The Effect of Environmental Smart Technology and Renewable Energy on Carbon Footprint: A Sustainability Perspective from the MENA Region

Abstract

This paper looks at the changing impact of renewable energy and green innovation on the carbon footprint of eight MENA nations between 2000 and 2020. We investigate this by using the panel Q-ARDL model for the first time, and we find that, with various impacts across different quantiles, a rise in green innovation and renewable energy greatly boosts environmental sustainability in the short run. In the long run, this effect becomes increasingly more noticeable. According to our analysis, the chosen MENA countries quickly embraced energy storage, solar hydrogen, and other technology pathways to diversify their energy mix, which was a turning point in the fight against climate change. Although these factors have been separately examined in different studies, our research merges them into a single non-parametric model. This research is significant as it provides empirical evidence on the efficiency of green innovation and renewable energy policies, and it will guide policymakers and energy stakeholders in developing strategies to achieve sustainable development goals.

1. Introduction

The overconsumption of natural resources frequently triggers severe environmental backlash, leading to widespread devastation for nature and society. Natural disasters are occurring more frequently all around the world as a result of the recent industrial activity that has accelerated the release of harmful gases into the atmosphere. For instance, a report by the United Nations suggests that, in the last fifty years, disasters linked to climate change have multiplied by five due to the unchecked exploitation of natural resources [1]. The imbalance between the demand and supply of these resources has continued to stir chaos, contributing to a surge in global emissions. The Natural Resources Defense Council warns that emission levels are yet to peak, indicating that emission rates might not stabilize soon, thus posing a significant environmental threat. Research suggests that the Earth has already heated up by over 1.5 °C from pre-industrial levels [2]. This trend could impose hefty environmental costs on countries, causing more ecological harm [3]. Consequently, regions suffering the most will likely experience more frequent and severe environmental repercussions.

Within the domain of carbon footprint, which encompasses sociocultural, economic, and environmental dimensions, ecological issues are particularly salient and have been extensively examined by researchers and policymakers [4,5]. Renewable energy sources and green patents significantly predict a carbon footprint. Societies can substantially mitigate CO₂ emissions and diminish air and water pollution while enhancing energy security by transitioning from non-renewable fossil fuels to RE alternatives [6]. The current theoretical framework posits that the extent of a nation’s fossil fuel reserves plays a pivotal role in determining its carbon intensity. In scenarios where other variables are constant, nations endowed with ample fossil fuel resources tend to adopt development trajectories more reliant on carbon emissions than those with scarce reserves. The Environmental Kuznets Curve (EKC) hypothesis is relevant here. It posits that environmental degradation initially increases with GDP but eventually decreases as economies mature and become wealthier, thus increasing investment in green technologies and stricter environmental regulations [7]. Green technology and modern growth theories support this by emphasizing the role of technological advancements in reducing environmental impacts [8]. These technologies include RE systems, energy-efficient processes, and sustainable practices, which are crucial for achieving long-term sustainability goals [9].

Furthermore, it is noteworthy that oil-rich economies often demonstrate a lower dependency on renewable energy. For example, in countries rich in petroleum, such as Saudi Arabia, the renewable energy contribution (REC) to the primary energy mix is a mere 0.09%, with Algeria and Iran showing similarly low figures of 0.25% and 1.64%, respectively. In stark contrast, nations with limited oil resources, such as Morocco, Vietnam, Malaysia, and Thailand, exhibit higher renewable energy percentages in their energy mix, accounting for 8%, 17%, 7%, and 6.17%, respectively [10].

In this study, considering the growing importance of limiting emissions and the ecological footprint, we explore how green energy and technologies affect the carbon footprint in the MENA region from 2000 to 2021. Through this study, we address the following research questions: (1) Do energy policy interventions, such as encouraging green energy and technologies, help limit the carbon footprint in the MENA bloc? (2) How does technology focusing on ecological management affect the ecological footprint in the MENA bloc? To address these research queries, we deployed advanced panel data models and tests that can generate robust and consistent estimates. The motivation behind this study lies in the sharp contrasts that exist between MENA policies that are aimed at curbing emissions.

