Environmental Impacts of Energy Intensity, Renewable Energy, and Globalization: Evidence from SAARC Countries

Abstract

The aim of this article was to determine the impact of energy intensity, renewable energy consumption, and globalization on environmental degradation in SAARC countries. Utilizing both CO₂ emissions and the ecological footprint (EF) as environmental indicators, the research provides a comprehensive evaluation of sustainability trends. The analysis employs the Method of Moments Quantile Regression (MMQR) technique to account for heterogeneity across different levels of environmental degradation. The study covered the period 2000–2021. The empirical results indicate that renewable energy usage significantly reduces both CO₂ emissions and EF, emphasizing the necessity of expanding clean energy sources in SAARC nations. Conversely, energy intensity exacerbates environmental degradation, highlighting the urgent need for efficiency improvements. Globalization exerts mixed effects, with economic integration increasing CO₂ emissions while reducing ecological footprint, suggesting a complex interplay between industrialization and sustainability. Additionally, economic growth consistently worsens environmental quality across all quantiles, reinforcing the challenge of balancing development with ecological preservation. The findings emphasize the necessity of policy interventions to promote renewable energy adoption, enhance energy efficiency, and leverage globalization for sustainable development. Future research should explore broader environmental indicators and adopt advanced econometric techniques to deepen insights into sustainable energy transitions in SAARC countries.

1. Introduction

Climate change stands out as one of the most urgent global issues of the 21st century, with its roots deeply embedded in human activities related to production and consumption. As economies grow, the demand for energy increases, leading to higher greenhouse gas (GHG) emissions and exacerbating environmental deterioration. Recognizing the urgency of this problem, the United Nations has established the Sustainable Development Goals (SDGs) to guide global efforts in tackling such challenges. In particular, SDG 7 (focused on clean energy access) and SDG 13 (dedicated to climate action) have become critical priorities for nations worldwide, highlighting the urgent need for sustainable energy systems and proactive measures to combat climate change [1].

1.1. Energy and Environmental Issues in SAARC Countries

The global pursuit of economic growth and development has frequently undermined environmental sustainability. This trade-off is particularly pronounced in developing regions, where rapid industrialization and urbanization have led to increased energy consumption and environmental harm. The nations within the South Asian Association for Regional Cooperation (SAARC) are no different in this regard. SAARC comprises eight member states: India, Pakistan, Bangladesh, Sri Lanka, Nepal, Bhutan, the Maldives, and Afghanistan, and is headquartered in Kathmandu, Nepal. As these nations strive to achieve economic prosperity, they face the dual challenge of satisfying growing energy needs while mitigating the adverse environmental impacts of their energy systems. With a cumulative GDP growth of 6.78% in 2023 [2], SAARC countries account for 21% of the world’s population and contribute 4.21% to the world economy, despite occupying only 3% of the world’s geographical area [3]. This rapid economic progress has resulted in higher per capita electricity consumption, placing immense pressure on energy resources and infrastructure. However, the region’s energy landscape is characterized by inefficiencies and disparities in electrification. For instance, access to electricity varies significantly, ranging from 30% in Afghanistan, 64% in Bangladesh, 93% in the Maldives, and 100% in Bhutan. While Bhutan and Nepal rely heavily on hydropower, the remaining SAARC nations continue to rely extensively on imported fossil fuels for electricity production, exacerbating environmental degradation and energy insecurity [3]. This reliance on non-renewable energy sources, coupled with high energy intensity—a measure of energy efficiency—has led to increased greenhouse gas emissions and environmental harm. Figure 1b shows that SAARC member countries experienced a rising trend in CO₂ emissions from 2000 to 2021, with the highest emissions observed in the Maldives. India, Bhutan, and Sri Lanka follow as the next highest emitters among SAARC nations. Figure 1a illustrates the trend of the ecological footprint for these countries, which has remained relatively stable over the same period.

1.2. Opportunities and Threats for SAARC Countries Resulting from Energy Intensity, the Implementation of Renewable Energy Sources, and Globalization

Amid these challenges, energy intensity (EI), renewable energy adoption, and globalization present both opportunities and risks. Energy intensity measures how much energy is needed to generate a unit of economic output (GDP) and is often linked to higher GHG emissions in fossil fuel-dependent economies. Improving energy efficiency—producing the same economic output with lower energy consumption—is a crucial strategy for reducing CO₂ emissions and addressing climate change challenges [5,6]. The movement towards a greener economy and net-zero emissions is reshaping energy consumption patterns in both advanced and emerging economies, encouraging decision-makers and enterprises to adopt energy-efficient solutions to tackle ongoing energy challenges [6]. In this context, shifting toward renewable energy (RE) sources, like wind, solar, and hydropower, provides an effective route to lower carbon emissions and enhance energy security. Governments have recently intensified their focus on RE as a central approach for mitigating environmental degradation [7]. In contrast to fossil fuels, RE sources produce electricity without releasing GHGs that cause air pollution [8]. Utilizing RE helps mitigate the overuse and combustion of fossil fuels like coal and petroleum [9]. Although extensive research highlights the environmental benefits of renewable energy consumption (REC), some studies [10,11] indicate that REC may, at times, contribute to environmental deterioration. However, the adoption of RE in SAARC countries remains uneven due to financial, technical, and infrastructural barriers. Globalization, characterized by increased economic integration, trade, and technological exchange, has further complicated the environmental landscape [12,13]. While globalization can facilitate the spread of clean technologies and promote sustainable practices [14], it can also exacerbate environmental harm by driving higher consumption, intensified resource extraction, carbon-heavy production, and the shift in pollution-intensive industries from developed to developing countries [15,16].

1.3. Research Gap

Traditionally, studies measuring environmental degradation have relied on CO₂ and GHG emissions, both of which primarily capture air pollution and its contribution to global warming. However, these indicators fail to account for other dimensions of environmental harm. To address this limitation, this study incorporates the ecological footprint (EF), a broader measure introduced by Rees [17]. EF measures humanity’s demand on the planet’s biological resources by contrasting the level of resource use with the capacity of biologically productive land and marine ecosystems, thereby reflecting the pressure placed on ecosystem services and natural resources [12,18]. This metric offers a broader assessment of environmental sustainability by capturing air, water, and soil pollution.

Given the interplay between EI, RE, globalization, and environmental degradation (measured using CO₂ emissions and EF), it is crucial to explore their combined impact, particularly in the context of SAARC nations. Limited literature exists on analyzing these factors together, especially in SAARC countries. Therefore, this study examines how energy intensity, renewable energy, and globalization affect the environment in SAARC nations, using annual data from 2000 to 2021.

The findings are expected to enrich the ongoing dialogue on sustainable development and inform policy decisions at both national and regional levels to achieve SDG 7 and SDG 13. This research makes several contributions to the literature: First, it analyzes the impact of energy intensity, renewable energy, and globalization on environmental quality within SAARC nations, a region that, to the best of our knowledge, has received limited attention in analyses of these combined factors. Second, whereas most prior studies have focused exclusively on CO₂ emissions to gauge environmental harm, this study employs both CO₂ emissions and EF, offering a more holistic assessment of environmental sustainability and enhancing the robustness of results. Third, the application of the advanced MMQR technique allows for the analysis of environmental quality across different quantiles, addressing the limitations of traditional econometric approaches such as fixed effects, pooled OLS, and random effects, which focus only on mean values and fail to capture distributional heterogeneity.

