Dr Fateh Belaid
email: fateh.belaid@univ-catholille.fr-fateh.belaid@gmail.com

Dr Maha Harbaoui Zrelli

Renewable and non-renewable electricity consumption, environmental degradation and economic development: Evidence from Mediterranean countries

Keywords: Renewable energy consumption, Non-renewable energy consumption, CO2 emissions, Panel cointegration, Energy policy, Mediterranean countries

P a g e 2 | 38

Introduction

Research has shown that the level of pollution released into the atmosphere has increased significantly since the beginning of the industrial era. Subsequently, in the last few decades, climate change recognized as the major environmental problem facing the globe, which is an intricate phenomenon arising from complex interactions between three distinct parameters-economics, energy and environment. Energy is necessary for economic production and therefore economic growth and the development of society, but it is also a major source of greenhouse gas emissions. The imperative to reduce CO2 emissions is stronger than ever. According to many studies, renewable energy has one of the most significant cost-effective potentials for reducing energy-related greenhouse gas emissions. Increasing the supply of renewable energy would allow for the replacement of carbon-intensive energy sources and significantly reduce pollutant emissions.

Fossil fuels remain the backbone of the world's energy system. At present, approximately 81% of all primary energy in the world is derived from fossil fuels, with oil accounting for 31.9%, coal for 27.1% and natural gas for 22.1% (IEA, 2018). Only 1.7% of the world's primary energy is derived from geothermal, wind, solar or other alternative energy sources.

Carbon emissions from a country's electricity generation depend on both the quantity of electricity produced and the generation mix. The mix, or types of sources used, determines the carbon intensity -the amount of CO2 emitted per unit of electricity.

P a g e 3 | 38

The exponential increase of energy consumption and the rapid growth of pollutant emissions are expected to have noticeable effect in the global environment: rising of global temperatures, erratic climate and weather extremes and altered ecosystems and habitats. All of these effects present increasing challenges for energy production and use and play a growing role in the design of the future energy system and energy policies.

In this context, several countries are paying close attention to climate change impacts and considering ways to adapt to adverse impacts by developing strategies with the aim of finding concert solutions to the problem. Incentivizing investment in renewable energy and low-carbon technologies will be a key challenge to achieve pollutant reduction targets.

However, increasing use of renewable energy has many potential benefits, including reduction of global warming emissions, diversification of energy supplies and reduced dependency on the fossil fuel energy market. In addition, renewable energy projects allow the replacement of carbon-intensive energy sources. The renewable energy industry is more labour-intensive, and thus, increasing renewable energy supply has the potential to stimulate employment through the creation of jobs in new 'green' technologies.

The Mediterranean region is endowed with a huge renewable energy (solar and wind) potential. Meanwhile, the electricity generation mix is still predominated by fossil fuels, and the renewable energy is poorly exploited. However, efforts have been taken recently, and the Mediterranean countries have tried to implement different actions and strategies P a g e 4 | 38 to resolve the energy and environmental problems and develop renewable energy. We can cite the Mediterranean Strategy for Sustainable Development (MSSD), the Mediterranean Solar Plan (MSP) and recently the Renewable Energy Solution for the Mediterranean (RES4MED). In fact, the development of large-scale renewable energy projects in this region would have many advantages, such as meeting the rising electricity demand at a lower cost, sustaining long-term economic growth, reducing energy bills in importing countries, creating new job opportunities, enhancing the quality of the environment and enhancing energy exchange cooperation between Mediterranean countries and the EU.

Many researchers have attempted to understand and define the causality relationship between energy consumption, economic growth and CO2 emissions [START_REF] Arouri | Energy consumption, economic growth and CO2 emissions in Middle East and North African countries[END_REF][START_REF] Shahbaz | Environmental Kuznets curve hypothesis in Pakistan: cointegration and Granger causality[END_REF][START_REF] Acheampong | Economic growth, CO2 emissions and energy consumption: What causes what and where?[END_REF]. The objective is to analyse the effects of economic growth and energy consumption on the environment, but few studies have focused on the causal relationship between CO2 emissions, renewable and/or nonrenewable energy (electricity) consumption and economic growth.

The main purpose of this paper is to explore the dynamic relationship between renewable electricity consumption, non-renewable electricity consumption, CO2 emissions and GDP for a panel of 9 Mediterranean countries 1 over the period 1980-2014 using the recently developed panel data methods. Therefore, the empirical estimates of this study are 1 Algeria, Egypt, France, Greece, Italy, Morocco, Spain, Tunisia and Turkey P a g e 5 | 38 important to guide policy-makers' decisions in terms of energy use, sustainable growth and CO2 emissions reduction the Mediterranean countries.

This study contributes to the existing body of knowledge in the following important ways. First, many studies have attempted to understand and define the causality relationship between energy consumption, economic growth and CO2 emissions, but a small body of literature has focused on the dynamic relationship between CO2 emissions and disaggregated energy consumption. Nevertheless, the focus of this paper is to contribute further to this literature by using disaggregated energy consumption (renewable and non-renewable electricity). Second, to address the limitation of prior research, we used the recently developed panel data methods, based on the secondgeneration estimators that consider cross-sectional dependence and slope homogeneity observed in the global panel. Accordingly, the PMG panel ARDL estimation approach accounts for cross-sectional heterogeneity through the short-term parameters and facilitates both long-run and short-run causalities inferences to be drawn, regardless of whether the variables used are integrated of order one or zero (I(1) or I(0)). More precisely, unlike most previous studies, we performed: (i) a cross-sectional dependence test to decide which unit root test would be appropriate, second generation unit root tests that assume homogeneity among each cross section, (ii) Westerlund's cointegration test that allows for cross-sectional dependence, (iii) Pesaran's Common Correlated Effects Mean Group (CCEMG) estimator to estimate the long-term estimators that account for cross-sectional dependence and (iv) the Pooled Mean Group (PMG) estimator proposed P a g e 6 | 38 by Pesaran to identify the sources of causality and distinguish between short-term and long-term relationships, which is robust to outliers and the choice of lag orders. To the best of our knowledge, only a few empirical studies have used the second-generation estimators (e.g., AMG and CCEMG) to investigate the emission-growth-renewables nexus [START_REF] Dong | CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions[END_REF].