This research paper examines the impact of GDP, FDV, RE, and green innovation on the carbon footprint across eight MENA countries: Algeria, Egypt, Tunisia, Morocco, Saudi Arabia, the United Arab Emirates, Jordan, and Lebanon. A marked effort within the MENA region to enhance and diversify energy sources by promoting RE is noted as a strategic approach to reducing carbon emissions [11]. The selection of the MENA region as the primary case study is attributed to the significant structural, economic, and societal changes it has undergone recently [12], and this is characterized by rapid economic development and the evolution of financial systems. The choice of these specific countries is based on their robust commitment to boosting their renewable energy sector, which is pivotal considering the considerable environmental impact of their mining and quarrying sectors, particularly in oil and gas production. Comprehensive environmental evaluations that concentrate on mining operations have shown that these industries are important causes of air pollution-related health problems, as well as considerable contributors to environmental deterioration. This scenario underscores the urgent need for a strategic pivot toward RE in these oil-abundant nations as part of an initiative to mitigate the adverse environmental effects associated with their conventional energy sectors, which aligns them with the carbon curse hypothesis.

The second essential contribution of this work is in exploring how ecological governance and finance through market-based intervention affects the carbon footprint in the MENA region. Additionally, international commitments also compel regional policymakers, notably the obligation to meet the Sustainable Development Goals (SDGs) by 2030. Agriculture and water resources are critical in the MENA region, serving as principal employment sources and political stability pillars. Yet, prevailing climatic and demographic trends are provoking concerns regarding the region’s sustainable capacity to support its population and economic stability in the long term [13].

The third contribution is using a dynamic panel quantile model to examine this field in MENA countries for the first time. Unlike previous studies that have employed static and traditional dynamic panel models, our research utilizes a non-parametric panel Q-ARDL model. This innovative approach allows us to capture the varying impacts of green innovation and renewable energy across different quantiles, thus providing a more nuanced and comprehensive analysis. We can gain a better understanding of the diverse impacts of numerous variables on environmental sustainability by utilizing this model as these effects are frequently obscured by traditional average-based approaches. This methodology also enables us to identify both immediate and long-term impacts, offering a clearer picture of how green innovation and renewable energy policies influence environmental outcomes over time. This contribution is particularly significant in the MENA region, where diverse economic and environmental conditions necessitate a more detailed and dynamic analytical framework.

Section 2 of the manuscript provides an analytical review of the recent literature; Section 3 outlines the theoretical framework and primary methodologies; Section 4 is devoted to a thorough presentation and discussion of the results; and Section 5 concludes with policy recommendations.

2. Literature Review

An important factor in the impact of carbon footprint is the use of green technologies, which is mainly achieved through green patents. Ref. Javed et al. [14] utilized a novel dynamic, simulated ARDL framework to analyze data from 1994–2019 in Italy. They found that ecological patents are instrumental in enhancing Italy’s environmental quality. Moreover, these patents demonstrate a more effective inhibition of ecological emissions when coupled with robust environmental governance. Expanding on their findings, Saqib et al. [15] suggested that the strategic implementation of ecological patents limits carbon footprint impacts and catalyzes sustainable economic development in emerging economies. The aforementioned authors emphasized the pivotal role of government policies in fostering an environment that is conducive to innovation and the adoption of green technologies. They recommend that policymakers in these regions focus on creating incentives for research and development in eco-friendly technologies, which could significantly transform their environmental and economic landscapes, mainly by reducing the carbon footprint associated with traditional industrial practices. Similarly, Appiah et al. [16] delved deeper into the mechanisms through which innovations influence ecological footprints. Their study highlights how crucial technical developments are in mitigating the negative effects of economic activity on the environment, including substantial reductions in carbon footprints. Applying the CS-ARDL approach provides empirical evidence showing that the proactive adoption of environmental patents diminishes the ecological footprint and sets a foundation for sustainable growth. This study calls for enhanced international collaboration to facilitate the transfer of green technologies, suggesting that such co-operation could be crucial for scaling the positive impacts of innovations across borders and achieving global sustainability goals. Abaas et al. [17] explored the nexus between green patents, energy use, R&D expenditure, and CO₂ emissions in Chinese cities under the Low Carbon City Program from 2003–2020. Via employing CS-ARDL and Q-ARDL models in 118 cities, green tech investments have significantly boosted environmental sustainability across quantiles, thus validating the EKC hypothesis. In contrast, Sarabdeen et al. [18] examined how digital revolution affects oil-exporting countries’ GDP, their contribution toward climate change, and their shift to sustainable energy. This study utilized panel data from the World Bank spanning from 2006 to 2020. This study found that digital technology improves environmental quality and mitigates climate change. However, internet and mobile access negatively impact ecological quality.

