Assessing the Saudi and Middle East Green Initiatives: The Role of Environmental Governance, Renewable Energy Transition, and Innovation in Achieving a Regional Green Future

Abstract

The transition to sustainable, innovation-driven economies has become a global imperative, particularly for resource-dependent regions like the Middle East, where environmental challenges, fossil fuel reliance, and economic diversification pressures intersect. In this context, green innovation plays a pivotal role in mitigating environmental degradation while supporting long-term economic growth. This study examines the short-term and long-term drivers of green innovation across 13 Middle Eastern countries from 1990 to 2023, with a focus on environmental governance, environmental pollution, economic growth, and natural resource abundance. Using a balanced panel dataset, this study applies Frees, Friedman, and Pesaran CSD tests to address cross-sectional dependency and second-generation unit root tests for data stationarity. Both first- and second-generation cointegration tests confirm long-run relationships among variables. The empirical analysis employs the cross-sectional autoregressive distributed lag (CS-ARDL) model, alongside Pooled Mean Group (PMG-ARDL), Average Mean Group (AMG), and Common Correlated Effects CCEMG estimators, ensuring robustness. The findings indicate that, in the long term, environmental governance, economic growth, population size, and natural resource abundance significantly promote green innovation, with respective coefficients of 0.3, 0.01, 0.02, and 0.4. Conversely, human development and environmental pollution exert a negative influence on green innovation, particularly over the long term. These results suggest that, while economic and governance factors drive innovation, human capital development may prioritize immediate growth over sustainability, and pollution may hinder long-term innovation. Enhancing environmental governance, accelerating renewables, using strategic resource revenue for green projects, integrating green growth, and regional collaboration can position Middle Eastern economies as green innovation leaders.

1. Introduction

Over the past several decades, addressing global environmental challenges has emerged as a central priority within the framework of the Sustainable Development Goals (SDGs). Notably, SDG 7 underscores the necessity of transitioning toward sustainable energy sources to supplant polluting fossil fuels, thereby promoting a more equitable and ecologically resilient world [1,2]. The adoption of renewable energy is particularly critical in mitigating atmospheric CO₂ emissions, considering that the energy sector is responsible for nearly two-thirds of global carbon emissions. Consequently, advancing low-carbon energy technologies and scaling up the utilization of renewable sources such as solar, hydropower, and biomass are essential strategies for addressing climate change [3].

International climate agreements have established binding regulations on energy consumption, thereby compelling significant polluters such as the United States and China to delineate explicit carbon neutrality targets [4,5]. Nevertheless, the widespread adoption of renewable energy continues to be impeded by high costs [6,7], technological constraints [8], and financial barriers [9]. Therefore, policy interventions, investments in innovation, and financial incentives are imperative to expedite this transition. Recent advancements in wind turbines, solar photovoltaics, battery storage, and smart grid technologies have significantly enhanced efficiency, reduced costs, and improved the competitiveness of renewable energy [3].

In the realm of renewable energy, technological innovation is pivotal in enhancing market accessibility and advancing sustainability standards [10]. Its significance has grown increasingly prominent in contemporary discussions on environmental management. The surge in patents for low-carbon technologies underscores a heightened focus on innovation as a key strategy for gaining competitive advantages in the global energy market [11]. However, despite this trend, the share of green innovation and environmental governance remains notably unstable in Middle Eastern nations (see Figure 1). Similarly, Figure 2 illustrates pronounced, irregular fluctuations in environmental governance among Middle Eastern countries from 1990 to 2023, indicating episodic, event-driven policy responses rather than consistent and sustained advancements in environmental regulation and governance. This instability poses significant challenges to transitioning away from fossil fuel dependency, driving a global shift toward a greener economy, and fostering the development of groundbreaking technologies [12].

The Middle East occupies a distinctive position at the intersection of environmental sustainability and economic development. With extensive oil reserves and challenging climate conditions, the region confronts pressing challenges, including desertification, water scarcity, and elevated carbon emissions. The Middle East Green Initiative (MGI) and the Saudi Green Initiative (SGI) represent significant efforts to address these environmental concerns through afforestation, pollution control, and investments in renewable energy [13]. Nevertheless, the implementation of these strategies remains complex, hindered by persistent reliance on fossil fuels, geopolitical tensions, and economic barriers.

Historically, Middle Eastern economies have been heavily reliant on natural resource exploitation, particularly in nations such as Saudi Arabia, Kuwait, Iraq, and Iran, which have dominated global oil exports for decades. While this dependence has conferred economic benefits, it has also exacerbated environmental degradation and heightened the region’s vulnerability to climate change [14,15]. The region’s exposure to extreme weather events, including prolonged droughts and severe heatwaves, further underscores the urgency of sustainable environmental management.

Environmental vulnerabilities differ significantly across Middle Eastern nations, reflecting diverse socio-political contexts. For example, Yemen, Bahrain, Israel, Jordan, and Lebanon experience severe water shortages due to a combination of natural limitations and geopolitical conflicts over resource access. In response, regional initiatives such as the MGI aim to promote environmental governance and sustainability; however, challenges persist, including weak regulatory frameworks, economic disparities, and inadequate infrastructure [3,16].

In response, the MGI, led by Saudi Arabia, signifies a paradigm shift in regional environmental governance. By emphasizing afforestation, pollution abatement, and the development of renewable energy infrastructure, the initiative seeks to mitigate the adverse effects of climate change. Complementary efforts, such as the SGI, are aimed at accelerating transitions toward sustainability. Nonetheless, the implementation of these initiatives encounters substantial challenges, including entrenched dependency on fossil fuels, political and economic barriers, and technological limitations [13].

Despite increasing international pressure for climate action, many Middle Eastern nations continue to grapple with structural and institutional barriers that hinder effective environmental governance and the transition to sustainable energy systems. These challenges include inadequate regulatory frameworks, limited regional coordination, and an entrenched dependence on fossil fuels, which collectively slow the adoption of renewable energy technologies and sustainability initiatives [13,17]. Additionally, political instability in conflict-affected countries such as Syria, Lebanon, and Iraq compound these obstacles, undermining long-term policy planning and deterring foreign investment in green infrastructure. Infrastructure deficiencies and persistent economic constraints in territories like Gaza and the West Bank further weaken environmental governance capacity, worsening problems related to pollution, waste management, and resource scarcity [2].

Even in more developed economies such as the UAE, Saudi Arabia, and Qatar, oil dependency constitutes a significant barrier to achieving a green transition. Although these nations have articulated ambitious commitments to diversify their economies and reduce emissions, progress is often hindered by fragmented environmental policies and a lack of regional coordination [18,19]. Furthermore, the renewable energy sector remains in its early stages, requiring substantial investments in innovation and financing to ensure the scalability and affordability of clean energy solutions [20,21]. Addressing these challenges necessitates a holistic approach that integrates environmental, economic, and technological strategies. Enhancing investments in renewable energy, strengthening governance frameworks, and promoting innovative sustainability practices are critical to overcoming obstacles and advancing the transition to a green economy.