Finally, although quite a number of empirical studies have been done on whether there is an existence of causal link amid renewable and non-renewable energy, economic growth, and carbon emissions; relatively little attention has been paid to the nexus amongst the aforementioned variables in Mediterranean countries. As far as we know, not much attention has been paid to discerning between North and the South Mediterranean countries.

The choice of the Mediterranean countries is motivated by the fact that little attention has been paid to these countries. Accordingly, like in many other countries, the literature on causality between renewable and non-renewable and other variables of Mediterranean countries is rather limited. However, to our knowledge, none of the empirical studies have focused on investigating the dynamic link between renewable and non-renewable electricity consumption-carbon emissions-GDP in this region. This paper sheds light on the possible sources and directions of the relationship between pollutant emissions, economic growth, renewable and non-electricity consumption. The study can also provide ideas on the design and the implementation of future economic and energy policies in the region. In embarking on this path, this article aims to answer the following P a g e 7 | 38 questions: (1) What is the role of renewable electricity consumption in reducing carbon emissions in the Mediterranean countries?; (2) Is there a possibility of substitution of renewable electricity for non-renewable electricity in the region in the GDP growth process?; (3) How can renewable energy contribute to reducing pollutant emissions and sustaining long-term economic growth?; and (4) What is the impact of the increasing electricity demand on the environmental quality in the Mediterranean countries?

In addition to the introductory section (Section 1), the rest of the paper is organized as follows: Section 2 presents the literature on the subject. Section 3 provides a brief overview of renewable energy in Mediterranean countries. Section 4 describes the data and the econometric model. We report our empirical findings in Section 5. Based on the results of the model, we draw conclusions and provide some policy implications in Section 6.

Literature review

There is an impressive body of literature concerning the relationship between energy consumption, economic growth and pollutant emissions. This issue is not a recent concern, hailing back to the 18 th century when Malthus (1798) discussed the impact of growing exploitation of natural resources in an environment with limited capacity to sustain an ever-increasing populace. In the literature of energy economy, we can distinguish three strands. The first group of studies has focused on the relationship between economic growth and environmental pollutant nexus. It tries to verify the Environmental Kuznets Curve (EKC) hypothesis. The EKC hypothesis postulates an P a g e 8 | 38 inverted-U-shaped relationship between different pollutants and per capita income, i.e., environmental degradation increases up to a certain level as income goes up; after a turning point, it starts declining. Therefore, the EKC hypothesis expresses a well-defined relationship between growth and environmental quality (see [START_REF] Arouri | Energy consumption, economic growth and CO2 emissions in Middle East and North African countries[END_REF][START_REF] Dinda | Environmental Kuznets curve hypothesis: a survey[END_REF][START_REF] Grossman | Environmental Impacts of the North American Free Trade Agreement[END_REF][START_REF] Pérez-Suárez | Growing green? Forecasting CO2 emissions with environmental Kuznets curves and logistic growth Models[END_REF][START_REF] Rüstemoğlu | Determinants of CO2 emissions in Brazil and Russia between 1992 and 2011: a decomposition analysis[END_REF][START_REF] Shahbaz | Environmental Kuznets curve hypothesis in Pakistan: cointegration and Granger causality[END_REF]. A second set of studies has focused on the relationship between energy consumption and economic growth. Since the pioneering study by [START_REF] Kraft | On the relationship between energy and GNP[END_REF], much causality literature has emerged [START_REF] Bélaïd | Electricity consumption and economic growth in Algeria: a multivariate causality analysis in the presence of structural change[END_REF][START_REF] Belloumi | Energy consumption and GDP in Tunisia: cointegration and causality analysis[END_REF][START_REF] Lise | Energy consumption and GDP in Turkey: is there a co-integration relationship?[END_REF][START_REF] Omri | CO2 emissions, energy consumption and economic growth nexus in MENA countries: evidence from simultaneous equations models[END_REF][START_REF] Tsani | Energy consumption and economic growth: a causality analysis for Greece[END_REF], and there is no consensus in results. The third strand has emerged from the two last sets of studies. It analyses the relationship between economic growth, energy consumption and pollutant emissions. We can mention [START_REF] Ang | CO2 emissions, energy consumption, and output in France[END_REF], [START_REF] Arouri | Energy consumption, economic growth and CO2 emissions in Middle East and North African countries[END_REF], [START_REF] Cowan | The nexus of electricity consumption, economic growth and CO2 emissions in the BRICS countries[END_REF] and [START_REF] Kasman | CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis[END_REF]. The results of these studies differ from one country to another. The mixed findings reflect several factors, including institutional differences between countries, model specification and econometric approach (See Table 1).

[Please Insert Table 1]

Recently, some researchers have focused on renewable energy and its role in enhancing growth and combatting global warming. The new trend in the literature of energy P a g e 9 | 38 economics is decomposing the effects of renewable and non-renewable energy consumption on the economy, but there are a few studies that have focused on the causal relationship between CO2 emissions, renewable and/or non-renewable energy (electricity) consumption and economic growth [START_REF] Bélaïd | Environmental degradation, renewable and non-renewable electricity consumption, and economic growth: assessing the evidence from Algeria[END_REF]. In this section, we will outline some results of this field of research (see Table 2).

