The moderating role of natural resources between governance and CO 2 emissions: Evidence from MENA countries

Abstract

Despite the diversity of theoretical studies, natural resources’ moderating role between governance indicators and environmental quality remains a controversial issue. The purpose of this paper is therefore to clarify the nature of this role in the Middle East and North Africa (MENA) countries from 1996 to 2017 relying on the generalized method of moments system estimators. The empirical results reveal that corruption control, political stability, rule of law, voice and accountability, and government effectiveness increase CO₂ emissions, while regulation quality does not affect CO₂ emissions. Our findings also show that FDI and GDP increase CO₂ emissions. However, natural resources moderate the governance indicators to reduce CO₂ emissions. Therefore, policy-makers should increase public awareness of the best use of natural resources. Thus, improving governments’ institutional framework will generally contribute to reducing the greenhouse gas emission levels in the MENA countries.

Keywords

natural resources economic growth governance indicators dynamic models

Introduction

The realization of environmental problems (the acidification of air, soil, and water, the accumulation of solid waste, and thermal pollution) during the 1960s and 1970s did not unduly worry economists who had a conceptual framework and analytical tools ready to study these problems: neoclassical economics, in particular welfare economics, with the resulting public policies consisting of regulatory or market instruments.¹ Indeed, the industrial development model has functioned for two centuries on the belief that natural resources are inexhaustible. When this belief collapsed, neoclassical economists tried to integrate the environment into the Walrasian general equilibrium model. The classics gave natural resources a prominent place in their theory of production, since they explicitly recognized their driving role both in industry and in agriculture.

In recent decades, developing as well as developed countries have been concerned with the preservation of environmental quality. Indeed, a clean natural environment is considered an essential element in improving the human life quality in societies.² Faced with this concern, governments and political decision-makers focused their attention on a proper natural resources management in order to preserve the ecosystem. Thus, the exploitation of natural resources has contradictory effects. The first attempts consider that natural resources are the main source of growth and economic development since they are key inputs for production.³ Thus, according to Balassa,⁴ natural resources contribute to industrial development because they represent the funds needed to form physical capital and increase the demand for industrial goods. Moreover, Deaton (1999), showed that resources rents represent a potential source of funds for physical capital accumulation. The author also showed that temporary price spikes offer windfalls that can enhance future growth. Indeed, Shahbaz et al.⁵ argued that natural resources contribute to financial development and economic growth. However, the second attempts predict that these resources’ massive exploitation—particularly fossil fuels—contributes to almost all CO₂ emissions and hence slow down economic growth.

Recently, this relationship between natural resources, growth, and environmental quality has been reinforced given the emergence of the resource curse concept.⁶ Indeed, the countries abundant in natural resources, oil in particular, have lower growth levels compared to those of the naturally oil-poor countries. A negative relationship seems to exist between the proportion of natural resources exports in gross domestic product and a country's growth rate.⁷ Faced with this ambiguity, new avenues of research showed that the so-called relationship between natural resources and development depends on other factors including primarily governance. However, despite the multiplicity of papers that have addressed the relationship between natural resources and growth on the one hand, and natural resources and environmental quality on the other, few studies have analyzed the moderating effect of natural resources between governance and the environment for a panel of 16 Middle East and North Africa (MENA) countries over the1996–2016 period.

The question to raise here is: why did we choose the MENA countries? Well, investigating the impact of the moderating role of natural resources between institutional quality and CO₂ emissions in the MENA countries is very crucial because these countries represent the weakest contributor to global carbon emissions. However, carbon emissions in this region have been increasing. For instance, carbon emissions increased from 864 million tons in 1990 to 2463 million tons in 2015 and further to about 2556 million tons in 2019.⁸ Therefore, it is crucial to understand the fundamental forces that are driving the increase in carbon emissions in the MENA countries in order to formulate adequate policies to mitigate this increase before it gets worse. Additionally, the MENA countries seem generally endowed with resources such as productive land, renewable natural wealth such as water and forests, and nonrenewable natural wealth such as gas, oil, and other minerals. These natural resources dominate the economy in many MENA countries and represent an important means of livelihood for the population living in rural areas. However, the sheer size of Africa's natural resources, which have been exploited for several decades, has not allowed the continent to experience real development. The abundance of natural resources has not translated into improved welfare for the people.

On the other hand, several studies argue that MENA countries have failed to harness the potential of their natural resources to drive industrialization, economic growth, poverty reduction, and sustainable development.^9–12 As a result, the MENA countries should make better use of their natural resources for their own development. It seems essential to implement strategies that can put the management of the MENA countries’ natural resources at the service of growth and the fight against poverty. Indeed, the exploitation of these natural resources must take into account the possible damage that could affect nature. The active involvement of stakeholders must be observed for the proper management of the wealth that natural resources provide to the MENA countries. Thus, the main actors—namely governments, businesses, civil society organizations, and local populations—must work together to ensure that this wealth leads to large-scale socioeconomic development and that future generations find a land in good condition.

Given the motivation for the study, this article contributes to the literature in several ways. First, this study not only presents the direct effect of governance indicators and natural resources on carbon emissions, but also investigates the indirect impact of natural resources on carbon emissions. Second, this study also shows the indirect channels through which natural resources impact carbon emissions. Thus, unlike previous studies, this paper extends the literature by examining the moderating effect between governance indicators, economic growth, and natural resources on carbon emissions. Third, the majority of existing empirical studies have used a single natural resource indicator to conclude the impact of natural resources on carbon emissions. It can be argued that using a single natural resource indicator could lead to biased results and wrong conclusions. Given this argument, this study uses two (2) natural resource indicators to examine their respective effect on carbon emissions. Finally, this study applies a two-stage dynamic system of generalized moment methods (GMM system) on a panel dataset to examine the impact of natural resources on carbon emissions. The GMM dynamic system helps to control the possible endogeneity and also helps to model short- and long-term impacts.

The remainder of our paper is organized as follows. The second section deals with a brief review of the literature. The third section presents the methodology we used. The fourth section presents the data and empirical results. Finally, the fifth section provides the main conclusions and policy implications.

Literature review

The relationship between governance indicators, growth, natural resources, and environmental quality was well presented in the literature. Several studies examined this relationship with reference to different countries and methodologies. This literature will be subdivided into subheadings to distinguish between direct and indirect effects and subsequently explain the relationships between governance, financial development, natural resources, and environmental quality. Thus, we will attempt to analyze the following hypotheses :

H₁: how does governance affect environmental quality?