A critical challenge in the Middle East’s sustainability transition lies in the interconnected dynamics of environmental governance, resource dependence, and pollution. The region’s vast natural resource wealth, particularly in fossil fuels, has historically shaped its economic trajectory, but weak governance structures have often resulted in unsustainable extraction practices and high pollution levels [15,22]. Resource-rich nations that lack strong regulatory frameworks frequently experience environmental degradation, as reliance on fossil fuels discourages investment in green technologies [23,24]. However, governance plays a crucial mediating role in breaking this cycle. Effective environmental policies can mitigate the adverse effects of resource dependence by promoting cleaner production methods, reinvesting resource revenues into sustainable innovation, and enforcing stricter pollution controls [25,26]. Without integrated governance mechanisms, pollution levels will continue to rise, hindering the effectiveness of green innovation in addressing environmental challenges [8]. Recognizing this interdependence is essential for crafting policies that balance economic growth, resource sustainability, and environmental protection in the Middle East.

This study aims to evaluate the interplay between environmental governance, renewable energy transitions, and sustainable innovations in shaping the Middle East’s green future. By focusing on key regional actors—including Bahrain, Iraq, Iran, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, the West Bank and Gaza, and Yemen—this research offers a comprehensive analysis of how policy, technology, and governance converge to drive environmental transformation. The inclusion of diverse nations with distinct political and economic contexts yields valuable insights into shared challenges and opportunities in regional sustainability efforts. While the existing literature extensively discusses the environmental consequences of fossil fuel dependency, this study provides new insights into how a coordinated strategy combining innovation, renewable energy, and governance can effectively address the Middle East’s environmental challenges.

Our research unfolds through the following stages: Firstly, we compile a panel dataset that includes environmental governance indicators, renewable energy metrics, innovation indices, and socio-economic variables from selected Middle Eastern countries. Secondly, we use the cross-sectional autoregressive distributed lag (CS-ARDL) model as our main analysis tool because it can manage differences between countries, allow for various time delays for each variable, and show strong long-term connections even when there are dependencies between countries [27,28]. Thirdly, we use other methods like the Pooled Mean Group (PMG-ARDL), Average Mean Group (AMG), and Common Correlated Effects Mean Group (CCEMG) to check how accurate and reliable the results from the CS-ARDL model are [28,29,30]. Fourthly, we examine both the short-term and long-term effects of the identified variables on green innovation, focusing on understanding the dynamics and causal relationships within the regional context. Finally, we derive practical insights and suggestions to guide Middle Eastern nations in their national green initiatives, helping them strike a balance between economic growth and environmental sustainability.

This study is structured as follows: Section 2 provides a review of the relevant literature. Section 3 outlines the methodology employed, detailing the analytical framework and data processing techniques. In Section 4, we present, interpret, and discuss the results of this study, analyzing their implications. Finally, Section 5 concludes this study and offers policy recommendations based on the findings, highlighting their significance for sustainable development and green innovation in the region.

2. Literature Review

Environmental governance, resource dependence, and pollution are intricately linked, shaping the path of green innovation and sustainability in the Middle East. Weak governance structures often exacerbate the negative effects of resource dependence, leading to high pollution levels and environmental degradation. In many Middle Eastern economies, natural resource wealth, primarily from fossil fuels—has driven economic expansion but has also created governance challenges that hinder environmental sustainability [13,31]. The absence of strong regulatory frameworks allows pollution-intensive industries to thrive, reinforcing a cycle where resource exploitation fuels environmental degradation without adequate innovation incentives. Conversely, countries with well-developed governance structures have demonstrated that effective policies can redirect resource revenues toward sustainable innovation, mitigating pollution while sustaining economic growth [32,33].

The “resource curse” hypothesis suggests that economies highly dependent on natural resource revenues often experience slower innovation growth and weaker institutional capacity, which, in turn, limits effective pollution control [24]. In the Middle East, oil and gas dependence has historically constrained efforts to transition toward a green economy, as fossil fuel revenues reduce the immediate economic pressure to invest in renewable energy and emissions reduction technologies [23,25]. However, governance plays a mediating role in breaking this pattern. Countries that strategically reinvest resource wealth into research and development (R&D) for green technologies have achieved more sustainable outcomes [34]. For example, Norway’s sovereign wealth fund model redirects oil revenues into sustainable projects, a model Middle Eastern economies could adopt to enhance green innovation while reducing pollution.

The lack of strict environmental regulations in resource-rich economies has also contributed to significant pollution levels, particularly in oil-dependent nations such as Saudi Arabia, Iraq, and Kuwait. Without governance mechanisms enforcing sustainable resource extraction practices, industries continue to prioritize short-term gains over long-term environmental stability [35]. Studies indicate that countries with robust environmental policies, such as carbon pricing, emissions caps, and investment in clean energy—are more likely to successfully integrate resource management with pollution control, reducing their reliance on fossil fuels while incentivizing green innovation [36,37]. In contrast, Middle Eastern countries with weak governance structures often struggle to implement these measures, leading to unchecked pollution despite growing awareness of environmental concerns [17].

Another critical factor is international climate governance and trade regulations, which increasingly pressure Middle Eastern economies to adopt cleaner technologies. Export-dependent oil economies face mounting challenges as global markets shift toward carbon border adjustment mechanisms, making green innovation essential for maintaining economic competitiveness [2]. Without proactive policy alignment between governance, resource dependence, and pollution control, Middle Eastern nations risk economic stagnation as global demand for fossil fuels declines. The European Union’s Carbon Border Adjustment Mechanism (CBAM) serves as an example of how external governance structures are influencing domestic environmental policies, forcing oil-exporting nations to reconsider their sustainability strategies [20].

Green innovation acts as the bridge between these interconnected issues, offering technological solutions to reduce pollution while ensuring economic resilience. However, without governance mechanisms that align resource revenues with sustainability investments, green innovation adoption remains slow [33]. Strengthening regional environmental policies—such as coordinated carbon pricing across GCC nations—could create incentives for businesses to shift toward low-carbon technologies while reducing pollution [38]. Furthermore, digital innovations, such as AI-driven emissions monitoring and blockchain-based resource tracking, offer new governance tools to ensure transparency and efficiency in managing pollution and resource use [39,40].

Ultimately, achieving a regional green future in the Middle East requires a holistic policy approach that integrates governance, natural resource management, and pollution control rather than treating them as separate challenges. Countries must transition from viewing resource wealth as an economic end goal to leveraging it as a tool for sustainable innovation. Establishing regional governance bodies to enforce unified pollution standards, reinvesting resource revenues into green technology R&D, and developing cross-border energy transition policies are essential steps toward breaking the resource–pollution cycle and fostering long-term environmental resilience.

