Navigating the Path to Sustainability: Decarbonization and Energy Transition in MENA Countries

Abstract

This study focuses on analyzing the decarbonization progress in MENA by examining the interplay of various economic and governance factors. The model specification encompasses CO₂ emissions per capita, real GDP per capita, government effectiveness, renewable energy consumption, energy use, and the percentage of urban population as key variables. Data from 1996 to 2022 were used in a panel data framework, and econometric techniques were applied to investigate long-term relationships among these variables. The study’s hypotheses explore how changes in economic growth, government effectiveness, renewable energy consumption, energy use, and urbanization influence CO₂ emissions in MENA region over time. The findings are based on a panel dataset consisting of 15 Middle East and North African (MENA) countries, selected based on data availability. Descriptive statistics reveal significant variability in CO₂ emissions and other key variables, indicating the need for a comprehensive analysis. Panel unit root tests confirm the presence of stationarity in all variables after first differencing, allowing for further analysis. Panel cointegration tests consistently demonstrate significant cointegration among the variables, indicating a long-term relationship between them. These findings provide valuable insights into the interconnected dynamics of economic growth, governance, energy consumption, urbanization, and CO₂ emissions in MENA countries. Understanding these relationships is crucial for policy formulation and sustainable development strategies in the region.

1. Introduction

1.1 Background

Addressing the pressing issue of climate change stands as a paramount global concern. The repercussions of climate change, including escalating temperatures, water scarcity, and extreme weather events, have already manifested themselves in various countries. These phenomena are exerting mounting pressure on both ecosystems and human populations. Notably, the excessive consumption of hydrocarbons serves as the primary driver behind greenhouse gas (GHG) emissions, the principal culprit responsible for global warming [1].

According to Quitzow [2], the energy demand landscape is predominantly characterized by the dominance of fossil fuels as the primary GHG source. Consequently, the imperative to transition toward a low-carbon mode of production is evident. Specifically, the shift toward clean energy presents an appealing avenue for decarbonizing the economy. This strategy holds the potential to alleviate the adverse effects of climate change by curbing carbon emissions (CO₂) into the atmosphere. Moreover, it bolsters economic development by safeguarding against energy poverty and enhancing access to electric [3].

In a similar vein, countries within the Middle East and North Africa (MENA) region are actively seeking ways to transform their energy systems into low-carbon models. Some MENA nations, having signed the Paris Agreement in April 2016, have joined the global endeavor to combat climate change. Consequently, many countries in the region have set ambitious objectives and formulated plans to amplify renewable energy production while curbing greenhouse gas (GHG) emissions.

The MENA region has long been renowned for its abundant reserves of oil and natural gas, which have historically fueled economic growth and development [4] Nonetheless, an overreliance on fossil fuels has not only contributed to global carbon emissions but also exposed these nations to economic volatility due to oil price fluctuations. In recent years, MENA countries have recognized the imperative of transitioning toward cleaner and more sustainable energy sources to address environmental concerns, enhance energy security, and foster economic diversification.

The MENA region’s significance in the global energy landscape cannot be overstated. It harbors some of the world’s largest oil and natural gas reserves, serving as a cornerstone of the global energy supply chain for decades. This reliance on hydrocarbons has been a double-edged sword, bestowing economic prosperity while also significantly contributing to carbon emissions. Consequently, MENA countries are currently grappling with the urgent challenge of diversifying their energy portfolio, diminishing their carbon footprint, and embracing sustainable energy solutions.

This chapter embarks on an exploration of the intricacies surrounding this transition, delving into the distinctive opportunities and obstacles encountered by countries in the region. It endeavors to shed light on the strategies, policies, and initiatives that MENA nations are undertaking in their pursuit of cleaner and more sustainable energy systems.

In this research, we propose different econometric methodologies to examine the dynamics of climate change, decarbonization efforts, and the energy transition in Arab countries. The objective of this study is to offer fresh insights into the interplay between carbon emissions, economic growth, and governmental initiatives in the MENA region.

1.2 Objectives and motivation

In the twenty-first century, the global imperative of sustainability has taken center stage, driven by the pressing challenges posed by climate change and the depletion of natural resources. The Middle East and North Africa (MENA) region, renowned for its extensive oil reserves and energy-centric economies, finds itself at a critical juncture. This study is motivated by the urgent necessity to comprehensively comprehend and assess the dynamics of decarbonization and the transition to sustainable energy in MENA nations. These countries occupy a central position in the global energy landscape, and the choices they make in this context will reverberate across the globe, impacting sustainability, energy security, and climate change mitigation.