H₂: how does financial development affect environmental quality?

H₃: do natural resources moderate the relationship between governance and environmental quality?

H_1: how does the quality of governance affect the quality of the environment?

Despite the importance of governance indicators (i.e. corruption control, law enforcement, political stability, and bureaucracy quality), these indicators are largely neglected in accounting for environmental quality degradation.^13,14 However, poor governance can lead to a degradation in the environmental quality in the sense that almost all the companies that exploit natural resources (e.g. the mining sector) form a monopoly that is very sensitive to the quality of the institutions. This finding can be confirmed with reference to the Transparency International¹⁶ report, where out of a total of 32 major mining countries, 23 countries admit to a (Consumer Price Index [CPI]) score below 5. Empirically, Apergis and Ozturk¹⁵ conducted a study on a group of 14 countries over the 1990–2011 period relying on four institutional quality indicators (political stability and lack of violence, government efficiency, quality of regulations, and fight against corruption). The authors concluded that the four indicators they used have significant effects on environmental quality. In the same sense and based on a group of countries in Sub-Saharan Africa, Abid¹⁷ studied the impact of the institutional framework on CO₂ emissions over the 1996 to 2010 period. The author found controversial results. He concludes that political stability, government effectiveness, democracy, and corruption control contribute to improved environmental quality, while quality of regulations and rule of law lead to environmental degradation. Additionally, to examine the impact of corruption on CO₂ emissions, Sekrafi et al.¹⁸ used quantile regression for a group of 18 African countries from 1992 to 2013. The authors noted that every time the corruption variable value moves away from zero (i.e. a lower corruption level), there is a lower negative direct effect of corruption on environmental quality. In addition, the authors found that corruption has a total positive effect on the environment quality, but each time we move from one quantile to another this effect decreases.

Introducing the informal sector, on a group of 100 countries over the period 1999–2005, Biswas et al.¹⁹ found that the impact of the informal economy on CO₂ emissions depends on the level of corruption, as it was less destructive for lower levels of corruption. In contrast, Danish et al.²⁰ analyzed the moderating effect of corruption on the growth–environment quality relationship for the Brazil, Russia, India, China, and South Africa (BRICS) countries over the period 1996–2016. The authors found that corruption reduces the economic growth–CO₂ emissions strong relationship. Thus, they came to the conclusion that controlling corruption leads to lower air pollution. Similarly, for a group of MENA countries, Sekrafi and Sghaier¹⁸ examined the effect of controlling corruption on the quality of the environment based on the GMM method. The authors calculated the direct and indirect effect of governance on the environment and concluded that corruption control positively and significantly affects environmental quality. In addition, corruption control affects environmental quality via the growth channel. Thus, the authors concluded that corruption control has a total positive effect on the quality of the environment.

H₂: how does financial development affect governance?

A large empirical literature^5,21–24 addressed the theoretical relationship between financial development and environmental quality. This literature can be presented in two different lines of thought. The first line of research addresses the existence of a relationship between financial development and the number of pollutants released into the atmosphere. Indeed, according to this approach, financial development is an important tool for attracting green and advanced technologies that lead to efficient energy use.^25,26 In this perspective, Tamazian et al.²⁴ relied on data for a panel of Brazil, Russia, India, and China (BRIC) countries over the 1992–2004 period to analyze the relationship between economic growth, financial development, and environmental degradation. The estimation results confirmed the important role of economic growth and financial development in reducing environmental degradation. In a study on the Chinese economy, the positive link between financial development and CO₂ emissions was confirmed by Zhao and Yang.²⁷ The researchers also confirmed that financial development represents a catalyst in creating green innovations since it entails environmentally-friendly technologies and improves businesses’ financial accessibility.

Based on an autoregressive distributed lag (ARDL) model, the impact of economic and financial development on carbon emissions in Pakistan was analyzed by Abbasi and Riaz²⁸ over two different time spans: from 1971 to 2011 for the first, and from 1988 to 2011 for the second. The results showed that financial variables only played a role in mitigating emissions in the latter period when a higher degree of financial sector liberalization and development occurred. For the case of China, Zhang²⁹ investigated the relationship between CO₂ emissions and financial development relying on tests of cointegration, causality, and variance decomposition. The author drew several findings: (1) China's financial development represents an important driver that increases carbon emissions. (2) The influence of financial intermediation scale on carbon emissions is greater than that of other financial development indicators. (3) Carbon emissions are affected by stock market scale. (4) FDI has a weak effect on changing carbon emissions.

The second line of literature advocates the absence of any relationship between financial development and environmental quality. With this in mind, Omri et al.³⁰ verified the absence of any relationship between financial development and CO₂ emissions for a panel of 12 MENA countries from 1990 to 2011. The authors relied on the private sector credit to GDP ratio as a proxy for financial development using the GMM method. Moreover, Jamel et al.³¹ used a sample of 40 European countries over the 1985–2014 period. They relied on the domestic credit index given by banks to the private sector to measure financial development. The authors relied on OLS regression and causality tests to examine the relationship between financial development and the quality of the environment. The absence of any causal relationship between the two variables is validated by the empirical analysis results. The latter corroborates the findings of Dogan and Turkekul³² for the case of the USA through an ARDL model.

H₃: do natural resources moderate the relationship between governance and environmental quality?

A major debate in environmental economics concerns the impact of natural resources on CO₂ emissions.^33–37 Indeed, no consensus seems to be emerging on the actual effect of natural resources abundance on CO₂ emissions.^17,38 Nevertheless, a vast literature has tried to find the different channels that can account for the effect (positive or negative) of natural resources on CO₂ emissions (i.e. corruption, insufficient public spending, lack of investment, diversion of productive forces to sectors with the lowest returns to scale). Among the various factors proposed, countries’ institutions and constitutional system represent a key element of natural resource management and determine the negative effect they can have on CO₂ emissions.³⁹

A major element to grasp the effect of natural resources abundance on a country's environmental quality is the latter's influence on the incentives to build quality institutions. So, what is the effect of natural resources on institutions? Nevertheless, the effect of natural resources on the evolution of governance indicators is ambiguous. Indeed, Robinson and Torvik⁴⁰ showed that natural resources have no impact on governance indicators in parliamentary systems, while natural resources have a nonlinear (inverted U curve) effect on governance indicators in presidential systems. Beyond a certain level of resources rents, the positive effect of rents becomes negative. Beyond this threshold, therefore, there is a deterioration in these indicators.