2.1. Conceptual Framework

The transition to a sustainable future in the Middle East hinges on the interplay between environmental governance, renewable energy adoption, and innovative sustainability solutions. Environmental governance provides the institutional, legal, and regulatory foundation necessary to drive sustainability policies, enforce compliance, and manage resource use efficiently [41,42]. In Saudi Arabia and the broader Middle Eastern region, recent policy shifts—including the SGI and MGI—demonstrate a growing commitment to balancing economic growth with environmental responsibility [13]. However, governance effectiveness varies across the region, affecting the speed and success of sustainable transitions.

A critical dimension of this transition is renewable energy adoption, which serves as a primary mechanism for reducing carbon emissions, diversifying energy sources, and decreasing fossil fuel dependency [21]. The energy transition model emphasizes the role of legislative support, technological innovation, and financial incentives in shifting from hydrocarbon-based economies to renewable energy systems [20]. Despite ambitious national policies, challenges such as high investment costs, technological limitations, and entrenched oil-based economic structures hinder rapid transformation [2].

At the same time, innovative sustainability strategies—including carbon capture technologies, green hydrogen, and smart infrastructure—offer pathways for mitigating climate risks while promoting economic resilience. Innovation theory suggests that technological breakthroughs, institutional adaptation, and market-driven solutions are central to achieving long-term environmental sustainability [19]. Middle Eastern economies increasingly integrate circular economy principles and digital sustainability tools to reduce carbon footprints, enhance resource efficiency, and address environmental challenges such as urbanization and water scarcity [43].

These three pillars—governance, renewable energy transition, and innovation—are deeply interconnected. Effective governance not only establishes regulatory frameworks and financial incentives for renewable energy adoption but also ensures that technological innovations are strategically implemented to support sustainability goals. In turn, successful renewable energy transitions and sustainability innovations reinforce governance by providing proof of concept for long-term environmental policies and encouraging further institutional reforms. Without strong governance mechanisms, renewable energy initiatives may remain underfunded, and sustainability innovations may fail to reach full-scale implementation [34].

Figure 3 illustrates how environmental governance, pollution control, renewable energy, and natural resources shape a sustainable future. Governance structures drive energy transitions, resource management, and sustainability efforts, while renewable energy adoption and innovation improve governance by influencing policy. Research and technological advancements act as moderators, enhancing the effectiveness of renewable energy and natural resources in achieving environmental goals. The model highlights the interconnected feedback loop where governance impacts sustainability, and innovation accelerates green progress through energy strategies and policy improvements.

2.2. Theoretical Framework

The Saudi and Middle East Green Initiatives represent a transformative policy shift in historically fossil fuel-dependent economies, aligning with the broader global energy transition movement. The energy transition model serves as a foundational theory for understanding this shift, as it describes how economies move from carbon-intensive energy systems to renewables through technological, institutional, and policy transformations [44]. This transition involves not only technological innovation but also changes in economic incentives, governance structures, and social acceptance of renewable energy solutions [45,46].

A key theoretical underpinning of this shift is environmental governance theory, which highlights the role of institutional quality, regulatory enforcement, and policy coordination in shaping sustainability outcomes [41]. Strong governance ensures that renewable energy policies are effectively implemented, compliance is enforced, and investments in sustainability are strategically allocated. In Saudi Arabia, for instance, government-led initiatives such as Vision 2030 and SGI demonstrate how governance can drive systemic energy transformations [13]. However, the region still faces institutional challenges, such as weak cross-border collaboration, policy inconsistencies, and slow regulatory adaptation [2].

Moreover, innovation theory provides a complementary perspective, emphasizing that technological advancements and market-based solutions are essential for achieving sustainable transitions [18]. Innovations such as solar PV efficiency improvements, energy storage breakthroughs, and AI-driven grid management play a pivotal role in ensuring that the energy transition is both technologically viable and economically competitive [47]. These advancements are not isolated but are deeply influenced by governance and policy frameworks, which determine the pace of adoption and diffusion [48].

The integration of these three theoretical pillars—energy transition, environmental governance, and innovation—creates a holistic analytical framework for assessing the effectiveness of Saudi and Middle East Green Initiatives. A key insight from transition studies is that successful energy shifts are not purely technological but also socio-political, requiring public support, economic incentives, and robust governance structures [46]. The challenge in the Middle East lies in navigating the complexities of economic diversification while maintaining political and financial stability [22,49].

By adopting a multidimensional theoretical framework, this study seeks to understand not only the technical feasibility of renewable energy transitions but also the governance and innovation strategies necessary to sustain these efforts. The success of these initiatives depends on whether environmental policies, institutional frameworks, and market-driven innovations can align to create a self-reinforcing cycle of sustainable transformation [45].

3. Materials and Methods

3.1. Data

The data utilized in this study are drawn from reputable sources, including the World Bank, the Global Material Flows Database, the World Health Organization (WHO), and the Organization for Economic Cooperation and Development (OECD). Table 1 provides a comprehensive overview of the data, detailing the units, abbreviations, measurements, sources, and references for each variable. To ensure a robust and well-rounded analysis, we constructed a balanced panel dataset comprising 13 Middle Eastern countries: Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, the West Bank and Gaza, and Yemen. The dataset spans the period from 1990 to 2023. These countries were selected based on the availability of consistent and reliable data across all variables, enabling a thorough and methodologically sound investigation.

This study investigates the determinants of green innovation in Middle Eastern countries within the framework of environmental governance, natural resource management, economic growth, and socio-environmental pressures. The selection of variables was guided by established theoretical frameworks and empirical findings in sustainability, environmental economics, and innovation studies. Below, we justify the inclusion of each variable based on its theoretical relevance and empirical evidence.

Green innovation (GIVN) serves as the dependent variable in this study, capturing the extent to which economies adopt environmentally friendly technologies, practices, and products. It represents a country’s capacity to reduce environmental pollution while maintaining economic growth. Following recent empirical work, patent data related to environmental technologies or renewable energy technologies is used as a proxy for green innovation, as it directly measures innovation output in environmentally relevant sectors [43,50].

Environmental governance (EVGN) reflects the quality of regulatory systems, policy enforcement, institutional frameworks, and stakeholder participation in managing environmental challenges. It is a critical determinant of the success of green transitions, as it influences both the formulation and implementation of sustainability policies [51]. Governance quality has been shown to mediate the relationship between economic activity and environmental outcomes [52]. Therefore, including an index or proxy for environmental governance (e.g., regulatory quality, environmental performance indices, or law enforcement indicators) captures institutional capacity to steer economies toward green innovation.

Table 1. Summary of the study variables.

