From Fossil Dependence on Sustainability: The Effects of Energy Transition, Green Growth, and Financial Inclusion on Environmental Degradation in the MENA Region

Abstract

Amid growing environmental concerns and an increasing push for sustainable development, countries in the Middle East and North Africa (MENA) region have taken proactive steps toward green growth, energy transition, and technological innovation. As a result, this study examines the effects of green growth, energy transition, technological innovation, financial inclusion, and urbanization on environmental sustainability in the Middle East and North Africa (MENA) region. Moreover, this study breaks new ground by exposing the hidden environmental costs of financial inclusion, urbanization, and technological innovation in the MENA region’s development trajectory, thereby providing compelling evidence for rethinking sustainability through an integrated approach that aligns economic ambition with ecological responsibility. Data for the studied variables were sourced from the World Bank database covering the period 1990 to 2021. The results show that green growth and energy transition significantly reduce CO₂ emissions, supporting the idea that economic expansion aligned with environmental priorities can contribute to ecological improvement. However, the impact of technological innovation is statistically insignificant, indicating that innovation in the region has not yet translated into meaningful environmental gains, possibly due to the dominance of non-green or industrial-focused innovation. Financial inclusion is found to increase CO₂ emissions, likely by facilitating greater access to credit and financial services that fuel energy-intensive consumption and production activities. Similarly, urbanization also contributes to rising emissions, reflecting the unsustainable nature of urban growth in many MENA region. Based on this study, we advocate for a coordinated regional approach to climate and energy policy, underpinned by shared governance and collective action.

1. Introduction

Over the past century, the world has been plagued by the escalating challenges of global warming and climate change [1,2,3]. These pressing issues, predominantly driven by anthropogenic activities and the extensive consumption of energy, have resulted in the release of hazardous pollutants, exacerbating ecological harm. The unanticipated rise in global temperatures, alongside the catastrophic consequences of environmental degradation, necessitates an integrated and urgent response to avert a climate disaster [4,5]. The continuation of the current climate trajectory poses significant threats, prompting widespread concern among policymakers and researchers regarding the unsustainable environmental impact of carbon emissions.

The escalating environmental crises have prompted academicians and policymakers globally to pursue an effective solution. In response, the United Nations introduced green growth as an alternative strategy to achieve sustainable development goals. Moreover, green growth is a forward-looking framework that seeks to stimulate economic development while safeguarding environmental integrity [6]. To attain green growth, it is essential to address demand-based emissions, achievable alone via the use of green technology, cleaner manufacturing methods, and innovations in the supply chain [7]. Achieving carbon neutrality objectives lies in the success of green growth, which may be accomplished by conserving natural resources and enhancing energy production efficiency via sustainable development. Green growth serves as a potential solution for energy conservation and the mitigation of CO₂ emissions, hence hindering environmental deterioration [8].

Moreover, the global environmental issues are caused by the rise in energy demand, highlighting the need for sustainable energy sources to mitigate CO₂ emissions. Numerous studies have emphasized the importance of reducing reliance on fossil fuel energy sources in favour of clean energy sources to achieve CO₂ reduction targets [9,10,11]. Although existing studies highlighted the positive role of cleaner energy towards economic growth, limited attention has been given to exploring the specific mechanisms through which green growth contributes to lowering CO₂ emissions [12,13,14]. Cleaner energy sources cover a diverse range of sources like bioenergy, solar energy, wind energy, geothermal energy, and hydropower energy, and can be classified as renewable energy sources (REC) due to their reliance on natural resources, which are inherently replenishable and sustainable over time. While not all REC are entirely environmentally benign, the majority of the REC have a significantly positive impact on environmental quality, making them critical components of efforts to reduce ecological degradation and promote green growth.

The implementation of a green growth strategy also relies heavily on the pivotal role of technological innovation (TI). Aligned with the major global environmental agreements, such as the COP 29, which emphasizes the impact of TI, but varies significantly across various nations. While some studies [15,16] have argued that TI increases environmental degradation. Meanwhile, another set of studies, such as Refs. [17,18,19] emphasized the potential of TI towards increasing ecological quality by reducing CO₂ emissions, highlighting its importance in achieving green growth objectives. Despite these insights, limited research explores the role of TI, particularly in enhancing environmental quality from a regional perspective, such as in MENA regions.

Financial inclusion (FI) significantly influences CO₂ emissions by driving economic activities that can have either positive or negative environmental impacts [20,21]. Improved access to financial services enables businesses and individuals to invest in economic growth, often resulting in increased energy consumption and industrial activities, which contribute to higher CO₂ emissions [22,23]. However, FI also offers an avenue to drive sustainable development by financing the green growth initiatives [24]. Moreover, the economic diversification strategy is critical for the MENA region, whereby FI could support the transition toward low-carbon economies by providing financing towards the development of TI and incentivizing cleaner production methods. Meanwhile, the environmental impact of FI depends on the extent to which financial resources are directed toward sustainable practices, highlighting the need for policies that align financial inclusion efforts with environmental goals to mitigate carbon footprints while fostering economic growth.

The objective of this study is to empirically examine how key economic and structural factors—namely green growth, energy transition, technological innovation, financial inclusion, and urbanization—affect environmental sustainability in the Middle East and North Africa (MENA) region. In line with this objective, the following research questions guide the investigation:

• How do green growth and energy transition initiatives influence environmental sustainability in MENA countries?
• What is the impact of technological innovation and financial inclusion on CO₂ emissions in the MENA region?
• To what extent does urbanization contribute to environmental degradation across the MENA region?

