Determinants of a Green Economy from the Perspective of Energy Sources in the GCC: The Role of Natural Gas Production

Abstract

Investigating the key factors that contribute to the development of a green economy is essential for governments and policymakers as they decide where to allocate their investments. However, the determinants of a green economy, particularly regarding different energy sources, remain an under-researched area, especially in the context of GCC (Gulf Cooperation Council) economies. This study aims to explore the roles of natural gas production, crude oil production, and electricity production from renewable energy sources in the transition towards green economic transformation. For our estimation, we employed panel data techniques, utilizing data from all six GCC economies covering the period from 2010 to 2023. Our results indicate that both the use of renewable energy sources and natural gas production have significantly contributed to advancing green economic transformation in these economies. In contrast, crude oil production has been found to be an irrelevant factor in explaining the transition to green growth in the GCC. The causality analysis revealed that there is a one-way causal relationship between natural gas production and green economic transformation and a two-way causal relationship between electricity generation from renewable sources and green economic transformation in GCC economies. Based on the study’s findings, we recommend that policymakers in GCC economies embrace green economic transformation by increasing the use of renewable sources and natural gas in production. Green economic transformation would help GCC economies pursue advanced, sustainable economic performance.

1. Introduction

In recent years, the term “green economy” has received considerable attention in the literature. A green economy is an economy that promotes economic performance without damaging the environment, creates numerous jobs, and further contributes positively to the health sector. The global financial crisis of 2008 and 2009 prompted a renewed focus on sustainable development and the need for a more environmentally friendly economic model. Green economic growth is a sustainable development model that emphasizes the responsible consumption of natural resources to preserve the quality of the environment. In 2008, the “Green Economy Initiatives” were launched by the “United Nations Environment Program” [1], which aims to help governments and companies with transitioning to a green economy. In 2012, a report [1] was published entitled, “Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication,” highlighting the potential benefits of investing in renewable energy, energy efficiency, and other environmentally friendly sectors [1]. From this standpoint, a green economy is defined as “an economic system that aims to promote sustainable development by reducing environmental impacts”. It should be noted that sustainable development is the main goal of all economic and non-economic activities as it protects the environmental quality.

Green economy is defined as the green economic growth of a system “that is efficient in its use of natural resources, clean in that it minimizes pollution and environmental impacts, and resilient in that it accounts for natural hazards and the role of environmental management in preventing physical hazards and excessive commodity price volatility” [2]. It has also been defined by the United Nations Environment Program as an economy that leads to improving human and societal well-being and achieving justice for future generations while significantly reducing environmental risks. Many countries across the world have increased their contribution to the green economy in recent years, as many are realizing the importance of sustainable development and how it reflects on individuals’ quality of life, creating job opportunities, achieving economic growth, and preserving the rights of future generations.

The member states of the “Gulf Cooperation Council” (hereafter GCC), namely Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, are very rich in terms of natural resources. The GCC member states have relatively higher incomes, which could in part be explained by their rich natural resource sector. The consumption of energy in GCC economies is also relatively high compared with developing and emerging economies, partly due to cheap prices. The abundance of hydrocarbon resources coupled with cheap prices has encouraged the overconsumption of energy in all GCC member states. To help reduce this overconsumption, GCC member policymakers are committed to investing in cleaner and greener energy sources to practice sustainability [3]. Furthermore, GCC economies are committed to building the infrastructure needed for renewable energy to be produced and used, as this type of energy is essential in green economic transformation [4]. All GCC economies are faced with the challenge of diversifying their economies amid increased environmental pollution [5]. Like all other members of the GCC, the Kingdom of Saudi Arabia is making efforts to contribute to the green economy. One of its main initiatives in this field is Vision 2030, which aims to diversify the country’s economy to ensure sustainable development. Vision 2030 includes several goals related to the green economy, such as increasing the use of renewable energy sources, reducing greenhouse gas emissions, and promoting sustainable practices in various sectors. Also, Oman’s policymakers are committed to producing 5.2 × 10³ electrolyzers and 300 million solar panels by 2050 to achieve green economic transformation [6]. Moreover, Qatar is attempting to achieve a sustainable economic model in line with the national plan of 2023. Therefore, green economic transformation is essential for the long-term sustainable economic growth of GCC economies.