Series	Measurement	Unit	Source	Reference
GIVN	Green innovation	Percent share of green or environmental patents/total	OECD	Wang and Juo [43]
EVGN	Environmental governance	Environmental Policy Stringency Index	OECD	Ozturk, Razzaq [2]
GPDC	GDP per capita	US Dollar	World Bank	Ghanem and Alamri [13]
EVPO	Environmental pollution	% of total pollution	WHO	Song, Meng [53]
HDIF	Human development index	scale 0–1	World Bank	Asongu and Odhiambo [54]
NRCS	Natural resource dependence	Tones per capita	Global Material Flows Database	Li, Ma [55]
TPOP	Total population	persons	World Population Prospects	Abdullahi, Ibrahim [19]

Source: Authors’ construct. Note: OECD: Organization for Economic Co-operation and Development, WHO: World Health Organization.

Table 1. Summary of the study variables.

Series	Measurement	Unit	Source	Reference
GIVN	Green innovation	Percent share of green or environmental patents/total	OECD	Wang and Juo [43]
EVGN	Environmental governance	Environmental Policy Stringency Index	OECD	Ozturk, Razzaq [2]
GPDC	GDP per capita	US Dollar	World Bank	Ghanem and Alamri [13]
EVPO	Environmental pollution	% of total pollution	WHO	Song, Meng [53]
HDIF	Human development index	scale 0–1	World Bank	Asongu and Odhiambo [54]
NRCS	Natural resource dependence	Tones per capita	Global Material Flows Database	Li, Ma [55]
TPOP	Total population	persons	World Population Prospects	Abdullahi, Ibrahim [19]

Source: Authors’ construct. Note: OECD: Organization for Economic Co-operation and Development, WHO: World Health Organization.

Environmental pollution (EVPO), typically measured via CO₂ emissions per capita or ecological footprint, represents the environmental pressure exerted by human and industrial activity. It is a core outcome variable in sustainability research and a critical driver of policy innovation [56]. Including pollution metrics allows for testing hypotheses such as the Environmental Kuznets Curve (EKC) and examining whether rising environmental pressures incentivize green innovation [57,58]. The selection of this variable is consistent with empirical studies that link pollution intensity to shifts in technological trajectories and policy reforms [59].

Human development, typically measured by the human development index (HDIF), was included as it reflects broader socio-economic capacity and welfare standards, which influence environmental behavior and innovation capacity. Higher human development levels often correlate with increased environmental awareness, demand for clean technologies, and institutional accountability [15]. Empirical studies suggest that countries with higher human development indices tend to prioritize environmental objectives and invest more in renewable energy and green technologies [23]. Therefore, including HDIF accounts for the role of education, healthcare, and income in driving sustainability transitions.

Economic growth, proxied by GDP per capita, has long been debated in environmental economics for its dual role in contributing to environmental degradation and providing resources for environmental improvement [56]. Its inclusion allows for testing the EKC hypothesis within the Middle Eastern context, where oil-driven economic models pose unique challenges to green transitions. Empirical evidence suggests that higher GDP levels can enable investments in renewable energy and clean technologies, provided appropriate governance mechanisms are in place [19,60].

Natural resource dependence (NRCS), typically measured by resource rent as a percentage of GDP or sub-indices like oil rents, is critical in Middle Eastern economies. Resource-rich nations face challenges associated with the resource curse, where wealth from natural resources can delay diversification and weaken innovation incentives [24,40]. This variable captures how resource abundance interacts with governance quality and innovation capacity, influencing the likelihood of adopting sustainable energy technologies and green innovations [55].

Population size (TPOP) directly influences both environmental pressures and market potential for green technologies. Larger populations typically lead to higher energy demand, greater emissions, and increased resource consumption, necessitating technological innovation and policy responses [19]. Population also affects labor market dynamics, urbanization patterns, and infrastructure requirements, all of which shape the feasibility and scale of renewable energy adoption and sustainability practices [3]. Empirical studies consistently control for the population when examining environmental and innovation outcomes to capture these demographic effects [2].

3.2. Estimation Techniques

This study investigates the intricate relationships between environmental governance, per capita GDP, environmental pollution, the human development index, natural resource availability, population dynamics, and green innovations across 13 Middle Eastern economies from 1990 to 2023. To achieve this, we begin with preliminary estimations and evaluate the presence of cross-sectional dependency (CSD) in the dataset. In panel data analysis, testing for CSD is a critical step before selecting the appropriate model, as cross-sectional units may exhibit interdependencies at both national and regional levels. Ignoring CSD can lead to inconsistent and inaccurate results, undermining the reliability of the conclusions drawn [61]. To address this, we employ the CSD test developed by Pesaran [62], which is outlined in Equation (1) below:

$$CSD=\sqrt{{\displaystyle \frac{2P}{N(N-1)}}({\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\rho }_{ij}})$$

(1)

Here, P represents the investigation period, N denotes the sample size, and the subscripts ${\rho }_{ij}$ indicate the cross-sectional dependency of country errors. The CSD test, a pivotal step, evaluates the alternative hypothesis, suggesting the presence of CSD in the data—against the null hypothesis, which posits no CSD.

In the second stage, we perform unit root tests to assess the stationarity of the selected variables. This step is crucial to confirming that all variables are integrated either at order I (0), I (1), or a mixed order but not at I (2). Given the presence of CSD in the dataset, first-generation unit root tests are unsuitable for evaluating the stationarity properties of the variables. Instead, we employ second-generation unit root tests, which are robust and reliable tools capable of effectively addressing both CSD and slope heterogeneity issues in time series data. This approach ensures the accuracy and validity of our analysis [63,64]. The Pesaran CADF is represented by Equation (2):

$${\Delta Z}_{it}={\vartheta }_i+{ϛ}_i{A}_{i, t-1}+{\delta }_i{Ā}_{t-1}+{\displaystyle \sum _{j=0}^p}{\sigma }_{ij}{\Delta Ā}_{t-1}+{\displaystyle \sum _{j=1}^p}{\gamma }_{ij}{\Delta Ā}_{i, t-1}+{\mu }_{it}$$

(2)

where ${\delta }_i$ and ${\mathrm{Ā}}_{i,t-1}$ denote the means for the lagged and the first differences in each cross-section series, and the CADF provides the basis of CIPS statistics, as presented in Equation (3) as follows:

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

(3)

The test statistic for the Fisher ADF test is given by Maddala and Wu [64], and it is represented by Equation (4):

$${\mathsf{\chi }}^2=-2\sum _{i=1}^N{lnP}_i$$

(4)

where χ² refers to a chi-squared distribution used to test the null hypothesis of the unit root test for panel data. $P_i$ is the p-value from the individual ADF test for each unit, and N is the number of cross-sectional units in the panel.

Cointegration tests, a key tool in econometrics, are used to assess the presence of a linear relationship between variables in their levels and first differences. These tests are vital in identifying a long-term equilibrium relationship among the variables under study. The field has seen significant evolution, with various cointegration tests now available, including the pioneering first-generation tests by Kao [65] and Pedroni [66]. Westerlund [67] further advanced this field with the introduction of a second-generation cointegration test. This model takes into account potential interdependencies among individual units in the panel. In this study, we explore the existence of a long-term equilibrium relationship among the variables by applying the tests proposed by Kao [65], Pedroni [66], and Westerlund [67].