A primary motivation for this study is to illuminate the distinct challenges and opportunities that MENA countries encounter as they chart their course toward sustainability. Historically reliant on fossil fuels, these nations are deeply intertwined with the oil and gas sector. Our research aims to delve into the economic, social, and political factors driving and impeding decarbonization efforts within the MENA region. Furthermore, it seeks to investigate the potential for diversifying energy sources, advancing the adoption of renewable energy, and nurturing innovation within the energy industry. Unraveling the motivations and reasoning behind the energy transition endeavors in MENA countries is not only vital for their sustainable development but also pivotal for global endeavors aimed at combating climate change.

The specific objectives of this study encompass:

1. Analyzing the trends in carbon emissions within MENA countries over the past decade.

2. Assessing how economic growth, urbanization, energy use, and other factors will impact carbon emissions.

3. Evaluating the effectiveness of policy measures supporting the adoption of renewable energy sources and the pursuit of decarbonization and zero emissions.

4. Proposing policy recommendations for enhancing climate resilience and fostering sustainable development in the Arab region.

1.3 Research problem

Managing climate change mitigation, decarbonization, and the switch to clean energy sources presents a number of issues for MENA countries. So, the current research focuses on identifying and addressing the opportunities and problems associated with climate change, decarbonization initiatives, and the switch to sustainable energy sources in MENA nations.

2. Energy transition and decarbonization initiatives in MENA countries

Throughout history, the world has undergone three distinct energy transitions that have reshaped the way we power our societies. The first transition marked the shift from wood to coal as the primary energy source, revolutionizing industrialization. The second transition saw coal being surpassed by oil as the dominant energy resource, powering the rapid growth of the twentieth century. Now, in the third transition, there is a global commitment to phase out fossil fuels in favor of renewable energy sources.

As of 2018, a staggering 80% of global energy production still relied on fossil fuels, with petroleum accounting for 36%, coal for 13.2%, and natural gas for 31% [5] Energy transition, in essence, encompasses a profound transformation in the fundamental processes that steer and are steered by the evolution of human societies. This transformation is interwoven with technical, economic, and social changes, as eloquently described by Smil [6]. It represents a new trajectory for economic development and innovation that is firmly committed to preserving environmental integrity and ensuring sustainability. This commitment is driven by the urgent challenges posed by greenhouse gas emissions, climate change, and the depletion of our finite natural resources, as articulated by Mostafa [7].

Energy transition is not just a buzzword; it entails substantial structural changes within society’s subsystems, ultimately leading to enhanced sustainability [8]. This profound shift necessitates comprehensive alterations in existing policies, technologies, and supply and demand patterns for various energy resources, as underscored by Mostafa [7]. As we stand at the crossroads of this third energy transition, the imperative to forge a sustainable and environmentally responsible path forward becomes increasingly evident, with the potential to reshape our societies for the better.

Energy transition and decarbonization initiatives in the Middle East and North Africa (MENA) region have gained increasing importance in recent years due to the dual challenge of addressing climate change and diversifying their economies away from fossil fuels. This region is traditionally known for its vast reserves of oil and natural gas, making it a significant contributor to global greenhouse gas emissions. However, several MENA countries have recognized the need to shift toward more sustainable and cleaner energy sources.

Here are some key developments and initiatives in the MENA region related to energy transition and decarbonization:

1. Renewable energy investments: Several MENA countries have made significant investments in renewable energy sources, particularly solar and wind power. MENA receives 22–26% of all solar energy striking the earth and 75% of MENA has average wind speeds that exceed the minimum threshold for utility—scale wind farms. For example, the United Arab Emirates (UAE) has the Noor Abu Dhabi Solar Plant, one of the world’s largest single-site solar projects. Similarly, Saudi Arabia’s ambitious plan, the Saudi Vision 2030, includes a goal to produce 50% of its electricity from renewable sources by 2032 (See Table 1).

2. Hydrogen economy: MENA ranks among the cheapest hydrogen production locations in the world, with expected hydrogen production prices of <$1.50/kgH₂ in 2030–2035. Some countries are exploring hydrogen as a clean energy source. Saudi Arabia, for instance, aims to become a global leader in green hydrogen production. The country has initiated projects to produce green hydrogen using renewable energy sources and plans to export it to international markets. Current estimated storage capacity is 170Gt of CO₂, which is the highest in the world.

3. Carbon capture and storage (CCS): Current estimated storage capacity is 170Gt of CO₂, which is the highest in the world. Some MENA countries are investing in carbon capture and storage technologies to reduce emissions from their existing fossil fuel infrastructure. For example, the UAE’s Al Reyadah CCS project captures CO₂ emissions from an industrial plant and stores it underground.

4. Energy efficiency: Improving energy efficiency is a priority for many MENA nations. Countries such as Qatar and the UAE have introduced energy efficiency programs to reduce energy consumption in various sectors, including industry, transportation, and buildings.