1.4. Research Objective, Questions, and Structure of the Article

By analyzing the interplay of EI, RE, globalization, and environmental degradation, this research aims to provide insights into how SAARC nations can achieve a balance between economic expansion and environmental sustainability. Specifically, it tackles the following research questions:

(i)

How does energy intensity impact environmental degradation in SAARC countries?

(ii)

Does renewable energy help SAARC economies transition toward net-zero emissions goals?

(iii)

Can globalization mitigate environmental degradation in SAARC countries?

To address these questions, we apply the Method of Moment Quantile Regression (MMQR) approach. This advanced technique offers significant advantages over traditional econometric methods by analyzing explanatory factors across different levels of environmental quality, capturing heterogeneity in panel data, and addressing non-linearity and endogeneity in the dataset.

The remainder of this paper is organized as follows: Section 2 reviews the relevant literature; Section 3 outlines the data and methodological approach; Section 4 discusses the findings; and Section 5 concludes by offering policy recommendations.

2. Literature Review

2.1. Energy Intensity and Environmental Degradation

Energy intensity (EI), defined as the amount of energy consumed per unit of economic output, is a widely used indicator of energy efficiency and technological advancement within an economy [19]. From a theoretical perspective, higher EI reflects inefficient production processes and excessive reliance on energy-intensive sectors, which often leads to increased fossil-fuel consumption, resource depletion, and environmental degradation [20]. Conversely, declining EI is typically associated with technological progress, structural transformation toward less energy-intensive activities, and improvements in environmental sustainability [21,22]. The energy–environment nexus has received considerable attention in the literature, with energy consumption frequently identified as a fundamental driver of environmental pollution [23,24]. However, compared to aggregate energy use, EI offers a more nuanced measure, as it captures the efficiency dimension of energy utilization relative to economic output [25,26]. Low improvements in EI amid expanding economic activity tend to amplify CO₂ emissions and undermine environmental sustainability [27].

A growing body of empirical studies suggests that higher EI is generally associated with deteriorating environmental quality, primarily through increased carbon emissions and ecological pressure. For example, Mirza et al. [28], analyzing 30 developing economies over the period 1990–2016, showed that improvements in energy efficiency significantly contribute to reducing CO₂ emissions. Similarly, Shokoohi et al. [20] found that EI is a key driver of both CO₂ emissions and ecological footprint in Iran, Iraq, and Türkiye during 1971–2015. Using PMG and ARDL approaches for 25 emerging economies, Rahman et al. [29] demonstrated that higher EI and industrialization increase carbon intensity in the long run, whereas renewable energy consumption and urbanization help mitigate environmental degradation.

However, the empirical evidence is not entirely conclusive. Somoye [30], examining Türkiye over the period 1970–2021, reported that both EI and renewable energy reduce CO₂ emissions in the long run, while simultaneously cautioning that their overall environmental effects may remain ambiguous. In contrast, Pata et al. [31] showed that fossil EI significantly worsens ecological efficiency in China, while Jabeen et al. [32], using provincial-level data, confirmed a positive association between energy utilization intensity and environmental degradation.

Evidence from developed economies largely supports the adverse environmental role of energy intensity. Danish et al. [33] demonstrated that rising EI increases CO₂ emissions in the United States over 1985–2017. Likewise, Marra et al. [5], employing a panel VAR model for 34 OECD countries, reported that improvements in EI contribute to emission reductions. Degirmenci et al. [34], focusing on G7 countries, revealed heterogeneous effects, with EI negatively affecting environmental outcomes in Italy, Germany, and the United States, while green energy transition mitigates environmental degradation in Japan.

Despite the extensive literature, empirical evidence on the energy intensity–environment nexus in SAARC countries remains scarce. Given the region’s rapid industrialization, urban expansion, and energy demand growth, a systematic examination of how energy intensity shapes environmental outcomes in SAARC economies represents a significant research gap that this study aims to address.

2.2. Renewable Energy and Environment

Embracing renewable energy consumption (REC) is broadly viewed as an essential approach to combating climate change, decreasing reliance on fossil fuels, and enhancing environmental sustainability. From a theoretical standpoint, renewable energy sources generate lower greenhouse gas emissions and impose fewer ecological externalities than conventional fossil fuels, thereby contributing to improved environmental quality and energy security [35,36]. Empirical evidence largely supports the environmental benefits of renewable energy adoption. Mohamed et al. [37], for instance, explored the link between REC and environmental excellence in Malaysia during the period 1985–2020, using the ARDL technique. Their findings indicated that, in the long run, REC contributes to CO₂ reduction, thereby improving environmental conditions. Similarly, Villanthenkodath and Pal [38], using the ARDL model, explored this relationship in India from 1990 to 2019, demonstrating that REC positively influenced environmental improvement by reducing both carbon emissions and EF, while also boosting the load capacity factor (LCF). Meanwhile, Faizi et al. [12] investigated how RE and globalization impact environmental degradation within the Organization of Turkic States (OTS) between 1996 and 2020. Their study revealed that RE enhances environmental quality, as reflected in lower CO₂ emissions and ecological footprint. In contrast, globalization was identified as a driver of environmental deterioration. Further evidence comes from Khan et al. [39], who explored the effects of REC on CO₂ in SAARC nations between 1990 and 2019, using FE panel quantile regression. Their findings showed an adverse link between REC and CO₂, notably in the higher quantile distributions. Analyzing data from 1994 to 2018, Jahanger et al. [7] assessed the association between REC and LCF in the top ten SDG countries using MMQR. Their results confirmed the beneficial impact of REC on LCF, thereby improving environmental quality. Dogan and Pata [40] also confirmed this positive relationship for G7 countries from 1986 to 2017. Conversely, Altıntaş et al. [11] observed that REC adversely affects environmental quality in Malaysia between 1980 and 2018, reducing LCF over both short and long periods. Similarly, Alola et al. [10], analyzing 16 EU member countries between 1997 and 2014 through a panel PMG-ARDL model, found that REC increased EF, signifying environmental degradation.

2.3. Globalization and Environmental Outcomes

Globalization (GL) has profound implications for environmental sustainability, both positive and negative. On the one hand, globalization facilitates the transfer of clean technologies and promotes international cooperation on environmental issues [14]. On the other hand, it can lead to increased resource use, carbon leakage, and environmental degradation in developing countries [16]. For example, Gaies et al. [41] applied ARDL and NARDL methods to study 17 MENA countries from 1980 to 2018. They found that trade globalization positively affects CO₂ emissions, whereas financial globalization had varying effects: it reduced CO₂ in OPEC MENA nations but increased it in non-OPEC MENA countries over the long term. Using the ARDL model, Leal et al. [15] examined this relationship across 32 developed and 26 developing nations from 1995 to 2017. Their findings indicated that globalization benefits environmental quality in developed nations while negatively impacting it in developing nations. This further supports the perspective that globalization can have both beneficial and harmful effects. Latif et al. [42] investigated this relationship in 48 Asian economies from 1996 to 2020 using the System GMM approach. They concluded that economic globalization increases LCF, indicating a beneficial impact on environmental performance. Conversely, several studies have reported that globalization has harmful effects on environmental consequences. Xia et al. [16], through panel analysis of 67 developed and developing nations between 1971 and 2018, determined that globalization is associated with increased CO₂ emissions in both groups of nations. Likewise, Aydin et al. [43] explored this link for 10 EU countries from 1990 to 2019 and found a significant adverse effect of globalization on LCF specifically for Austria. Using the PMG-ARDL model, Pata et al. [44] investigated data spanning 1990 to 2018 and found that globalization negatively correlates with the LCF in Latin American and Caribbean (LAC) nations. In the SAARC region, globalization has been accompanied by rapid economic expansion and increased trade activities. Yet, its environmental implications remain largely unclear. Although the connection between globalization and environmental outcomes has been examined in other parts of the world, research that specifically addresses this relationship within SAARC countries is notably limited.