Added to the conditionality role played by governance indicators, many authors have shown that, depending on the natural resource, highly dependent countries have weak governance indicators.^41–43 Nevertheless, Couttenier⁴⁴ showed that natural resources have a nonlinear effect (inverted U curve) on governance indicators. In fact, he demonstrates that the rent from natural resources is an incentive for the establishment of institutions geared to maximizing rent extraction. However, this incentive is opposed to the initial institutional and legal framework. Indeed, governance indicators, if they are of sufficient quality, can provide superior incentives that differ from those provided by natural resources. For example, natural resources do not systematically degrade national institutions. The rent from natural resources abundance may even positively affect governance indicators.

Given the inconsistency in the literature coupled with the knowledge gaps, this study contributes to the literature by using different natural resources indicators (two indicators) to investigate their direct and indirect effect on carbon emissions for a panel of 16 MENA countries over the period 1996–2016 using a dynamic system—GMM.

Methodology

The present paper's objective is to examine the direct as well as the indirect impact of governance on CO₂ emissions. We relied on the dynamic panel estimation technique to examine the natural resources’ moderating effect between governance indicators and CO₂ emissions in MENA countries. Our dynamic panel estimation technique is the system GMM approach. To meet our objective, the empirical model we tested is given as follows:

\begin{aligned} C O_{2 i, t} = α_{1} C O_{2 i, t - 1} + α_{2} N R_{i, t} + α_{3} G I_{i, t} + β^{'} X_{i, t} \\ + η_{i} + ν_{t} + ε_{i, t} \end{aligned}

(1)

where

C O_{2 i, t}

is the CO₂ emissions per capita of country i in year t,

T R N

is total natural resources.

G I

is the governance indicators, namely government effectiveness (

G E

), political stability and absence of violence (

P S

), regulations quality (

R Q

), rule of law (

R L

), voice and accountability (

V A

), and corruption control (

C C

). X is the vector for the other explanatory variables, which include real GDP per capita and financial development.

α_{1}, α_{2}, α_{3}, and β

are parameters or vectors of parameters to be estimated.

η_{i}, ν_{t},

and

ε_{i, t}

are country-specific effects, time-specific effects, and an error term, respectively.

After that, the interaction term of governance indicators and natural resources (governance indicators [GI] × natural resources [NR]) was introduced to study the indirect effect of governance on environmental deterioration. Hence, the indirect effect of governance on environmental degradation is given in model (2).

\begin{aligned} C O_{2 i, t} = α_{1} C O_{2 i, t - 1} + α_{2} T R N_{i, t} + α_{3} G I_{i, t} + β^{'} X_{i, t} + δ (N R \times G I)_{i, t} \\ + η_{i} + ν_{t} + ε_{i, t} \end{aligned}

(2)

where

δ

is the governance indicators and natural resources interaction term.

Indeed, estimating the dynamic panel data models by the OLS method yields potentially biased results.⁴⁵ There are several problems in such models, namely the environmental regression ones. First of all, since the explanatory variables are likely to be endogenous, they can be measured with errors, especially in a short time span.⁴⁶ Second, a biased estimation can be given by the omitted variables. For these problems to be addressed, Arellano and Bond⁴⁷ came up with a dynamic panel data model that relies on the GMM model. This model includes an explanatory variable, which is the lagged endogenous variable. Based on Arellano and Bond's estimation strategy, it is important to have a first difference equation to overcome the country-specific effect.

The lagged dependent variable is correlated with the error term; consequently, the “within” estimator is also biased. According to Blundell and Bond,⁴⁸ the first difference GMM estimators can have a poor performance with persistent time series and shorter periods. This can be explained by the fact that the series’ lagged levels provide only weak instruments for the differentiated equations. A further limitation of the difference estimator is that the differentiation process to remove the country-specific effect also requires discarding any data about the variation in levels between countries. It is for this reason that we opted for an instrumental variable approach in our paper by finding the appropriate instruments to be correlated with the endogenous explanatory variables but not correlated with the dependent variable. Therefore, we rely on the GMM in a two-step system, proposed by Arellano and Bovier,⁴⁹ Blundell and Bond,⁴⁸ Bond et al.⁵⁰ and introduced by Roodman,⁵¹ since it is a more reliable approach. This is a common approach that allows us to avoid the aforementioned limitations, mainly for the empirical environmental regressions. Indeed, the system-based GMM estimator's contribution is its use of moment conditions based on level equations, added to the usual Arellano and Bond (1991) conditions of orthogonality. Nevertheless, the most important advantage of this estimator is that it does not need any external instrument to deal with endogeneity; instead, its instruments are the lagged values and the differences between the two time periods of the endogenous explanatory variables. There are two main assumptions for the GMM estimator's consistency in a system. The first requires the validity of the instrumental variables, that is, they have to be uncorrelated with the error terms. We test this assumption with the Sargan and Hansen test for restrictions of identification. The second assumption requires verifying the absence of second-order autocorrelation (AR(2)) in the residuals, while a negative first-order autocorrelation (AR(1)) can be detected. We test this assumption with the Arellano-Bond tests for AR(1) and AR(2).

Data and empirical analysis

Data

In the present paper, we studied a group of 16 MENA countries over the 1996–2016 period to examine the moderating impact of natural resources on the relationship between governance and environmental quality. We used CO₂ emissions measured in metric tons per capita as a proxy for environmental quality. We used two indicators to measure natural resources rents. The first indicator measures total natural resources rents as a percentage of GDP (NR). The second indicator is petroleum rents as a percentage of GDP (PR). To measure economic activity, we used GDP per capita, and the measure of financial development is approximated by the ratio of domestic credit to the private sector to GDP. Finally, to measure the governance indicators, we used Kaufman's six indicators, namely government effectiveness (GE), political stability and absence of violence (PS), regulation quality (RQ), rule of law (RL), voice and accountability (VA), and corruption control (CC). The governance indicators are presented in scores between −2.5 and 2.5. The closer the score to 2.5, the better the governance quality; but the closer to −2.5, the worse the governance quality. We extracted our variables from the World Bank database. We display the full list of countries in Table A, in the Appendix.

In Table 1 hereunder, we present the correlation between the different variables used in our empirical analysis. It can clearly be seen that the correlation coefficients between each of the two endogenous variables (RN and PR) and those of the exogenous variables are all below the critical threshold of 0.7 used by Kennedy.⁵² This confirms the absence of any correlation problem between the variables. However, the results show that the correlation coefficients between the different governance indicators are above the threshold of 0.7. To solve this problem, we managed to introduce the indicators separately, that is, we used individual models for each indicator.