The interrelationship between RE and economic expansion constitutes a multifaceted and progressively pertinent study area, especially in terms of sustainable development and carbon footprint [19,20,21,22,23]. The traditional economic growth paradigm is largely dependent on fossil fuels, which, while effective in driving industrialization and economic progress, has led to environmental degradation and an increased carbon footprint [24,25,26]. This suggests that a greater adoption of renewable energy could enhance financial inclusion and decrease the risks associated with economic policy uncertainty. This study asserts that the intricate relationship between REC, GI, and GDP profoundly influences policymakers’ renewable energy frameworks. With adequate energy supply, a nation can benefit from increased productivity and other economic advantages such as enhanced competitiveness, fulfilling unmet needs, and creating new value [27]. Additionally, innovative strategies for transitioning economies from traditional to greener energy sources are essential for achieving the objectives set for reducing carbon footprints by 2030 [17,28]. Usman et al. [29] emphasized the indispensable role of renewable energy consumption in meeting these carbon footprint reduction goals.

Financial development (FDV) is critical in accelerating the transition toward renewable energy consumption and in reducing carbon footprint by facilitating investments, mitigating risks, and fostering technological innovations [30]. Moreover, FDV influences policy decisions, as well as the potential for advocating for renewable-friendly policies and subsidies. FDV remains a significant barrier to the adoption of renewable energy in developing regions, such as African and Arab countries [31]. FDV has been identified as a crucial driver of economic progress, and it has been highlighted that a consequent increase in production reduces carbon footprint and heightens energy consumption. According to the study of [32], FDV is associated with advanced technologies that reduce energy consumption, but it is contingent on GDP growth. Shahbaz et al. [33] observed that factors like reform methods, financial openness, structural changes, financial crises, energy costs, and inflation affect the financial sector and significantly influence carbon footprint and energy consumption levels. Danish [34] argued that a more efficient financial system enables individuals, businesses, and governments to procure more energy products. The significance of renewables is increasingly acknowledged as they are pivotal to reducing carbon footprints and can enhance socio-economic aspects by satisfying energy demands, lessening pollution impacts, and improving population well-being. Dogan et al. [35] focused on the function of research and development (R&D) in realizing SDG goals and CO₂ emissions in the USA during the period of 1990 to 2022 in the context of COP28. They confirmed a long-term cointegrating link between CO₂ emissions, GDP, human capital, R&D, eco-friendly technologies, and RE. In the short run, all of the variables except R&D expenditures showed significant effects. Moreover, rising income levels and human capital tend to worsen environmental problems by contributing to an increase in CO₂ emissions. Conversely, expenditures on the R&D, technological innovation, and the adoption of RE reduce carbon emissions and foster sustainability.

3. Data and Methodology

3.1. Data Specification

This study examined the impacts of renewable energy, green innovation, economic growth, and financial development on carbon footprint using annual data. The yearly statistics were collected from 8 MENA nations (Algeria, Egypt, Morocco, Tunisia, Saudi Arabia, United Arab Emirates, Lebanon, and Jordan) from 2000 to 2020 to generate a balanced panel for this study. The research data end in 2020, mostly because of limitations in data accessibility. This study is grounded in the EKC and the LCC hypotheses. The EKC hypothesis suggests that environmental degradation initially rises with economic growth but decreases after reaching a certain GDP threshold, thus exhibiting an inverted U-shaped pattern [7]. In contrast, the LCC hypothesis highlights the continuous pressure that economic growth can place on environmental resources [36,37]. Based on this theoretical background and the streaming literature [17,20,38,39,40], the indicators used in this study—GDP, FDV, RE, and GIV—were chosen for their critical roles in sustainability analysis. GDP is essential for understanding the association between the economic growth and environmental impact that exists between the EKC and LCC hypotheses. Financial development facilitates investments in sustainable technologies and RE projects, which are crucial for reducing carbon footprints. RE is essential for lowering greenhouse gas emissions, which helps to mitigate climate change. Green innovation drives technological advancements that lower environmental impact and promote sustainable development. These indicators provide a comprehensive view of how economic and technological factors influence environmental sustainability in the MENA region.

All the data were converted to a logarithm. Figure 1 shows the data trend, while the corresponding source and measures are displayed in

Table 1. Variable descriptions.

Variables	Unit	Acronym	Sources
Carbon Footprint	GHA per capita	CF	Global Footprint Network
Renewable Energy Consumption	Terajoule	RE	Sustainable Energy for All database WDI
Financial Development	GDP (constant 2015 USD)	FDV	WDI
Economic Growth	GDP (constant 2015 USD)	GDP	WDI
Green Innovation	Share of environmental patents over total patents (%)	GIN	OECD

.