Although the existing literature offers important perspectives on how EI, RE, and globalization affect the environment, several gaps remain. First, few studies have examined these factors in the context of SAARC countries, despite the region’s unique economic and environmental challenges. Second, little research has explored how these factors interact and collectively impact environmental changes. This study seeks to fill these gaps by delivering a comprehensive assessment of the environmental impacts of EI, RE, and globalization across SAARC nations.

3. Materials and Methods

This study applies a sequence of econometric techniques to examine environmental quality in SAARC countries, with the methodological framework summarized in Figure 2. The analysis integrates data collection, model specification, preliminary diagnostics tests, and model estimation using MMQR, followed by panel causality and robustness checks to ensure result reliability.

3.1. Data and Sources

This study analyzes the effects of REC, EI, globalization (GL), and economic growth (GDP) on environmental degradation in SAARC members (India, Bangladesh, Maldives, Nepal, Afghanistan, Bhutan, Pakistan, and Sri Lanka) during the period between 2000 and 2021. The time frame was chosen based on data availability. Environmental degradation is examined using two dependent variables: CO₂ emissions (CO₂) and the ecological footprint (EF), applied in two separate models to identify the determinants of environmental changes. Higher levels of CO₂ and EF signify greater environmental degradation. Most prior research has primarily focused on CO₂ as the sole indicator of environmental degradation. While CO₂ emissions measure air pollution and its direct contribution to global warming, they fail to capture other dimensions of environmental harm. To address this limitation, this study incorporates EF as a broader indicator of environmental degradation. Unlike measures that focus solely on air pollution, EF captures the effects on soil and water as well, reflecting the impacts of land use, waste generation, and natural resource depletion [12,45]. The data for EF are collected from the Global Footprint Network [4], while the data on globalization are sourced from the KOF Swiss Economic Institute [46]. The rest of the data for REC, EI, and GDP are obtained from the World Bank [2]. All variables are transformed into their logarithmic forms to address potential heteroscedasticity and to facilitate the interpretation of coefficients as elasticities. A summary of variable descriptions and sources is provided in

Table 1. Description of variables and sources.

Symbol	Variable	Measurement	Source
EF	Ecological footprint	Per capita ecological footprint (global hectares)	[4]
CO2	CO2 emissions	Total annual emissions of carbon dioxide (CO2), per capita	[2]
EI	Energy intensity	Energy intensity of primary energy use (MJ/$2017 PPP GDP)	[2]
REC	Renewable energy	Share of renewable energy in total final energy consumption (%)	[2]
GDP	Economic growth	Constant 2015 US$, per capita GDP	[2]
GL	Globalization	A combined index reflecting economic, political, and social dimensions of globalization, measured on a scale from 0 to 100.	[46]

.

EI is defined as the total energy consumed to produce one unit of GDP, expressed as primary energy divided by GDP. Figure 3a illustrates the trend of energy intensity among SAARC member countries from 2000 to 2021, showing a general decline over this period. Among these nations, Bhutan exhibits the highest energy intensity, followed by Nepal and India. The remaining countries have relatively lower and more stable energy intensity levels. For further clarity,

Table 2. Energy intensity of SAARC member countries. Source of data: [2].

Country	2000	2021	Changes (%)
Afghanistan	1.5	2.94	96
Bangladesh	2.45	1.93	−21
Bhutan	19.34	9.72	−50
India	7.01	4.21	−40
Maldives	2.04	2.87	41
Nepal	6.89	5.63	−18
Pakistan	5.08	4.21	−17
Sri Lanka	3	1.67	−44

presents the percentage change in energy intensity between 2000 and 2021. Afghanistan and the Maldives experienced a positive change in energy intensity, increasing by 96% and 41%, respectively. In contrast, all other countries saw a decline over the same period, with the highest reductions observed in Bhutan (50%), Sri Lanka (44%), the Maldives (41%), and India (40%). On the other hand, Figure 3b shows REC as a percentage of total energy use. The data reveal that all SAARC member nations experienced a decline in REC usage in 2021 compared to 2000. Bhutan, Nepal, and Sri Lanka had the highest REC shares in 2021, at 81.8%, 73.7%, and 48.8%, respectively.

3.2. Model Specification

Following the studies of Shokoohi et al. [20] and Jahanger et al. [47], this research investigates the impact of REC, EI, GL, and GDP on environmental quality across eight SAARC member states. Based on both theoretical foundations and empirical studies, the baseline model is specified as follows:

$$\mathrm{EQ}=f\left(\mathrm{REC},\mathrm{EI},\mathrm{GL},\mathrm{GDP}\right)$$

(1)

Here, EQ denotes environmental quality, the dependent variable, which is measured by CO₂ emissions and ecological footprint. The model’s independent variables comprise renewable energy consumption (REC), energy intensity (EI), globalization (GL), and economic growth (GDP).

To estimate the baseline model empirically, two separate models are constructed to evaluate environmental quality using different indicators: CO₂ emissions (CO₂) and ecological footprint (EF). The models derived from the baseline model are as follows:

$${\mathrm{lnCO}}_{2_{it}}={\alpha }_{it}+{\beta }_{1,it}{\mathrm{lnREC}}_{it}+{\beta }_{2,it}{\mathrm{lnEI}}_{it}+{\beta }_{3,it}{\mathrm{lnGL}}_{it}+{\beta }_{4,it}{\mathrm{lnGDP}}_{it}+{\epsilon }_{it}$$

(2)

$${\mathrm{lnEF}}_{it}={\alpha }_{it}+{\beta }_{1,it}{\mathrm{lnREC}}_{it}+{\beta }_{2,it}{\mathrm{lnEI}}_{it}+{\beta }_{3,it}{\mathrm{lnGL}}_{it}+{\beta }_{4,it}{\mathrm{lnGDP}}_{it}+{\epsilon }_{it}$$

(3)

here α represents the intercepts, β denotes the coefficients of the explanatory variables for cross-section i over time t, and ε indicates the error term.

3.3. Econometric Approach

This study employed various econometric methods to examine the environmental quality of SAARC countries. These methods were designed to assess the influence of REC, EI, GL, and GDP on environmental quality (CO₂ and EF).