This study is significant as it addresses the urgent need for sustainable environmental strategies in the fossil fuel-dependent MENA region, which faces increasing climate-related risks. By examining the roles of green growth, energy transition, technological innovation, financial inclusion, and urbanization, the research offers practical insights for policymakers striving to balance economic development with environmental sustainability. The findings are especially relevant for guiding region-specific climate and energy policies in similarly vulnerable developing economies.

Based on the above discussion, this current study offers several valuable contributions. Firstly, it explores the role of green growth, ET, and TI on CO₂ emissions, a subject matter that remains underexplored, particularly in a region heavily reliant on fossil fuel energy sources. Secondly, the study also included control variables such as urbanization and financial inclusion in the model. Thirdly, this study focuses on the MENA region, which relies heavily on oil production, making it highly vulnerable to climate change impacts, such as extreme heat, droughts, and floods, which threaten agriculture, human lives, and coastal cities. Moreover, this set of nations is dedicated to implementing the green growth initiatives by prioritizing the development of ET and TI. Additionally, with 66% of its population already urbanized and an additional 70 million urban residents expected by 2025, rapid urbanization further strains natural resources. Fourthly, the method of moment quantile regression (MMQR) estimator is the main estimator used in the empirical analysis, while the Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) approaches were used for the robustness check for the MMQR result. From the empirical findings, green growth, renewable energy, technological innovation, and urbanization contribute to carbon neutrality, while financial inclusion positively impacts CO₂ emissions in MENA nations. Furthermore, the generalizability of the findings represents a key strength of this research. By examining a diverse set of countries within the MENA region, the study provides insights that are highly relevant to other developing nations with comparable economic and environmental challenges. This approach not only enhances the applicability of the results but also contributes to the global dialogue on sustainable development, offering evidence-based recommendations that policymakers can leverage to address similar issues in various regional contexts.

Meanwhile, the other structure of this study is: Section 2, which outlines prior research on the subject matter, and Section 3 delineates the methodologies used in this study. The findings of the analysis are presented in Section 4, and Section 5 addresses the conclusion and policy implications.

2. Literature Review

In response to escalating environmental and global warming issues, several scholars have conducted studies to identify effective strategies for minimizing emissions. In recent years, mitigating environmental pollution has garnered considerable interest from scholars and policymakers worldwide. Prior studies on the nexus between CO₂ emissions and green growth, CO₂ emissions and REC, CO₂ emissions and technological innovation (TI), CO₂ and urbanization, and CO₂ and financial inclusion.

2.1. Green Growth and CO₂ Emissions

The nexus between economic growth and environmental degradation has remained a central theme in environmental economics since the 1970s, when early theoretical models raised alarm over unchecked industrial expansion, resource depletion, and ecological collapse [25,26,27]. These foundational frameworks have since evolved to capture the multidimensional and region-specific nature of growth-environment dynamics, particularly within developing economies where economic expansion often coincides with environmental stress. Moreover, a substantial body of research has examined the growth-environment relationship in resource-dependent economies, often invoking theoretical frameworks such as the “resource curse” hypothesis, which suggests that nations endowed with abundant natural resources tend to experience volatile growth patterns and environmental degradation. [28]. Another theoretical framework is the Environmental Kuznets Curve (EKC) framework posits that environmental degradation initially rises with economic growth but declines after surpassing a certain income threshold. For instance, a study conducted by [29] on oil-exporting nations, established that economic growth initially had a positive impact on CO₂ emissions; however, beyond a certain GDP level, the relationship turned negative, supporting the EKC hypothesis.

Furthermore, in a study focused on oil-exporting African countries by [30], the authors emphasized that green growth initiatives, such as increased renewable energy consumption, are often stimulated by rising carbon emissions and energy prices, further reinforcing the dynamic interaction between economic performance and environmental response. This has sharpened subsequent research on sustainable development, thereby forming the contemporary debates on green growth. The relationship between green growth (GG) and CO₂ emissions has been widely examined, with most studies reporting a negative association, suggesting that GG can reduce environmental degradation. For instance, the study conducted by [13] also included other variables such as REC, eco-innovation. To observe the impact of GG on CO₂ emissions in the G7 nations. The authors also considered other factors such as human capital and environmental-related taxes. The result of the CS-ARDL method revealed that REC, GG, eco-innovation, human capital, and environmental-related tax reduce CO₂ in G7 nations.

Furthermore, the study of [31] in the Chinese provinces also established the negative impact of GG on CO₂, suggesting that the decoupling of economic expansion from environmental degradation through the promotion of efficiency, sustainable production, and low-carbon technologies. However, the effectiveness of GG is partly offset by rising income inequality and trade openness, which intensify environmental pressures. Likewise, the study of [32] also observed the effectiveness of GG in mitigating CO₂ in OECD nations. However, the positive environmental contribution of GG is moderated by institutional quality and population dynamics, suggesting that governance standards and demographic factors play critical roles in shaping green growth outcomes. Meanwhile, the work of [33] focus on Pakistan and show that GG, along with labour force dynamics, significantly reduces environmental degradation, thereby highlighting how sustainable economic strategies can be effective even in developing nations.