In the final stage, we employ the CS-ARDL model, an extension of the standard ARDL model, a practical and powerful tool in panel data analysis. This CD-ARDL approach can capture short-term dynamics and long-term relationships between variables across different cross-sectional units in a panel. Its unique ability to handle cases of heterogeneous panels, where the relationships between the variables might differ across cross-sectional units, makes it a reliable and applicable model [68]. The general equation for a CD-ARDL model for panel data is as follows (Equation (5)):

$$\begin{array}{ll}{lGIVN}_{it}=S_i+& {\displaystyle \sum _{j=1}^{P_y}}{K}_{ij}{lGIVN}_{it-j}+{\displaystyle \sum _{j=0}^{P_X}}{\delta }_1{EVGN}_{it-j}+{\displaystyle \sum _{j=0}^{P_X}}{\delta }_2{lGDPC}_{it-j}+{\displaystyle \sum _{j=0}^{P_X}}{\delta }_3{EVPO}_{it-j}+{\displaystyle \sum _{j=0}^{P_X}}{\delta }_4{HDIF}_{it-j}\\ & +{\displaystyle \sum _{j=0}^{P_X}}{\delta }_5{NRCS}_{it-j}+{\displaystyle \sum _{j=0}^{P_X}}{\delta }_6{lTPOP}_{it-j}+{\displaystyle \sum _{j=1}^{P_y}}{\alpha }_1{lGIVN}_{t-j}+{\displaystyle \sum _{j=0}^{P_X}}{\alpha }_2{lEVGN}_{t-j}+{\displaystyle \sum _{j=0}^{P_X}}{\alpha }_3{lGDPC}_{t-j}\\ & +{\displaystyle \sum _{j=0}^{P_X}}{\alpha }_4{EVPO}_{t-j}+{\displaystyle \sum _{j=0}^{P_X}}{\alpha }_5{HDIF}_{t-j}+{\displaystyle \sum _{j=0}^{P_X}}{\alpha }_6{NRCS}_{t-j}+{\displaystyle \sum _{j=0}^{P_X}}{\alpha }_7{lTPOP}_{t-j}+{\mathrm{\mu }}_{it}\end{array}$$

(5)

where $\mathrm{l}\mathrm{G}\mathrm{I}\mathrm{V}\mathrm{N}$, $EVGN$, lGDPC, EVPO, HDIF, NRCS, and $\mathrm{l}\mathrm{T}\mathrm{P}\mathrm{O}\mathrm{P}$ represent the cross-sectional means, $S_i$ denotes the effect specifications of unobserved economies, $K_{ij}$ symbolizes the effect of the lagged criterion variable, and ${\delta }_i,\dots ,{\delta }_6$ are the coefficients of the lagged input series. Additionally, ${\alpha }_1,\dots ,{\alpha }_7$ represent the average cross-sectional values of the lagged series, and p refers to the cross-sectional mean lags. The CD-ARDL incorporating the Error Correction Model (ECM) structure to account for both short-run dynamics and long-run equilibrium relationships, with cross-sectional dependence, can be represented by Equation (6):

$$Y_{it}={\alpha }_i+{\displaystyle \sum _{p=1}^{P_Y}}{\beta }_{ip}{Y}_{it-p}+{\displaystyle \sum _{q=0}^{P_X}}{\gamma }_{iq}{X}_{it-q}\Pi \sum _{j\ne i}{\displaystyle \sum _{p=0}^{P_Y}}{\vartheta }_{ij,p}{Y}_{jt-p}+\sum _{j\ne i}{\displaystyle \sum _{q=0}^{P_X}}{\phi }_{ij,q}{X}_{jt-q}+{\Psi }_{it}+{\mathrm{\mu }}_{it}$$

(6)

where ${\vartheta }_{ij,p}$ and ${\phi }_{ij,q}$ capture the lagged dependent and independent variables from other cross-sectional units j ≠ I, ${\Psi }_{it}$ is the error correction term (ECM), capturing the deviation from the long-run equilibrium, and ${\mathrm{\mu }}_{it}$ is the idiosyncratic error term.

The CS-ARDL model is a key component of our study. However, we combine it with other methods to ensure the robustness of our findings. The Pooled Mean Group (PMG) ARDL, Average Mean Group (AMG), and Common Correlated Effects Mean Group (CCEMG) methods are employed to bolster the reliability and consistency of the CS-ARDL estimates. We can provide more dependable results across different modeling approaches by considering heterogeneous slopes and potential standard shocks. These techniques enable a comprehensive validation of the long-run relationships and dynamics, which are the core of the CS-ARDL model [27,28]. Moreover, we depict the empirical procedures of our study in Figure 4.

4. Results and Discussion

4.1. Preliminary Results

The descriptive statistics of the study variables are presented in

Table 2. Descriptive statistics.

Variable	Obs.	Mean	Std. Dev.	Min.	Max.	Skewness	Kurtosis
GIVN	442	154.495	211.193	8.060	951.831	2.275	7.305
EVGN	442	14.513	34.671	0.004	128.500	2.796	9.321
GDPC	442	13,759.698	35,887.803	186.024	66,1062.910	13.563	40.930
EVPO	442	67.973	30.770	3.400	100	−0.383	1.800
HDIF	442	0.407	0.413	0.000	1.000	0.343	1.354
NRCS	442	20.592	17.097	0.001	67.443	0.361	2.057
TPOP	442	14,322,359	19,338,723	354,170	89,172,767	2.103	7.167

Source: Authors’ construct. Note: GIVN: green innovation, EVGN: environmental governance, GDPC: per capita gross domestic product, EVPO: environmental pollution, HDIF: human development index, NRCS: natural resource dependence, TPOP: total population.

. The variable with the highest mean value is the population (TPOP), which also shows a significant level of volatility with a standard deviation of 19,338,723. In contrast, the human development index (HDIF) has the lowest average value and demonstrates the least volatility with a standard deviation of 0.413. All series are positively skewed, except for environmental pollution (EVPO = −0.383). All variables exhibit mesokurtic distributions, with per capita GDP (GDPC) having the highest value of 40.93.