[Please Insert Table 2]

With rapidly increasing deployment of renewable energy, a growing body of research has investigated the role of renewable energy consumption in affecting environmental quality and economic development at the country-specific, regional, and global level. For example, [START_REF] Sadorsky | Renewable energy consumption and income in emerging economies[END_REF] analysed the relationship between renewable energy consumption, GDP and CO2 emissions in G7 countries from 1980-2005. Panel cointegration estimates show that a 1% increase in real income per capita increases renewable energy consumption in G7 countries by 8.44% or 7.24% according to whether the estimation technique used is FMOLS or DOLS, and a 1% increase in CO2 emissions per capita increases renewable energy consumption in G7 countries by 5.23% (FMOLS technique). So, real GDP and CO2 emissions have positive effects on renewable energy consumption. [START_REF] Menyah | Energy consumption, pollutant emissions and economic growth in South Africa[END_REF] explored the causal relationship between CO2 emissions, nuclear energy, renewable energy and growth in USA over the period 1960-P a g e 10 | 38 2007 using a [START_REF] Toda | Statistical inference in vector autoregressions with possibly integrated processes[END_REF] test. The investigation confirms unidirectional causality from nuclear to carbon emissions. This result confirms the negative impact of non-renewable energy on the environment. The second important result is the unidirectional causality from CO2 emissions to renewable energy consumption. [START_REF] Salim | Why do some emerging economies proactively accelerate the adoption of renewable energy?[END_REF] analysed the relationship between CO2 emissions and renewable energy consumption and income for 6 emerging countries using the dynamic OLS and fully modified OLS techniques. They concluded that renewable energy consumption is significantly determined by income and CO2 emissions in Brazil, China, India and Indonesia. For the same countries, the Granger causality test confirms bidirectional causality between renewable energy consumption and CO2 emissions in the short term and bidirectional causality between income and CO2 emissions in Brazil, China and Turkey. The last result implies that decreased CO2 emissions can negatively affect economic growth in these countries. [START_REF] Sebri | On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: fresh evidence from BRICS countries[END_REF] investigated the relationship between renewable energy consumption, CO2 emissions and economic growth for the BRICS countries over the period 1971-2010 within multivariate frameworks. The ARDL bounds testing approach to cointegration and Vector Error Correction Model are used to examine the long-term and causal relationships. The ARDL approach confirms the positive impact of renewable energy consumption on economic growth and vice versa. The causality test concludes that there is bidirectional causality between economic growth and renewable energy consumption in the short and long terms for all studied countries except India. They also find bidirectional causality among P a g e 11 | 38 all the variables in the long term. [START_REF] Shafiei | Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis[END_REF] explored the relationship between non-renewable and renewable energy consumption and CO2 emissions in OECD countries using the STIRPAT model over the period 1980-2011. Their results support the existence of an Environmental Kuznets Curve between CO2 emissions and urbanization. They find that renewable energy consumption has a negative impact on CO2 emissions, whereas non-renewable energy consumption has a positive and significant impact on carbon emissions. They conclude that policy makers should design and develop effective support policies to promote investment in new renewable energy technologies. Sinah et al. (2018) analysed the relationship between economic growth, CO2 emissions, fossil fuel and renewable energy consumption in Next 11 countries between 1990-2016. They conducted both parametric and non-parametric causality tests (Granger, 1969 andDocks andPanchenko, 2006) and introducing the Geweke (1982) causality test to compare the results. The authors emphasize the importance of causality results in the determination of energy policies. They find a bidirectional causality between CO2 emissions fossil fuel consumption, and economic growth.

Despite the growing interest on the relationship between renewable energy and carbon emissions, up to now, there is no consensus on the results of the recent empirical studies.

The findings are diverse, and the results regarding the direction of causality are inconclusive. To fill this knowledge gap and contribute to the emerging literature, the potential innovations of this paper are as follows: (i) First, it investigates the relationship among renewable and non-renewable electricity consumption, CO2 emissions and P a g e 12 | 38 economic growth for Mediterranean countries, (ii) Second, we use a causality framework including panel unit-root, cointegration, estimation, and causality tests allowing fro cross-sectional dependence and slope homogeneity. .

Brief overview of energy context in Mediterranean countries

The Mediterranean basin faces many environmental and energy challenges. The population of approximately 450 million (more than half live in: Egypt, Turkey, France and Italy) consumes almost 1,000 million tons of oil equivalent (Mtoe) of energy each year, i.e., approximately 8.2% of global demand. To date, approximately 94% of the energy consumption of MED-11 countries is covered by hydrocarbons. In the north, more than 90% of fossil fuels are imported, while the south enjoys an export capacity of 26%.

The two shores of the Mediterranean are complementary, and energy constitutes a strong link between them: the European Union depends on the south for 35% of its gas and 22% of its oil, which represents respectively approximately 85% and 50% of the south's export volumes. The southern Mediterranean region is the third largest supplier of natural gas into the EU.

The first consequence of this strong production and consumption is the global warming caused by pollutant emissions. The carbon emission has grown dramatically over the last few decades (see Fig. 1), and the single largest source of CO2 emissions in the southern Mediterranean region is the combustion of fossil fuels to generate electricity. Indeed, the trend scenario of the Energy World Organization shows that the energy consumption in the Mediterranean region will be based on oil in 2030. These countries will import 39% of P a g e 13 | 38 their needed oil and 28% of their needed natural gas. This consumption will increase CO2 emissions and, according to different reports and studies, the countries on the south shore of the Mediterranean are particularly vulnerable to global warming because of their geographical position and their dependence on climate sensitive economic sectors (such as agriculture, fisheries and tourism). The climate change affects the entire area, and common risks have already been detected. This is an example of common contrasts between the northern shore countries (developed) and the southern shore countries (still in development).

[Please Insert Fig.1.]