1.1. Motivation for the Study

In recent years, there has been increased interest in using natural gas to generate electricity. This interest stems from the abundance of natural gas in Saudi Arabia and other GCC economies due to newly discovered gas fields. The consumption of natural gas generally produces relatively low CO₂ emissions compared with other fossil fuel sources due to its high thermal efficiency. However, the impact of natural gas on the development of green economic transformation is not well understood, particularly in GCC economies. Therefore, this study attempts to discover the effect of using alternative energy sources on green economic transformation in GCC economies, with a particular focus on natural gas. The previous literature has largely not compared different energy sources and their impact on green economic transformation, particularly in the context of GCC economies.

1.2. Objectives of the Study

Against this backdrop, with this study, we aim to analyze the role of natural gas in GCC countries and its possible use to achieve a greener economy. To achieve this, we study the historical development of natural gas in GCC economies since its discovery until now, in addition to using a standard method to compare the use of natural gas, petroleum, and renewable energy and how this contributes to the development of green economic transformation. In other words, this research aims to determine the impact of natural gas production on the development of green economic transformation in GCC economies.

1.3. Contribution of the Study

Furthermore, the scientific contribution of this research is that we determine the impact of natural gas production on the development of green economic transformation in GCC economies. How this study differs from the previous literature is twofold. First, previous studies were limited to the concept of the green economy and how to transition to it, without addressing the role of natural gas. Second, unlike previous studies focused on one energy source, the current study compares all energy sources, petroleum, natural gas, and renewable energy. Hence, we expect that this paper will shape policymaking related to green economic transformation in GCC economies.

The remainder of this study is organized as follows. Section 2 will discuss the previous literature on the subject. Section 3 describes the model, the hypotheses to be tested, and the data used. Section 4 includes a discussion of the descriptive results, main regression results, and sensitivity results. Finally, Section 5 presents the conclusions and policy recommendations of this paper.

2. Literature Review

Green economic transformation has received some attention from researchers in recent times due to its immense importance. To provide a detailed understanding, we have divided the available literature into several sections, as provided below. In the first step, we have provided a commentary on the literature for GCC members. In the second step, we have also discussed some important case studies and regional studies focusing on green economic transformation. However, research evidence remains limited and largely inconclusive on the green economic transformation, despite its importance. Green economic transformation is the only way forward for long-term sustainable economic performance, as it has numerous benefits.

2.1. The Literature on GCC

Despite its importance, research evidence on green economic transformation is limited and largely inconclusive. Some research studies have highlighted the importance of green economic transformation, such as the fact that the development of a green economy helps increase the well-being of society, create job opportunities, and increase economic growth, that is, increase quality of life. The authors of [7] reviewed the green economic transformation of GCC economies and concluded that the performance of GCC economies is not satisfactory, although several important initiatives have been taken over the years. The authors of [8] also found mutual relationships between environmental, economic, cultural, and social dimensions; therefore, adopting a green economy and improving the environment may contribute to advancing the comprehensive development process. Moreover, focusing on the data of GCC economies, ref. [9] indicated that economies with a higher level of renewable energy have achieved significant improvements in terms of green economic transformation. In low-income countries, there are major obstacles to achieving the development and growth of the green economy.

2.2. The Global Literature

Another strand of the literature has emerged that supports a state’s adoption of the green economy, the negative impact of the use of oil on the environment, and indicators of the green economy in different countries, such as the effects of oil spills, air pollution, and CO₂ emissions resulting from its consumption. For example, the authors of [10] aimed to analyze the relationship between economic growth, CO₂ emissions, and energy consumption in Saudi Arabia. Based on the slow gap model, this study found that carbon dioxide emissions increase with an increase in per capita GDP, which is consistent with Kuznets’s theory. The results also showed that carbon dioxide elasticities are high with oil consumption but low with natural gas, which means that increasing oil consumption increases carbon emissions, while gas consumption reduces them. The authors of [11] also addressed the European context in terms of reducing the environmental costs of economic development. This study aimed to explain the relationship between the impact of economic growth and carbon emissions by applying Kuznets’s approach to the environment using double-panel data and data from 27 countries. One of the most important results of this study is that a positive impact of renewable energy on carbon dioxide emissions was found. The authors of [12] addressed the environmental impacts of economic growth resulting from oil consumption in Saudi Arabia using the cointegration method. The most important results indicate that economic growth based on oil consumption increases carbon dioxide emissions in both the long and short term.