2.2. Technological Innovation and CO₂ Emissions

Unlike the consistent findings on the impact of GG on CO₂, prior studies on the environmental impact of technological innovation (TI) present notable contradictions. Prior studies had reported the mitigating role of TI towards CO₂, thereby suggesting that facilitating the adoption of cleaner production processes, energy efficiency improvements, and the diffusion of low-carbon technologies. For instance, Refs. [18,34] reported that found that TI significantly lowers emissions in Bangladesh and Pakistan, respectively. This shows that in developing nations, TI could also reduce CO₂. Likewise, another study conducted in Africa and China by [17,19] also obtained evidence supporting the effectiveness of TI towards mitigating CO₂, especially when TI is linked with a sustainable target. With the developed nation context, Chien et al. [35] reported the reducing effect of TI on CO₂ in the USA, with environmental taxes and renewable energy (REC) also playing a mitigating role. Meanwhile, prior studies provide a different perspective on the increasing role of TI towards CO₂. For instance, these studies conducted in BRICS nations by [36,37] obtained the negative impact of TI on environmental degradation. The authors argued that the use of TI to drive industrial productivity will trigger a surge in energy consumption and carbon output. Furthermore, the study of [15] pinpointed the positive impact of TI on CO₂ in China, possibly due to its orientation toward industrial expansion and high-energy-output sectors rather than environmental sustainability.

2.3. Renewable Energy Consumption and CO₂ Emissions

Developing nations continue to experience increasing environmental degradation due to their energy usage and economic structure. Furthermore, studies dating back to the 1970s established resource depletion as a major factor responsible for the surge in pollution levels during the industrialization period [27]. Additionally, extensive studies such as [3,38,39,40] pinpointed fossil fuel dependence as a major driver of environmental pollution, since gas flaring and oil extraction induce CO₂ emissions. The energy ladder hypothesis suggests that as economies progress, they undergo a transition from reliance on traditional biomass fuels to the adoption of modern, cleaner energy sources such as solar, hydro, and wind, reflecting improvements in income levels, technology, and energy infrastructure [41]. Meanwhile, for developing regions such as the MENA regions, such transition has been hindered by their economic disparities and policy inertia, particularly for oil and gas exporting nations. Furthermore, the seminal work of [26] underscores how energy sector structures in resource-rich developing countries shape labour markets and economic policies, often prioritizing short-term gains over long-term environmental sustainability. This is particularly relevant for the MENA region, where the oil and gas sector plays a major role in economic growth. Meanwhile, developing nations are faced with issues related to limited access to technology and financing, which serve as a barrier to sustainable energy transitions [42]. Moreover, the resource curse hypothesis and rentier state theory further illuminate the challenges of energy transitions in oil and gas exporting nations. The former highlights how resource wealth can stifle diversification and environmental reforms [43], while the latter underscores how oil revenues shape governance, often prioritizing short-term gains over sustainability [44]. These theories are essential for understanding the unique challenges of energy transition in major oil and gas exporting nations, in which many nations in the MENA region, especially the Gulf Cooperation Council nations, are in this group. Moreover, the empirical study of [45] conducted in 38 oil-abundant developing nations, in which these nations are included, in which rejection of the EKC in the respective nations and a weak and monotonically increasing relationship. For developed oil-producing nations such as the United States, Ref. [46] found that renewable energy increases ecological quality. The study of [47] focused on Mexico, which is another oil-exporting nation, and found that renewable energy reduces CO₂. An empirical study on Russia, which is a major oil and gas exporting nation [48], found that renewable energy improves ecological quality.

Given that the MENA region is a unified bloc, which comprises highly heterogeneous economies that differ in their energy profiles, governance structures, and environmental vulnerabilities. Given that some MENA countries, such as the United Arab Emirates, Qatar, and Saudi Arabia, are heavily dependent on oil and gas revenues, operate within rentier state models, and possess substantial fiscal space to invest in energy transitions. In contrast, others, such as Tunisia, Morocco, and Jordan, are energy importers with limited hydrocarbon reserves and more constrained institutional capacities. Thus, this heterogeneity is crucial when analyzing the link between energy transition and environmental degradation. Policies that may be effective in oil-rich, high-income economies may not be applicable or effective in low- or middle-income energy-importing countries. As a result, prior studies have been conducted for different nations in the MENA region and provided varying outcomes on the environmental impacts of energy transition in different MENA nations. For instance, in the United Arab Emirates, Ref. [49] found that the energy transition had no significant effect on environmental outcomes. In contrast, in a study conducted by [50] in Saudi Arabia concluded that transitioning to renewable energy sources significantly reduces CO₂ emissions. Moreover, for Non-oil MENA countries, the study of [51] in Morocco, reported that renewable energy adoption contributes to notable reductions in carbon emissions. Furthermore, Ref. [52] demonstrated that in Tunisia, renewable energy not only facilitates the shift towards a sustainable energy strategy but also serves as a catalyst for socioeconomic stability and long-term development. The study of [53] further concluded that the energy transition process can only provide short-term gains towards ecological quality in Tunisia.

Meanwhile, prior empirical studies on the nexus between renewable energy and CO₂ are presented as follows: the work of [13] probed the G7 nations, and found that REC reduces CO₂ with the inclusion of eco-innovation and strong institutional support. Similarly, with differences in economic dynamics, Refs. [46,54] obtained evidence that increased REC contributes to environmental outcomes in the United States and Malaysia, respectively. In Africa, the study of [19] find that REC contributes meaningfully to emissions reduction, aligning with sustainable development goals in low-income regions. Likewise, Refs. [9,55] reported that REC has a strong decarbonizing effect in China and India. In Italy, the work of [56] also established REC as an important driver of sustainable effort due to it capability of driving structural change in energy systems, thereby strengthens the argument that REC is essential for long-term environmental sustainability. The study of [57] also reported the negative impact of REC on CO₂ in ten nations, thereby contributes to environmental outcomes. Furthermore, Ref. [58] examined the Organization of the Petroleum Exporting Countries (OPEC) and concluded that the current energy transition policies implemented by these nations are inadequate in effectively advancing their environmental sustainability agendas.