Electricity is at the heart of economic and social development of many countries, and it plays a crucial role in the Mediterranean region, particularly on the south shore. In the south, electricity consumption had an annual growth rate of approximately 6% between 1990 and 2010, a rate three times greater than the north Mediterranean countries whose consumption grew by an annual growth rate of 1.8% (FEEM, 2015). In the future, higher levels of economic growth and population will increase demand for electricity and put additional pressure on the existing electricity infrastructure, requiring major investment in the construction of new electricity generation facilities, transmissions lines and distribution networks. However, the regional electricity generation mix still dominated by fossil fuel. In this situation, the development of renewable and efficient energy sources P a g e 14 | 38 represents an important contribution to the sustainability of the region, particularly considering the huge potential of existing renewable energy resources, such as wind, solar and biomass energies. The countries on the southern shore of the Mediterranean have a high rate of sunshine, between 2,700 and 3,400 hours per year, and the average annual radiation is between 1,900 kWh per m 2 in the coastal areas and 3,200 kWh per m² in the desert areas. In the framework of the "Solar Atlas for the Mediterranean", the economic potential of CSP in the south shore could be estimated at 431,382 TWh/year and in the north shore at 1,450 TWh/year in 2030. The potential of photovoltaic energy is calculated to be 122 TWh/year in the south shore and only 22 TWh/year in the north shore. The wind potential is also high; the wind speed is between 6 and 11 m/s. The technical potential is estimated to be 21,967 TWh/year in the south and 648 TWh/year in the north [START_REF] Feem | The Future of Renewable Energy in the Mediterranean[END_REF]. Despite these high potentials, the share of renewable energy production is still below what is needed. Therefore, cooperation between both shores can have many advantages. The north shore has human skills, technology and experience, while the south shore has abundant natural resources.

Worldwide, in 2016 2 , renewable energy represented 19.3% of global final energy consumption, and the share of renewable energy in electricity production in 2016 represented 23.7%, less than fossil fuel at 78.4% but more than nuclear at 2.3%. In Mediterranean countries, the share fluctuates (Fig. 2 Despite the great potential of renewable energy and all efforts deployed in this region, the share of renewable energy remains low, and the projects fail to start. This is explained essentially by the different barriers (commercial, infrastructural regulatory and financial) prevailing particularly on the southern shore. The most significant barrier is energy subsidies. The main justification of this political economy of energy is to limit energy poverty and boost domestic supply. However, a negative consequence of these subsidies is that they discourage efficient energy use, limit financial resources available to invest in the energy sector and limit the competitiveness of renewable energy sources [START_REF] Feem | The Future of Renewable Energy in the Mediterranean[END_REF].

The huge potential of renewable energy sources in the region might benefit all the countries in the region in terms of energy security, replacing carbon-intensive energy sources, providing affordable electricity, stabilizing energy prices, promoting economic growth, creating new jobs, open new business and market opportunities. Nevertheless, reinforcing Regional cooperation would facilitate the penetration of a higher share of renewable energy in the power grid, foster technology transfer and promote business and market opportunities for power trade. Therefore, policymakers in the Mediterranean P a g e 17 | 38 region should support Clean Energy initiative, which aims at supporting Mediterranean countries and creating synergies among them in the integration of larger shares of renewable energy on their energy mix. The final goal is to reach an integrated power grid covering the whole region, enabling for cleaner-based energy exchanges. Regional cooperation could help alleviate issues and create an effective coordination mechanism across main countries stockholders.

Given these facts, we develop an econometric model to verify whether the development of renewable energy would limit CO2 emissions and boost economic growth in the Mediterranean countries.

Methodology

Data and model

Our empirical analysis is based on annual time series data over the period 1980-2014 for 9 Mediterranean countries. Data on electricity consumption, renewable and nonrenewable (Billion kilowatt hours) are obtained from the International Energy Agency (IEA, 2014). The data on real GDP per capita (constant 2010 US$), CO2 emissions (metric tons) are taken from the World Development Indicators of the World Bank (WDI, 2018).

In this paper, we investigate the causality between renewable and non-renewable electricity consumption, CO2 emissions and GDP. Consequently, the logarithmic form of the estimated equation is as follows:

CO = + + + ln + (1)
where CO2, GDP, NREC, REC denote CO2 emissions, gross domestic product, non-P a g e 18 | 38 renewable electricity consumption and renewable electricity consumption, respectively.

is the error term. The subscript i refers to countries, and t denotes the year.

Estimation strategy

To explore the dynamics of the relationships between CO2 emissions, electricity consumption and GDP, the following steps are performed. The steps of the model are summarized in Fig. 4.

[Please Insert Fig. 4]

Testing cross-sectional dependence

One important issue in panel causality analysis is considering possible cross-sectional dependence across regions. Therefore, several cross-sectional dependencies tests have been used, including Freidman (1937), Frees (1995), and [START_REF] Breusch | The lagrange multiplier test and its applications to model specification in econometrics[END_REF], and [START_REF] Pesaran | General Diagnostic Tests for Cross Section Dependence in Panels[END_REF], tests. This step allows us to choose the appropriate unit-root tests. The Lagrange Multiplier test (LM), developed by [START_REF] Breusch | The lagrange multiplier test and its applications to model specification in econometrics[END_REF], is favourable when T is larger than N. However, [START_REF] Pesaran | General Diagnostic Tests for Cross Section Dependence in Panels[END_REF] presence of common shocks and unobserved components that ultimately become part of the error term [START_REF] Anselin | Spatial econometrics[END_REF][START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF][START_REF] Pesaran | General Diagnostic Tests for Cross Section Dependence in Panels[END_REF][START_REF] Robertson | Factor Residuals in SUR Regressions: Estimating Panels Allowing for Cross Sectional Correlation[END_REF].

The presence of some form of cross-sectional correlation of errors in panel data applications in economics is likely to be the rule rather than the exception. According to De Hoyos and Sarafidis ( 2006), one reason for this result may be that during the last few decades we have experienced an ever-increasing economic and financial integration of countries and financial entities, which implies strong interdependencies between crosssectional units. This is because a high degree of economic and financial integration makes a region sensitive to economic shocks.