Table 1.

Correlation matrix.

	NR	PR	CO₂	GDP	FDI	VA	PS	GE	RQ	RL	CC
NR	1
PR	0.978	1
CO₂	0.417	0.370	1
GDP	0.400	0.369	0.979	1
FDI	−0.501	−0.495	0.300	0.333	1
VA	−0.442	−0.439	0.028	0.067	0.537	1
PS	−0.060	−0.065	0.560	0.608	0.601	0.307	1
GE	−0.236	−0.251	0.514	0.556	0.773	0.514	0.765	1
RQ	−0.246	−0.273	0.479	0.508	0.695	0.510	0.671	0.927	1
RL	−0.242	−0.270	0.560	0.593	0.791	0.500	0.826	0.920	0.904	1
CC	−0.170	−0.200	0.617	0.659	0.705	0.534	0.781	0.925	0.894	0.940	1

We present the variables’ descriptive statistics in Table 2. The first finding is that the value of the total rent is very close to the value of the oil products’ rent. This confirms that the MENA countries are characterized by a dominance in the exploitation of oil to the detriment of other natural resources. Moreover, if we look at the governance indicators, we see that they are very low on average compared to the upper bound of 2.5. This confirms these countries’ poor governance. We also notice that some countries have very close scores to the lower bound of −2.5 (i.e. they have very poor governance) such as Libya, Iraq, Yemen, and the Syrian Arab Republic. This is due to internal wars and the Arab Spring Revolution.

Table 2.

Descriptive statistics.

Variable	Obs	Mean	SD	Min	Max
NR	315	2.147	2.473	−5.461	4.230
PR	315	1.822	3.023	−7.309	4.212
CO₂	336	1.800	1.195	−0.940	4.249
GDP	336	8.971	1.201	6.542	11.151
FDI	316	3.346	1.107	0.236	4.655
VA	336	−0.993	0.480	−2.050	0.303
PS	336	−0.444	1.040	−2.180	1.223
GE	336	−0.237	0.760	−2.279	1.509
RQ	336	−0.274	0.779	−2.346	1.120
RL	336	−0.247	0.767	−2.090	0.958
CC	336	−0.283	0.748	−1.688	1.567

Empirical analysis

After giving an overview of our variables, we used a dynamic model (system GMM). This model allowed us to address the endogeneity problem between some independent variables. In addition, we used the lagged independent variables as instruments with reference to the literature.^53–54 This suggests that taking the lag of endogenous variables as instruments is the simplest method to deal with the problem of endogeneity.

We present the results of the estimates in the absence of interaction factors in Table 3, that is, we summarize the direct effects between our model's variables. Table 4 shows the estimates in the presence of interaction variables between natural resources rents and governance indicators. This interaction accounts for the moderating effect, that is, the indirect transmission channels that transmit the effect of governance indicators on environmental quality. The results show that the CO₂ variable delayed by one period positively and significantly affects current emissions at the 1% threshold. In addition, our results show that the natural resources rent positively and significantly affects CO₂ emissions in all models. Moreover, our results confirm that natural resources are the main contributor to the increase in CO₂ emissions in the MENA countries. Indeed, a 1% rise in natural resources rents contributes to a 0.3% drop in CO₂ emissions. We can account for this observation by the fact that any increase in natural resources rents requires the massive extraction of the resources. This leads to the destruction of the reserves and the transformation of these products, which represents one of the most polluting industries. In other words, mismanagement and high abundance of natural resources rents are not environmentally friendly and therefore damage the global environment.⁵⁶ Hence, countries rich in natural resources are confronted with the problem of the “resource curse” that leads to environmental problems. These results are confirmed in the works of Gerelmaa and Kotani⁵⁷ and Shao and Yang.⁵⁸

Table 3.

System-GMM results for CO₂ emissions (without interaction).

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
L1.CO₂	0.604***	0.622***	0.649***	0.624***	0.617***	0.613***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
NR	0.019*	0.028***	0.031***	0.022**	0.031***	0.030***
	(0.058)	(0.008)	(0.006)	(0.039)	(0.010)	(0.010)
FDI	0.011	0.038**	0.018	0.032**	0.027*	0.033**
	(0.535)	(0.015)	(0.322)	(0.018)	(0.054)	(0.013)
GDP	0.381***	0.336***	0.300***	0.352***	0.338***	0.340***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
VA	0.111***
	(0.005)
PS		0.041***
		(0.009)
GE			0.078**
			(0.014)
RQ				0.028
				(0.216)
RL					0.066**
					(0.038)
CC						0.055**
						(0.042)
Constant	−2.653***	−2.487***	−2.151***	−2.616***	−2.477***	−2.501***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(1)	−4.737***	−4.748***	−4.736***	−4.759***	−4.692***	−4.681***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(2)	−1.174	−0.881	−1.092	−0.943	−1.037	−1.089
	(0.244)	(0.376)	(0.274)	(0.346)	(0.304)	(0.281)
Sagan	(0.214)	(0.234)	(0.211)	(0.143)	(0.218)	(0.187)

Note: *,^**, ***, significant at the 10%, 5%, 1% threshold, respectively. The Sargan test validates the instruments used (H0: accepted), and the p-values exceed the 10% threshold. There is no autocorrelation between the second-order error terms according to the AR(2) test, and p-values are above the 10% threshold.

Table 4.

System-GMM results for CO₂ emissions (with interaction).