2.4. Financial Inclusion and CO₂ Emissions

There is a complex issue in the environmental literature regarding the effect of financial inclusion (FI) on CO₂. A set of studies conducted by emerging nations such as [20] observed how FI, and trade openness impact on CO₂ in BRI nations. The empirical results showed that FI, and trade openness increase CO₂. Furthermore, prior studies conducted by [59] in lower-income regimes, Ref. [21] in 31 Asian nations, and Ref. [60] in Pakistan, reported that FI tends to exacerbate environmental degradation, due to increasing access to credit and financial services that stimulate consumption and production. This shows that nations with lax regulatory oversight or a high dependence on carbon-intensive industries, increased financial access can lead to heightened energy demand, expanded industrial output, and increased vehicular and household consumption, thereby contributing to higher emissions. This shows that FI expands without an accompanying green finance agenda or environmental safeguards, it can act as a catalyst for ecological harm. Meanwhile, the work of [23] provides an alternative perspective, showing that FI can also serve as a tool for environmental improvement, particularly when targeted toward sustainable investments, clean energy adoption, and digital financial technologies that reduce transaction costs and promote energy-efficient behaviour. This points to a dual effect: while FI can increase emissions by fueling economic activity, it can also contribute to environmental sustainability if guided by supportive financial and regulatory institutions.

2.5. Urbanization and CO₂ Emissions

Given the growing attention on the environmental effect of urbanization (URB), prior studies had reported the positive impact of URB on CO₂. For instance, the study of [17,34,36] observed that rapid surge in URB contribute to the increase in CO₂ for China, Bangladesh, and BRICS nations. These findings suggest that as urban populations grow, so does the demand for housing, transportation, energy, and infrastructure. This increasing demand led to increase in fossil fuels, which lead to greater ecological strain. Moreover, many in developing countries, unplanned urban sprawl, limited public transit systems, and weak regulatory enforcement exacerbate the carbon intensity of urban growth, making URB is a significant driver of environmental degradation. However, the work of [55] obtained different evidence, establishing that URB mitigates CO₂ in China, especially when proactive urban environmental reforms, such as investments in green public transportation, energy-efficient infrastructure, and stricter pollution control policies are adopted. Meanwhile, findings on the impact of URB on CO₂ in oil exporting countries such as OPEC remain mixed. For instance, Ref. [58] reported a negative relationship, indicating that urbanization may contribute to emissions reduction through improved infrastructure and energy efficiency. In contrast, Ref. [61] found a positive impact, suggesting that increased urbanization in OPEC nations may exacerbate environmental degradation due to higher energy consumption and greater pressure on urban resources.

Existing literature on the environmental implications of GG, TI, REC, FI, and URB present mixed and often contradictory findings. These inconsistencies stem from the application of diverse econometric techniques, variations in country selection, differing study periods, and the inclusion of heterogeneous explanatory variables. Despite growing interest in environmental sustainability, particularly among MENA nations committed to green growth and energy transition, there remains a notable gap in comprehensive, region-specific analyses that jointly examine these key drivers. This study seeks to fill that void by offering a systematic and integrated investigation into the environmental effects of GG, TI, REC, FI, and URB within the MENA context. In doing so, it contributes to clarifying the fragmented empirical discourse and offers nuanced insights into the sustainability pathways of resource-intensive economies undergoing structural transformation.

3. Material and Method

3.1. Data

This study focused on investigating how technological innovation, green growth, energy transition, financial inclusion and urbanization affect CO₂ emissions in MENA nations. The study period for this study spans the dataset between 1990 and 2021 for MENA nations (Algeria, Egypt, Jordan, Morocco, Qatar, Tunisia, Saudi Arabia, UAE, Iran and Yemen). However, the study’s ending period (2021) is constrained by limited data availability for financial inclusion, green growth and CO₂ emissions. These set of variables: CO₂ emissions, green growth, technological innovation, financial inclusion, urbanization and energy transitions were gotten from the world bank database. The sources of the variables and their unit of measurement are presented in

Table 1. Variables Information.

Indicators	Codes	Metric	Sources
CO2 emissions	CO2	CO2 emissions (metric tons per capita)	World bank database
Green growth	GG	Particulate emission damage as adjusted net savings (% of GNI)	World bank database
Technological innovation	TI	Addition of Patent applications for non-residents and residents	World bank database
Energy transition	ET	% of total final energy consumption	World bank database
Financial inclusion	FI	the number of ATMs per 100,000 adults	World bank database
Urbanization	URB	Urban population (% of total population)	World bank database

. However, Figure 1 and Figure 2 shows the trend of carbon emissions for each MENA nation used in this study. Figure 1 shows the trend for moderate CO₂ emitter, while Figure 2 shows the trend for major CO₂ emitter in MENA regions. The trend of percentage of urban population for each MENA nation used in this study is presented in Figure 3 and Figure 4. Figure 3 shows the trend for moderate percentage of urban population, while Figure 4 shows the trend for higher percentage of urban population.