As highlighted by Gujarati [69], the issue of multicollinearity becomes significant when the correlation coefficient exceeds 0.8. This high level of correlation can lead to unreliable estimates and inflated standard errors, undermining the accuracy of the results. In contrast, Hausman [70] suggests that maintaining a correlation below 0.8 can lead to more accurate results, indicating that the variables are not excessively correlated. This emphasis on accuracy underscores the value of the research to the economist, particularly those interested in panel data analysis. Importantly, the results in

Table 3. Matrix of correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	VIF	1/VIF
(1) GIVN	1.000
(2) EVGN	0.659	1.000						1.040	0.962
(3) GDPC	0.137	0.140	1.000					1.141	0.876
(4) EVPO	0.047	0.019	0.127	1.000				1.033	0.968
(5) HDIF	−0.013	−0.013	0.311	0.154	1.000			1.135	0.881
(6) NRCS	0.293	0.120	0.062	0.024	0.092	1.000		1.026	0.975
(7) TPOP	0.022	−0.047	−0.014	0.030	0.002	−0.042	1.000	1.005	0.995
Mean VIF								1.063

Source: Authors’ construct. Note: GIVN: green innovation, EVGN: environmental governance, GDPC: per capita gross domestic product, EVPO: environmental pollution, HDIF: human development index, NRCS: natural resource dependence, TPOP: total population.

illustrate that the highest correlation is between GIVN and EVGN, which is 0.66. The variance inflation factor (VIF) and tolerance tests (1/VIF) were below 5 and above 0.2, respectively, indicating a reassuring absence of collinearity among the predictors and confirming the reliability of this study’s results.

The results of the CSD tests, shown in

Table 4. Cross-sectional dependency test.

Series	Statistics	p-Value
Frees	1.263	0.000 ***
Pesaran CD	−3.483	0.001 ***
Breusch and Pagan Lagrangian multiplier	32.56	0.000 ***

Source: Authors’ construct. *** indicates statistical significance at 1% level.

, are derived from the Fress, Pesaran CD, and Breusch–Pagan Lagrangian multiplier tests. All three methods indicate that CSD is present in the dataset, suggesting interdependence among the countries. This means a shock in one country could create a ripple effect, impacting other countries. However, the need for first-generation unit root tests in examining the stationarity properties of the variables due to their failure to account for CSD is a pressing issue [19,71]. Therefore, it is crucial to utilize a second-generation unit root test to underline the importance of this solution.

Table 5. Panel unit root test.

	Pesaran CADF		CIPS		Fisher ADF
Series	Level	1st Diff.	Level	1st Diff.	Level	1st Diff.
GIVN	−11.452 ***	−15.239 ***	−5.395 ***	−6.190 ***	−16.381 ***	24.799 ***
EVGN	−8.824 ***	−15.006 ***	−4.854 ***	−6.128 ***	−14.625 ***	−24.231 ***
GDPC	2.564	−5.565 ***	−0.935	−4.383 ***	−0.359	−16.351 ***
EVPO	7.473	3.957	0.307	−1.065 ***	2.003	−18.418 ***
HDIF	−0.810	−8.792 ***	−2.476	−5.622 ***	−2.453 ***	−20.066 ***
NRCS	−11.072 ***	−14.034 ***	−5.798 ***	−6.190 ***	−19.012 ***	−27.148 ***
TPOP	−10.504 ***	−16.726 ***	−6.190 ***	−6.190 ***	−21.780 ***	−29.190 ***

Source: Authors’ construct. Note: *** indicates statistical significance at 1% level, CADF: Cross-section Augmented Dickey–Fuller, CIPS: Cross-sectionally Augmented IPS, ADF: Augmented Dickey–Fuller, GIVN: green innovation, EVGN: environmental governance, GDPC: per capita gross domestic product, EVPO: environmental pollution, HDIF: human development index, NRCS: natural resource dependence, TPOP: total population.

displays the outcomes of the Pesaran CADF, CIP, and Fisher ADF unit root test in levels and first difference. The results indicate that all the variables are stationary at levels I (0) and I (1) except HDIF and NRCS, which are stationary only at the first difference. No variable in the series was integrated in the I (2) order, and I (2) can lead to deceptive results [71,72].

Our research comprehensively examined the long-term equilibrium relationship between environmental governance, per capita GDP, environmental pollution, the human development index, natural resources, population, and green innovations to ascertain the presence of any convergence among the variables. We employed a range of tests, including the Pedroni [66], Kao [65], and Westerlund [67] tests, to evaluate long-term cointegration. The findings, presented in

Table 6. Panel cointegration test.

Pedroni Residual	Statistics	p-Value
Modified Phillips–Perron t	0.591	0.277
Phillips–Perron t	−11.696 ***	0.000
Augmented Dickey–Fuller t	−10.355 ***	0.000
Kao residuals
Modified Dickey–Fuller t	−4.001 ***	0.000
Dickey–Fuller t	−7.149 ***	0.000
Augmented Dickey–Fuller t	−4.629 ***	0.000
Unadjusted modified Dickey–Fuller t	−30.749 ***	0.000
Unadjusted Dickey–Fuller t	−16.288 ***	0.000
Westerlund
Variance ratio	−2.467 ***	0.007

Source: Authors’ construct. Note: *** indicates statistical significance at 1% level.

, show a compelling rejection of the null hypothesis (H₀), offering strong evidence of long-term cointegration among the variables GIVN, EVGN, GDPC, EVPO, HDIF, NRCS, and TPOP.

4.2. Main Results

Table 7. CS-ARDL estimate.

	Coef.	Std. Err.	Z	p > |z|
Panel A
$\mathrm{L}.\mathrm{l}\mathrm{G}\mathrm{I}\mathrm{V}\mathrm{N}$	0.301 ***	0.047	6.36	0.000
EVGN	0.014 ***	0.001	12.34	0.000
$\mathrm{l}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}$	0.014 **	0.003	4.67	0.013
EVPO	−0.001	0.007	−0.18	0.856
HDIF	−1.968	6.356	−0.31	0.757
NRCS	0.040 ***	0.018	8.22	0.000
$\mathrm{l}\mathrm{T}\mathrm{P}\mathrm{O}\mathrm{P}$	0.018 ***	0.004	4.32	0.003
Panel B
ECT	−0.699 ***	0.047	−14.79	0.000
EVGN	0.022 ***	0.001	13.40	0.000
$\mathrm{l}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}$	0.447 ***	0.176	5.88	0.007
EVPO	−0.005	0.012	−0.41	0.680
HDIF	−2.293	8.510	−0.28	0.779
NRCS	0.064 ***	0.011	5.88	0.000
$\mathrm{l}\mathrm{T}\mathrm{P}\mathrm{O}\mathrm{P}$	0.031 ***	0.002	15.51	0.003
CD statistics	−3.06
Root MSE	0.59

Source: Authors’ construct. Note: ***, ** indicates statistical significance at 1% and 5% levels respectively, CS-ARDLs: cross-sectional autoregressive distributed lags, GIVN: green innovation, EVGN: environmental governance, GDPC: per capita gross domestic product, EVPO: environmental pollution, HDIF: human development index, NRCS: natural resource dependence, TPOP: total population.

presents the long-run and short-run effects of environmental governance, environmental pollution, per capita GDP, human development index, natural resources, and total population on green innovation in Middle Eastern economies using CS-ARDL estimate. To ensure the reliability of the estimation results, we also employed the PMG-ARDL, AMG, and CCEMG methods (see Table 8). The error correction term (ECT) indicates that EVGN, GDPC, EVPO, HDIF, NRCS, and TPOP are converging toward their long-term trajectories at rates of −0.669 (CS-ARDL) and −0.642 (PMG-ARDL), with a statistical significance of 1% level, attributed to the influence of their independent variables. The statistically significant ECTt−1 confirms the equilibrium relationship among the variables. The explanatory factors account for roughly 66.9% and 64.2% of the annual correction toward equilibrium divergence.