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
L1.CO₂	0.604***	0.620***	0.647***	0.615***	0.618***	0.600***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
RN	0.030	0.006	0.018	0.007	0.020	0.028*
	(0.129) **	(0.686) **	(0.187) **	(0.547) **	(0.158) **	(0.059) **
FDI	0.050**	0.021	0.017	0.033*	0.027	0.040**
	(0.013)	(0.243)	(0.356)	(0.059)	(0.117)	(0.014)
GDP	0.378***	0.356***	0.310***	0.372***	0.344***	0.323***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
VA	0.301***
	(0.000)
NR*VA	−0.063***
	(0.003)
PS		0.100***
		(0.000)
NR*PS		−0.024***
		(0.007)
GE			0.151***
			(0.000)
NR*GE			−0.032***
			(0.010)
RQ				0.153***
				(0.002)
NR				−0.046***
				(0.000)
RL					0.143***
					(0.002)
NR*RL					−0.033**
					(0.016)
CC						0.156***
						(0.001)
NR*CC						−0.038**
						(0.023)
Constant	−2.495***	−2.561***	−2.223***	−2.762***	−2.515***	−2.346***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(1)	−4.591***	−4.733***	−4.745***	−4.641***	−4.693***	−4.545***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(2)	−1.224	−0.992	−1.088	−1.037	−1.012	−1.144
	(0.222)	(0.322)	(0.282)	(0.302)	(0.311)	(0.255)
Sagan	(0.534)	(0.363)	(0.319)	(0.239)	(0.142)	(0.146)

Note: *, **, ***, significant at the 10%, 5%, 1% threshold respectively. The Sargan test validates the instruments used (H0: accepted), and p-values exceed the 10% threshold. There is no autocorrelation between the second-order error terms according to the AR(2) test, and p-values are above the 10% threshold.

The negative effect of natural resources rents leads us to conclude that States are called upon to give more importance to this factor through good regulation that will allow for better control of natural resources extraction. Also, decision-makers must encourage the exploitation of sustainable natural resources such as the sun and the wind in order to substitute fossil fuels with renewable energies.

For the GDP variable, which is the proxy for economic growth, its coefficient is positive and significant at the 1% threshold. Indeed, a 1% rise in the GDP level contributes to a 0.38% rise in CO₂ emissions into the atmosphere. The MENA countries are still in their first phase where any increase in GDP is accompanied by an increase in pollutants. These results are well confirmed by Muhammad et al.⁵⁹ who concluded that economic evolution pushes industrial companies to consume more natural resources; which urges the extinction of these resources. Moreover, the financial development variable positively and significantly affects CO₂ emissions. Indeed, a 1% rise in the level of financial development generates a 0.3% rise in CO₂ emissions. We can account for this outcome by the fact that almost all investments are in the field of petroleum products extraction and processing. This represents a source of pollution and degradation of the environmental quality. The works of Acheampong,⁶⁰ Acheampong et al.,⁶¹ and Avom et al.⁶² confirm the positive effect of financial development. However, our findings do not meet those of Jalil and Feridun²² for China; Shahbaz et al.⁵ for Indonesia; Zafar et al.⁶³ for the OECD countries; and Zaidi et al.⁶⁴ for the organization of the petroleum exporting countries (OPEC) countries which proved a negative relationship. This disparity in the results obtained can be explained by differences in the approximation of financial development, the sampling, and the estimation approach.

As for the institutional quality indicators, we note that they all have positive and significant coefficients. Indeed, the poor governance indicators that characterize most MENA countries contribute to a large extent to the degradation of environmental quality in these countries. Taking the CC indicator as an example, any increase in corruption levels will have adverse effects on environmental quality through the evasion of polluting companies to pay taxes on their emissions. Also, the bribes given to decision-makers will allow the installation of polluting industries in these countries. These results are well confirmed by Sekrafi and Sghaier¹⁸ who examined the relationship between corruption, economic growth, environmental degradation, and energy consumption for the 13 MENA countries over the period 1984–2012. The authors showed that increased corruption directly affects economic growth, environmental quality, and energy consumption. However, corruption has an indirect effect on environmental quality via economic growth.

Thus, good governance can turn the natural resource curse into a blessing. As Subramanian and Sala-I-Martin⁷ point out, the natural resource curse is a purely institutional phenomenon. If institutions are of good quality (favorable to productive activities), natural resources promote growth. On the other hand, the presence of institutions favorable to predatory activities contributes to turning natural resources into a curse.

Mavragani et al.⁶⁵ examined the relationship between governance indicators and carbon emissions in a sample of 75 countries. They came to the conclusion that the index of environmental performance has a positive correlation with each governance indicator.

We present the analysis of the indirect effects in Table 4 using the interaction variables. The estimation results show that the interaction of natural resources with the different governance indicators improves environmental quality. Indeed, a 1% increase in the interaction term (NR*GI) contributes to a reduction in CO₂ emissions of (0.063, 0.024, 0.32, 0.046, 0.033, and 0.038%) for (VA, PS, GE, RQ, RL, and CC), respectively. In a good governance situation, natural resources rents will be more efficiently exploited; which contributes to increased investment in renewable energy production. Also, the control of the extractive industries encourages the installation of filters and the efficient exploitation of resources. Thus, the moderating role played by governance indicators between natural resources and environmental quality seems to be essential. For the impact of natural resources on environmental quality to be curbed, the MENA countries need to further improve their governance by increasing the level of CC, law enforcement, and good governance and fostering a stable political environment.

Robustness tests

Tables 5 and 6 illustrate the robustness of our estimates. Indeed, and given that the MENA countries’ natural resources rent is dominated by the oil rent, we used the oil rent as a proxy for the natural resources rent in what follows. The results show that the governance indicators kept their positive and significant effects on CO₂ emissions. Indeed, the MENA countries’ poor institutional quality contributes to the degradation of their environmental quality. In addition, the natural resources rent variable keeps its positive and significant sign. The MENA countries, despite their important natural resources rents volume, poorly exploit these rents given their political instability and high corruption levels. Natural resources rents are used in some countries to finance terrorism (Libya, Iraq) and also used by policy-makers to maintain their power.

Table 5.

System-GMM results for CO₂ emissions (without interaction).

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
L1.CO₂	0.609***	0.636***	0.665***	0.637***	0.630	0.611***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
PR	0.016*	0.021**	0.025***	0.016*	0.023**	0.028**
	(0.059)	(0.020)	(0.010)	(0.085)	(0.025)	(0.012)
FDI	0.011	0.031**	0.011	0.026*	0.019	0.039***
	(0.535)	(0.049)	(0.533)	(0.060)	(0.172)	(0.010)
GDP	0.377***	0.327***	0.285***	0.346***	0.334***	0.327***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
VA	0.109***
	(0.007)
PS		0.048***
		(0.004)
GE			0.087***
			(0.008)
RQ				0.033
				(0.179)
RL					0.070**
					(0.036)
CC						0.059**
						(0.049)
Constant	−2.616***	−2.387***	−2.006***	−2.545***	−2.409***	−2.389***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(1)	−4.682	−4.667	−4.645	−4.676	−4.594	−4.597
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(2)	−1.152	−0.864	−1.098	−0.936	−1.027	−1.079
	(0.252)	(0.392)	(0.274)	(0.353)	(0.308)	(0.286)
Sagan	(0.239)	(0.372)	(0.317)	(0.193)	(0.281)	(0.162)

Table 6.