3.2. Methodology

3.2.1. Theoretical Framework

The empirical analysis of this study mirrors both the IPAT framework formulated by [62,63] improved the IPAT framework into a the STIRPAT model, denoting the Stochastic Impacts by Regression on Population, Affluence and Technology. Moreover, this model quantifies environmental impact based on the interplay among population, affluence, and technology, mathematically expressed as follows:

$$I=C· P_i^{\alpha }· A_i^{\beta }·T_i^{\gamma }·{\epsilon }_i$$

(1)

where $I$ represents environmental impact, $A$ denotes affluence, $T$ represents technology, and $P$ depicts population. Wherein environmental impact ($I$) is influenced by affluence ($A)$, technology $\left(T\right)$, and population $\left(P\right)$. Specifically, affluence represents economic wealth, technology reflects technological advancement, and population accounts for demographic factors, which is depicted as urbanization. Moreover, the STIRPAT model’s structure has been widely extended in empirical literature to accommodate evolving socio-economic and institutional dynamics that indirectly influence environmental degradation. Aligning with prior works such as [64,65,66], we proposed the inclusion of renewable energy and financial inclusion into the model. Moreover, financial inclusion is linked with both affluence and technology. From the affluence perspective, FI enables broader access to financial services thereby influencing household consumption patterns and industrial activity levels, key mediators of ecological pressure. From the technological standpoint, financial inclusion serves as a critical enabler for investments in energy-efficient appliances, low-carbon infrastructure, and digital financial technologies that enhance environmental performance. Thus, FI influences both the level and structure of economic activity and the channels through which technology adoption occurs, consistent with the extended interpretations of STIRPAT. Furthermore, energy transition is linked with the technology aspect of the model, representing a structural technological shift toward cleaner energy systems, which serves as a mitigating factor to environmental degradation. The inclusion of energy transition enables the capturing of the decoupling potential of renewable technologies from emissions and ecological harm. Thus, the extended STIRPAT model is expressed as follows:

$${CO}_{2it}=f\left({GG}_{it}, {TI}_{it}, {URB}_{it}, {ET}_{it}, {FI}_{it}\right)$$

(2)

3.2.2. Econometric Approaches

For the panel analysis, it is essential to provide unbiased stationarity and co-integration analysis by addressing cross-sectional dependence (CSD) issue and slope heterogeneity (SH) issues. This study addresses the CSD issue by employing the Pesaran Scaled LM CSD test and the Bias-corrected Scaled LM CSD test to investigate whether there is evidence of a shocks in one MENA nations will affect other MENA nations. This study also addresses the SH issues by using the Pesaran and Yamagata (2008) slope homogeneity test, which test whether the slope of the model is homogenous in nature [67].

It is important to consider the CSD issue when evaluating the stationarity of panel data. The first-generation unit root tests have the potential to provide biassed results due to their limited capacity for homogenous and independent panel cross-sections. This study used the Cross-Sectionally Augmented Dickey–Fuller (CADF) unit root test and the Cross-Sectionally Augmented Im, Pesaran, and Shin (CIPS) unit root test developed by [68], to evaluate the stationarity of the variable. When the CSD and SH exist, these two-unit root tests provide more reliable and accurate results.

Panel cointegration analysis offers essential statistical requirements for confirming the long-term dynamic connections among the chosen factors. This study employed the Westerlund (2007) cointegration analysis, which is a second-generation cointegration approach, which has the potential to confirm long-run cointegration within the dataset within the CSD framework [69]. The statistics for this approach may be expressed as follows:

$$ai\left(\mathrm{L}\right)\Delta {\mathrm{y}}_{i,t}=\gamma 1_{i,t}+\gamma 2_{i,t}+{\beta }_i\left(y_{i,t}-1-a_i{x}_{i,t}-1\right)+{\lambda }_i\left(\mathrm{L}\right){\nu }_{i,t}+{\eta }_{i }$$

(3)

This study used a well-established MMQR approach coined by Machado and Santos Silva, (2019) to evaluate long-term results [70]. This approach offers a comprehensive basis for our research by accounting for various quantiles of the response variable and entity-specific alterations (fixed effects). The MMQR method offers a significant advantage over traditional panel quantile regressions by allowing for both location and scale heterogeneity, thereby capturing unobserved individual effects more comprehensively. Unlike fixed-effects quantile models that focus on specific quantiles, MMQR enables estimation of the entire conditional distribution of the dependent variable. It is also robust to non-normality and heteroskedasticity, making it particularly suitable for macro-panel data with structural variation. Furthermore, MMQR helps reduce the risk of bias in our estimated relationships due to unobservable individual-specific traits. It enables us to examine how the relationships among factors vary over different quantiles of the response variable. This is crucial for gaining insights into how these issues may disrupt various conditional of the variables. The equation for MMQR approach can be expressed as follows:

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

(4)

To confirm the MMQR output’s reliability, this study employed the DOLS and FMOLS methods for robustness checks. The DOLS approach incorporates lagged and leading values of explanatory variables to account for trends in the error term of the co-integration equation, enabling the modelling of cointegrated patterns with variables integrated in different orders by estimating the dependent variable based on explanatory factors in levels, as well as their leads and lags. Additionally, the FMOLS method is a semi-parametric, unbiased extension of least squares designed to address endogeneity in independent variables and cointegration-related serial correlation, making it effective for detecting spurious regressions by applying ordinary least squares (OLS) to nonstationary data with unit roots. Figure 5 shows the estimation pathway of this study.

4. Result and Discussion

4.1. Result

This study used a series of CSD tests such as the Pesaran Scaled LM CSD test, and the Bias-corrected scaled LM CSD test. The outcomes of these set of CSD test are presented in

Table 2. Outcome of the CSD test.

Variable	Pesaran Scaled LM	Bias-Corrected Scaled LM
CO2	65.810 *	65.649 *
GG	17.665 *	17.536 *
ET	29.208 *	29.047 *
TI	21.463 *	21.301 *
FI	47.932 *	47.667 *
URB	117.311 *	117.150 *

Note: * depicts <1%.