On the other hand, many studies have analyzed the role of natural gas and its importance in the process of achieving the sustainable development goals as a clean alternative to oil. Natural gas is one of the leading sources of energy in GCC economies [12]. Some research studies have concluded that natural gas has a positive impact on transformation and the achievement of the sustainable development goals. This study indicated that natural gas is expected to rank more highly compared with various other types of global energy due to its beneficial characteristics, including its low carbon emissions, its thermal efficiency, and the support it receives from environmentalists. The authors of [5] concluded that natural gas is important in achieving sustainable development in several areas, such as transportation and electricity. The authors of [13] also found that there is a one-sided causal relationship extending from natural gas consumption to real GDP in the Kingdom of Saudi Arabia, which means that natural gas consumption leads to an increase in output and environmental quality. Natural gas is characterized by lower carbon dioxide emissions and calorific value. Furthermore, the authors of [13] commented on the importance of relying on natural gas to achieve sustainable development, especially in the electric energy and fuel sector, as its CO₂ emissions are lower than those of oil and coal, so gas is important for the transition to a green economy and sustainable development.

Some studies have examined the role of policy interventions to explore the factors behind successful green economic transformation. For example, ref. [14] utilized data from 271 cities in China for the period 2003–2019 to explore the exact factors underlying green transformation. Their results showed that digital infrastructure is indeed very important for successful economic transformation. This means that digital infrastructure development could be used as an instrument to accelerate the pace of green economic transformation. In addition to green economic transformation, digital infrastructure will also foster growth and development. Moreover, ref. [15] demonstrated that policy interventions, such as green finance policies, are essential for advancing the process of green economic transformation. These studies highlighted the important role that policies could play in shaping the process of green economic transformation.

In conclusion, various studies have focused on assessing the importance of green economic transformation and its potential determinants. However, to the best of our knowledge, the specific role of natural gas from the perspective of green economic transformation is yet to be researched comprehensively, particularly in the context of GCC member states. More specifically, the role of natural gas in the transformation towards a greener economy is yet to be fully elucidated, mostly due to limited research. Against this backdrop, the present study is interested in comparing energy sources and their impact on the development of green economic transformation. We expect that this study will be significant for the policymakers and government authorities of GCC economies.

3. Materials and Methods

3.1. Econometric Model and Data

The specification of the model is a crucial step in applied research studies, as it aids in achieving the objectives of the study. Recognizing the importance of model specification, we describe the design of the model in this section. We use trade openness and income level, measured by GDP per capita, as control variables, following the prior literature [5,16]. Therefore, we based the specification of the model for analysis based on previous research. More specifically, model 1 employed for the quantitative analysis is given below.

$${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}={\mathrm{B}}_0+{{\mathrm{B}}_1\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}+{{\mathrm{B}}_2\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}+{{\mathrm{B}}_3\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}+{{\mathrm{B}}_4\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}+{{\mathrm{B}}_5\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathrm{U}}_{\mathrm{i}\mathrm{t}}$$

(1)

The functional form presented in Equation (1) illustrates that green economic transformation (GET_it) in GCC economies can be explained by several factors: electricity generation using renewable energy sources (ELC_it), total natural gas production (NGAS_it), total crude oil production (COIL_it), openness to international trade and income (TOPEN_it), and GDP per capita (PGDP_it). By assuming linearities and expressing the results in elasticities, we transformed the variables in Equation (1) using a natural logarithmic transformation. The subscripts (i) and (t) represent the cross-sectional and time dimensions, respectively. The last term of model 1 is the error term, which accounts for the impact of all other factors not included in the model.