3.2. Model

The following model was used to examine how green innovation, economic growth, and financial development affect the use of renewable energy. We used the logarithmic form of all the variables.

$${lnCF}_{it}=a_0+{ a}_1{lnGDP}_{i t}+{ a}_2{lnFDV}_{i t}+{ a}_3{lnGI}_{i t}+{ a}_4{lnRE}_{i t}+v_{it},$$

(1)

where $a_0$ is the intercept; $a_1,a_2, a_3$ and $a_4$ are the coefficients of the GDP, FDV, GI, and RE, respectively; and $v_{it}$ is the error term.

3.3. Methodology

In this section, we will move on to the statistical descriptive and correlation matrix; then, we shall use a basic analysis and estimation of the panel data by starting with the dependence relationships among the cross-sections. Panel data frequently have relationships according to recent empirical studies. The cross-dependency in the relevant variables has been investigated using the CSD approach proposed by [41]. Indeed, employing the unit root (UR) test with the Cross-Sectionally Augmented IPS (CIPS) test, as proposed by M. in [42], is a suitable method for examining the dynamics of variables while addressing the presence of Cross-Sectional Dependence (CSD). This test accounts for the heterogeneity in the autoregressive coefficient within the Augmented Dickey–Fuller (ADF) regression, thus accommodating for the differences among the panel members. It is particularly adept at encapsulating potential uniform attributes across the panel, with the dataset allowing for incorporating a single latent common factor, which is further enhanced by various factor loadings.

Creating the CIPS statistic involves conducting panel-specific ADF regression analyses on the cross-sectional mean values of both the dependent and independent variables, which is achieved by employing lagged variances to mitigate the serial correlations. These regressions are described using the Cross-Sectionally Augmented Dickey–Fuller (CADF) approach, which considers the potential interactions and interdependencies among the observations by addressing cross-sectional dependence.

After we assessed the integration order of each variable through these rigorous tests, we advanced our analysis to the panel cointegration test proposed by [43]. This test represents a second-generation CSD test for cointegration, providing a robust framework for identifying the long-term equilibrium relationships among the panel data variables despite the complexities introduced by cross-sectional dependencies.

After that, this study used the quantile ARDL proposed by [44], a methodology that has gained popularity in the recent literature for analyzing how the impact of quantile series depends on percentile explanatory data across diverse market conditions [45,46,47]. This approach proved significantly more effective and efficient in capturing outcomes over short and long terms. The panel quantile ARDL model is suitable for periods exceeding 20 and for those that are cointegrated and have mixed effects [47,48]. The Panel Quantile-ARDL approach is particularly suitable when the data display a mix of levels, and when the first level and when outliers are present. The PMG ARDL technique addresses the CSD and heterogeneity among panel units. While permitting various short-run adaptations between nations, this approach integrates the long-run equilibrium relationships and short-run dynamics. This is crucial for our analysis, as it recognizes the unique short-term responses of different MENA countries while providing a unified long-term perspective, thus enhancing the robustness and reliability of our findings. By integrating these advanced methodologies, our study offers nuanced insights into the dynamic interactions between environmental and economic variables.

Causality Tests

We used the causality tests used in [44] to accomplish this objective. This causality approach also includes the time series causality test that was formulated by [49]. This method can still resolve scale error and parameter bias when N is big, as was the case in the MENA data. As a result, the method makes panel regression inference more trustworthy by successfully evaluating the correlation. Last but not least, panel data models enable more precise model inference to regulate the impacts of omitted variables while improving data quantity and quality [50,51]. Therefore, the Granger causality test equations are as follows:

$${\mathrm{M}}_t=\alpha 1+{\sum }_{k=0}^n{\beta }_{1t}{N}_{t-1}+{\sum }_{k=0}^n{\beta }_{2t}{M}_{t-1}+e_{1t},$$

(2)

$${\mathrm{N}}_t=\alpha 2+{\sum }_{k=0}^n{\beta }_{3t}{N}_{t-1}+{\sum }_{k=0}^n{\beta }_{4t}{M}_{t-1}+e_{2t}\mathrm{}$$

(3)

4. Empirical Findings

4.1. Descriptive Statistics

Reviewing some preliminary statistical data before diving into the core analysis is crucial.

Table 2. Descriptive statistics.