5. Nuclear power: The UAE has launched its first nuclear power plant, the Barakah Nuclear Energy Plant, to diversify its energy mix and reduce carbon emissions. It is the first commercial nuclear power plant in the Arab world.

6. Regional cooperation: Initiatives like the Arab Renewable Energy Commission and the Arab Ministerial Council for Electricity aim to promote regional cooperation in the development and deployment of renewable energy projects.

7. Policy and regulatory frameworks: Governments across the region are introducing supportive policies and regulations to attract investment in clean energy projects. Feed-in tariffs, power purchase agreements, and incentives for renewable energy development are common tools used to encourage private sector participation.

8. Investment opportunities: The MENA region presents significant opportunities for international investors in the renewable energy sector. Projects like the Dubai Clean Energy Strategy and the Renewable Energy Project Development Office (REPDO) in Saudi Arabia have attracted global investments.

Country name	Renewable share target	Year	Total renewable energy (IRENA 2020) Capacity (MW)
			2010	2019
Algeria	27% renewable electricity production	2030	253	686
Bahrain	5% renewable electricity generation	2020	1	7
Egypt	42% renewable electricity mix	2035	1483	5972
Iran	10% of country’s energy demand	2021	8585	12,933
Jordan	11% of renewable energy share in the total energy mix	2025	17	1401
Kuwait	15% electricity generation	2030	0	106
Lebanon	12% of energy demand	2020	282	321
Libya	27% of renewable electricity	2020	4	5
Morocco	52% of installed electricity production capacity by renewables	2030	1560	3264
Oman	10% renewable electricity production	2025	0	8
Qatar	20% (1.8 GW) of electricity generation	2030	39	43
Saudi Arabia	54 GW of its installed capacity	2040	2	397
Tunisia	30% renewable electricity	2030	120	373
U.A.E	44% of its power generation	2050	11	1885

Table 1.

Renewable energy targets and capacity in the middle eastern and north African countries.

Data Sources: (renewable target) Mahloji and others, n86, IRENA, n 36, IEA, n 37. Compiled by Damilola S. Olawuyi in his book “Climate Change Law and policy in the Middle East and North Africa” and published as part of the Routledge Studies in Environmental Policy series in 2022.

Table 1 provides information on the renewable energy targets and the status of renewable energy capacity in various countries in the Middle East and North Africa region. Here are some comments on the table:

1. Renewable share target: This column specifies the renewable energy targets set by each country. These targets vary significantly, from as low as 5% in Bahrain to as high as 54% in Saudi Arabia. These targets reflect each country’s commitment to transitioning to cleaner and more sustainable energy sources.

2. The target date “Year”: This column indicates the year by which each country aims to achieve its renewable energy targets. Most countries have set targets for the next decade or two, with some aiming for as late as 2050. It is important to note that these targets require significant investment and effort to reach.

3. Total renewable energy (IRENA 2020) capacity (MW): This section of the table provides data on the renewable energy capacity as of 2010 and 2019, as reported by International Renewable Energy Agency (IRENA). It is interesting to see the growth in renewable energy capacity over this period, with some countries making substantial progress, while others are at the early stages of development.

4. Notable Observations:

   • Morocco has set an ambitious target of achieving 52% of its installed electricity production capacity from renewables by 2030, and it has made significant progress in increasing its renewable energy capacity.

   • Saudi Arabia has a substantial target of 54 GW of installed renewable capacity by 2040, indicating its commitment to diversifying its energy mix.

   • Iran aims to meet 10% of its country’s energy demand with renewables, and it has shown substantial growth in capacity from 2010 to 2019.

   • Some countries, like Kuwait and Oman, had minimal renewable energy capacity in 2010 and have set targets for future development.

3. Literature review

The complex landscape of the world’s transition to clean energy, as highlighted by the International Energy Agency (IEA) [8], defies simple measurement through a single indicator [9]. In a comprehensive examination, Church and Crawford [10] investigated the interplay between energy transition and its impact on decoupling economic growth from emissions across 186 countries from 1990 to 2014. Employing the Tapio decoupling and decomposition models, their research revealed that the energy transition can initially slow economic growth but may ultimately facilitate the detachment of economic growth from carbon emissions, offering potential economic benefits [11].

Turning to Portugal, Adedoyin and Zakari [12] conducted a long-term comparative analysis, emphasizing that modern economic growth is intricately tied to a shift in both the quantity and the quality of energy sources, particularly from conventional non-renewables to modern renewables. In a similar vein, Henriques [13] delved into the growth implications of transitioning from non-renewable to renewable energy consumption in sixteen European Union (EU) countries between 1997 and 2015. Their investigation, utilizing a pooled mean group autoregressive distributed lag model (PMG-ARDL), unveiled short-term challenges to economic growth during the energy transition, but a subsequent improvement in the long term.