System-GMM results for CO₂ emissions (with interaction).

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
L1.CO₂	0.614***	0.618***	0.656***	0.625***	0.663***	0.672***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
PR	0.004	0.018*	0.025***	0.021**	0.028***	0.025**
	(0.570)	(0.061)	(0.008)	(0.024)	(0.005)	(0.015)
FDI	0.051***	0.035**	0.023	0.045***	0.027*	0.034**
	(0.005)	(0.018)	(0.189)	(0.005)	(0.067)	(0.023)
GDP	0.376***	0.342***	0.290***	0.337***	0.272***	0.264***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
VA	0.054*
	(0.076)
PR*VA	−0.001**
	(0.016)
PS		0.068***
		(0.000)
PR*PS		−0.001***
		(0.006)
GE			0.124***
			(0.000)
PR*GE			−0.001***
			(0.006)
RQ				0.092***
				(0.007)
PR*RQ				−0.001***
				(0.000)
RL					0.103***
					(0.002)
PR*RL					−0.001**
					(0.026)
CC						0.098***
						(0.005)
PR*CC						−0.001**
						(0.027)
Constant	−2.828***	−2.500***	−2.081***	−2.515***	−1.959***	−1.915***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(1) z =	−4.802***	−4.558***	−4.512***	−4.493***	−4.765***	−4.684***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AR(2) z =	−1.18	−1.06	−1.23	−1.16	−1.17	−1.27
	(0.240)	(0.290)	(0.217)	(0.245)	(0.240)	(0.203)
Sagan	(0.109)	(0.177)	(0.215)	(0.086)	(0.102)	(0.128)

For the moderating effect, we note that it also keeps its negative impact. Thus, to boost environmental quality and reduce the amount of CO₂ emissions released into the atmosphere, the MENA countries need to further improve their governance indicators through enhanced control, law enforcement, and political stability.

Conclusions and policy implications

The issue of the effect of governance indicators on environmental degradation has raised much debate among economists in recent years, particularly after the emergence of the Dutch Disease theory and the Governance Curse theory. Researchers are trying to investigate the channel through which this curse occurs, by comparing different resource-rich countries that achieved different emissions levels. Many recent studies confirmed that the abundant natural resources represent the channel, which determines whether governance indicators are a blessing or a curse for countries’ environmental quality. The present paper examines the moderating effect of natural resources between governance indicators and CO₂ emissions in the MENA countries from 1996 to 2016 while checking the effect of economic growth and financial development. For the impact of governance indicators on CO₂ emissions to be properly investigated, we used six governance variables (i.e. VA, GE, RQ, RL, and CC) to study the effect of governance on CO₂ emissions. Relying on the system generalized method of moments (GMM), we present our findings as follows:

First, our findings prove that economic growth, natural resources, and financial development have positive and statistically significant signs. Regarding governance variables specifically, our findings indicate that governance—which is measured using VA, political governance, GE, RL, and corruption control—increases CO₂ emissions while RQ does not significantly affect CO₂ emissions in the MENA countries. Although not all measures of governance indicators affect environmental quality, it is obvious that these indicators degrade it. The main argument in this work is that the governance indicators’ degrading effect is assimilated to the market economy where group interests are put forward and which do not necessarily go in the direction of the search for a better environment. Indeed, Fredriksson and Svensson⁶⁶ showed that the RL alone cannot lead to an improvement in the environment quality because investors will make choices about their production methods and the use of resources only in accordance with their private interests.

Second, our findings show that natural resources moderate governance indicators to influence CO₂ emissions. We find that natural resources moderate GI to curb CO₂ emissions. Thus, GIs enable the transfer and allocation of green technologies to environmentally-friendly economic sectors, and hence reduce CO₂ emissions. Similarly, the moderating effect (GI*NR) on environmental degradation suggests that the MENA governments provide efficient resources (capital) allocation to companies and other economic sectors that rely on or consume a lot of nonrenewable energy, thus contributing to the reduction of CO₂ emissions. What is implied is that governance indicators increase carbon emissions directly, but also decrease carbon emissions indirectly through the natural resources channel. Third, we also tested the robustness of the natural resources moderating effect between the governance indicators and environmental degradation. We substituted our natural resources moderator (NR) with the petroleum rent moderator (PR). The results are, for the most part, significant and consistent with those obtained in the previous specifications.

Finally, this study not only adds to the existing literature that presents the moderating role of natural resources between governance and environmental quality, but also has quite important implications for policy-makers. Indeed, our findings imply that omitting natural resources from environmental degradation models will result in unsustainable carbon mitigation strategies. Our paper recommends that although natural resources hinder environmental quality, governance should encourage industries or companies to invest in environmentally-friendly projects. Thus, policy-makers must find ways to take into account the true value of natural resources in their decisions and thus allow better management of these resources. Learning to value and set a price for ecosystem services and natural resources will reduce pressure on the environment. In addition, future environmental policies should require companies as well as industries to disclose their environmental performance. It is also advisable that environmental policy-makers rely on other policy instruments like emission-trading schemes or carbon taxes to mitigate polluting emissions. As natural resources moderate governance indicators to decrease carbon emissions, policy-makers should increase public awareness about the best use of natural resources. Thus, strengthening the freedom of expression would allow citizens and the civil society to calmly denounce attitudes and activities that could harm their environment. In addition, these countries could develop cleaner institutional arrangements by improving their anti-corruption mechanisms, regulatory compliance, and GE. They should also encourage all that would remove the deficiencies that limit law implementation and the respect of companies’ and citizens’ commitments in the fight against pollution. Thus, good governance has a double effect on the quality of the environment; the first through the control of polluting industries and the second through the improvement of growth, and hence the increase in income levels which will have a positive effect on the quality of the environment.

These policy implications apply not only to the MENA countries, but can also be extended to developing countries. Therefore, future researchers should be more cautious when drawing conclusions about the effect of governance on CO₂ emissions, since different governance indicators could lead to a different impact (positive, negative, or insignificant) on polluting emissions. Further research avenues could extend this paper by examining the nonlinear relationship between governance indicators and natural resources. This is because the nonlinear effect of natural resources on governance depends on the nature of the country's system (i.e. parliamentary or presidential).

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at Jouf University for funding this work through research grant No ( DSR-2021-04-0113).