	CF	RE	GIN	GDP	FDV
Mean	1.003	8.906	2.377	25.391	24.831
Median	0.708	8.956	2.549	25.364	24.881
Maximum	2.629	12.181	3.885	27.244	26.681
Minimum	0.165	5.235	0.231	23.593	22.335
Std. Dev.	0.648	1.894	0.699	1.033	1.065
Skewness	1.073	−0.150	−0.796	0.112	0.181
Kurtosis	2.960	2.249	3.300	1.687	2.079
Jarque–Bera	32.255	4.575	18.382	12.425	6.863
Probability	0.000	0.101	0.000	0.002	0.032
Observations	168	168	168	168	168

displays the dataset’s descriptive statistics. The average value of carbon footprint (CF) was 1.003, with values ranging from 0.165 to 2.629. The distribution of CF did not follow a normal pattern, as indicated by a Jarque–Bera probability of 0.000, thus rejecting the null hypothesis of normality. Renewable energy (RE) averaged at 8.906, fluctuating between 5.235 and 12.181 with a Jarque–Bera probability of 0.101, which is above the 0.05 threshold, suggesting that the distribution of RE could be considered normal. The green innovation (GIN) had an average value of 2.377, with variations from 0.231 to 3.885, but it did not follow a normal distribution, as evidenced by its Jarque–Bera probability below 0.05. The gross domestic product (GDP) was averaged at 25.391, ranging from 23.593 to 27.244, and it also did not follow a normal distribution with a Jarque–Bera probability of 0.002. Finally, the average value of the financial development (FDV) was 24.831, with fluctuations from 22.335 to 26.681 and a Jarque–Bera probability of 0.032, thus indicating non-normality in its distribution. These statistics provide a detailed picture of the data’s central tendencies, dispersion, and distribution shape, which is essential for further analysis.

Table 3. Correlation results.

	CF	RE	GIN	GDP	FDV
CF	1
RE	−0.221	1
GIN	−0.049	−0.224	1
GDP	−0.452	−0.303	0.206	1
FDV	−0.353	−0.206	0.114	0.766	1

presents the findings of the pairwise correlation between the variables. The association between carbon footprint (CF) and several vital variables was depicted as follows: CF and renewable energy (RE) had a correlation coefficient of −0.221, indicating a negative relationship. Similarly, CF negatively correlated with economic growth (GDP) at −0.452 and financial development (FDV) at −0.353. In contrast, the CF and green innovation (GIN) relationship was fragile, with a correlation coefficient of −0.049. These figures state the statistical relationships without delving into deeper interpretations or implications.

An assessment for multicollinearity was performed to ascertain its presence and to prevent any bias from the omitted variables in the model. The results in

Table 4. Multicollinearity findings.

Data	VIF	1/VIF
GDP	2.61	0.383290
FDV	2.43	0.411172
RE	1.14	0.879903
GIN	1.08	0.925048
Mean		1.81

indicate no signs of multicollinearity, as demonstrated by the lack of significant coefficients for the individual variables.

4.2. CSD Test

Testing the CSD between the series in investigations is important using panel data analysis. The CSD between the series is examined before the analysis is started.

Table 5. Results from the CD test.

Test	Statistic	Prob.
B-P LM	308.2231	0.00 *
Pes LM	37.44638	0.00 *
Pes CD	8.571892	0.00 *

Note: * indicates 1% significance levels.

displays the model’s CSD and the variables used in the CD test from [41]. It can be seen that none of the variables’ probability values exceeded the threshold of 0.05 per cent. As a result, we could not rule out the alternative theory, which suggests a CSD in all of the variables across the countries or regions.

4.3. CIPS Test Results

As the cross-sectional dependencies of each variable across the various nations or regions have been established, we can now consider the second-generation panel stationarity test, which considers this circumstance. The following table displays the Pesaran panel CSD unity root for four of the variables of this study.

The results from

Table 6. Pesaran test results.

	Without Trend		With Trend
	Zt-Bar Statistic	p-Value	Zt-Bar Statistic	p-Value
lnFC	−2.97 *	0.0	−2.94 *	0.0	I(0)
lnREC	0.110	0.559	2.94	0.99	I(1)
D_lnREC	−4.12 *	0	−3.587 *	0
lnEG	0.358	0.640	−0.763	0.99	I(1)
D_lnEG	−5.01 *	0	−4.43 *	0
lnFD	2.319	0.990	1.702	0.96	I(1)
D_lnFD	−4.87 *	0	−3.24 *	0
lnGI	−7.25 *	0	−6.87 *	0	I(0)

Note: ‘*’ refers to the confidence intervals at 99%.

indicate that, except for lnGI and CF (which were integrated at level), all other variables achieved stationarity at the first difference, thus being classified as first integrated I(1). This suggests that, while some variables exhibit robust stochastic trends, others show minimal or barely noticeable stochastic patterns. These findings are crucial for determining the most appropriate method to analyze cointegration relationships. Consequently, the Westerlund panel cointegration test, which adequately accounts for (CSD), should be prioritized in our analysis. A primary focus of this research involved the panel cointegration assessment, which accommodates a heterogeneous order of integration and is especially pertinent when the dependent variable is non-stationary.