The dependent variable, green economic transformation, is measured by the green growth index, which has four main components. These components include “efficient and sustainable resource use”, “natural capital protection”, “green economic opportunities”, and “social inclusion”. The green growth index basically reflects the progress of nations in terms of progress in achieving the SDGs, Paris Climate Agreement, and Aichi Biodiversity. The index ranges from 0 to 100, where higher values reflect better performance in terms of green economic transformation and vice versa. The crude oil production in GCC economies is quantified using “Crude oil including lease condensate (Mb/d)”, while natural gas production is measured in “Dry natural gas production (bcf)”. Similarly, electricity generation from renewable sources is used to highlight the importance of green energy. Finally, both openness to international trade and economic growth are used as control variables. From a measurement perspective, trade openness is measured as “total trade as a percentage of GDP”, while economic growth is measured by the “GDP per capita in constant USD”.

Data reliability and validity play a central role in research studies based on secondary data. We collected data on the green growth index from the GGGI database, which is freely available to researchers. Similarly, data on crude oil production and natural gas production were gathered from the EIA database. Finally, we collected data on the control variables and electricity generation using the renewable sources of energy from the World Development Indicators (WDI). Data were collected for the period 2010 to 2023, as prior data on the green growth index are not available because the index launched in 2010.

3.2. Estimation Tools

For the estimation of the specified model (1), several contending econometric tools are available. Fixed effects (FEs) and random effects (REs) are the main estimation tools relevant to the models based on panel or longitudinal data. Both of these tools have been utilized by numerous researchers over the years for studies with larger time dimensions compared with cross-sectional dimensions. The fixed-effects tool generally works well when addressing the issue of serial correlation, which may be present between the regressor of the model and the error component. However, the fixed-effects model generally faces the issue of dummy variable traps; hence, it is unable to quantify the impacts of time-invariant factors. Random effects are generally considered to not be suitable for addressing the serial correlation issue, but they can effectively assess the influence of time-invariant factors on the dependent variable. The decision of selecting fixed or random techniques is generally made with the help of the well-established testing procedure developed by [17].

The present study also employed “Feasible Generalized Least Squares” (hereafter FGLS) to assess the sensitivity of the findings. This practice is consistent with the prior literature [17]. Similarly, to solve the most likely endogeneity problem, we also employed “Two-Stage Least Squares” (hereafter TSLS) for the model estimates. TSLS is preferred over the “Generalized Method of Moments” (GMM) as the number of cross-sectional units is less than the period (N < T).

3.3. Preliminary Testing and Analysis

Before the formal analysis, we conducted preliminary testing of the models using the collected data. The Pesaran cross-sectional dependence test [18] was performed to assess the possibility of cross-sectional dependency. Our results, provided in Appendix A (

Table A1. CD testing.

Test Name	Test Score	Prob.
“Pesaran CD Test”	−0.560	0.572

), showed that the selected cross-sectional data are independent as the null hypothesis is not rejected. Similarly, we also followed the testing procedure of Hausman’s test [17] to choose the appropriate estimator. The results provided in

Table A2. Hausman test.

Test Name	Test Score	Prob.
“Cross-Section Random”	28.008	0.000

(Appendix A) confirmed the appropriateness of the FE modeling, as the null hypothesis is rejected as the standard level.

4. Results

4.1. Descriptive Analysis

The results of our descriptive analysis for the selected dependent and independent variables are shown in

Table 1. Descriptive statistics.

Descriptives	${\mathbf{G}\mathbf{E}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{N}\mathbf{G}\mathbf{A}\mathbf{S}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{C}\mathbf{O}\mathbf{I}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{E}\mathbf{L}\mathbf{C}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{T}\mathbf{O}\mathbf{P}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{P}\mathbf{G}\mathbf{D}\mathbf{P}}_{\mathbf{i}\mathbf{t}}$
Mean	43.068	2278.491	3038.062	4.13 × 108	112.182	34,582.210
Maximum	53.060	6067.221	10,644.390	6.26 × 109	202.332	81,608.570
Minimum	31.980	414.3509	170.857	0.000000	47.533	17,743.890
Std. Dev.	5.532	1896.360	3262.575	1.23 × 109	38.604	17,427.410
Observations	84	84	84	84	84	84

. The average value of the green growth index is 43.068, which shows that the GCC economy is moving steadily towards a green economy. The highest value of green growth (53.060) among GCC economies was witnessed for the UAE in 2023, while the lowest value of the green growth index (31.980) was recorded for Bahrain in 2010. The 2023 statistics show that Bahrain has made slight progress in terms of green economic transformation. Qatar, Oman, and Saudi Arabia have made significant progress over the last few years in terms of green economic transformation. Green economic transformation is a vital factor for long-term sustainable growth and development.