The panel data model estimations reveal a notable result: environmental governance (EVGN) exerts a consistently positive and statistically significant influence on green innovation in the Middle East, both in the short and long run. At a 1% significance level, a one-unit improvement in environmental governance is associated with an increase in green innovation of 0.014 units (CS-ARDL), 0.022 units (PMG-ARDL), and 0.018 units (AMG and CCEMG) in the long run. While these coefficient values may appear modest, their practical significance lies in their cumulative effect over time. In policy terms, incremental improvements in governance quality such as enhanced regulatory enforcement, improved policy coherence, and strengthened institutional capacity could create a favorable environment for sustained technological innovation and market entry of green firms [2,14,26]. These findings corroborate the theoretical assumption that governance reforms are not merely administrative necessities but strategic levers for economic diversification and green industrialization in resource-dependent regions [73,74,75].

Similarly, the GDP per capita coefficient has a significant and positive impact on environmentally friendly innovation. Depending on the model used, a one percent increase in GDP per capita could lead to a green innovation increase between 0.112% and 0.447% in the long run. In the short term, for a significance level of 1%, lGDPC could result in an increase of 0.447% (CS-ARDL) and 0.112% (PMG-ARDL) in green innovation. This suggests that an increase in Middle Eastern countries’ per capita GDP could stimulate green innovation by boosting the allocation of financial resources for research and development, promoting the use of environmentally friendly technologies, and creating a robust market for environmentally friendly goods and services [19,53]. This highlights the critical need to align economic growth strategies with environmental incentives, ensuring GDP gains translate into measurable ecological benefits rather than worsening degradation. The findings of Hao, Li [76] and Nan, Wang [77] further support this conclusion, highlighting the collective effort behind our research.

Table 8. Robustness using PMG-ARDL, AMG, and CCEMG estimators.

	PMG-ARDL		AMG		CCEMG
Panel A	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.
EVGN	0.022 ***	0.002	0.018 ***	0.001	0.018 ***	0.001
$\mathrm{l}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}$	0.112 **	0.048	0.129 **	0.004	0.132 **	0.010
EVPO	−0.001	0.002	−0.000	0.001	0.001	0.005
HDIF	−0.509 *	0.272	−0.531	1.098	0.569	1.482
NRCS	0.065 ***	0.006	0.027 ***	0.000	0.028 ***	0.004
$\mathrm{l}\mathrm{T}\mathrm{P}\mathrm{O}\mathrm{P}$	0.500 ***	0.047	0.443 ***	0.014	0.118 ***	0.021
Panel B
ECT	−0.642 ***	0.032
EVGN	0.010 ***	0.001
$\mathrm{l}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}$	0.211 ***	0.001
EVPO	−0.001	0.002
HDIF	−3.339 ***	0.186
NRC [78] S	0.020 ***	0.002
$\mathrm{l}\mathrm{T}\mathrm{P}\mathrm{O}\mathrm{P}$	0.005 ***	0.007

Source: Authors’ construct. Note: ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively. ECT: error correction term, PMG-ARDL: Pooled Mean Group-Autoregression Distributed Lags, AMG: Average Mean Group, CCEM: Common Correlated Mean Group, GIVN: green innovation, EVGN: environmental governance, GDPC: per capita gross domestic product, EVPO: environmental pollution, HDIF: human development index, NRCS: natural resource dependence, TPOP: total population.

Table 8. Robustness using PMG-ARDL, AMG, and CCEMG estimators.

	PMG-ARDL		AMG		CCEMG
Panel A	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.
EVGN	0.022 ***	0.002	0.018 ***	0.001	0.018 ***	0.001
$\mathrm{l}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}$	0.112 **	0.048	0.129 **	0.004	0.132 **	0.010
EVPO	−0.001	0.002	−0.000	0.001	0.001	0.005
HDIF	−0.509 *	0.272	−0.531	1.098	0.569	1.482
NRCS	0.065 ***	0.006	0.027 ***	0.000	0.028 ***	0.004
$\mathrm{l}\mathrm{T}\mathrm{P}\mathrm{O}\mathrm{P}$	0.500 ***	0.047	0.443 ***	0.014	0.118 ***	0.021
Panel B
ECT	−0.642 ***	0.032
EVGN	0.010 ***	0.001
$\mathrm{l}\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}$	0.211 ***	0.001
EVPO	−0.001	0.002
HDIF	−3.339 ***	0.186
NRC [78] S	0.020 ***	0.002
$\mathrm{l}\mathrm{T}\mathrm{P}\mathrm{O}\mathrm{P}$	0.005 ***	0.007

Source: Authors’ construct. Note: ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively. ECT: error correction term, PMG-ARDL: Pooled Mean Group-Autoregression Distributed Lags, AMG: Average Mean Group, CCEM: Common Correlated Mean Group, GIVN: green innovation, EVGN: environmental governance, GDPC: per capita gross domestic product, EVPO: environmental pollution, HDIF: human development index, NRCS: natural resource dependence, TPOP: total population.

Furthermore, the HDIF is negatively affecting green innovation. The result is only significant at the 10% level from the PMG-ARDL in the long run and at 1% in the short run, indicating that a percentage decrease in the human development index will result in a decline in green innovation in Middle Eastern nations. While this appears counterintuitive, several contextual factors could explain the finding. The negative and statistically significant relationship between HDIF and green innovation likely reflects regional dynamics where improvements in welfare indicators have historically coincided with fossil fuel-driven economic growth and industrialization, often sidelining environmental innovation. This paradox may result from policy trade-offs common in rentier economies, where rising living standards do not necessarily translate into enhanced environmental awareness, green R&D investment, or innovation capacity [78]. Additionally, the positive effects may manifest over longer time horizons, with current institutional frameworks and governance priorities limiting immediate benefits. Some studies, however, have suggested that human capital, a key driver of innovation, remains vital for fostering green innovation. As human capital increases, it enhances employee creativity, collaboration, and learning, advancing technology, resources, and innovation capacity within local firms [47,79]. An elevated skilled workforce leads to a denser population of highly educated individuals, crucial for knowledge generation and technological advancement. Yet, in the case of Middle Eastern countries, as development progresses, there may be a tendency to prioritize immediate economic growth and industrialization over environmental sustainability, resulting in less emphasis on investing in green technologies [19,43]. Moreover, higher living standards and economic security may reduce the urgency to pursue environmentally friendly solutions in the short term [80]. This underscores the need for future research and policy reforms to align human development progress with sustainability objectives by integrating targeted measures such as environmental education, green employment opportunities, and sustainability-oriented human capital development programs.

