C.; Omari-Sasu, A.Y.; Zhu, D.; Ampimah, B.C.; Quarcoo, A. Analysis on the nexus of economic growth, fossil fuel energy consumption, CO₂ emissions and oil price in Africa based on a PMG panel ARDL approach. J. Clean. Prod.; 2019; 228, pp. 161-174. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.04.281]

58. Pesaran, M.H.; Yamagata, T. Testing slope homogeneity in large panels. J. Econom.; 2008; 142, pp. 50-93. [DOI: https://dx.doi.org/10.1016/j.jeconom.2007.05.010]

59. Pesaran, M.H. Testing weak cross-sectional dependence in large panels. Econom. Rev.; 2015; 34, pp. 1089-1117. [DOI: https://dx.doi.org/10.1080/07474938.2014.956623]

60. Tufail, M.; Song, L.; Adebayo, T.S.; Kirikkaleli, D.; Khan, S. Do fiscal decentralization and natural resources rent curb carbon emissions? Evidence from developed countries. Environ. Sci. Pollut. Res.; 2021; 28, pp. 49179-49190. [DOI: https://dx.doi.org/10.1007/s11356-021-13865-y]

61. Westerlund, J.; Edgerton, D.L. A simple test for cointegration in dependent panels with structural breaks. Oxf. Bull. Econ. Stat.; 2008; 70, pp. 665-704. [DOI: https://dx.doi.org/10.1111/j.1468-0084.2008.00513.x]

62. Pesaran, M.H. General Diagnostic Tests for Cross-Sectional Dependence in Panels; Mimeo: New York, NY, USA, University of Cambridge: Cambridge, UK, 2004.

63. Breusch, T.S.; Pagan, A.R. The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud.; 1980; 47, pp. 239-253. [DOI: https://dx.doi.org/10.2307/2297111]

64. Baltagi, B.H.; Feng, Q.; Kao, C. A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model. J. Econom.; 2012; 170, pp. 164-177. [DOI: https://dx.doi.org/10.1016/j.jeconom.2012.04.004]

65. Pedroni, P. Purchasing power parity tests in cointegrated panels. Rev. Econ. Stat.; 2001; 83, pp. 727-731. [DOI: https://dx.doi.org/10.1162/003465301753237803]

66. Pedroni, P. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econom. Theory; 2004; 20, pp. 597-625. [DOI: https://dx.doi.org/10.1017/S0266466604203073]

67. Kao, Y.C.; Zhou, C.; Sherman, M.; Laughton, C.A.; Chen, S. Molecular basis of the inhibition of human aromatase (estrogen synthetase) by flavone and isoflavone phytoestrogens: A site-directed mutagenesis study. Environ. Health Perspect.; 1998; 106, pp. 85-92. [DOI: https://dx.doi.org/10.1289/ehp.9810685] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/9435150]

68. Westerlund, J. Testing for error correction in panel data. Oxf. Bull. Econ. Stat.; 2007; 69, pp. 709-748. [DOI: https://dx.doi.org/10.1111/j.1468-0084.2007.00477.x]

69. Tenaw, D.; Hawitibo, A.L. Carbon decoupling and economic growth in Africa: Evidence from production and consumption-based carbon emissions. Resour. Environ. Sustain.; 2021; 6, 100040. [DOI: https://dx.doi.org/10.1016/j.resenv.2021.100040]

70. Eberhardt, M.; Teal, F. Productivity Analysis in Global Manufacturing Production, 515. 2010; Available online: https://ora.ox.ac.uk/objects/uuid:ea831625-9014-40ec-abc5-516ecfbd2118 (accessed on 21 August 2022).