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mehdi Abid

Naima Sadaoui is an Assistant Professor at the Department of Business, at Jouf University, Saudi Arabia. He holds a PhD in Economics from the Faculty of Economics and Management of University of Algeria 3. His research areas cover, among others, Environmental resources management, natural resources, and economic development.

Lotfi Zabat is an Assistant Professor at the Department of Business, at Jouf University, Saudi Arabia. He holds a PhD in Economics from the Faculty of Economics and Management of University of Algeria 3. His research areas cover, among others, Environmental resources management, natural resources, and economic development.

Habib Sekrafi is an Assistant Professor at the Department of Economics, at Jandouba, Tunisia. He holds a PhD in Economics from the Faculty of Economics and Management of Sousse, University of Sousse. His research areas cover, among others, ecological economics, energy, environmental governance, and climate change adaptation.

Mehdi Abid is an Associate Professor at the Department of Finance and Investment, at Jouf University, Saudi Arabia. He holds a PhD in Economics from the Faculty of Economics and Management of Sousse, University of Sousse. His research areas cover, among others, international economics, energy, resource economics, and climate change.

Appendix

Table A.

List of countries

Algeria, Bahrain, Egypt, Jordan, Kuwait, Lebanon, Morocco, Oman, Saudi Arabia, Tunisia, United Arab Emirates, Qatar, Syrian Arab Republic, Yemen, Iraq, Libya.

References

Baumol

Oates

, et al. The theory of environmental policy. Cambridge University Press, 1988.

Benzerrouk

Abid

Sekrafi

. Pollution haven or halo effect? A comparative analysis of developing and developed countries. Energy Reports 2021; 7: 4862–4871.

Peach

Starbuck

. Oil and gas production and economic growth in New Mexico. J Econ Issues 2011; 45: 511–526.

Balassa

. The process of industrial development and alternative development strategies. Washington, DC: World Bank, 1980.

Shahbaz

Muhammad

Hye

, et al. Economic growth, energy consumption, financial development, international trade and CO₂ emissions in Indonesia. Renew Sust Energ Rev 2013; 25: 109–121.

Richard Auty. Resource-based industrialization: sowing the oil in eight developing countries. Oxford: Clarendon Press, 1990.

Sala-I-Martin

Subramanian

. Addressing the natural resource curse: An illustration from Nigeria. Working Paper WP/03/139. The International Monetary Fund. 2003.

WDI, World development indicators | DataBank . 2022. https://databank.worldbank. org/source/world-development-indicators#

Carrère

Gourdon

Olarreaga

. Regional integration and natural resources: who benefits? Evidence from MENA. Econ Int 2012; 131: 23–41.

10.

Jiang

Rahman

Zhang

, et al. An assessment of the impact of natural resources, energy, institutional quality, and financial development on CO₂ emissions: evidence from the B&R nations. Resour Pol 2022; 76: 102716.

11.

Polyzos

Kuck

Abdulrahman

. Demographic change and economic growth: the role of natural resources in the MENA region. Res Econ 2022; 76: 1–13.

12.

Yang

Shi

, et al. Renewable energy resources, natural resources volatility and economic performance: evidence from BRICS. Resour Pol 2022; 76: 102621.

13.

Cole

. Corruption, income and the environment: an empirical analysis. Ecol Econ 2007; 62: 637–647.

14.

Ibrahim

Law

. Institutional quality and CO₂ emission—trade relations: evidence from Sub-Saharan Africa. S Afr J Econ 2016; 84: 323–340.

15.

Apergis

Ozturk

. Testing environmental Kuznets curve hypothesis in Asian countries. Ecol Indic 2015; 52: 16–22.

16.

Transparency International. Global corruption barometer: Citizens’ voices from around the world. 2020. https://www.transparency.org/news/feature/glob al_corruption_barometer_citizens_voices_from_ around_the_world

17.

Abid

. Impact of economic, financial and institutional factors on CO₂ emissions: evidence from Sub-Saharan Africa economies. Util Policy 2016; 41: 85–94.

18.

Sekrafi

Sghaier

. Examining the relationship between corruption, economic growth, environmental degradation, and energy consumption: a panel analysis in MENA region. J Knowl Econ 2018; 9: 963–979.

19.

Biswas

Farzanegan

Thum

. Pollution, shadow economy and corruption: theory and evidence. Ecol Econ 2012; 75: 114–125.

20.

Danish

Ahmad

Hashim

, et al. Comparison of surface properties of wood biomass activated carbons and their application against rhodamine B and methylene blue dye. Surfaces and Interfaces 2018; 11: 1–13.

21.

Dasgupta

Laplante

Mamingi

. Pollution and capital markets in developing countries. J Environ Econ Manag 2001; 42: 310–335.

22.

Jalil

Feridun

. The impact of growth, energy and financial development on the environment in China: a cointegration analysis. Energy Econ 2011; 33: 284–291.

23.

Saidi

Mbarek

. The impact of income, trade, urbanization, and financial development on CO₂ emissions in 19 emerging economies. Environ Sci Pollut Res 2017; 24: 12748–12757.

24.

Tamazian

Chousa

Vadlamannati

. Does higher economic and financial development lead to environmental degradation: evidence from BRIC countries? Energy Policy 2009; 37: 246–253.

25.

Farhani

Solarin

. Financial development and energy demand in the United States: new evidence from combined cointegration and asymmetric causality tests. Energy 2017; 134: 1029–1037.

26.

Kahouli

. The short and long run causality relationship among economic growth, energy consumption and financial development: evidence from South Mediterranean Countries (SMCs). Energy Econ 2017; 68: 19–30.

27.

Zhao

Yang

. Does financial development influence CO₂ emissions? A Chinese province-level study. Energy 2020; 200: 117523.

28.

Abbasi

Riaz

. CO₂ Emissions and financial development in an emerging economy: an augmented VAR approach. Energy Policy 2016; 90: 102–114.

29.

Zhang

. The impact of financial development on carbon emissions: an empirical analysis in China. Energy Policy 2011; 39: 2197–2203.

30.

Omri

Daly

Rault

, et al.

Financial development, environmental quality, trade and economic growth: what causes what in MENA countries?

Energy Econ 2015; 48: 242–252.

31.

Jamel

Maktouf

Charfeddine

. The nexus between economic growth, financial development, trade openness, and CO₂ emissions in European countries. Cogent Econ Finance 2017; 5: 1341456.

32.