However, ignoring cross-sectional dependence of errors (as is commonly done by practitioners) can have serious consequences. It is well known that ignoring crosssectional dependence may affect the first-order properties (unbiasedness and consistency) of standard panel estimators and lead to incorrect statistical inference. The decrease in estimation efficiency can become so large that the pooled (panel) least-squares estimator may provide little gain over the single-equation ordinary least squares [START_REF] Phillips | Dynamic panel estimation and homogeneity testing under cross section dependence[END_REF].

Panel unit root tests

As a first step, it is necessary to check whether each variable of interest is stationary. Since the seminal works of Levin andLin (1992, 1999) and [START_REF] Quah | Exploiting cross-section variation for unit root inference in dynamic data[END_REF] Several panel unit root tests have been developed in the literature [START_REF] Choi | Unit Root Tests for Panel Data[END_REF][START_REF] Hadri | Testing for stationarity in heterogeneous panel data[END_REF][START_REF] Harris | Inference for unit roots in dynamic panels where the time dimension is fixed[END_REF][START_REF] Im | Testing for Unit Roots in Heterogeneous Panels[END_REF][START_REF] Levin | Unit root tests in panel data: asymptotic and finite-sample properties[END_REF]Levin and Lin, 1992;[START_REF] Maddala | A comparative study of unit root tests with panel data and a new simple test[END_REF][START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF]. Two generations of tests can be distinguished.

The first generation of panel unit root tests is based on the cross-sectional independency hypothesis and includes the contributions of [START_REF] Maddala | A comparative study of unit root tests with panel data and a new simple test[END_REF], [START_REF] Choi | Unit root tests for panel data[END_REF][START_REF] Hadri | Testing for stationarity in heterogeneous panel data[END_REF] and [START_REF] Im | Testing for unit roots in heterogeneous panels[END_REF].

Various tests have been proposed in response to the need for panel unit root tests that relax the cross-sectional independence assumption and allow for cross-sectional dependence. The second-generation unit root tests include the contributions of [START_REF] Bai | A PANIC attack on unit roots and cointegration[END_REF], [START_REF] Moon | Testing for a unit root in panels with dynamic factors[END_REF], [START_REF] Smith | More powerful panel data unit root tests with an application to mean reversion in real exchange rates[END_REF], [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF] and [START_REF] Pesaran | Testing slope homogeneity in large panels[END_REF]. This last category of tests is still under development, given the diversity of the potential cross-sectional correlations. In the presence of cross-sectional dependence, "first generation" panel unit root tests tend to reject the null hypothesis of a unit root excessively.

Hence, we propose two different panel unit root tests: the [START_REF] Breitung | The local power of some unit root tests for panel data[END_REF] test, which assumes homogeneity within each cross section, and a more recent CADF test suggested by [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF].

P a g e 21 | 38

Panel cointegration tests

The next step in our analysis is to apply the cointegration test. When both series are integrated of the same order, we can proceed to test for the presence of cointegration, i.e., whether there is a long-term relationship between the variables. Consequently, panel cointegration test can be used to study the long-term equilibrium process. For this purpose, we used the recent Durbin Hausman group mean cointegration test developed by [START_REF] Westerlund | A simple test for cointegration in dependent panels with structural breaks[END_REF]. This test allows for cross-sectional dependence and does not rely heavily on a priori knowledge regarding the integration orders of the variables, which allows the stability ranks of the independent variables to be different.

Thus, it can be applied under very general conditions.

Estimation of long-term relationship

As stated by [START_REF] Phillips | Bias in dynamic panel estimation with fixed effects, incidental trends and cross section dependence[END_REF], when models suffer from the problem of cross- the variables and possible contemporaneous dependence of the observed regressors with the unobserved factors [START_REF] Chudik | Weak and strong cross-section dependence and estimation of large panels[END_REF].

In this step, we employ the Common Correlated Effects Mean Group (CCEMG) estimator, proposed by [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF], to estimate the long-term estimators that account for crosssectional dependence. [START_REF] Eberhardt | Estimating panel time-series models with heterogeneous slopes[END_REF] stated that the CCEMG approach is robust to the presence of a limited number of "strong" factors and an infinite number of "weak" factors. In addition, the estimator is robust to non-stationary common factors [START_REF] Kapetanios | Panels with non-stationary multifactor error structures[END_REF].

Granger causality test: panel short-term and long-term causality test

Given the existence of a cointegration relationship, the next step is to determine the source and the direction of causality between the variables. Panel Granger causality is tested following the two-step Engle-Granger causality procedure [START_REF] Engle | Co-integration and error correction: representation, estimation, and testing[END_REF]. To identify the sources of causality and distinguish between short-term and longterm relationships, we apply the Pooled Mean Group (PMG) estimator proposed by [START_REF] Pesaran | Pooled mean group estimation of dynamic heterogeneous panels[END_REF]. The PMG estimator (see [START_REF] Pesaran | Pooled mean group estimation of dynamic heterogeneous panels[END_REF][START_REF] Pesaran | Pooled mean group estimation of dynamic heterogeneous panels[END_REF] relies on a combination of pooling and averaging of coefficients. This particular estimator allows us to address an important problem that confronts empirical panel studies: parameter heterogeneity. The major advantage of PMG is that it allows short-term coefficients including the intercepts, the speed of adjustment to the long-term equilibrium values and error variances to be heterogeneous across countries, while the long-term slope coefficients are restricted to be homogeneous across countries. In addition, the PMG

P a g e 23 | 38

estimation technique is robust to outliers and the choice of lag orders. The basic PMG estimator involves estimating an ARDL model of order (pi, qi). In this case, the ARDL dynamic panel is specified as follows:

= , + ! * # , + $ % &' ( &) + * (2)
where # (, * 1) is the vector of explanatory variables; $ represent the fixed effects;

are scalars; and ! * are (, * 1) coefficient vectors. It is convenient to work with the reparameterization (see [START_REF] Pesaran | Pooled mean group estimation of dynamic heterogeneous panels[END_REF] of Eq. ( 2):

-= . / , ) + 0 1 # 2 + * ∆ , ) + ! 1 * ∆# , + $ ( ) &' ( ) &) + * (3)
where:

. = -6- ; > = 1, 2, … , @ -1

! 1 * = ! ,; % ;& <) ; > = 1, 2, … , A -1
. represents the error-correction speed adjustment term. The long-term equilibrium relationship can be tested statistically using the significance of . . If the null hypothesis . = 0, , then there would be evidence of long-term equilibrium, i.e., the variables are P a g e 24 | 38 cointegrated and there is evidence of long-term causality from the independent to the dependent variables. The direction of short-term causality can be determined by testing the significance of the coefficients of each explanatory variable, that is, ! 1 * = 0 in Eq. 3.

In our case, we can specify Eq. 4 in terms of variables in Eq. 1 as follows:

-LnCO = . / , ) + 0 1 # 2 + * ∆LnCO , ) + ! 1 * ∆# , + $ ( ) &' ( ) &) + * (4)
Where X is the vector of explanatory variables: LnGDP, LnNREC and LnREC. In the same way, we can specify equations for other variables.

Empirical analysis and results discussion

Cross dependence tests

We begin our analysis by examining the potential contemporaneous correlation across the units (countries) and assess the types of unit root and cointegration tests that best fit our data set we employed various cross-sectional dependencies tests, including Freidman (1937), Frees (1995[START_REF] Breusch | The lagrange multiplier test and its applications to model specification in econometrics[END_REF], and [START_REF] Pesaran | General Diagnostic Tests for Cross Section Dependence in Panels[END_REF], tests. The results of these tests are illustrated in Table 3. We notice from this table that the results indicate that the null hypothesis of cross-sectional dependence is statistically rejected by all the tests at the 1% significant level. Therefore, each series contains cross-sectional dependence, suggesting that a shock occurring in one of these Mediterranean countries seems to be transmitted to other countries.

To deal with this issue we implemented various second-generation unit root tests, all these tests are robust to the presence of the cross-sectional dependence.

P a g e 25 | 38 [Please Insert Table 3]

Unit-root test

To examine the stochastic properties of the four series (unit roots and stationarity). The first-generation test LLC(Lev-in-LIN-Chu) and Breitung [START_REF] Breitung | The local power of some unit root tests for panel data[END_REF] Pesaran CADF

(2007) and [START_REF] Breitung | The local power of some unit root tests for panel data[END_REF] tests have been applied, as we can see in Table 4.

[Please Insert Table 4]

Once we found the presence of dependence in the variables, we studied their order of integration using different tests that account for dependence. All are representative of the "second generation" panel unit root tests. These tests relax the restrictive assumption of cross-sectional independence. We apply IPS (Im-Pesaran-Skin), Fisher-PP and Fisher-ADF test [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF] CADF test (Cross Augmented Dickey Fuller).

To eliminate the cross dependence, the standard DF regressions are augmented with cross-sectional averages of lagged levels and first differences of the individual series. The proposed test has the advantage of being relatively robust with respect to cross-sectional dependence, even if the autoregressive parameter is high. In addition, the approach is intuitive and simple to implement. It is also valid for panels where N and T are of the same orders of magnitudes. Second, we also apply the Breitung test [START_REF] Breitung | The local power of some unit root tests for panel data[END_REF][START_REF] Breitung | Panel unit root tests under cross-sectional dependence[END_REF]. This test is a suitable approach when cross-correlation is

P a g e 26 | 38
pervasive, as in this case. The Breitung test assumes that the error term * is uncorrelated across both i and t. Breitung test adjusts the data before fitting a regression model so that bias adjustments are not needed. In addition, the Breitung procedure allows for a prewhitening of the series before computing the test. The null hypothesis of these unit root tests is that all series contain a unit root.

Cointegration tests

Given that each of the variables presents a panel unit root, we need then to check whether there is a long-term relationship between the variables using the error correction based cointegration test for (unbalanced) panels developed by [START_REF] Westerlund | Testing for error correction in panel data[END_REF]. The existence of a negative error correction term is taken as proof for cointegration. To accommodate cross-sectional dependence, critical values are obtained through bootstrapping. The test is meaningful for application in our case for the following reasons: First, it is general enough to allow for a large degree of heterogeneity, both in the long-term cointegration relation and in the short-term dynamics [START_REF] Persyn | Error-correction-based cointegration tests for panel data[END_REF]. Second, it is developed to cope with cross-sectionally dependent data. Third, the test comes along with an optional bootstrap procedure that allows for multiple repetitions of the cointegration tests, which is meaningful because we have indications of cointegration in the panel. While the group-mean tests (Gt and Ga) examine the alternative hypothesis that at least one unit is cointegrated, the panel tests (Pt and Pa) have the alternative hypothesis that the panel is cointegratede [START_REF] Persyn | Error-correction-based cointegration tests for panel data[END_REF]. As we can see in Table 5, the results of Westerlund's test show that Groupe-t and Panel-a that Groupe-P a g e 27 | 38 t and Panel-t test statistics are significant and reject the null hypothesis of no panel cointegration, thus results from Johansen Fisher panel cointegration test confirm the existence of at least two cointegration relationship between variables, suggesting that there exist a long-run cointegration between the variables considered in the model. In other words, our variables are associated for long-run.