Regarding natural resource dependence (NRCS), the long-run coefficients of 0.04 (CS-ARDL), 0.07 (PMG-ARDL), and 0.03 (AMG and CCEMG) are relevant economically and in terms of policy. These results suggest that resource wealth, when effectively managed, can be leveraged to support investment in renewable energy, eco-friendly infrastructure, and green innovation initiatives. Moreover, the positive relationship between natural resources and green innovation in Middle Eastern countries suggests that abundant natural resources can drive investment in sustainable technologies and solutions. The implication here is critical for Middle Eastern countries where oil and gas exports dominate GDP: resource wealth need not be a barrier to sustainability but can be an opportunity for financing green transitions. Redirecting resource revenues into green R&D, innovation clusters, and clean technology subsidies can help to break the resource curse cycle and contribute to global decarbonization targets [15,55].

The effect of the total population on green innovation was found to be significant and positive. At a 1% level of significance, TPOP is found to increase GIVN by 0.018% (CS-ARDL and CCEMG), 0.005% (PMG-ARDL), and 0.043% (AMG) in the long run, while it increases it by 0.031% (CS-ARDL) and 0.005% (PMG) in the short run. These moderate effect sizes are practically significant in the rapidly urbanizing and densely populated regions of the Middle East. They suggest that population growth drives demand for cleaner, more efficient technologies, creating opportunities for green industries and prompting governments to invest in sustainable infrastructure. This finding underscores the idea that demographic transitions can serve as catalysts for environmental innovation, especially when accompanied by cohesive urban, energy, and innovation policies [81,82,83]. It also highlights the potential for green innovation to address environmental and demographic challenges.

4.3. Discussion

This study offers new empirical insights into the drivers of green innovation in 13 Middle Eastern countries over the period 1990–2023, focusing on environmental governance, economic development, human capital, and natural resource utilization. The findings make several key contributions. First, the strong and consistent positive impact of environmental governance on green innovation both in the short and long term reinforces the theoretical claim that institutional quality is fundamental for stimulating sustainability transitions. This supports earlier findings from Ghanem and Alamri [13] and Wang, Yang [35], while adding regional nuance by demonstrating that, even in resource-dependent contexts, governance reforms can catalyze innovation.

Second, the positive effect of GDP per capita aligns with the Environmental Kuznets Curve (EKC) hypothesis, suggesting that rising income levels can enable environmental innovation when supported by proper governance and incentives. However, this relationship is conditional, as shown by the negative association between human development (HDIF) and green innovation. This counterintuitive result diverges from studies in more industrialized regions (e.g., Ulucak, Danish [15]) and suggests that, in rentier economies, rising welfare does not automatically translate into sustainability-driven innovation. This finding highlights the need to rethink how human capital investments are aligned with environmental goals in the Middle East.

Third, this study finds that natural resource abundance, often seen as a barrier to green development, can positively influence innovation when resource revenues are strategically allocated to support clean technology, as argued by Gao, Sun [84]. Similarly, population growth was found to stimulate green innovation [85], likely due to growing urbanization pressures and rising demand for sustainable infrastructure, an encouraging signal for environmentally conscious development planning in the region.

This study offers valuable insights into the drivers of green innovation in Middle Eastern economies; certain limitations must be acknowledged. The analysis is constrained by its reliance on six primary variables and a fixed timeframe, limiting its capacity to uncover the mediating mechanisms, such as policy diffusion pathways or institutional capacity-building processes, through which factors like environmental governance and R&D investments translate into tangible innovation outcomes. Moreover, the model does not account for potential non-linear dynamics or threshold effects; for instance, green innovation may only accelerate once regulatory stringency or economic development reaches a critical level. To fill these gaps, future studies should use analytical methods that focus on understanding the mechanisms, like structural equation modeling (SEM), to separate direct and indirect effects in the relationship between policy and innovation. Additionally, applying non-linear econometric techniques, system dynamics modeling, or agent-based simulations could offer more profound insights into recursive feedback loops. For instance, we might gain insights into how growing market demand for green technologies might progressively amplify regulatory impact and institutional innovation capacity over time. Broadening the range of factors studied, breaking down different sectors, and including how policies interact over time would make future research in this area more useful and relevant.

5. Conclusions and Policy Implications

This study examines the roles of environmental governance, renewable energy transition, economic growth, and natural resource abundance in driving green innovations across 13 Middle Eastern countries from 1990 to 2023. Using a panel dataset, we first detected cross-sectional dependency (CSD) through Frees, Friedman, and Pesaran CSD tests. Consequently, second-generation unit root tests were employed to confirm data stationarity and address the issue of CSD. Both first- and second-generation cointegration tests were utilized to validate long-run relationships among the variables. The analysis relied on the CS-ARDL model as the baseline, with PMG-ARDL, AMG, and CCEMG estimators used to corroborate the findings.

The results reveal that environmental governance, economic growth, total population, and natural resource abundance consistently exert a positive influence on green innovation in both the short and long term. In contrast, the human development index and environmental pollution negatively affect green innovation, with these adverse effects becoming significant only in the long run. This pattern suggests that, while governance, economic expansion, and demographic dynamics stimulate innovation, rising human development may at times prioritize immediate economic gains over sustainability goals, while persistent pollution progressively undermines innovation capacity over time.

Based on the findings of this study, the following policy recommendations are proposed to promote green innovation in the Middle Eastern region: First, it is imperative for governments to implement robust environmental regulations and policies that incentivize sustainable practices. Establishing transparent monitoring systems and enforcing compliance can create an environment conducive to green innovation. Second, policymakers should prioritize investments in renewable energy infrastructure, including solar, wind, and hydropower. The provision of subsidies, tax incentives, and the establishment of public–private partnerships can encourage the adoption of clean energy technologies. Third, it is recommended to redirect revenues from natural resource exploitation toward funding green innovation projects. The establishment of sovereign wealth funds or green investment funds could facilitate support for research and development in sustainable technologies. Fourth, it is essential to integrate green growth strategies into national development plans. Industries should be encouraged to adopt eco-friendly practices through grants, low-interest loans, and carbon pricing mechanisms. Fifth, stricter pollution control measures should be implemented, along with investments in waste management systems. Promoting circular economy models would contribute to waste reduction and resource efficiency. Sixth, aligning human capital development programs with sustainability goals is crucial. Incorporating green skills training and environmental education into curricula can foster a workforce capable of driving green innovation. Seventh, the launch of public awareness campaigns to highlight the benefits of green innovation is necessary. Encouraging community participation in sustainability initiatives can help build a culture of environmental responsibility. Finally, it is advisable for Middle Eastern countries to collaborate on regional green innovation initiatives, sharing knowledge, technology, and best practices to address common environmental challenges. By adopting these policies, Middle Eastern economies can leverage their strengths, address existing challenges, and position themselves as leaders in green innovation and sustainable development.