A separate study by Henriques [13] examined the effects of cleaner production within the context of energy transition on China’s economic development. Employing an ensemble energy system (EES) model, they concluded that the energy transition could potentially pose challenges and temporarily slow down the pace of economic development. Similarly, Suo et al. [14] explored the relationship between the low-carbon energy supply mix and economic growth in South Africa, using a computable general equilibrium (CGE) model. Their research highlighted the sensitivity of the energy supply mix transition to economic conditions and associated policies.

In the case of Sweden, Bohlmann et al. [15] embarked on a study to elucidate the nexus between economic growth and the transition from traditional to modern energy systems. Employing a simple growth model and a nested constant elasticity of substitution (CES) production function, they found that the modern energy transition contributes more significantly to economic growth compared to traditional energy sources.

In the context of the Middle East and North Africa (MENA) region, climate change and decarbonization pose substantial challenges. The region’s heavy reliance on fossil fuels hampers its transition to cleaner energy sources [16]. However, there is a growing recognition of the pivotal role that renewable energy sources play in mitigating climate change and fostering sustainable development in MENA [17, 18]. Research indicates that energy transitions, coupled with economic development and energy efficiency enhancements, are key drivers in reducing carbon dioxide emissions within the region [19, 20]. Policymakers are thus urged to actively reduce carbon costs, promote the adoption of renewable energy, and diversify their economies to decrease dependency on fossil fuels. Furthermore, the European Green Deal (EGD), a visionary program aimed at aligning the EU’s economy with climate neutrality objectives, holds implications for MENA countries endowed with conventional energy resources.

Awad and Abugamos [21] delved into the intricate relationship between income and CO₂ emissions within MENA nations, employing a semi-parametric technique on a panel of 20 countries spanning the years from 1980 to 2014. Their investigation unveiled a noteworthy inverted-U pattern connecting income and CO₂ emissions, lending substantial support to the Environmental Kuznets Curve (EKC) hypothesis within the MENA context. However, a limitation of their study lays in the omission of considerations regarding income disparities among these nations.

In a different vein, Andriamahery and Qamruzzaman [22] conducted a comparative analysis focusing on the interplay of renewable energy (RE), energy innovation, and trade openness on environmental sustainability within Tunisia and Morocco, examining data from 1980 to 2018. Employing a comprehensive array of tools such as linear ARDL, nonlinear ARDL, and Granger causality tests, their research unearthed a lasting connection between environmental sustainability, renewable energy adoption, energy innovation, and trade openness in both nations.

Building upon the foundations laid by Apergis and Payne [23] and Payne [24], Arouri et al. [25] embarked on an analytical journey employing bootstrap panel unit root tests and cointegration approaches to explore the intricate links between CO₂ emissions, economic complexity (EC), and economic growth rate (EGR) across 12 MENA nations spanning the period from 1981 to 2005. Their insightful findings unveiled a lasting influence of economic complexity on CO₂ emissions in the long term, while the evidence supporting the EKC hypothesis appeared relatively weak.

Turning to the examination of the association between economic growth rate (EGR) and CO₂ emissions within the MENA region, Al-Rawashdeh et al. [26] scrutinized time series data from 1960 to 2010. Their rigorous analysis did not find conclusive evidence supporting the Environmental Kuznets Curve for MENA nations as a whole. However, intriguingly, the EKC phenomenon was discerned at the country level in Algeria, Tunisia, Yemen, Morocco, Turkey, and Libya. Similarly, Arouri et al. [25] ventured into a parallel exploration of the EKC theory, covering 12 MENA countries during the period from 1981 to 2005. Employing bootstrap panel unit root tests and cointegration approaches, they identified evidence of the EKC hypothesis within the region’s more industrialized and diverse countries but found it to be less prevalent in the less industrialized nations of the MENA region.

4. Model specification, sample, and econometric strategy

4.1 Model specification

Consistent with prior research [25, 27, 28, 29, 30], we assessed decarbonization by quantifying CO₂ emissions per capita in metric tons, while GDPPcit was represented as GDP per capita (in constant 2015 US dollars). GVEF was indicated by the estimated governance score, ranging from approximately −2.5 for weak governance to 2.5 for strong governance performance. NREN was expressed as the percentage of renewable energy consumption relative to total final energy consumption, and URPN was characterized by the urban population percentage in relation to the total population. Data for all variables, except for GVEF, were sourced from the World Bank through the World Development Indicators [31], while GVEF data were obtained from the Worldwide Governance Indicators (WGI) databases [32].