4.4. Cointegration Test

We investigated the potential for a long-term relationship between the relevant variables by taking into account the previously described cross-sectional dependency and (UR) test results. The results from the Westerlund, Koa, and Padroni tests, as presented in

Table 7. Cointegration test results.

	Value	p-Value
Westerlund	1.691 **	0.04
Kao	−4.35 *	0.00
Pedrouni	−4.015 *	0.00

Note: ‘**’, and ‘*’ refer to the confidence intervals at 95%, and 99%.

, indicated a long-term association between the variables within a model that includes only an intercept term. This finding suggests differing levels of integration among the variables.

4.5. Panel QARDL

The outcomes of the panel quantile autoregressive distributed lag (QARDL) model are delineated in

Table 8. QARDL results.

dCF	Coefficient	Std. Err.	Z	P > |z|
	0.05 Q Extremely Low Quantile
	Short Run
dGDP	0.3723357	0.0374563	9.94	0.000
dRE	−0.0040827	0.002296	−1.78	0.075
dGIN	0.008412	0.0015519	5.42	0.000
dFDV	0.0210109	0.0256222	0.82	0.412
	Long Run
GDP	0.0048599	0.0016894	2.88	0.004
RE	−0.0054889	0.0008357	−6.57	0.000
GIN	−0.0331313	0.001939	−17.09	0.000
FDV	−0.0211153	0.0008962	−23.56	0.000
	0.5 Q Median Quantile
	Short Run
dGDP	0.0987813	0.0350019	2.82	0.005
dRE	0.0015617	0.0054205	0.29	0.773
dGIN	−0.0004463	0.0031003	−0.14	0.886
dFDV	0.055936	0.0224142	2.50	0.013
	Long Run
GDP	0.0016127	0.0030996	0.52	0.603
RE	−0.0082196	0.0024804	−3.31	0.001
GIN	−0.0224775	0.0058183	−3.86	0.000
FDV	−0.0122197	0.001207	−10.12	0.000
	0.9 Q Extreme Upper Quantile
	Short Run
dGDP	0.1297526	0.053222	2.44	0.015
dRE	−0.0054189	0.0032524	−1.67	0.096
dGIN	−0.0077389	0.0038756	−2.00	0.046
dFDV	0.0477708	0.0166599	2.87	0.004
	Long Run
GDP	0.0058261	0.0019288	3.02	0.003
RE	−0.0051176	0.0003801	−13.46	0.000
GIN	−0.0134756	0.0026465	−5.09	0.000
FDV	−0.0130267	0.0016368	−7.96	0.000

Note: the estimations encompass both the short-term and long-term dynamics across the three distinct quantile thresholds—the 5th, 50th, and 95th quantiles.

. The estimations encompass both short-term and long-term dynamics across three distinct quantile thresholds—the 5th, 50th, and 95th quantiles. In the short run, the substantial increase in GDP at the 5th quantile, with a coefficient of 0.372, signified that economic expansion is strongly associated with increased carbon. In contrast, the RE showed a marginally significant reduction in CF, implying that renewable energy initiatives may only slightly mitigate environmental degradation at the lower extremes. At the 50th quantile, GDP continued to show a significant, albeit smaller, impact on environmental degradation, with a coefficient of 0.098, suggesting that economic activity still contributes to the CF at median levels of environmental quality but to a lesser extent than at the lower extremes. Except for the medium quantile in the short run, our analysis revealed that energy significantly influences global ecological quality. The substantial role of RE across different quantiles on ecological footprint is a major contributor to environmental degradation. Environmental stakeholders and energy economists emphasize the need to decouple economic growth from reliance on petroleum-based hydrocarbons. This shift is particularly crucial for countries with high emission intensities and conventional energy procedures, which are advised to transition gradually toward clean energy without disrupting economic growth cycles. Several studies [19,20,21,22,23] support the idea that incorporating clean energy into economic growth strategies can enhance the ecological quality in various regions, including the MENA region [52]. The findings also concord with preceding scholarly works [14,17,53,54], which underscore the pivotal interaction of RE and green patents in mitigating the carbon footprint in different regions. Also, this finding is in line with Saqib et al. [15], who suggested the strategic implementation of ecological patents and RE adoption to mitigate carbon footprint impacts and to catalyze sustainable economic development in emerging economies. As a result, both the public and private sectors will probably exhibit a greater inclination toward adopting and investing in renewable energy sources, consequently increasing their utilization. These outcomes align with prior studies [55,56,57].