In terms of natural gas production, the average value for GCC economies was 2278.491 (bcf) during the 2010–2023 period. The highest value of gas production was seen in Qatar in 2023; on the other hand, the minimum value of gas production was experienced by the economy of Kuwait in 2010. All other economies of the GCC region are producing significant volumes of gas, which are playing an important role in their overall growth and development process. Moreover, GCC economies are producing significant amounts of crude oil. The mean value of crude oil is 3038.062, while its standard deviation is 170.857. The highest crude oil production was experienced by Saudi Arabia in 2022. Likewise, the lowest crude oil production was experienced by Bahrain in 2020. All other members of GCC economies are producing moderate amounts of crude oil. Finally, the mean value of electricity production from renewable energy sources is 4.13 × 10⁸ (kWh) for GCC economies.

Furthermore, in terms of trade openness, the average value of GCC economies is 112.182 percent, which is excellent. The highest trade openness index was recorded for the UAE in 2023. Likewise, the minimum value of the trade openness index was recorded for Saudi Arabia in 2020. Finally, the income statistics show that the average value of GDP per capita is 34,582.210 for GCC economies. The highest income level in the GCC was experienced by Qatar in 2011, while the lowest income was experienced by Oman in 2020. Overall, these income statistics are reasonable, and their significance could be explained by the rich hydrocarbon sector.

4.2. Unit Root Analysis

This section presents the results of unit roots extracted using the Levin, Lin, and Chu testing procedures, which are the most widely used tests for this purpose (

Table 2. Unit Roots (Levin, Lin and Chu).

Variables	Level	First Difference	Conclusion
${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	−2.757 ***	−3.259 ***	I (0)
${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$	−15.845 ***	−26.111 ***	I (0)
${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$	−2.127 **	−4.786 ***	I (0)
${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$	−2.481 **	−6.590 ***	I (0)
${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	−2.47 **	−3.729 ***	I (0)
${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	−5.519 ***	−6.373 ***	I (0)

*** and ** donate significance at the 1% and 5% respectively.

). Our unit root results show that the variables are stationary both at level and in first differences. Therefore, the use of Fixed-effects modeling is appropriate.

4.3. Correlation Analysis

The findings of the correlation analysis are shown in

Table 3. Correlations.

Variables	${\mathbf{G}\mathbf{E}\mathbf{T}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{N}\mathbf{G}\mathbf{A}\mathbf{S}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{C}\mathbf{O}\mathbf{I}\mathbf{L}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{E}\mathbf{L}\mathbf{C}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{T}\mathbf{O}\mathbf{P}\mathbf{E}\mathbf{N}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{P}\mathbf{G}\mathbf{D}\mathbf{P}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$	1	0.624	0.050	0.457	−0.001	0.718
${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$	0.624	1	0.333	−0.022	−0.395	0.720
${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$	0.050	0.333	1	0.038	−0.482	−0.197
${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$	0.457	−0.022	0.038	1	0.491	0.115
${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	−0.001	−0.395	−0.482	0.491	1	0.080
${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	0.718	0.720	−0.197	0.115	0.080	1

. The highest correlation is between income and green economic transformation, while the lowest is between trade and green economic transformation. All other variables in our model are moderately correlated with one another.

4.4. Multicollinearity Testing

This section presents the results of the multicollinearity analysis, which were obtained using the “Variance Inflation Factor (VIF)”. The VIF results in

Table 4. VIF test.

Variables	Variance	VIF	Decision
${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$	3.016	1.096	No Multicollinearity
${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$	0.007	2.102
${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$	3.822	2.849
${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	1.127	1.250
${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	3.255	3.122

indicate that multicollinearity is not a concern in the estimated model. All VIF values are below 5, indicating no multicollinearity.

4.5. Regression Analysis

In this section, we report the regression results of the “Pooled Least Squares” (POLS) and FE estimators (

Table 5. Regression findings.