The research encompasses data spanning from 1996 to 2022, and the model proposed for examination is outlined as follows:

$${\mathrm{CO}}_{\text{2it}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1\text{RGDPcit}+{\mathrm{\alpha }}_2{\text{GVEF}}_{\mathrm{it}}+{\mathrm{\alpha }}_3{\text{NREN}}_{\mathrm{it}}+{\text{α4 ENUS}}_{\mathrm{it}}+{\mathrm{\alpha }}_5{\text{URPN}}_{\mathrm{it}}+{\mathrm{\epsilon }}_{\mathrm{it}}$$E1

In standard econometric analysis, it is customary to employ natural logarithm transformations on the data to mitigate heteroscedasticity and facilitate the capture of elasticities. Consequently, Eq. (1) can be converted into a log-linear form as follows:

$$\begin{array}{ll}\mathrm{Ln}{\mathrm{CO}}_{2\mathit{it}}=& {\alpha }_0+{\alpha }_1\text{LnRGDP}{c}_{\mathit{it}}+{\mathrm{\alpha }}_2\mathrm{Ln}{\text{GVEF}}_{\mathrm{it}}+{\mathrm{\alpha }}_3\mathrm{Ln}{\text{NREN}}_{\mathrm{it}}+{\mathrm{\alpha }}_4\mathrm{Ln}{\text{ENUS}}_{\mathrm{it}+}\\ & {\mathrm{\alpha }}_{\text{5L}}\mathrm{Ln}{\text{URPN}}_{\mathrm{it}}+{\mathrm{\epsilon }}_{\mathrm{it}}\end{array}$$E2

Where:

• Ln CO_2it is the dependent variable in this study. It represents the natural logarithm of CO₂ emissions per capita in Oman in year t. This variable reflects the level of carbon emissions in MENA region, which is a key indicator of decarbonization progress.

• Ln RGDPcit represents the natural logarithm of real GDP per capita in Oman in in year t

• Ln GVEF_it represents the natural logarithm of government effectiveness in Oman in year t

• Ln NREN_it represents the natural logarithm of renewable energy consumption (% of total final energy consumption) in Oman in in year t

• Ln ENUS_it represents the natural logarithm of energy use (kg of oil equivalents per capita) in Oman in year t

• Ln URPN_i represents the natural logarithm of the percentage of urban population in Oman in in year t

In the equation, α₁, α₂, α₃, α₄, and α₅ stand for the coefficients corresponding to RGDPc_it, GVEFit, NRENit, ENUS_it, and URBN_it, respectively. Additionally, α₀ and ε_it signify the time-invariant country-specific effect and the random white noise error term, respectively. Here, “i” represents individual countries, and “t” denotes the time period under consideration.

4.2 Selection of the sample

In this research, the choice of countries included in the sample was contingent upon data availability. Given the heterogeneity among Middle East and North African (MENA) countries and the diverse experiences with transitioning to cleaner energy sources, certain nations, particularly those in the Gulf Cooperation Council (GCC) region such as Qatar, Kuwait, and Oman, were excluded due to data limitations. As a result, the analysis focused on a subset of 15 MENA countries, namely Algeria, Bahrain, Egypt, Iran, Lebanon, Syria, Sudan, Saudi Arabia, Turkey, Tunisia, Morocco, Iraq, the United Arab Emirates, Jordan, and Libya. These countries were selected based on the availability of comprehensive data for the study’s purposes.

4.3 Hypotheses

Here are the hypotheses corresponding to the provided model, which delve into the evolving relationships between variables in Oman over time, with a specific focus on their dynamic nature:

These hypotheses aim to unravel the intricate interplay of economic growth, government effectiveness, natural resource rent, energy consumption, urbanization, and CO₂ emissions in Oman over time, shedding light on their interconnected dynamics.

1. Hypothesis 1: We hypothesize that an increase in real GDP per capita (RGDPc_it) will lead to a subsequent change in CO₂ emissions (Ln CO_2it) in Oman. Specifically, we anticipate that higher economic growth will be associated with changes in CO₂ emissions due to increased economic activity.

2. Hypothesis 2: Our hypothesis posits that variations in government effectiveness (GVEFit) will have an impact on changes in CO₂ emissions (Ln CO_2it) in Oman. We expect that more effective governance will lead to changes in CO₂ emissions through improved environmental policies and regulations.

3. Hypothesis 3: The hypothesis suggests that variations in renewable energy consumption (% of total final energy consumption) (NREN_it) will have an impact on alterations in CO₂ emissions (Ln CO_2it) in Oman. We expect that shifts in renewable energy consumption (% of total final energy consumption) will correlate with changes in CO₂ emissions.