71. Fodha, M.; Zaghdoud, O. Economic growth and pollutant emissions in Tunisia: An empirical analysis of the environmental Kuznets curve. Energy Policy; 2010; 38, pp. 1150-1156. [DOI: https://dx.doi.org/10.1016/j.enpol.2009.11.002]

72. Tang, Y.; Zhu, H.; Yang, J. The asymmetric effects of economic growth, urbanization and deindustrialization on carbon emissions: Evidence from China. Energy Rep.; 2022; 8, pp. 513-521. [DOI: https://dx.doi.org/10.1016/j.egyr.2022.05.076]

73. Pata, U.K. Renewable energy consumption, urbanization, financial development, income and CO₂ emissions in Turkey: Testing EKC hypothesis with structural breaks. J. Clean. Prod.; 2018; 187, pp. 770-779. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.03.236]

74. Mikayilov, J.I.; Galeotti, M.; Hasanov, F.J. The impact of economic growth on CO₂ emissions in Azerbaijan. J. Clean. Prod.; 2018; 197, pp. 1558-1572. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.06.269]

75. Bozkurt, C.; Yusuf, A.K.A.N. Economic growth, CO₂ emissions and energy consumption: The Turkish case. Int. J. Energy Econ. Policy; 2014; 4, pp. 484-494.

76. Yousaf, A.; Erum, N.; Bibi, F. Economic Growth, Foreign Direct Investment and the Environment: An Empirical Investigation for SAARC Countries. Glob. Strateg. Secur. Stud. Rev.; 2016; I, pp. 1-12. [DOI: https://dx.doi.org/10.31703/gsssr.2016(I-I).01]

77. Ozgur, O.; Yilanci, V.; Kongkuah, M. Nuclear energy consumption and CO₂ emissions in India: Evidence from Fourier ARDL bounds test approach. Nucl. Eng. Technol.; 2022; 54, pp. 1657-1663. [DOI: https://dx.doi.org/10.1016/j.net.2021.11.001]

78. Lu, W.C. Renewable energy, carbon emissions, and economic growth in 24 Asian countries: Evidence from panel cointegration analysis. Environ. Sci. Pollut. Res.; 2017; 24, pp. 26006-26015. [DOI: https://dx.doi.org/10.1007/s11356-017-0259-9]

79. Saad, W.; Taleb, A. The causal relationship between renewable energy consumption and economic growth: Evidence from Europe. Clean Technol. Environ. Policy; 2018; 20, pp. 127-136. [DOI: https://dx.doi.org/10.1007/s10098-017-1463-5]

80. Al-Mulali, U.; Ozturk, I.; Solarin, S.A. Investigating the environmental Kuznets curve hypothesis in seven regions: The role of renewable energy. Ecol. Indic.; 2016; 67, pp. 267-282. [DOI: https://dx.doi.org/10.1016/j.ecolind.2016.02.059]

81. Iwata, H.; Okada, K.; Samreth, S. Empirical study on the environmental Kuznets curve for CO₂ in France: The role of nuclear energy. Energy Policy; 2010; 38, pp. 4057-4063. [DOI: https://dx.doi.org/10.1016/j.enpol.2010.03.031]

82. Voumik, L.C.; Rahman, M.; Akter, S. Investigating the EKC hypothesis with renewable energy, nuclear energy, and R&D for EU: Fresh panel evidence. Heliyon; 2022; 8, e12447. [DOI: https://dx.doi.org/10.1016/j.heliyon.2022.e12447]

83. Voumik, L.C.; Sultana, T. Impact of urbanization, industrialization, electrification and renewable energy on the environment in BRICS: Fresh evidence from novel CS-ARDL model. Heliyon; 2022; 8, e11457. [DOI: https://dx.doi.org/10.1016/j.heliyon.2022.e11457]

84. Polcyn, J.; Voumik, L.C.; Ridwan, M.; Ray, S.; Vovk, V. Evaluating the Influences of Health Expenditure, Energy Consumption, and Environmental Pollution on Life Expectancy in Asia. Int. J. Environ. Res. Public Health; 2023; 20, 4000. [DOI: https://dx.doi.org/10.3390/ijerph20054000]

85. Voumik, L.C.; Rahman, M.H.; Nafi, S.M.; Hossain, M.A.; Ridzuan, A.R.; Mohamed Yusoff, N.Y. Modelling Sustainable Non-Renewable and Renewable Energy Based on the EKC Hypothesis for Africa's Ten Most Popular Tourist Destinations. Sustainability; 2023; 15, 4029. [DOI: https://dx.doi.org/10.3390/su15054029]

86. Bekun, F.V.; Alola, A.A.; Sarkodie, S.A. Toward a sustainable environment: Nexus between CO₂ emissions, resource rent, renewable and non-renewable energy in 16-EU countries. Sci. Total Environ.; 2019; 657, pp. 1023-1029. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.12.104] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30677870]