[Please Insert Table 5]

Long-term estimation

Empirical evidence suggests that CO2, GDP, REC and NREC are cointegrated. To further explore the sustainability condition, we estimate the long-term parameters in the cointegration relation of each panel using the Cross Correlated Effects (CEE) and the Common Correlated Effects Mean Group (CCE-MG) estimation procedures developed by [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF] CCE-MG estimations. In Table 6, we report estimates of Pesaran's longterm CCEMG. The results show that GDP per capita and non-renewable electricity have positive and significant effects on CO2 emissions, implying that an increase in both GDP per capita and non-renewable electricity consumption in Mediterranean countries leads to increased CO2 emissions. In addition, we can observe that the coefficient for GDP is greater than that for NREC. This result demonstrates that in the long term, GDP per capita contributes more to increased pollutant emission than NREC in Mediterranean countries.

The coefficient of GDP suggests that a 1% increase in this factor leads to an increase in CO2 emissions by 0.35%. Similar results have been found by [START_REF] Shafiei | Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis[END_REF] for P a g e 28 | 38 the OECD countries. However, it is found that renewable electricity consumption has a negative effect on CO2 emissions, and thus 1% increase in renewable electricity consumption reduces CO2 emissions by 0.002% in the long term. This finding is consistent with the negative relationship found by [START_REF] Shafiei | Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis[END_REF] for the OECD countries.

[Please Insert Table 6]

Short-term and long-term causality test

The results of short-and long-term Granger causality test are reported in Table 7 below.

The findings are interpreted essentially for the relationships between CO2 and the other variables.

[Please Insert Table 7]

Regarding long-term causality, the ECT's coefficients are negative and statistically significant where CO2 and non-renewable electricity are the dependent variables. This implies that there is bidirectional long-term causality between CO2 emissions and nonrenewable electricity consumption. This result is consistent with the findings of [START_REF] Shafiei | Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis[END_REF]. In addition, we also find unidirectional long-term causality from renewable electricity consumption to CO2 emissions, in contrast with the results of [START_REF] Shafiei | Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis[END_REF] for OECD countries and Menyah and Wolde-Rufael (2010) for the US.

The finding of a unidirectional causal relationship from GDP to CO2 emissions is in line

P a g e 29 | 38
with the results of [START_REF] Acaravci | Electricity consumption-growth nexus: evidence from panel data for transition countries[END_REF] and [START_REF] Akpan | Electricity consumption, carbon emissions and economic growth in Nigeria[END_REF]. It indicates that controlling and reducing CO2 emissions does not affect economic growth for this panel. Similarly, there is unidirectional causality from GDP to non-renewable electricity consumption. This result was found by [START_REF] Apergis | The renewable energy consumption-growth nexus in Central America[END_REF] and [START_REF] Tugcu | Renewable and non-renewable energy consumption and economic growth relationship revisited: evidence from G7 countries[END_REF] for Japan and England and implies that energy conservation policies will have no adverse effect on the real output growth. When the dependent variable is GDP, the ECT coefficient is negative but not significant, so there is no causality from CO2 emissions, renewable and non-renewable electricity consumption to GDP in the long term. The same conclusion can be made if the dependent variable is the renewable electricity consumption. There is no causality from CO2 emissions, non-renewable electricity consumption and GDP to renewable electricity consumption. The non-causality between renewable electricity consumption and GDP is a finding of [START_REF] Payne | On the dynamics of energy consumption and output in the US[END_REF] and [START_REF] Menegaki | Growth and renewable energy in Europe: a random effect model with evidence for neutrality hypothesis[END_REF]. This is in line with the low share of renewable electricity consumption in this panel. Fig. 5 recapitulates the long-term causal relationship between the four series for the panel.

[Please Insert Fig. 5]

Turning to the short-term Granger causality relationship, the empirical results indicate the existence of bidirectional causality between renewable electricity consumption and GDP. This result is consistent with the finding of [START_REF] Sadorsky | Renewable energy consumption and income in emerging economies[END_REF], [START_REF] Apergis | Renewable and non-renewable energy consumption-growth nexus: evidence from a panel error correction model[END_REF], [START_REF] Bildirici | Economic growth and biomass energy[END_REF] and [START_REF] Sebri | On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: fresh evidence from BRICS countries[END_REF]. It indicates that economic growth granger causes renewable energy consumption, and each mutually influences the other P a g e 30 | 38 in Mediterranean countries. Therefore, the development of renewable energy resources may lead to a significant positive impact on economic growth. However, any negative shock in the process may have a negative impact on economic growth in the region.

Renewable energy is one determinant of growth in Mediterranean countries, and the increase in income is a core factor driving the development of the renewable energy sector. Therefore, the governments of the Mediterranean countries considered in this study should develop more renewable energy consumption and promote investment in new renewable energy technologies. Similarly, we find bidirectional causality between CO2 emissions and renewable electricity consumption. This result is in line with findings by [START_REF] Salim | Why do some emerging economies proactively accelerate the adoption of renewable energy?[END_REF]. Thus, CO2 emissions lead policy makers to take different policy measures to scale down fossil energy consumption and develop more renewable energy.

A bidirectional causal relationship is confirmed between CO2 emissions and GDP; this result is consistent with the findings of [START_REF] Salim | Why do some emerging economies proactively accelerate the adoption of renewable energy?[END_REF] for 6 emerging countries and [START_REF] Omri | CO2 emissions, energy consumption and economic growth nexus in MENA countries: evidence from simultaneous equations models[END_REF] for MENA countries. This implies that degradation of the environment has an impact on economic growth. The evidence suggests that, by reducing pollutant emissions, a country may sacrifice its economic growth. Finally, the empirical result suggests that there is a bidirectional causal relationship between renewable electricity consumption and non-renewable electricity consumption.

As shown in Table 7, there is unidirectional causality from non-renewable electricity consumption to GDP. The same result is found by [START_REF] Alshehry | Energy consumption, carbon dioxide emissions and economic growth: the case of Saudi Arabia[END_REF]. Nonrenewable electricity consumption is one determinant of growth in Mediterranean P a g e 31 | 38 countries. Similarly, we find a unidirectional causality from non-renewable electricity consumption to CO2 emissions, and this result is consistent with the findings of [START_REF] Menyah | Energy consumption, pollutant emissions and economic growth in South Africa[END_REF]. This finding implies that energy conservation policies are determinant to limit pollutants and environment degradation.