Financial development (FDV) at this quantile showed a significant increase in CF, as indicated by a coefficient of 0.055, suggesting that financial growth may correlate with increased environmental degradation in the short term. At the upper quantile, i.e., the 95th, economic growth showed a significant impact with a coefficient of 0.129, indicating that CF increases even at higher quantiles as economies expand. Green innovation displayed a significant negative coefficient, suggesting that innovation is associated with reducing the carbon footprint at the upper extremes, thus highlighting the potential of green technology and practices in mitigating environmental degradation when fully integrated into the economic system. The observed positive coefficient underscores the pivotal role of financial development in facilitating and fostering the proliferation and assimilation of renewable energy resources. An adept and judicious orchestration of credit distribution to the private sector is instrumental in augmenting economic growth (EG). These findings are congruent with the investigations conducted by [58,59], who elucidated the consequential impact of FDV and the consumption of RE on the carbon footprint.

In the long run, the consistent negative effects of RE, GIN, and FDV across all quantiles reveal a concerning trend despite their positive short-term impacts. The adverse long-run effects suggest that the initial environmental benefits of renewable energy consumption and green innovation might not be sustained over time, or they may have unintended negative consequences, potentially through the inefficient allocation of resources or a lag in adopting such innovations at an upper quantile.

4.6. Robustness Model

To emphasize the robustness of the data analysis process and to address potential biases, rigorous data validation techniques and sensitivity analyses were conducted to confirm the reliability of the results, thus enhancing this study’s credibility. As detailed in

Table 9. PMG-ARDL.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
	Long Run
RE	−0.218260	0.051072	−4.273569	0.0000 *
GIN	−0.123058	0.022573	5.451523	0.0000 *
GDP	0.521872	0.182914	2.853096	0.0052 *
FDV	−0.236191	0.111778	2.113040	0.0369 **
	Short Run
COINTEQ01	−0.443426	0.159561	−2.779033	0.0064 *
D(RE)	0.006540	0.088911	0.073555	0.9415
D(GIN)	−0.044465	0.024404	−1.822038	0.0712 ***
D(GDP)	−0.677153	0.234452	−2.888242	0.0047 **
D(FDV)	−0.153896	0.089865	−1.712512	0.0897 ***
C	−7.472891	2.891587	−2.584356	0.0111 **

Note: “***”, ‘**’, and ‘*’ refer to the confidence intervals at the 90%, 95%, and 99% levels.

, the long-run estimation analysis applied robust methodologies using the PMG-ARDL model to confirm the QARDL results. A drop in the CF was linked to a rise in the use of RE, as indicated by the negative coefficient of −0.218 for RE. Thus, there was a reduction in the environmental damage or carbon footprint. A positive coefficient of 0.123 for GIN indicates that advancements in green innovation correlate with an increase in the dependent variable over the long term. This could imply that green innovation positively contributes to environmental quality or potentially to economic performance depending on the specific context of the dependent variable. The coefficient of 0.52 for GDP signifies a strong positive relationship with the CF in the long run. In the short-term effects, a change in the RE by 0.006 suggests a small but positive short-run effect of changes in RE consumption on the dependent variable.

The results also show a negative coefficient of green innovation at −0.044, indicating that short-term changes in green innovation are associated with a decreased CF. The short-run effect of changes in financial development, with a coefficient of −0.153, implies a negative association with the dependent variable. This suggests that, in the short term, financial development may reduce the CF, which could mean a lower carbon footprint or an improvement in the environmental quality if that is what the dependent variable represents. Overall, the PMG ARDL results suggest that renewable energy consumption and green innovation protect ecological quality in the long run. In contrast, economic growth may lead to increased environmental degradation. The findings indicate a complicated short-term association between these factors and environmental quality, with renewable energy and financial development not immediately reflecting their long-term ecological benefits. These findings confirm the majority of results that were obtained from the panel QARDL model.

4.7. Causality Findings

We investigated the causality proposed by [60], as shown in

Table 10. DH results at lag 3.