Dogan

Turkekul

. CO₂ Emissions, real output, energy consumption, trade, urbanization and financial development: testing the EKC hypothesis for the USA. Environ Sci Pollut Res Int 2016; 23: 1203–1213.

33.

Baloch

Mahmood

Zhang

. Effect 514 of natural resources, renewable energy and economic development on CO₂ emissions in BRICS countries. Sci Total Environ 2019; 678: 632–638.

34.

Dong

Sun

Hochman

. Do natural gas and renewable energy consumption lead to less CO₂ emission? Empirical evidence from a panel of BRICS countries. Energy 2017; 141: 1466–1478.

35.

Balsalobre-Lorente

Shahbaz

Roubaud

, et al. How economic growth, renewable electricity and natural resources contribute to CO2 emissions. Energy policy 2018; 113: 356–367.

36.

Wegenast

. Opening Pandora’s box? Inclusive institutions and the onset of internal conflict in oil-rich countries. Int Political Sci Rev 2013; 34: 392–410.

37.

Eregha

Mesagan

. Oil resource abundance, institutions and growth: Evidence from oil producing African countries. J Policy Model 2016; 38: 603–619.

38.

Abid

. Does economic, financial and institutional developments matter for environmental quality? A comparative analysis of EU and MEA countries. J Enviro Manage 2017; 188: 183–194.

39.

Bekun

Alola

Sarkodie

. Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci Total Environ 2019; 657: 1023–1029.

40.

Robinson

Torvik

. Endogenous presidentialism. NBER Working Papers 14603, National Bureau of Economic Research, Inc. 2008.

41.

Bulte

Damania

Deacon

. Resource abundance, poverty and development. Technical Report. 2004.

42.

Isham

Woolcock

Pritchett

, et al. The varieties of resource experience: natural resource export structures and the political economy of economic growth. World Bank Econ Rev 2005; 19: 141–174.

43.

Sala-I

Subramanian

. Addressing the natural resource curse: An illustration from Nigeria. NBER Working Papers 9804. National Bureau of Economic Research, Inc. 2003.

44.

Couttenier

. Relationship between natural resources and institutions 2008; 60.

45.

Fetahi-Vehapi

Sadiku

Petkovski

. Empirical analysis of the effects of trade openness on economic growth: An evidence for South East European countries. Procedia Econ Financ 2015; 19: 17–26.

46.

Nickell

. Biases in dynamic models with fixed effects. Econometrica: Journal of the econometric society 1981: 1417–1426.

47.

Arellano

Bond

. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies 1991; 58: 277–297.

48.

Blundell

Bond

. Initial conditions and moment restrictions in dynamic panel data models. J Econom 1998; 87: 115–143.

49.

Arellano

Bover

. Another look at the instrumental variable estimation of error-components models. Journal of econometrics 1995; 68: 29–51.

50.

Bond

Hoeffler

and Temple J. GMM Estimation of empirical growth models. Economic Papers, W21. Nuffield College, University of Oxford, 2001.

51.

Roodman

(2006, July). How to do xtabond2. In North American stata users’ group meetings 2006 (No. 8). Stata Users Group.

52.

Kennedy

. Getting counted: Markets, media, and reality. American sociological review, 2008; 73: 270–295.

53.

Chen

Separation of geochemical anomalies from the sample data of unknown distribution population using Gaussian mixture model. Computers & Geosciences 2019; 125: 9–18.

54.

Liu

Wang

Shen

, et al. Compositional model based fisher vector coding for image classification. IEEE transactions on pattern analysis and machine intelligence, 2017; 39: 2335–2348.

55.

Zhang

Jin

Wang

, et al. Pedestrian intrusion detection based on improved GMM and SVM. In 2016 13th international computer conference on wavelet active media technology and information processing (ICCWAMTIP) (pp. 311-315). IEEE., 2016.

56.

Tugcu

Ozturk

Aslan

. Renewable and non-renewable energy consumption and economic growth relationship revisited: evidence from G7 countries. Energy Econ 2012; 34: 1942–1950.

57.

Gerelmaa

Kotani

. Further investigation of natural resources and economic growth: do natural resources depress economic growth? Resour Pol 2016; 50: 312–321.

58.

Shao

Yang

. Natural resource dependence, human capital accumulation, and economic growth: a combined explanation for the resource curse and the resource blessing. Energy Policy 2014; 74: 632–642.

59.

Muhammad

. The moderating role of natural resources between institutional quality and CO₂ emissions: evidence from developing countries. 2021. https://doi.org/10.21203/rs.3.rs-299004/v1.

60.

Acheampong

. Modelling for insight: does financial development improve environmental quality. Energy Econ 2019; 83: 156–179.

61.

Acheampong

Adams

Boateng

. Do globalization and renewable energy contribute to carbon emissions mitigation in sub-Saharan Africa? Sci Total Environ 2019; 677: 436–446.

62.

Avom

Nkengfack

Fotio

, et al. ICT And environmental quality in sub-Saharan Africa: effects and transmission channels. Technol Forecast Soc Change 2020; 155: 120028.

63.

Zafar

Zaidi

SAH

Sinha

, et al. The role of stock market and banking sector development, and renewable energy consumption in carbon emissions: insights from G-7 and N-11 countries. Resour Pol 2019; 62: 427–436.

64.

Zaidi

SAH

Zafar

Shahbaz

, et al. Dynamic linkages between globalization, financial development and carbon emissions: evidence from Asia Pacific economic cooperation countries. J Clean Prod 2019; 228: 533–543.

65.

Mavragani

Nikolaou

Tsagarakis

. Open economy, institutional quality, and environmental performance: a macroeconomic approach. Sustainability 2016; 8: 601.

66.

Fredriksson

Svensson

. Political instability, corruption and policy formation: the case of environmental policy. Journal of public economics 2003; 87: 1383–1405.

The moderating role of natural resources between governance and CO 2 emissions: Evidence from MENA countries

Abstract

Keywords

Introduction

Literature review

H1: how does the quality of governance affect the quality of the environment?

H2: how does financial development affect governance?

H3: do natural resources moderate the relationship between governance and environmental quality?

Methodology

Data and empirical analysis

Data

Empirical analysis

Robustness tests

Conclusions and policy implications

Acknowledgements

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

Appendix

References

H_1: how does the quality of governance affect the quality of the environment?

H₂: how does financial development affect governance?

H₃: do natural resources moderate the relationship between governance and environmental quality?