In recent years, the interdependency among countries has grown significantly due to increasing economic, cultural, and political integration. This interconnectedness can result in spillover effects, where policy implementations or shocks in one country affect others. Such dynamics are particularly pronounced in a globalized world. Ignoring cross-sectional dependence (CSD) in panel data analysis may lead to biased and unreliable estimates. Therefore, detecting CSD at the initial stage of empirical analysis is essential. To assess the presence of CSD, we employed the Pesaran [48] CSD test, which evaluates the null hypothesis of no cross-sectional dependence among panel units. The test statistic is formulated as follows:

$$\mathrm{CSD}=\sqrt{{\displaystyle \frac{2T}{N\left(N-1\right)}}}\left({\displaystyle \sum _{i=1}^{N-1}}{\displaystyle \sum _{j=i+1}^N}{\widehat{\rho }}_{ij}\right)~N\left(0,1\right)i,j$$

(4)

Following the CSD test, we conducted a slope homogeneity test to examine whether the slope coefficients are homogeneous across the panel. This step ensures the appropriateness of subsequent empirical estimations. For this purpose, we applied the slope homogeneity test proposed by Pesaran and Yamagata [49], which is mathematically expressed as follows:

$${\stackrel{\sim }{∆}}_{SH}={\left(N\right)}^{\frac{1}{2}}{\left(2k\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\stackrel{\sim }{S}-k\right)$$

(5)

$${\stackrel{\sim }{∆}}_{ASH}={\left(N\right)}^{\frac{1}{2}}{\left(2k\left({\displaystyle \frac{T-k-1}{T+1}}\right)\right)}^{-\frac{1}{2}}\left(\frac{1}{N}\stackrel{\sim }{S}-k\right)$$

(6)

Here, ${\stackrel{\sim }{∆}}_{SH}$ indicates the delta tilde statistic, while ${\stackrel{\sim }{∆}}_{ASH}$ refers to its adjusted counterpart. The null hypothesis (H₀) tested assumes slope homogeneity across the panel units.

To assess the stationarity of the data over the study period, we account for the presence of CSD in our dataset. Conventional unit root tests are incapable of handling CSD, making them unsuitable for such datasets. Therefore, we utilize Pesaran’s [50] 2nd-generation approach, the Cross-Sectionally Augmented Im–Pesaran–Shin (CIPS) unit root test. This test effectively accounts for CSD, which is commonly observed in panel data. The CIPS statistic is derived by averaging the Cross-Sectionally Augmented Dickey–Fuller (CADF) statistics, and its formulation is shown below:

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

(7)

where the term $t_i\left(N,T\right)$ represents the CADF statistic in the equation below:

$$\mathrm{CIPS}=\frac{1}{N}{\displaystyle \sum _{i=1}^N}{\mathrm{CADF}}_i$$

(8)

Considering the existence of CSD and slope heterogeneity in the dataset, it is crucial to utilize a heterogeneous estimation method to assess long-run cointegration. Accordingly, we employ the Westerlund [51] cointegration test, which is well-suited for datasets exhibiting heterogeneity and CSD, such as ours. This test evaluates the null hypothesis of no cointegration [47]. The Westerlund test utilizes group statistics (G_a and G_t) and panel statistics (P_a and P_t) to evaluate cointegration, which are formulated as follows:

$$G_t=\frac{1}{N}{\displaystyle \sum _{i-1}^N}{\displaystyle \frac{{\phi }_i}{SE\left({\widehat{\phi }}_i\right)}}$$

(9)

$$G_a=\frac{1}{N}{\displaystyle \sum _{i-1}^N}{\displaystyle \frac{T{\phi }_i}{{\acute{\phi }}_i\left(1\right)}}$$

(10)

$$P_t=\frac{a{\phi }_i}{SE\left({\widehat{\phi }}_i\right)}$$

(11)

$$P_a=T{\widehat{\phi }}_i$$

(12)

3.4. The MMQR Estimation Technique

In this research, we adopted the Method of Moments Quantile Regression (MMQR) approach with fixed effects, as outlined by Machado and Silva [52]. The choice of MMQR was guided by specific data features, including non-normality, outliers (as shown in Figure 4), slope heterogeneity, and mixed orders of integration, which make traditional linear models less appropriate. MMQR allows for analyzing how independent variables impact environmental quality across different quantiles, offering several benefits. Unlike linear models that focus only on average effects, MMQR captures how covariates influence the lower, median, and upper tails of the conditional distribution, thereby providing richer insights into heterogeneous environmental responses. While MMQR does not rely on external instrumental variables, it mitigates endogeneity concerns through the inclusion of fixed effects, robustness to outliers, and allowance for heterogeneous slope parameters across quantiles. It also extends standard quantile regression by integrating moment restrictions [12]. Nevertheless, MMQR does not inherently establish causal relationships. To address this limitation, we conducted additional robustness checks using the Dumitrescu and Hurlin [53] panel Granger causality test, which strengthens the causal association of our findings by providing evidence on the directionality of the relationships among the variables. The MMQR methodology is expressed as follows:

$$Q_{y_{it}}\left(\tau |X_{it}\right)=\left({\alpha }_i+{\delta }_{i}q\left(\tau \right)\right)+X_{it}^{\prime }\beta +Z_{it}^{\prime }\gamma q\left(\tau \right)$$

(13)

$$i=1,\dots ,N t=1,\dots ,T \tau \in \left(0,1\right)$$

here $Q_{y_{it}}\left(\tau |X_{it}\right)$ denotes the conditional quantile function of the dependent variable $Y_{it}$ (environmental quality), conditioned on the independent variables in $X_{it}$. The vector $X_{it}^{\prime }$ includes explanatory variables, namely REC, EI, GL, and GDP. The expression $X_{it}^{\prime }-{\alpha }_i\left(\tau \right)\equiv {\alpha }_i+{\delta }_{i}q\left(\tau \right)$ defines the scalar coefficient, representing the fixed effect at the quantile (τ) for each cross-section unit i. Unlike the intercept in traditional OLS fixed-effects models, these individual effects are incorporated differently. The τ-th quantile $q\left(\tau \right)$, is obtained by solving the optimization problem described below:

$${min}_q{\displaystyle \sum _i\sum _t}{\rho }_{\tau }\left(R_{it}-\left({\delta }_i+Z_{it}^{\prime }\gamma \right)q\right)$$

(14)

From Equation (14), ${\rho }_{\tau }\left(R_{it}-\left({\delta }_i+Z_{it}^{\prime }\gamma \right)q\right)$ refers to the check function, which ensures that the quantile-specific residuals are minimized.

4. Results and Discussion

4.1. Summary Statistics, Correlation Analysis, Slope Homogeneity, and Multicollinearity Test Results for the Energy and Environmental Issues Variables in SAARC Countries

The descriptive statistics in

Table 3. Descriptive statistics.