			W-Stat.	Zbar-Stat.	Prob.
RE	Cause	CF	0.94074	−0.30635	0.7593
CF	Cause	RE	1.79836	1.05785	0.2901
GIN	Cause	CF	3.22287	3.32379	0.0009 *
CF	Cause	GIN	1.83963	1.12350	0.2612
GDP	Cause	CF	3.30856	3.46009	0.0005 *
CF	Cause	GDP	2.43806	2.07540	0.0379 **
FDV	Cause	CF	1.30175	0.26790	0.7888
CF	Cause	FDV	2.30036	1.85637	0.0634 ***

Note: “***”, ‘**’, and ‘*’ refer to the confidence intervals at 90%, 95%, and 99%.

. The findings established that there were causal relationships between GIN and GDP and CF, whereas the RE value indicated a non-existent causality direction to CF.

5. Conclusions

This study examined the impact of GDP growth, GIN, REC, and FDV on the carbon footprint using the panel data from eight MENA countries from 2000 to 2020. Our findings highlight the presence of CSD among the panel countries, indicating the potential influence of common unobserved factors. Additionally, the evidence is consistent with a long-term equilibrium relationship between the factors listed above. Given these findings, we estimated the underlying relationships using the QARDL and PMG ARDL methods. Following these estimates, the DH test was used to validate the selected model specification. We established a significant and positive effect of green innovation, greener energy, and financial development on the carbon footprint of MENA-selective nations.

The results suggest that the enterprises within these MENA countries have a strategic impetus to channel investments into renewable energy to sustain GDP growth and stabilize financial conditions, which is a paramount long-term objective. Moreover, it is imperative to safeguard the evolution of financial markets over the medium to long term. It is anticipated that there will be considerable transformations within the financial and industrial architectures propelled by socio-environmental consciousness and technological innovation. Economic growth (EG) and financial markets are projected to experience concomitant continued expansion with an escalation in demand for renewable energy consumption (REC) and green patents. The results also highlight the significant potential for RE to drive sustainable economic growth and to reduce carbon footprints, suggesting several key policy actions. Firstly, policymakers in the MENA region should prioritize developing and implementing supportive regulatory frameworks that encourage investment in RE. This includes setting clear targets for RE adoption, providing financial incentives such as subsidies and tax breaks, and simplifying administrative processes for RE projects. Secondly, the findings underscore the importance of investing in R&D to advance green technologies. Governments should allocate resources to R&D initiatives that focus on improving the efficiency and cost-effectiveness of RE technologies. Thirdly, to ensure a just transition to a green economy, it is crucial to develop training programs that equip workers with the necessary skills for jobs in the RE sector. Additionally, new energy laws and environmental technology patents must be created to promote the growth of the RE sector in this situation. Environmental stakeholders stress the importance of decoupling economic growth from dependence on petroleum-based hydrocarbons. This transition is especially critical for countries with high emissions intensities and traditional energy practices, such as the MENA region.

Our manuscript has a few limitations that can serve as a foundation for future research. The manuscript acknowledges several constraints that merit attention and could inform subsequent scholarly inquiries. Primarily, the analytical focus on the MENA region may limit the generalizability of the findings, particularly concerning the interplay between sustainability and global difficulties such as the pandemic, regional geopolitical tensions like the hostilities involving Russia and Ukraine, and conflicts in the Red Sea area. Such circumscription potentially narrows the scope of insights into the effects on developing countries within and beyond the MENA cohort. Secondarily, this study does not encompass critical macroeconomic variables like green productivity. Anticipated future scholarly contributions are expected to delve deeper into institutional and geopolitical indices via assessing whether adopting RE sources facilitated by energy policies and green productivity could substantially mitigate CO₂ emissions. Thirdly, we recognize the several limitations of the methodologies. The Panel Quantile ARDL and PMG ARDL models are robust in capturing dynamic relationships across quantiles and heterogeneous panels. However, these models rely on certain assumptions that may affect the results. For instance, the PMG ARDL model assumes a homogeneity in the long-run coefficients across countries while allowing for short-run coefficients to vary. This assumption may not hold; thus, it could lead to potential biases in long-term estimates.

Future research in environmental sustainability and green energy in the MENA region can be directed toward technological innovations that also hold significant potential; research could investigate the applicability of advanced solar and wind technologies, energy storage solutions, smart grid systems, and AI use that is specific to the MENA region. We will also conduct a comparative analysis to complement our findings for the MENA region against other regions, as well as provide a more comprehensive understanding of the dynamics of renewable energy adoption and its implications for environmental sustainability.