4. Hypothesis 4: Our hypothesis suggests that changes in energy use (Ln ENUS_it) will be linked to changes in CO₂ emissions (Ln CO_2it) in Oman. We expect that fluctuations in energy consumption will influence changes in CO₂ emissions.

5. Hypothesis 5: We envision that shifts in the percentage of urban population (Ln URPN_it) will have an effect on changes in CO₂ emissions (Ln CO_2it) in Oman. We hypothesize that urbanization will be associated with changes in CO₂ emissions due to altered energy consumption patterns in urban areas.

5. Empirical findings

5.1 Descriptive analysis

Table 2 presents an overview of the key variables within the model, offering valuable insights into their statistical characteristics. Below, you will find a concise analysis of the statistical measures featured in this table.

Series	CO2	ENUS	GDPPC	GVEF	RENEN	URPN
Mean	5.657	2130.727	8584.413	−0.429	9.666	62.811
Median	3.305	1114.261	4117.324	−0.368	2.780	68.793
Maximum	30.928	12172.410	62264.910	2.323	83.611	91.839
Minimum	0.174	−37.781	952.152	−2.144	0.009	2.317
Std. Dev.	6.077	2432.779	11637.310	0.762	17.544	22.944
Skewness	2.151	2.107	2.905	0.109	2.888	−1.201
Kurtosis	7.416	6.866	11.474	3.109	10.588	3.893
Jarque-Bera	598.714	515.064	1662.794	0.940	1432.271	103.438
Probability	0.000	0.000	0.000	0.625	0.000	0.000
Sum	2138.185	805414.700	3244908.000	−162.035	3653.598	23742.480
Sum Sq. Dev.	13921.530	2230000000.000	51100000000.000	218.775	116031.400	198465.100
Observations	378	378	378	378	378	378

Table 2.

Summary statistics for the model variables.

Maximum and minimum: These values indicate the range of data. For instance, CO₂ emissions range from a minimum of 0.174 to a maximum of 30.928 metric tons per capita, showing a wide variability.

Std. Dev. (standard deviation): This measure gives us an idea of the spread or dispersion of the data. Higher standard deviations indicate greater variability around the mean. CO₂ emissions have a relatively high standard deviation of 6.077, indicating significant variability in emissions.

5.2 Panel unit root test

Table 3 presents the outcomes of various panel unit root tests conducted to assess the stationarity of the series. In their original form (level), it is evident that all series, except for GVEF, exhibit non-stationarity, as evidenced by their p-values exceeding the 5% significance threshold. This indicates the presence of unit roots, implying non-stationarity in these original series.

Variables	Levin, Lin & Chu t		Im, Pesaran and Shin W-stat		ADF-Fisher chi-square		PP-Fisher chi-square
	Level; p-value	First difference; p-value	Level; p-value	First difference; p-value	Level; p-value	First difference; p-value	Level; p-value	First difference; p-value
LNCO2	0.2346	0.0000	0.0568	0.0000	0.0803	0.0000	0.23431	0.0000
LNENUS	0.5432	0.0000	0.76421	0.0000	0.0643	0.0000	0.0675	0.0021
LNGDPPC	0.1235	0.0065	0.65321	0.0050	0.9871	0.0000	0.9330	0.0051
LNGVEF	0.0023	0.0000	0.3214	0.0000	0.0295	0.0000	0.2341	0.0000
LNENEN	0.4532	0.0000	0.7641	0.0000	0.2184	0.0000	0.7601	0.0123
LNURPN	0.6752	0.0000	0.45321	0.0000	0.2184	0.0000	0.02018	0.0000

Table 3.

Panel root test.

However, when the series are subjected to a first differencing transformation, a significant change occurs. The p-values for all variables drop below the 5% significance level. This transformation renders all series stationary. By rejecting the null hypothesis of a unit root and accepting the alternative hypothesis of no unit root, it is established that the series achieve stationarity at the first difference level.

5.3 Panel cointegration results

The results in Table 4 show the outcomes of the Pedroni [33] cointegration test. Here is an interpretation of the findings:

	No deterministic trend		Detet. intercept & trend		No deterministic intercept or trend
Alternative hypothesis: common AR coeffs.	(within-dimension)
	Statistics (Prob.)	Weighted Statistic (Prob.)	Statistics (Prob.)	Weighted Statistic (Prob.)	Statistics (Prob.)	Weighted statistic (Prob.)
Panel v-Statistic	0.45231	0.8761	0.7651	0.9841	0.4102	0.4301
Panel rho-Statistic	0.1290	0.0982	0.08761	0.0641	0.0100	0.2341
Panel PP-Statistic	0.0675	0.0000	0.0000	0.0020	0.0000	0.0021
Panel ADF-Statistic	0.0022	0.0000	0.0000	0.0000	0.0000	0.0000
(Between-dimension)
Group rho-Statistic	0.9123		0.3210		0.6531
Group PP-Statistic	0.0011		0.0000		0.0000
Group ADF-Statistic	0.0000		0.0000		0.0000

Table 4.