Variables	N	Mean	SD	Min	Max	Skewness	Kurtosis	Jarque–Bera
lnCO2	176	−0.41	1.07	−3.27	1.67	−0.47	3	−0.41
lnEF	176	0.29	0.88	−0.82	2.34	1.05	2.55	0.29
lnREC	176	3.45	1.23	0.18	4.52	−1.75	4.85	3.45
lnEI	176	1.32	0.64	0.19	2.96	0.53	2.51	1.32
lnGL	176	3.8	0.24	3.16	4.14	−0.72	2.85	3.8
lnGDP	176	7.34	0.90	5.62	9.33	0.53	2.57	7.34

highlight the distributional properties of each variable. The standard deviations indicate the degree of variation around the mean. For instance, CO₂ emissions show high variability (SD = 1.07), while globalization is the most stable (SD = 0.24). The range of values indicates significant differences across countries, with GDP varying from 5.62 to 9.33 and EF from −0.82 to 2.34. Skewness and kurtosis suggest deviations from normality, particularly for REC (−1.75 skewness, 4.85 kurtosis), reflecting a distribution that is left-skewed and higher-peaked than normal. The Jarque–Bera statistics confirm non-normality in most variables, highlighting the need for robust econometric methods. These distributional characteristics are further visualized in Figure 4 using box plots for all variables.

The correlation matrix in

Table 4. Correlation matrix.

Variables	lnCO2	lnEF	lnREC	lnEI	lnGL	lnGDP
lnCO2	1.000
lnEF	0.667 *	1.000
lnREC	−0.490 *	−0.595 *	1.000
lnEI	0.163	0.169	0.473 *	1.000
lnGL	0.465 *	−0.256 *	−0.034	−0.209 *	1.000
lnGDP	0.867 *	0.780 *	−0.602 *	−0.098	0.323 *	1.000

Note: * denotes significance at 1%.

shows several significant relationships among the variables, many of which are significant at the 1% level. GDP shows a strong positive correlation with CO₂ (0.867) and EF (0.780). In contrast, REC exhibits a significant negative correlation with both CO₂ (−0.490) and EF (−0.595), while EI shows no significant correlation with CO₂. GL is positively correlated with CO₂ (0.465) and negatively with EF (−0.256).

The multicollinearity test, assessed using the Variance Inflation Factor (VIF), shows that all variables have VIF values below the critical threshold of five, indicating no severe multicollinearity concerns in the models (see

Table 5. Slope homogeneity test and multicollinearity test results.

Statistics	Model 1: CO2		Model 2: EF
		p-Value		p-Value
Delta tilde	9.235 ***	0.000	11.604 ***	0.000
Delta tilde Adjusted	10.829 ***	0.000	13.606 ***	0.000
Variables	VIF		1/VIF
lnREC	2.50		0.400679
lnEI	1.59		0.628804
lnGL	1.34		0.745585
lnGDP	2.15		0.464316

Notes: *** denotes the significance at 1%.

). The slope homogeneity test results in Table 5 show significant statistics for both models, leading to a rejection of the null hypothesis of homogeneous slopes at the 1% significance level for Model 1 and Model 2. Overall, these results suggest that while the models exhibit slope heterogeneity, multicollinearity is not a major issue, ensuring the robustness of the regression analysis.

4.2. Results of Cross-Sectional Dependence (CSD), Panel Unit Root, and Cointegration Tests for the Energy and Environmental Issues Variables in SAARC Countries

The results from the Pesaran [48] CSD test, as presented in

Table 6. CSD test results.

Variables	CD-Test	p-Value	Corr	Abs(Corr)
lnCO2	20.520 ***	0.000	0.827	0.827
lnEF	0.580	0.564	0.023	0.423
lnREC	18.850 ***	0.000	0.759	0.759
lnEI	4.720 ***	0.000	0.190	0.842
lnGL	21.890 ***	0.000	0.882	0.882
lnGDP	22.130 ***	0.000	0.891	0.891

Note: *** denotes p-value at 1%.

, demonstrate significant CSD among all variables except for EF. These results suggest that changes in one country’s values for these variables are significantly linked to changes in other countries, highlighting the necessity to account for CSD. Consequently, we employed the second-generation unit root test to account for CSD and determine the stationary properties of all variables.

Table 7. Panel unit root test results.

	CIPS					Outcome
Variables	Level I (0)			1st Difference I (1)		Order of Integration
	Constant	Constant and Trend		Constant	Constant and Trend
lnCO2	−2.249 *	−2.426		−4.670 ***	−4.934 ***	I (1)
lnEF	−2.663 ***	−3.121 ***		−5.327 ***	−5.301 ***	I (0)
lnREC	−1.675	−2.287		−4.831 ***	−5.068 ***	I (1)
lnEI	−2.297 *	−2.449		−4.824 ***	−4.874 ***	I (1)
lnGL	−2.831 ***	−3.064 **		−4.510 ***	−4.448 *	I (0)
lnGDP	−1.695	−1.972		−3.566 ***	−4.193 ***	I (1)

Note: *, **, and *** indicate significance at the 10%, 5%, and 1%, respectively.

provides the results of the CIPS (Cross-sectionally Augmented Im, Pesaran, and Shin) test. The outcomes reveal that CO₂, REC, EI, and GDP variables do not reject the null hypothesis of a unit root at levels but achieve stationarity after first differencing, suggesting that they are integrated of order one, I(1). Conversely, EF and GL exhibit stationarity at levels, demonstrating they are integrated of order zero, I(0). These mixed integration orders necessitate a cointegration analysis to identify any long-term relationships. To address this, we conducted the Westerlund [51] cointegration test. As shown in

Table 8. Westerlund panel cointegration results.

Statistic	Value	Z-Value	p-Value	Robust
Gt	−2.931 ***	−2.619	0.004	0.000
Ga	−7.228 *	1.054	0.854	0.090
Pt	−7.802 ***	−2.549	0.005	0.010
Pa	−8.279 **	−0.838	0.201	0.020

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

, the results confirm a long-run equilibrium relationship among the variables. The Gt, Pt, and Pa statistics are all significant at the 1% level, leading to the rejection of the null hypothesis of no cointegration. Overall, these findings validate the presence of a stable long-term association among the panel variables.

4.3. MMQR Estimation Results for the Energy and Environmental Issues Variables in SAARC Countries

The results of the Method of Moments Quantile Regression (MMQR) estimations for Model 1 and Model 2 are presented in

Table 9. MMQR estimation results.