87. Chen, Y.; Zhao, J.; Lai, Z.; Wang, Z.; Xia, H. Exploring the effects of economic growth, and renewable and non-renewable energy consumption on China’s CO₂ emissions: Evidence from a regional panel analysis. Renew. Energy; 2019; 140, pp. 341-353. [DOI: https://dx.doi.org/10.1016/j.renene.2019.03.058]

88. Mekhzoumi, L.; Harnane, N.; Ayachi, A.; Abdellaoui, O. The Environmental Kuznets Curve Hypothesis in Industrialized Countries: A Second Generation Econometric Approach. Int. J. Econ. Financ. Issues; 2022; 12, pp. 96-103. [DOI: https://dx.doi.org/10.32479/ijefi.12771]

89. Murshed, M.; Ali, S.R.; Banerjee, S. Consumption of liquefied petroleum gas and the EKC hypothesis in South Asia: Evidence from cross-sectionally dependent heterogeneous panel data with structural breaks. Energy Ecol. Environ.; 2021; 6, pp. 353-377. [DOI: https://dx.doi.org/10.1007/s40974-020-00185-z]

90. Sahoo, M.; Sethi, N. The intermittent effects of renewable energy on ecological footprint: Evidence from developing countries. Environ. Sci. Pollut. Res.; 2021; 28, pp. 56401-56417. [DOI: https://dx.doi.org/10.1007/s11356-021-14600-3]

91. Vasylieva, T.; Lyulyov, O.; Bilan, Y.; Streimikiene, D. Sustainable economic development and greenhouse gas emissions: The dynamic impact of renewable energy consumption, GDP, and corruption. Energies; 2019; 12, 3289. [DOI: https://dx.doi.org/10.3390/en12173289]

92. Li, F.; Chang, T.; Wang, M.C.; Zhou, J. 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, pp. 10908-10927. [DOI: https://dx.doi.org/10.1007/s11356-021-17900-w]

93. Siddiqi, T.A. Viable and Environment-Friendly Sources for Meeting South Asia’s Growing Energy Needs; East-West Center: Honolulu, HI, USA, 2007; Available online: http://www.jstor.org/stable/resrep16008 (accessed on 20 May 2022).

94. Adewuyi, A.O.; Awodumi, O.B. Biomass energy consumption, economic growth and carbon emissions: Fresh evidence from West Africa using a simultaneous equation model. Energy; 2017; 119, pp. 453-471. [DOI: https://dx.doi.org/10.1016/j.energy.2016.12.059]

95. Sebitosi, A.B.; Pillay, P. Renewable energy and the environment in South Africa: A way forward. Energy Policy; 2008; 36, pp. 3312-3316. [DOI: https://dx.doi.org/10.1016/j.enpol.2008.05.013]

96. Voumik, L.C.; Hossain, M.S.; Islam, M.A.; Rahaman, A. Power Generation Sources and Carbon Dioxide Emissions in BRICS Countries: Static and Dynamic Panel Regression. Strateg. Plan. Energy Environ.; 2022; 41, pp. 401-424. [DOI: https://dx.doi.org/10.13052/spee1048-5236.4143]

97. Rahman, M.H.; Voumik, L.C.; Islam, M.J.; Halim, M.A.; Esquivias, M.A. Economic Growth, Energy Mix, and Tourism-Induced EKC Hypothesis: Evidence from Top Ten Tourist Destinations. Sustainability; 2022; 14, 16328. [DOI: https://dx.doi.org/10.3390/su142416328]

98. Azam, M.; Uddin, I.; Khan, S.; Tariq, M. Are globalization, urbanization, and energy consumption cause carbon emissions in SAARC region? New evidence from CS-ARDL approach. Environ. Sci. Pollut. Res.; 2022; 29, pp. 87746-87763. [DOI: https://dx.doi.org/10.1007/s11356-022-21835-1]

99. Nahrin, R.; Rahman, M.H.; Majumder, S.C.; Esquivias, M.A. Economic Growth and Pollution Nexus in Mexico, Colombia, and Venezuela (G-3 Countries): The Role of Renewable Energy in Carbon Dioxide Emissions. Energies; 2023; 16, 1076. [DOI: https://dx.doi.org/10.3390/en16031076]