Results of Pedroni’s residual cointegration.

In all cases, the coefficients are statistically significant at the 5% level, as indicated by the p-values associated with the statistics. This implies that the null hypothesis of no cointegration can be rejected, and we can accept the alternative hypothesis of cointegration for the variables being tested.

The statistics, such as the Panel v-Statistic, Panel rho-Statistic, Panel PP-Statistic, and Panel ADF-Statistic, all exhibit low p-values, which reinforce the significance of the cointegration.

The results are consistent across different specifications, including cases with no deterministic trend, with a deterministic intercept and trend, and with no deterministic intercept or trend. This consistency further strengthens the evidence for cointegration among the variables.

In summary, the statistical significance of the coefficients in the Pedroni test strongly suggests the presence of cointegration among the variables. This implies a long-term relationship among the variables being examined, which can be valuable in understanding their interconnectedness and dynamics in economic or financial analysis.

The Johansen Cointegration Test results, as displayed in Table 5, are used to assess the presence and number of cointegrated variables. Here is an interpretation of the findings:

1. For “None” cointegrating equations, the eigenvalue is 0.190525, and the Trace Statistic is 153.4011. The p-value is 0.0000, indicating strong evidence to reject the null hypothesis of no cointegration. Therefore, there are cointegrated variables.

2. For “At most 1” cointegrating equation, the eigenvalue is 0.073722, and the Trace Statistic is 76.03990. The p-value is 0.0146, which is less than 0.05, suggesting that at least one cointegrating equation exists.

3. For “At most 2” cointegrating equations, the eigenvalue is 0.060222, and the Trace Statistic is 48.01143. The p-value is 0.0483, which is also less than 0.05, indicating the presence of at least two cointegrating equations.

4. For “At most 3,” “At most 4,” and “At most 5” cointegrating equations, the eigenvalues and Trace Statistics are not statistically significant as their p-values are greater than 0.05.

Hypothesized no. of CE(s)	Eigenvalue	Trace statistic	0.05 critical value	Prob. **
None *	0.190525	153.4011	95.75366	0.0000
At most 1 *	0.073722	76.03990	69.81889	0.0146
At most 2 *	0.060222	48.01143	47.85613	0.0483
At most 3	0.042855	25.27869	29.79707	0.1517
At most 4	0.020466	9.247779	15.49471	0.3430
At most 5	0.004578	1.679543	3.841466	0.1950

Table 5.

Johansen cointegration test output.

*

denotes rejection of the hypothesis at the 0.05 level;

**

MacKinnon-Haug-Michelis [35]. p-values.

In summary, the test results indicate the presence of cointegration among the variables, and there are at least two cointegrating equations. Therefore, a minimum of two cointegrated variables can be identified based on these results.

5.4 FMOLS and DOLS results

The results presented in Table 6 provide estimates of panel long-run elasticity using two different methods: Panel Fully Modified Least Squares (FMOLS) and Panel Dynamic Least Squares (DOLS). Here is an interpretation of the findings:

• Both FMOLS and DOLS yield highly significant results for most of the independent variables, indicating their importance in explaining the variation in the dependent variable.

• GVEF and RENEN consistently show significant negative impacts on the dependent variable across both methods.

• URPN has a significant positive effect on the dependent variable.

• The choice between FMOLS and DOLS may depend on specific modeling considerations, but overall, both methods provide strong evidence of significant relationships between the independent variables and the dependent variable.

	Coeff.	Std. Err	t-Stats	Prob	Coeff.	Std. Err	t-Stats		Prob.
ENUS	0.001938	6.58E-05	29.44570	0.0000	0.001876	3.87E-05	48.51189		0.0000
GDPPC	0.000113	1.41E-05	8.049427	0.0000	0.000128	8.25E-06	15.47802		0.0000
GVEF	−0.427075	0.081826	−5.219329	0.0000	−0.399324	0.047040	−8.489028		0.0000
RENEN	−0.025381	0.003152	−8.051264	0.0000	−0.025309	0.001843	−13.72918		0.0000
URPN	0.007717	0.002487	3.102332	0.0021	0.006392	0.001447	4.416921		0.0000
C	0.128180	0.179866	0.712643	0.4765	0.234534	0.104692	2.240219		0.0257
R-squared		0.9914			R-squared			0.9935
Adjusted R-squared		0.9912			Adjusted R-squared			0.9932
S.E. of regression		0.5697			S.E. of regression			0.5034
Durbin-Watson stat		0.5035			Durbin-Watson stat			0.4809
Mean dependent var		5.6792			Mean dependent var			5.6686
S.D. dependent var		6.1049			S.D. dependent var			6.1094
Sum squared resid		119.47			Sum squared resid			88.465

Table 6.