. The results of the Pesaran Scaled LM CSD test and the Bias-corrected scaled LM CSD test reveal the rejection of the null hypothesis of no CSD at 1% significance level. The results establish the presence of CSD across MENA countries in all selected variables. Consequently, a shock in the core variables in one nation could have implications in other MENA nations. Additionally,

Table 3. Outcomes of the heterogeneity test.

	Delta	p-Value
Delta Tilde	20.443 *	0.000
Delta Tilde Adjusted	23.128 *	0.000

Note: * depict <0.01.

shows the result of the slope heterogeneity test, revealing the rejection of the null hypothesis of homogeneous slope coefficients across all cross-sectional units. It suggests that the nation-specific changes in all slope coefficients of GG, ET, TI, FI, and URB.

For the unit root test, this study used the CADF and CIPS unit root tests, which are presented in

Table 4. Unit root test.

	CIPS		CADF
	I(0)	I(1)	I(0)	I(1)
CO2	−2.118	−5.299 *	−2.406	−3.911 *
GG	−2.086	−4.515 *	−1.574	−3.131 *
ET	−0.973	−4.860 *	−1.206	−3.567 *
TI	−3.167	−5.901 *	−1.777	−3.247 *
FI	−1.678	−4.306 *	−1.416	−4.306 *
URB	−2.334	−3.120 *	−2.172	−2.918 **

Note: * and ** depict <1% and <5%.

. The results of the CIPS show that all variables are stationary at the first difference at 1% significance level except URB. Additionally, the results of the CADF unit roots test confirm that none of the variables are stationary at level, while we observed that all variables have no unit root issues at the first difference at 1% significance level. Therefore, all variables are integrated at first difference. After conducting stationarity test, we advance to the cointegration analysis.

The result of the Westerlund cointegration analysis is presented in

Table 5. Result of Westerlund (2007) Cointegration.

	Value	Z-Value
${\mathrm{G}}_{\mathrm{t}}$	−2.734 *	0.962 *
${\mathrm{G}}_{\mathsf{\alpha }}$	−8.335 *	3.749 *
${\mathrm{P}}_{\mathrm{t}}$	−7.006 *	1.811 *
${\mathrm{P}}_{\mathsf{\alpha }}$	−8.363 *	2.565 *

Note: * represents p < 1%.

. The results show the rejection of the null hypothesis of no cointegration in the four different statistics at 1% significance level. This shows that there is a long-term relationship between CO₂ emissions and its regressors. The result presents the basis for evaluating long-term elasticities using the MMQR method.

Furthermore, this present study used the MMQR method to inspect the effect of GG, FI, TI, URB and REC on CO₂ emissions across various quantiles.

Table 6. Result of MMQR estimation.

	Locati	Scale	Qtile0.1	Qtile0.2	Qtile0.3	Qtile0.4	Qtile0.5	Qtile0.6	Qtile0.7	Qtile0.8	Qtile0.9
GG	−0.021	0.006	−0.031 *	−0.027 *	−0.024 *	−0.023 *	−0.022 *	−0.018 *	−0.017 *	−0.015 *	−0.011 **
REC	−0.249	−0.028	−0.206 *	−0.223 *	−0.235 *	−0.244 *	−0.250 *	−0.263 *	−0.269 *	−0.280 *	−0.297 *
TI	0.002	−0.005	0.010	0.007	0.005	0.003	0.002	−0.000	−0.001	−0.004	−0.006
FI	0.042	0.009	0.027	0.033	0.037 **	0.040 **	0.042 *	0.046 *	0.048 *	0.052 *	0.058 *
URB	2.432	−0.002	2.436 *	2.435 *	2.434 *	2.433 *	2.432 *	2.431 *	2.431 *	2.430 *	2.428 *
Consta	−3.586	0.149	−3.815	−3.728	−3.664	−3.614	−3.583	−3.516	−3.483	−3.424	−3.332

Note: * and ** represent p < 1% and p < 5%, respectively.

reveals the findings of the MMQR method. Precisely, the negative effect of GG on CO₂ emissions is evident across all quantiles (0.1–0.9). Moreover, the degree of the negative impact shows a downward trend as the quantiles progress. The coefficients of GG spans from −0.031 (0.1Q) to −0.011 (0.9Q). Thus, GG negatively impact CO₂ emissions in MENA regions. Furthermore, we observed that the adverse effect of REC on CO₂ emissions across all quantiles, with the coefficients increasing from 0.206 (in Q0.1) to 0.297 (Q0.9). Thus, REC negatively impact CO₂ emissions in MENA regions. However, in the lower quantiles (0.1–0.2), FI has no significant influence on CO₂ emissions, while in the other quantiles (0.3–0.9), FI has a significant and positive effect on CO₂ emissions. The degree of the coefficient slightly progresses as the quantiles increases from 0.037 (Q3) to 0.058 (Q9). Thus, FI contributes to CO₂ emissions in MENA regions. Likewise, the positive effect of URB on CO₂ emissions is evident across all quantiles (0.1–0.9). Moreover, the coefficients of URB slightly drop from 2.436 (0.1Q) to 2.428 (0.9Q). Thus, URB positively impact CO₂ emissions in MENA regions. Meanwhile, we observed that TI has insignificant and positive effect CO₂ emissions in the lower quantiles, while in the medium and upper quantiles, TI also has insignificant and negative effect CO₂ emissions. Thus, TI has no significant impact on CO₂ emissions in MENA regions. The Graphical representation is provided in Figure 6.

The FMOLS and DOLS methods are used as a robustness analysis for the estimations of the MMQR approach. The result of these two methods is presented in

Table 7. Result of robustness analysis.