	Model 1: CO2
Variables	Q10	Q20	Q30	Q40	Q50	Q60	Q70	Q80	Q90
lnREC	−0.270 *** (0.056)	−0.277 *** (0.045)	−0.287 *** (0.030)	−0.293 *** (0.021)	−0.296 *** (0.018)	−0.298 *** (0.016)	−0.299 *** (0.015)	−0.302 *** (0.014)	−0.304 *** (0.015)
lnEI	0.588 *** (0.100)	0.645 *** (0.084)	0.727 *** (0.058)	0.778 *** (0.041)	0.800 *** (0.033)	0.817 *** (0.029)	0.830 *** (0.027)	0.849 *** (0.026)	0.871 *** (0.026)
lnGL	1.637 *** (0.297)	1.615 *** (0.238)	1.585 *** (0.157)	1.565 *** (0.111)	1.557 *** (0.095)	1.550 *** (0.085)	1.546 *** (0.079)	1.538 *** (0.075)	1.530 *** (0.077)
lnGDP	0.807 *** (0.086)	0.775 *** (0.070)	0.729 *** (0.047)	0.700 *** (0.033)	0.688 *** (0.028)	0.678 *** (0.025)	0.671 *** (0.023)	0.660 *** (0.022)	0.648 *** (0.023)
	Model 2: EF
lnREC	−0.224 *** (0.024)	−0.210 *** (0.021)	−0.201 *** (0.019)	−0.192 *** (0.019)	−0.185 *** (0.018)	−0.172 *** (0.019)	−0.161 *** (0.020)	−0.151 *** (0.022)	−0.134 *** (0.026)
lnEI	0.409 *** (0.046)	0.397 *** (0.040)	0.390 *** (0.037)	0.383 *** (0.036)	0.376 *** (0.035)	0.366 *** (0.036)	0.357 *** (0.039)	0.349 *** (0.043)	0.335 *** (0.051)
lnGL	−1.489 *** (0.115)	−1.555 *** (0.100)	−1.599 *** (0.093)	−1.642 *** (0.089)	−1.678 *** (0.089)	−1.738 *** (0.092)	−1.788 *** (0.098)	−1.839 *** (0.108)	−1.919 *** (0.125)
lnGDP	0.798 *** (0.034)	0.794 *** (0.030)	0.792 *** (0.028)	0.789 *** (0.026)	0.787 *** (0.026)	0.784 *** (0.027)	0.781 *** (0.029)	0.779 *** (0.032)	0.774 *** (0.038)
Observations	176	176	176	176	176	176	176	176	176

Note: Standard errors are shown in parentheses, and *** denotes p-value at 1%.

, which presents coefficient estimates along with their robust standard errors. To examine whether the effects of the regressors vary across the conditional distribution, Wald tests of coefficient equality across quantiles are conducted, with the results reported in Appendix A 

Table A1. Wald tests of coefficient equality across quantiles (Q10–Q90).

	Model 1: CO2
Variables	Q10–Q20	Q20–Q30	Q30–Q40	Q40–Q50	Q50–Q60	Q60–Q70	Q70–Q80	Q80–Q90
lnREC	0.35 (0.556)	0.35 (0.552)	0.35 (0.554)	0.33 (0.565)	0.35 (0.554)	0.34 (0.559)	0.35 (0.553)	0.35 (0.554)
lnEI	4.95 ** (0.026)	6.22 ** (0.013)	4.95 ** (0.026)	2.68 (0.102)	5.15 ** (0.023)	3.81 * (0.051)	5.74 ** (0.017)	5.35 ** (0.021)
lnGL	0.12 (0.727)	0.12 (0.726)	0.12 (0.727)	0.12 (0.731)	0.12 (0.727)	0.12 (0.728)	0.12 (0.727)	0.12 (0.727)
lnGDP	2.55 (0.110)	2.92 * (0.087)	2.66 (0.103)	1.85 (0.174)	2.67 (0.102)	2.27 (0.132)	2.80 * (0.094)	2.71 * (0.100)
	Model 2: EF
lnREC	4.99 ** (0.026)	4.77 ** (0.029)	5.18 ** (0.023)	4.11 ** (0.043)	5.54 ** (0.019)	4.82 ** (0.028)	4.82 ** (0.028)	5.71 ** (0.017)
lnEI	1.17 (0.280)	1.15 (0.283)	1.18 (0.278)	1.11 (0.292)	1.19 (0.275)	1.16 (0.282)	1.16 (0.282)	1.21 (0.272)
lnGL	4.95 ** (0.026)	4.71 ** (0.030)	5.12 ** (0.024)	4.01 ** (0.045)	5.41 ** (0.020)	4.74 ** (0.029)	4.79 ** (0.029)	5.80 ** (0.016)
lnGDP	0.23 (0.634)	0.23 (0.634)	0.23 (0.633)	0.22 (0.635)	0.23 (0.633)	0.23 (0.634)	0.23 (0.634)	0.23 (0.633)

Notes: This table reports Wald tests of coefficient equality across quantiles based on the MMQR estimates. The null hypothesis is that the estimated coefficient is equal across the specified quantiles. Rejection of the null hypothesis indicates statistically significant heterogeneity in the estimated effects across the conditional distribution of the dependent variable. Robust standard errors are used in all tests. χ² statistics are asymptotically distributed. χ² statistics are from Wald tests of coefficient equality across adjacent quantiles; p-values are in parentheses, and ** and * denote significance at the 5% and 10% levels, respectively.

. The Wald test results confirm statistically significant differences in the estimated coefficients across quantiles, thereby validating the presence of quantile heterogeneity and supporting the use of the MMQR framework.

For Model 1, renewable energy consumption shows a consistent negative and statistically significant effect on CO₂ across all quantiles, with the magnitude increasing from −0.27 at the 10th to −0.304 at the 90th quantile. This indicates that higher REC contributes to lower CO₂ emissions, with the impact being stronger at higher emission levels. In contrast, energy intensity, globalization, and GDP exhibit positive and significant relationships with CO₂ emissions, suggesting that higher energy intensity, globalization, and economic expansion lead to increased CO₂ emissions. This effect intensifies at higher quantiles.

For Model 2, REC also demonstrates a negative and significant influence on the ecological footprint across all quantiles, with the effect gradually diminishing from −0.223 at the 10th to −0.134 at the 90th quantile. Similarly, EI has a positive and significant influence on the EF, but the effect decreases at higher quantiles. Conversely, GL consistently shows a negative and significant value, indicating that increased globalization reduces the ecological footprint, with the effect becoming more pronounced at higher quantiles. Finally, GDP remains positively significant across all quantiles, with only marginal variations, implying that economic progress consistently increases the EF. These results highlight the varying impacts of each variable on environmental quality across different quantiles, highlighting the necessity for the consideration of heterogeneity in environmental-economic relationships.

Our findings indicate that REC has a negative impact on CO₂ emissions and the EF, suggesting that a higher proportion of REC in overall energy use enhances environmental quality in SAARC member nations. Notably, the mitigation effect of REC is stronger at higher CO₂ quantiles, suggesting that renewable energy delivers larger marginal emission reductions in high-emission settings where fossil-fuel substitution is more pronounced. This asymmetry does not imply diminishing returns at lower pollution levels; rather, it reflects structural heterogeneity in energy systems across SAARC economies. In relatively high-emission countries such as the Maldives and Bhutan, where energy demand is driven by tourism, transport, and infrastructure expansion, aggressive renewable substitution can yield substantial environmental gains. In contrast, for lower-emission SAARC countries such as Nepal, Sri Lanka, Pakistan, India, Bangladesh, and Afghanistan, renewable energy expansion should be complemented by improvements in energy efficiency and grid integration to sustain environmental benefits. These findings align with the studies of Jahanger et al. [7] on the top ten SDG nations, Wang et al. [54] on Asian countries, and Aydin and Erdem [55] on 18 EU nations, all of which confirm that greater adoption of renewable energy helps lower carbon emissions, particularly those generated by fossil-fuel combustion [9]. For instance, hydropower plants harness the kinetic energy of flowing water to produce electricity, offering a renewable and environmentally friendly energy source. By utilizing the natural flow of water, hydropower serves as an environmentally friendly alternative to fossil fuels, reducing GHG emissions and contributing to a more sustainable energy landscape. Similarly, solar and wind energy systems generate electricity without producing air pollution. Biomass energy sources, including waste, wood, and biofuels, not only generate electricity and heat but also help reduce landfill waste. Additionally, geothermal energy systems utilize the Earth’s internal heat to produce electricity while emitting little to no harmful emissions [47].