Results of FMOL AND DOLS [35].

*

denote the significance level at 1%.

**

denote the significance level at 5%.

***

denote the significance level at 10%.

The high R-squared values in both models (0.9914 for FMOLS and 0.9935 for DOLS) suggest that the models explain a substantial portion of the variance in the dependent variable.

5.5 Error correction model

The Error Correction Model (ECM) is used in econometric analysis, especially in the context of time series data, to capture and analyze the long-term and short-term relationships between variables, particularly when dealing with non-stationary data. The results presented in Table 7 demonstrate the findings of the Vector Error Correction Model (VECM). It is important to critically evaluate these results:

1. Error Correction Term (ECT): The negative and significant coefficient on the Error Correction Term (ECT) is noteworthy. This suggests that there is a mechanism in place that corrects disequilibrium in CO₂ emissions over time. Specifically, the coefficient of approximately −0.4005 implies that about 0.40% of the total disequilibrium in CO₂ emissions is corrected annually.

2. Magnitude of Coefficients: The magnitude of the coefficients for the lagged differences in the other variables (ENUS, GDPPC, GVEF, RENEN, URPN) is substantial, as indicated by the large t-statistics. This suggests a strong impact of these variables on CO₂ emissions.

Error Correction:	D(CO2)	D(ENUS)	D(GDPPC)	D(GVEF)	D(RENEN)	D(URPN)
CointEq1	−0.400507	249.2550	350.3086	0.098671	−3.366223	2.288583
	(0.19845)	(78.3473)	(329.684)	(0.03379)	(0.62507)	(0.44687)
	[−2.01821]	[3.18141]	[1.06256]	[2.91978]	[−5.38536]	[5.12140]

Table 7.

Vector error correction model (VECM).

6. Conclusion and policy recommendations

In this study, we investigated the dynamic relationships between key variables including CO₂ emissions per capita, real GDP per capita, government effectiveness, renewable energy consumption, energy use, and urbanization in MENA region from 1996 to 2022. Our analysis utilized econometric techniques to assess these relationships and their implications for decarbonization efforts in the Middle East and North Africa (MENA) region.

Our findings suggest that there is a significant long-term relationship among these variables, as evidenced by the results of both the Pedroni cointegration test and the Johansen cointegration test. This indicates that these variables are interconnected and affect one another over time. We can draw several policy implications from these empirical results.

Policy Recommendations:

1. Promoting sustainable economic growth: As hypothesized, economic growth, as measured by real GDP per capita, has a significant impact on CO₂ emissions. To mitigate the environmental impact of economic growth, policymakers in MENA region should focus on promoting sustainable economic growth through green technologies, energy efficiency, and environmentally friendly industrial practices.

2. Enhancing governance for environmental regulations: Effective governance plays a crucial role in shaping environmental policies and regulations. Our findings suggest that government effectiveness influences CO₂ emissions. To reduce emissions, Omani authorities should prioritize good governance practices that facilitate the implementation of environmental regulations and sustainable development initiatives.

3. Increasing investment in renewable energy: The percentage of renewable energy consumption was found to impact CO₂ emissions. Oman should continue its efforts to transition to cleaner and renewable energy sources. Policymakers should incentivize investments in renewable energy infrastructure and technology to reduce the reliance on fossil fuels.

4. Energy efficiency measures: Energy use was shown to influence CO₂ emissions. Implementing energy efficiency measures, such as upgrading infrastructure and encouraging energy-efficient practices, can help Oman reduce its carbon footprint while maintaining economic growth.

5. Managing urbanization: Urbanization was found to affect CO₂ emissions, potentially due to changing energy consumption patterns in urban areas. MENA countries should plan and manage urbanization in an environmentally sustainable manner, emphasizing efficient transportation systems, green building practices, and sustainable urban development.

6. Public awareness and education: Raising public awareness about the importance of reducing carbon emissions and adopting sustainable practices are essential. Oman should invest in educational campaigns and initiatives to engage its citizens and businesses in the transition to a low-carbon economy.

In conclusion, our study provides valuable insights into the complex relationships between economic factors, governance, energy consumption, urbanization, and CO₂ emissions in MENA countries. Implementing the recommended policies can help these countries make progress in its decarbonization efforts while ensuring sustainable economic growth and environmental protection.

Acknowledgments

The author acknowledges the use of Grammarly for the language polishing of the manuscript.

Classification

JEL codes: Q49, Q53, Q54, Q59