	DOLS	FMOLS
GG	−0.021 **	−0.041 **
ET	−0.446 *	−0.320 *
TI	0.008	−0.055
FI	0.062 *	0.118 *
URB	0.477 *	0.652 *

Note: *, and ** represent p < 1%, and p < 5%, respectively.

. The result of the FMOLS method disclosed that GG exerts a significant and negative effect on CO₂ emissions, in which the 1% surge in GG will reduce CO₂ emissions by 0.041%. ET exhibit a significant and mitigating role towards CO₂ emissions. A 1% rise in ET will trigger the decrease in CO₂ emissions by 0.320%. Thus, ET contribute to the decreasing role of CO₂ emissions in MENA region. Furthermore, URB and FI positively impact CO₂, in which the 1% surge in URB and FI will increase CO₂ by 0.652% and 0.118%. Meanwhile, it is observed that there is an insignificant and negative effect of TI on CO₂.

Additionally, the outcome of the DOLS also disclose the positive impact of FI and URB on CO₂ in MENA nations. Precisely, a percentage rise in FI and URB by 1% will induce CO₂ emissions by 0.477% and 0.062%, respectively. Meanwhile, ET and GG exhibit a negative association with CO₂ emissions, whereby an increase of 1% in ET and GG will lead in a reduction of CO₂ emissions by 0.446%, 0.021%, respectively. However, we observed that there is an insignificant and negative effect of TI on CO₂.

For the model’s diagnostic analysis,

Table 8. Diagnostics Test.

Variance Inflation Factor
	VIF		1/VIF
FI	1.798		0.556
GG	1.008		0.991
ET	1.142		0.875
TI	1.033		0.967
URB	1.774		0.563
Mean VIF	1.351		N/A
Normality test
Jarque–Bera (p-value)		1.889 (0.388)

presents the outcomes of the sensitivity tests conducted to validate the empirical framework. The Variance Inflation Factor test, all falling below the critical value of 5, demonstrate that the explanatory variables are sufficiently independent of one another, thereby eliminating concerns related to multicollinearity and ensuring the stability of the coefficient estimates. In addition, the residual normality test fails to reject the null hypothesis, indicating that the distribution of the model’s error terms aligns with the assumption of normality, which reinforces the credibility and statistical soundness of the estimated results.

4.2. Discussions

The outcome shows that GG reduces CO₂ emissions in MENA region, indicating that the drive for a structural shift from carbon-intensive activities to other sustainable alternatives, will contributes to ecological quality. Given that several countries in the MENA region have introduced ambitious green growth initiatives aimed at fostering sustainable development while addressing environmental challenges. For example, the United Arab Emirates’ Green Agenda 2030 and Saudi Arabia’s Vision 2030 promote large-scale investments in renewable energy, clean technologies, and sustainable urban infrastructure. Morocco’s National Energy Strategy aims to achieve over 50% renewable energy in its power mix by 2030, while Egypt has committed to scaling up solar and wind capacity through public–private partnerships. These initiatives reflect a regional shift toward integrating environmental priorities into national development plans, marking a strategic move toward balancing economic growth with ecological sustainability. This result can be corroborated by prior studies such as: Ref. [33] in Pakistan, Ref. [32] in OECD nations, Ref. [31] in Chinese provinces and [71] in BRICS nations, which established the negative impact of GG on CO₂.

ET plays a pivotal role in mitigating CO₂ in MENA region, underscoring its significance in the region’s broader environmental and sustainability agenda. The findings indicate that a systematic and sustained energy transition from fossil fuels to renewable sources is essential for achieving long-term carbon neutrality targets and addressing the escalating impacts of climate change. By reducing the reliance on carbon-intensive energy sources, ET helps to lower the region’s greenhouse gas footprint while enhancing energy security and diversification. Moreover, energy transitions contribute substantially to the reduction in fossil fuel-related externalities, such as poor air quality and associated health risks, particularly in densely populated urban areas. Cleaner energy sources minimize particulate emissions and other pollutants, thereby improving public health outcomes and reducing healthcare costs. The expansion of renewable energy infrastructure not only supports climate goals but also catalyzes broader socioeconomic benefits. It paves the way for the development of a net-zero emissions society by fostering innovation, attracting green investment, and supporting the creation of new industries. Prior studies such as: Ref. [11] in BRICS-1 nations; Ref. [36] in BRICS nations; and Ref. [72] in E7 nations align with this present study’s finding.

Our results highlight the positive effect of FI on CO₂ emissions across all quantiles. The degree of the coefficient slightly progresses as the quantiles increases from 0.562 (Q1) to 0.590 (Q9). This result concurs with prior studies of [7] in E7 nations; Ref. [73] in top CO₂ emitting nations and [74] in Asian nations, but disagree with the work of [23] in APEC nations. This result indicates that FI contributes to environmental degradation in MENA nations, implying the FI promotes economic activity by expanding access to financial services for individuals and businesses, also contributes to an increase in carbon emissions. This relationship is likely driven by the surge in economic activities that FI enables, such as increased production, consumption, and infrastructure development, which often rely on carbon-intensive processes and industries.

The findings reveal that TI exhibits no statistically significant impact on CO₂. Specifically, in the lower quantiles, TI shows a positive but insignificant effect. In contrast, in the medium and upper quantiles, TI shows a negative but still statistically insignificant effect. This result does not align with prior studies of [75] in G7 nations, Ref. [76] in South Africa, Ref. [77] in Africa, Ref. [34] in Bangladesh and [18] in Pakistan, Ref. [17] in Africa; Ref. [19] in China; Ref. [35] in USA; Refs. [36,37] in BRICS nations. Moreover, TI is widely regarded as a pathway to reducing environmental degradation, the findings of this study indicate that, in the context of MENA countries, such TI has not necessarily led to improved environmental outcomes. This paradox can be explained by the nature and direction of TI in the region, which is often geared toward economic and industrial expansion rather than environmentally sustainable development. The limited diffusion of clean technologies, coupled with weak institutional support for green related TI and the persistent dominance of fossil fuel-based infrastructure, reduces the environmental effectiveness of technological progress. Moreover, in the absence of stringent environmental policies and incentives, innovation tends to reinforce existing carbon-intensive production systems. Thus, while TI is occurring, it does not automatically translate into environmental gains without targeted efforts to align innovation with sustainability goals.