Variables	POLS	FE
	Coefficients	Coefficients
${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$	0.701 *** (0.153)	0.143 * (0.082)
${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$	6.908 *** (0.644)	5.345 ** (1.927)
${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$	7.916 *** (1.066)	−2.256 (2.351)
${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	10.877 *** (1.321)	1.946 * (1.115)
${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	−9.586 *** (1.454)	13.718 *** (1.988)
${\mathrm{B}}_0$	117.308 (23.586)	−265.966 (43.412)
Diagnostics Examination	Adjusted (R-Square): 0.964 SER: 2.687 F: 54.738 ***	Adjusted (R-Square): 0.964 SER: 1.067 F: 212.854 ***

The asterisks represents significance at the 1 percent (***), 5 percent (**), and 10 percent (*) levels. Standard errors are reported in parentheses.

). According to the POLS results, natural gas production, crude oil production, and renewable energy sources are important for green economic transformation in GCC economies. According to our findings, electricity consumption from renewable energy sources appeared to be the main driving force behind the transition towards a green economy in GCC economies. Similarly, trade openness appeared to be an integral part of green economic transformation, while income level appeared to be one of the main hurdles for green economic transformation. However, we do not need to emphasize these findings, as the Hausman test [17] indicated the appropriateness of the FE modeling. The POLS estimation is important in the first step of the analysis as it provides some valuable information about the possible impacts of the independent variable on the dependent variable, as elucidated by [19].

Next, we turn to the findings of the FE modeling displayed in the final column of Table 5. The results demonstrate that electricity production using renewable energy has positively contributed to green economic transformation in GCC economies (${\mathrm{B}}_1$ = 0.143, S. E = 0.082, p < 0.10). It is a fact that renewable energy sources are environmentally friendly, as they produce very few emissions in the atmosphere. Our results are supported by the recent research in [20], where the authors focused on 33 economies for the period 1990–2021 and demonstrated that renewable energy sources are important for successful green economic transformation. Focusing on GCC economies, [9] found that economies with a higher level of renewable energy have achieved significant improvements in terms of green economic transformation. Therefore, GCC economies need to encourage the use of renewable energy both in the transport sector and industrial sector to achieve the objective of green economic transformation successfully.

Our results further reveal that the production of natural gas and its consumption are two of the main factors underlying the achievement of green economic transition in GCC economies. In the estimated model, the coefficient of the gas production variable is positive and statistically significant, which confirms its positive role (${\mathrm{B}}_2$ = 5.345, S. E = 1.927, p < 0.05). Natural gas production generally emits less emissions compared with other forms of energy. Therefore, a positive influence of natural gas on green economic transformation was expected. Moreover, our results showed that crude oil production also negatively impacts green economic transition in GCC economies; however, its role is statistically insignificant (${\mathrm{B}}_3$ = −2.256, S. E = 2.351, p > 0.10). This implies that other sectors are playing a more vital role in green economic transformation in GCC economies. Generally, crude oil production and ultimately its consumption produce enormous emissions in the atmosphere, hence adversely affecting the transition towards a green economy.

The control variables were also shown to play a role in green economic transformation in GCC economies. Openness to international trade is important for green economic transformation in GCC economies, as evident from the results (${\mathrm{B}}_4$ = 1.946, S. E = 1.115, p < 0.10). Trade openness impacts green economic transformation mostly through the technique channel. The technique channel implies that trade openness improves environmental quality and supports green economic transformation with the help of advanced tools and production techniques. The authors of [21] also reported similar findings regarding the role played by trade openness from the perspective of green economic transformation. Finally, our findings also confirmed the positive influence of income on the transition towards a green economy (${\mathrm{B}}_2$ = 13.718, S. E = 1.988, p < 0.001). Higher economic stakes force individuals to act much more responsibly in their efforts to achieve a green economy.

Finally, our model estimates carry excellent explanatory power. The model estimated with POLS explains 0.775 percent of the variation in the dependent variable, while the model with the FE estimator explains 0.964 percent. The F-test is also significant at the 1 percent level, which emphasizes the overall fitness of the estimated models.