Our analysis also reveals that EI exerts a positive and significant impact on both CO₂ and EF across all quantiles. This indicates that higher energy intensity contributes to environmental harm in SAARC nations by increasing CO₂. Given these outcomes, reducing energy intensity is crucial for mitigating environmental damage and achieving SDG 7, which promotes affordable and sustainable energy access. Several studies support our results, including Shokoohi et al. [20], who identified energy intensity as a key driver of environmental degradation in Iran, Iraq, and Türkiye. Similarly, Rahman et al. [21] found that energy intensity exacerbates carbon intensity in 25 emerging economies, while Degirmenci et al. [26] found that higher energy intensity undermines environmental sustainability within G7 countries.

Regarding globalization, our results present mixed effects on CO₂ emissions and EF, highlighting the multidimensional nature of environmental pressure. Specifically, globalization exhibits a positive relationship with CO₂ emissions but a negative correlation with EF. This divergence reflects distinct theoretical transmission channels rather than a contradiction. On the one hand, globalization intensifies CO₂ emissions through scale and composition effects. In SAARC countries, deeper integration into global markets has expanded export-oriented and energy-intensive production, increased fossil-fuel consumption, and encouraged the relocation of carbon-intensive industries from advanced economies. These mechanisms primarily affect air pollution and energy-related emissions, leading to higher CO₂ levels. This finding is consistent with those of Xia et al. [16] for 67 developed and developing countries, Aydin et al. [43] for 10 EU countries, and Pata et al. [44] for LAC nations. On the other hand, globalization reduces the ecological footprint by improving resource-use efficiency and lowering pressure on biocapacity. Through trade openness, foreign direct investment, and knowledge spillovers, developing economies gain access to cleaner technologies, advanced management practices, and more efficient production processes. These channels contribute to lower land use intensity, improved productivity, and reduced aggregate ecological pressure, even if energy-related emissions rise during industrial expansion. This result aligns with the findings of Latif et al. [42] for 48 Asian nations, Leal et al. [15] for developed countries, and Erdoğan et al. [14] for resource-rich SSA nations.

To place our findings within the existing literature,

Table 10. Comparison of the present study with previous research.

Study	Region	Indicator(s)	Method	Key Finding	Gap Addressed by This Study
Jahanger et al. [7]	Top SDG countries	CO2	MMQR	REC reduces CO2	No EF
Alola et al. [10]	EU countries	EF	PMG-ARDL	REC increases EF	Developed countries; mean effects only
Wang et al. [54]	Asia	LCF	Panel quantile	REC improves LCF	Excludes CO2–EF comparison
Mirza et al. [28]	Developing economies	CO2	2nd-gen panel	EI increases emissions	No distributional heterogeneity
Somoye [30]	Türkiye	CO2	Nonlinear ARDL	EI and REC reduce CO2	Single-country focus
Xia et al. [16]	Developed and developing	CO2	System GMM	GL raises CO2	Ignores EF and quantiles
Latif et al. [42]	Asia	LCF	System GMM	GL improves LCF	No CO2–EF divergence
This study	SAARC	CO2 and EF	MMQR	REC lowers both CO2 and EF; GL increases CO2 but reduces EF; quantile asymmetry	Combining CO2 and EF with quantile heterogeneity

Note: CO₂ = carbon dioxide emissions; EF = ecological footprint; LCF = load capacity factor; REC = renewable energy consumption; EI = energy intensity; GL = globalization.

provides a comparative assessment of previous studies and the present research. Unlike earlier studies that focus on a single environmental indicator—primarily CO₂ emissions (e.g., Jahanger et al. [7]; Xia et al. [16]) or ecological footprint alone (Alola et al. [10])—this study simultaneously examines both CO₂ emissions and ecological footprint, allowing for a more comprehensive evaluation of environmental quality. Methodologically, while prior research relies mainly on mean-based estimators (PMG-ARDL, second-gen panel, system GMM), our use of MMQR captures distributional heterogeneity and reveals asymmetric effects across pollution levels. Notably, we show that REC exerts stronger mitigation effects at higher CO₂ quantiles while globalization exhibits divergent impacts—aggravating CO₂ emissions but alleviating broader ecological pressure—an outcome not documented in previous SAARC-focused studies.

To further validate these findings, we perform exclusion-based sensitivity checks by re-estimating the MMQR models after removing potential outlier countries (Maldives and Bhutan). The results, reported in Appendix A 

Table A2. Robustness checks excluding the Maldives and Bhutan from the SAARC panel.

	Model 1: CO2
Variables	Q10	Q20	Q30	Q40	Q50	Q60	Q70	Q80	Q90
lnREC	−0.92 ***	−0.83 ***	−0.79 ***	−0.73 ***	−0.69 ***	−0.66 ***	−0.62 ***	−0.57 ***	−0.53 ***
	(0.101)	(0.0783)	(0.0706)	(0.0654)	(0.0660)	(0.0688)	(0.0716)	(0.0811)	(0.0958)
lnEI	0.604 ***	0.609 ***	0.612 ***	0.615 ***	0.617 ***	0.619 ***	0.621 ***	0.624 ***	0.626 ***
	(0.141)	(0.111)	(0.102)	(0.0961)	(0.0963)	(0.100)	(0.107)	(0.123)	(0.134)
lnGL	2.641 ***	2.521 ***	2.465 ***	2.399 ***	2.35 ***	2.30 ***	2.26 ***	2.18 ***	2.14 ***
	(0.503)	(0.396)	(0.362)	(0.341)	(0.342)	(0.356)	(0.379)	(0.434)	(0.479)
lnGDP	0.673 ***	0.612 ***	0.584 ***	0.550 ***	0.526 ***	0.500 ***	0.477 ***	0.439 ***	0.417 ***
	(0.161)	(0.127)	(0.116)	(0.109)	(0.110)	(0.114)	(0.122)	(0.139)	(0.153)
	Model 2: EF
lnREC	−0.47 ***	−0.39 ***	−0.36 ***	−0.29 ***	−0.23 ***	−0.19 ***	−0.16 ***	−0.12 **	−0.080 *
	(0.0924)	(0.0705)	(0.0683)	(0.0597)	(0.0543)	(0.0503)	(0.0480)	(0.0490)	(0.0487)
lnEI	0.485 ***	0.370 ***	0.330 ***	0.235 ***	0.145 *	0.0932	0.0540	−0.0149	−0.0664
	(0.126)	(0.0953)	(0.0929)	(0.0815)	(0.0745)	(0.0687)	(0.0652)	(0.0666)	(0.0656)
lnGL	−1.52 ***	−1.29 ***	−1.21 ***	−1.02 ***	−0.84 ***	−0.73 ***	−0.65 ***	−0.52 **	−0.414 *
	(0.391)	(0.311)	(0.295)	(0.253)	(0.226)	(0.213)	(0.207)	(0.211)	(0.217)
lnGDP	0.78 ***	0.68 ***	0.64 ***	0.56 ***	0.48 ***	0.43 ***	0.40 ***	0.34 ***	0.29 ***
	(0.154)	(0.122)	(0.116)	(0.0999)	(0.0896)	(0.0841)	(0.081)	(0.0829)	(0.0844)
Observations	132	132	132	132	132	132	132	132	132

Note: Standard errors are in parentheses; *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

, confirm that the relative sensitivity patterns and coefficient signs remain intact, reinforcing the robustness of the estimated relationships.