URB contributes to carbon emissions in MENA region. This finding reflects the largely unplanned or unsustainable nature of urban development in many MENA countries, where rapid population growth, weak infrastructure, and limited environmental regulation often led to increased energy consumption, traffic congestion, industrial activity, and waste generation. The urbanization process in the region tends to be resource-intensive and heavily reliant on fossil fuels for transportation, electricity, and construction, thereby exacerbating CO₂. Moreover, the minimal variation in the coefficients across quantiles implies that the negative environmental impact of urbanization does not vary significantly with different emission levels, pointing to a structural issue that transcends income or development disparities within the region. This underscores the urgent need for sustainable urban planning policies, the promotion of energy-efficient technologies in cities, investment in public transportation systems, and the implementation of green building codes. Moreover, prior studies such as: Ref. [17] in China; Ref. [36] in BRICS and Ref. [34] in Bangladesh also corroborate this present study’s findings by establishing that rapid surge in URB contribute to the increase in CO₂. However, prior studies such as: Ref. [78] in BRI nations; Ref. [12] in Sweden and Ref. [79] in Finland also do not corroborate this present study’s findings by establishing that URB mitigates CO₂ emissions. The summary of this study’s finding is presented in Figure 7.

5. Conclusions and Policy Recommendation

Environmental deterioration is becoming a significant and complex challenges globally. The MENA region is currently committed towards promoting ecological quality by decreasing the environmental degradation from carbon emissions. As a result, this study gives new insights into the synergistic effects of green growth, ET, financial inclusion, TI and URB in MENA region from 1990 to 2021. This study confirms the presence of CSD issue and the validity of the model’s slope heterogeneity. Furthermore, this study used the Westerlund cointegration to evaluate the influence of explanatory factors on CO₂ in MENA region. The findings of the MMQR method indicate that ET and GG decrease CO₂ emissions in MENA region. Meanwhile, financial inclusion and URB positively impacts CO₂ emissions, while TI has no significant effect on CO₂ in MENA region. Additionally, this study used two different estimators, FMOLS and DOLS methods as a robustness analysis for the result of the MMQR method.

These empirical results could aid in the formulating policy recommendations. GG is essential for mitigating pollution, and it is vital to establish a more coherent shared vision regarding energy and climate while enhancing collaboration and coordinated advancement across MENA region. The advancement of GG is significantly reliant on governmental endorsement; hence, it is essential for governments at all tiers to assume a pivotal position in strategic planning and to include green growth cohesively into various planning levels. It is imperative to actively advocate for investment and development in clean energy sector, which is vital for green growth. Given the resource endowments of nations, the advancement of electricity production projects should be vigorously encouraged. Furthermore, the promotion of alternative energy sources should be advocated, with the encouragement of complete multi-energy complementing initiatives. Additionally, the government of these nations may implement laws that foster sustainable urban development, including building rules that encourage energy-efficient structures, endorsing sustainable transportation alternatives, and enhancing green areas to mitigate CO₂ emissions across the MENA nations.

Policymakers should implement rules pertaining to FI, facilitate the use of new technologies, and enhance environmental quality. Furthermore, policymakers must recognize the impact of TI; when supported by FI, it will not only decrease emissions but simultaneously foster sustainable economic development. These policies could encompass the integration of sustainable practices within the financial sector, the promotion of green financing, and the provision of subsidies for environmentally conscious enterprises. Furthermore, MENA region has to prioritize investments in R&D related to TI to facilitate the progression and implementation of green technology. These economies may achieve a degree of technological innovation that fosters economic growth and enhances environmental quality by investing in green initiatives, enforcing stringent regulations to compel firms to adopt new, eco-friendly technologies, and providing subsidies to facilitate the implementation of these technologies. Furthermore, the government of the MENA region must engage in public–private partnerships to promote sustainable technology advancements. Furthermore, the data indicate a correlation between financial inclusion and CO₂ emissions, making it imperative to enforce rigorous environmental policies to mitigate pollution and improve environmental quality.

Limitation of Study and Future Direction

Although this study offers fresh insights into the environmental sustainability literature, it has several limitations that require consideration. First, the study does not account for environmentally related technological innovations, such as clean-tech patents or green R&D, which could offer a more accurate understanding of the role of innovation in emission reduction. Incorporating such variables in future analyses may yield more conclusive and policy-relevant findings. Second, while the study period (1990–2021) was chosen based on data availability and consistency, more recent developments—particularly post-2021 climate commitments and technological shifts are not captured, which may limit the applicability of the findings to current policy contexts. Additionally, although the focus on MENA provides valuable regional insights, the absence of comparative analysis with other regions limits the generalizability of the conclusions. Future studies could address this by conducting cross-regional or country-specific analyses to better contextualize the unique environmental and economic dynamics of MENA economies. Lastly, this study primarily focuses on CO₂ emissions as the sole environmental indicator. Expanding the scope to include broader metrics such as ecological footprints, or load capacity factor would allow for a more comprehensive assessment of sustainability performance.