4.6. Discussion of Regression Findings

Our results indicate that natural gas production is one of the main and most important channels by which the GCC economies could address environmental issues and achieve the objective of green economic transformation. Natural gas production is associated with relatively less emissions, and hence, its production is expected to help the policymakers of GCC economies to transform their economies. Similarly, our findings also supported the view that green economic transformation responds positively to renewable energy sources. Particularly, electricity, which is an important component of the production process in modern day production, if produced with renewable energy sources, would advance the process of green economic transformation in the GCC economies. Unlike traditional energy sources such as fossil fuels, renewable sources of energy are considered environmentally friendly as they emit much less pollution. In terms of relative importance, our results confirmed the superiority of natural gas production over renewable energy sources. On the other hand, our results indicated the detrimental influence of crude oil production on green economic transformation. Although crude oil is an engine for production processes and the transport sector, excessive production of crude oil contributes enormously to environmental degradation and hence adversely impacts the journey towards green economic transformation. Therefore, targeted and well-coordinated efforts on the part of authorities are required to achieve the goal of green economic transformation successfully by encouraging the production of natural gas and the utilization of renewable energy sources, and discouraging the production of crude oil production.

Moreover, liberalized trade policies bring multiple benefits, including a positive influence on green economic transformation. Liberalized trade policies provide opportunities for advancement, and the latest technologies are associated with less emissions in contrast to traditional and outdated technologies, and hence, they are useful for the journey towards green economic transformation. Therefore, the GCC economies must take steps to relax the restrictions of trade to experience the full benefits of trade liberalization, including green economic transformation. Lastly, our analysis inferred that increased income levels could also be used as a main tool to move successfully towards green economic transformation. A higher level of income without a doubt creates awareness among the population regarding the importance of a green and clean environment, and hence, they act more sensibly towards environmental problems. Therefore, efforts should be made by the authorities of GCC economies to move towards liberalized trade regimes and further enhance the income of the population for better environmental outcomes in the form of green economic transformation.

4.7. Robustness Analysis

Recently, assessing the robustness of a study’s findings has become considerably more important in the literature. Due to this, we assessed the sensitivity of our findings using the FGLS and TSLS estimators, with the results outlined in

Table 6. Sensitivity results.

Variables	FGLS	TSLS
	Coefficients	Coefficients
${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$	0.156 * (0.087)	0.747 *** (0.151)
${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$	4.537 ** (1.955)	1.276 *** (0.461)
${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$	−1.795 (1.736)	−1.059 *** (0.291)
${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$	0.815 (1.061)	1.573 ** (0.799)
${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	11.890 *** (1.804)	6.247 *** (0.792)
${\mathrm{B}}_0$	−223.179 (43.250)	−59.416 (5.970)
Diagnostics Examination	Adjusted (R-Square): 0.980 SER: 1.053 F: 212.851 ***	Adjusted (R-Square): 0.778 SER: 2.665 F: 47.882 **

The asterisks represents significance at the 1 percent (***), 5 percent (**), and 10 percent (*) levels. Standard errors are reported in parentheses.

. The quantitative results of the FGLS validated the earlier results by demonstrating favorable impacts of electricity use from renewable energy sources and gas production on green economic transformation in GCC economies. Similarly, the results also validated the earlier findings by showing a negative influence of crude oil production on green economic transformation in GCC economies. Finally, in the FGLS results, the control variables maintained their significant positive relationships with green economic transformation.

The findings of the TSLS also provided sound support for the results reported earlier. According to the results of the TSLS, natural gas production and electricity production from renewable energy sources maintained their affirmative significant influences on green economic transformation in GCC economies. Moreover, crude oil production appeared to be harmful for green economic transformation in GCC economies, which is consistent with the earlier findings. Lastly, trade openness and income maintained their positive impacts on green economic transformation in GCC economies.

4.8. Causality Analysis

In this section, we highlight the findings of causality testing. Our results, provided in

Table 7. Causality analysis.