4.4. Robustness Check and Causality Test Results for the Energy and Environmental Issues Variables in SAARC Countries

The robustness check results, presented in

Table 11. Results of robustness check.

	Model 1: CO2					Model 2: EF
	FMOLS		Pooled OLS			FMOLS		Pooled OLS
Variables	Coeff.	t-Stat.	Coeff.	t-Stat.		Coeff.	t-Stat.	Coeff.	t-Stat.
lnREC	−1.36 *	−44.85	−0.291 *	−9.71		−0.78 *	−27.52	−0.182 *	−8.96
lnEI	0.66 *	39.43	0.765 *	16.47		0.02	0.28	0.373 *	11.92
lnGL	0.08 *	20.59	1.57 *	14.10		0.15 *	−8.43	−1.69 *	−22.50
lnGDP	0.91 *	39.40	0.707 *	18.54		−0.18	1.46	0.786 *	30.53

Note: * denotes significance at the 1% level.

, support the main findings from the MMQR analysis, confirming the consistent significance and directional impact of key variables such as REC, EI, GL, and GDP on environmental quality. The robustness check, conducted using FMOLS and Pooled-OLS, reveals that REC shows a significant negative impact on both CO₂ and the EF, which aligns with the MMQR results that indicated a mitigating role of REC across all quantiles. Energy intensity consistently exhibits a positive effect on environmental degradation across all methods in both models (CO₂ and EF). Similarly, economic growth demonstrates a positive and significant impact on environmental degradation in both approaches, except for EF in the FMOLS, where an insignificant negative effect is observed. The positive relationship between globalization and CO₂ and its negative association with EF, as observed in the MMQR, are also supported by the robustness check results. Although the robustness models do not capture the varying impacts across different levels of pollution as effectively as the MMQR, the consistency in the direction and significance of the coefficients validates the primary findings, reinforcing the robustness of the MMQR estimations.

The Dumitrescu–Hurlin panel causality test results reported in

Table 12. Dumitrescu–Hurlin panel predictability test results.

Direction	W-Bar	Z-Bar	p-Value	Directional Predictability	Outcome
REC → CO2	4.8611	7.2235 ***	0.0000	REC → CO2	Bidirectional
CO2 → REC	2.8400	3.4423 ***	0.0006	CO2 → REC
EI → CO2	2.3835	2.5884 ***	0.0096	EI → CO2	Bidirectional
CO2 → EI	3.8317	0.7288 ***	0.0000	CO2 → EI
GL → CO2	5.8115	9.0016 ***	0.0000	GL → CO2	Unidirectional
CO2 → GL	1.3796	0.7102	0.4776	No
GDP → CO2	2.7313	3.2390 ***	0.0012	GDP → CO2	Bidirectional
CO2 → GDP	3.3394	4.3766 ***	0.0000	CO2 → GDP
REC → EF	5.7538	8.8936 ***	0.0000	REC→ EF	Bidirectional
EF → REC	2.1323	2.1183 **	0.0342	EF → REC
EI → EF	2.5286	2.8598 ***	0.0042	EI → EF	Bidirectional
EF → EI	3.8613	5.3530 ***	0.0000	EF → EI
GL → EF	7.8239	12.7664 ***	0.0000	GL → EF	Unidirectional
EF → GL	1.0169	0.0317	0.9747	No
GDP → EF	5.9317	9.2263 ***	0.0000	GDP → EF	Bidirectional
EF → GDP	2.6646	3.1142 ***	0.0018	EF → GDP

Note: *** denotes significance at the 1% level.

provide evidence on the directional predictability among the examined variables rather than on structural or policy-interpretable causality. This test evaluates whether past realizations of one variable contain statistically significant information for predicting another variable within the panel framework, conditional on the model specification. The results indicate bidirectional Granger predictability between REC and CO₂ emissions. Similar bidirectional predictive relationships are observed between EI and CO₂ emissions, as well as between economic growth and GDP and CO₂ emissions. In contrast, GL exhibits unidirectional Granger predictability toward CO₂ emissions. For the ecological footprint, the results reveal bidirectional Granger predictability with REC, EI, and GDP, whereas globalization again shows a unidirectional predictive association toward EF. These findings should be interpreted as evidence of dynamic interdependence and temporal precedence rather than as confirmation of structural causal mechanisms.

5. Conclusions and Policy Implications

This study examines the interplay between energy intensity, renewable energy consumption, globalization, and environmental degradation in SAARC countries using annual data from 2000 to 2021. Recognizing the limitations of conventional environmental indicators, this research employs both CO₂ emissions and the ecological footprint, offering a more holistic assessment of environmental sustainability. The empirical findings reveal several key insights:

• REC exhibits a consistently negative and significant impact on both CO₂ and EF, suggesting that increasing the proportion of renewable energy within the total energy use enhances environmental quality. This reinforces the need for SAARC countries to accelerate their transition towards green energy sources such as hydropower, solar, and wind energy.
• Energy intensity is positively associated with both CO₂ emissions and EF across all quantiles, underscoring the importance of improving energy efficiency to mitigate environmental degradation.
• Globalization presents mixed effects: while it increases CO₂ emissions, it reduces EF, indicating that economic integration can both facilitate clean technology transfers and contribute to carbon-intensive industrialization.
• Economic growth significantly exacerbates environmental degradation across all quantiles, highlighting the persistent trade-off between economic expansion and sustainability.

Based on the results, several policy recommendations emerge:

• SAARC governments should intensify efforts to promote renewable energy adoption by providing targeted subsidies, tax incentives, and investments in green infrastructure. Given the region’s dependence on fossil-fuel imports, expanding the renewable energy sector can enhance both environmental and energy security.
• Policies aimed at reducing energy intensity, such as enforcing energy efficiency regulations, encouraging industrial modernization, and incentivizing research in low-carbon technologies, are crucial for mitigating CO₂ emissions.
• Regional collaboration should be strengthened to harness the positive aspects of globalization, particularly in facilitating clean technology transfers and knowledge-sharing initiatives.
• Balancing economic growth with sustainability requires integrating environmental concerns into long-term development planning, including stricter environmental regulations and the promotion of circular economy practices.

Future research should explore additional dimensions of environmental sustainability, including biodiversity loss, water pollution, and resource depletion, to provide a more holistic understanding of environmental challenges. Furthermore, incorporating additional econometric approaches, such as dynamic panel data models, could enhance the robustness of findings. Given the rapid technological advancements in renewable energy and energy efficiency, future studies should also investigate the role of digital innovations and smart grid technologies in shaping sustainable energy transitions in SAARC nations. By expanding the scope of research and incorporating new methodological approaches, scholars can further contribute to the global discourse on achieving SDG 7 (affordable and clean energy) and SDG 13 (climate action).