“Hypothesis”	“F-Statistics”	“Prob.”
“${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$”	0.286	0.594
“${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$”	0.043	0.835
“${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$”	4.877 **	0.030
“${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$”	4.542 **	0.036
“${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$”	2.875 *	0.094
“${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$”	0.013	0.908
“${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$”	0.961	0.329
“${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$”	0.012	0.910
“${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$”	0.150	0.699
“${\mathrm{G}\mathrm{E}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$”	0.157	0.692
“${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$”	1.142	0.288
“${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$”	5.770 **	0.019
$“{\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$”	0.599	0.441
“${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$”	0.012	0.911
“${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$”	0.482	0.489
$“{\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$”	2.174	0.144
“${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$”	0.073	0.787
“${\mathrm{C}\mathrm{O}\mathrm{I}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$”	0.001	0.964
“${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$”	0.303	0.583
“${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$”	1.235	0.270
“${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$”	0.012	0.911
“${\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$”	0.077	0.781
“${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$”	0.496	0.483
$“{\mathrm{E}\mathrm{L}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$”	0.616	0.435
“${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$”	1.283	0.260
“${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$”	0.278	0.599
“${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$”	0.122	0.727
“${\mathrm{N}\mathrm{G}\mathrm{A}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$”	1.009	0.318
“${\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$”	0.151	0.698
“${\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$$\mathrm{to} {\mathrm{P}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$”	0.032	0.857

Note: The asterisks (**, *) show significance at the 5 and 10 percent levels, respectively.

, show that natural gas production unilaterally causes green economic transformation in GCC economies. This means that natural gas production is a vital factor in the green economic transformation of GCC economies. Similarly, our results proved a unilateral causal relationship between crude oil production and electricity production from renewable energy sources. Finally, the causality analysis demonstrated a two-way causal relationship between electricity production from renewable energy sources and green economic transformation in GCC economies.

5. Conclusions

5.1. Summarizing Remarks

In this research paper, we attempted to elucidate the comparative role played by and the effect of different energy sources on green economic transformation, which is an under-researched area in the literature. We focused on GCC economies and utilized data for the period 2010–2023. To measure green economic transformation, we used the green economic index, which has four main components.

Our results confirmed that electricity consumption produced using renewable energy sources has played a dominant role in advancing green economic transformation in GCC economies. Therefore, the use of renewable energy should be encouraged by the member states of the GCC to generate electricity. Similarly, our study found that natural gas production is a vital factor for advancing green economic transformation in GCC economies, hence making it a preferred source of energy. On the other hand, crude oil production appeared to be an unimportant factor for green economic transformation as, according to the estimated model, its impact is insignificant. Moreover, the empirical analysis revealed that trade openness and income have helped GCC economies to achieve successful green economic transformation. Finally, the causality analysis also displayed a one-way causal relationship between natural gas production and green economic transformation and a two-way causal relationship between electricity generation using renewable energy sources and green economic transformation in GCC economies. We expect that our research results will shape policy formulation regarding green economic transformation in GCC economies.

5.2. Policy Implications

The implications of the current study based on the results are highlighted below for the consideration of policymakers of GCC economies.

• GCC member states must embrace the use of renewable energy sources as this will help them to achieve green economic transformation. Similarly, the use of natural gas should be given priority for the purpose of energy production as it emits relatively less emissions. Finally, the use of crude oil production is discouraged due to its harmful effects on the process of green economic transformation in GCC economies, as indicated by our results.
• More attention needs to be paid to the trade liberalization process in GCC economies by relaxing a variety of restrictions on the movement of goods and services. Increased trade openness based on advanced and environmentally friendly technologies would contribute to the process of green economic transformation significantly.
• As demonstrated by our results, higher economic growth and eventually an increased income level of the population are integral parts of green economic transformation. Therefore, the government authorities of GCC countries need to formulate policies that diversify their economies amid the resource depletion problem, which is associated with the natural resource sector. Economic diversification would bring enormous benefits to GCC economies, including increased incomes for the population, which would help in the achievement of green economic transformation.

5.3. Limitations and Future Research Directions

The limitations and implications of the current research paper are outlined below.

• This study has focused only on the GCC economies, which share unique characteristics in terms of their political and economic structures. Therefore, the results could not be generalized on a large scale. Therefore, future studies on this subject are advised to focus on other regional blocks to confirm the validity of our results.
• This study has explored the determinants of green economic transformation for the overall GCC region. Hence, individual variations are neglected. Future research studies are advised to conduct case studies focusing on individual members of the GCC to determine whether the results reported in this paper hold or are altered.
• Our study’s analysis is based on traditional FE estimation. Therefore, we suggest that future researchers use additional econometric techniques, such as panel cointegration, alongside FE to provide more detailed insights into the role of natural gas in green economic transformation.