Fig. 6 recapitulates the short-term causal relationships between the four series for the panel.

[Please Insert Fig. 6] P a g e 32 | 38

Conclusion

In this study, we explored the dynamic relationship between renewable, non-renewable electricity consumption, CO2 emissions and GDP for 9 Mediterranean countries over the period 1980-2012. To address the limitations of prior research, we used recently developed panel data methods that consider cross-sectional dependence across regions.

In addition, the statistical techniques used in this study better distinguish between the short-and long-term causality and take into account possible endogeneity and heterogeneity. Our panel cointegration and causality test found several interesting findings, and the results have clear implications for the implementation of future policies promoting renewable energy in combination with macroeconomic policies in Mediterranean countries.

First, the results indicate the presence of short-term bidirectional causality from CO2 emissions to GDP and renewable electricity consumption. We find also a feedback hypothesis between real GDP and renewable electricity consumption. So, renewable energy is a determinant factor of growth and vice versa. The unidirectional causality from non-renewable electricity consumption to GDP confirms that energy conservation policy will have an adverse effect on real output in the short term. The unidirectional causality from non-renewable electricity consumption to CO2 emissions confirms the role of nonrenewable electricity in environmental degradation. Therefore, it is important to limit this kind of energy, which is more pollutant than renewable energy, and substitute renewable for non-renewable energy consumption.

P a g e 33 | 38

Second, in the long term, there seems to be a bidirectional causal relationship between non-renewable electricity consumption and dioxide carbon emissions and a non-causal relationship from CO2 emissions and non-renewable electricity consumption to real GDP.

These results imply that energy conservation policy can be conducted without affecting real output in the long term. The nonexistence of a causal relationship between renewable energy consumption and real GDP results from the low share of renewable energy in the energy mix of the Mediterranean countries.

The results of this research will be interesting as they provide important implications on energy and economic policy for the Mediterranean countries. Our results imply that the adoption of policies designed to encourage the development of renewable energy sources (e.g., wind and solar) and increased energy efficiency are the primary ways to reduce pollutant emissions and sustain the economic growth in Mediterranean countries. The results of this study could provide policy makers with a better understanding of the energy demand trends and help them measure progress towards efficient and renewable energy to better target new developments, thus offering some guidance on energy performance policy. Following [START_REF] Westerlund | Testing for error correction in panel data[END_REF], the maximum lag length is selected according to 4(T/100) 2/9 . See [START_REF] Persyn | Error-correction-based cointegration tests for panel data[END_REF] for the details.

[

  Fig. 3 illustrates the change in renewable electricity consumption in Mediterranean

  cross-sectional dependence (CD) test is robust when T<N and can be used with balanced and unbalanced panels. A growing body of the panel data literature concludes that panel data models are likely to exhibit substantial cross-sectional dependence in the errors[START_REF] De Hoyos | Testing for cross-sectional dependence in panel-data models[END_REF]. Cross correlations of errors could be due to omitted common effects, spatial effects or the P a g e 19 | 38

  , the investigation of integrated series in panel data has known great development, and panel unit root tests have been applied to various fields of research. For this purpose, it is common practice in P a g e 20 | 38 the literature to perform several panel unit root tests, given the shortcomings of any single test with regard to sample size and power properties.

  sectional dependence, heteroskedasticity and serial correlation panel estimators can result in misleading inference and even inconsistent estimators. Pesaran has suggested an estimation method to alleviate some of these difficulties, called Common Correlated Effects (CCE), which has been further developed by Kapetanios et al. (2011) and Chudik et al. (2011). The Pesaran (2007) CCE estimator exhibits more advantages. It does not involve estimation of unobserved common factors and factor loadings. It allows for unobserved factors to be correlated with exogenous regressors and idiosyncratic components to be independent across countries. Furthermore, the proposed estimator is still consistent under different situations such as serial correlation in errors, unit roots in P a g e 22 | 38

Fig. 1 .Fig. 2 .Fig. 3 .FigFig. 5 .Fig. 6 .

 12356 Fig. 1. Per capita CO2 emissions by country from 1980-2014

  below). In 2016, 36.6% of Spain's electricity consumption was powered by renewable energy, in Turkey 32.37%, in Italy 34%, in France 19.2% and Greece 23.8%. The share is higher in developed countries than in developing countries. Only Morocco is making efforts to increase its renewable electricity consumption, which rose from 5.94% in 2000 to 34% in 2017 (MASEN). The share is still low for Tunisia (3%) and Algeria (0.5%) in 2016.

	2 REN21, 2017.
	P a g e 15 | 38

  The 2020 projections for growth in demand for energy and particularly electricity in the EU Mediterranean are very high. In this context, expanding renewable energy sources is a cornerstone of the MED-09 countries' efforts to address security of energy supply, CO2 emissions and climate change issues.

	emissions in the north and south Mediterranean countries. Policy makers should
	encourage more efforts to promote renewable energy and energy efficiency across
	countries on north and south shores of Mediterranean. It is urgent to promote deeper
	regional energy cooperation and develop strategies to exploit the strong
	complementarities and interdependence between the different northern and southern
	countries by considering the unique characteristics of each country in the region. The
	huge potential of renewable energy sources in the Mediterranean basin might benefit
	both sides of the Mediterranean in terms of energy security, replacing carbon-intensive
	energy sources, providing affordable electricity, stabilizing energy prices, promoting
	economic growth and creating employment.
	The huge potential of renewable energy is failing to be realized in many Mediterranean
	countries. Therefore, the policymakers in Mediterranean region face several choices to
	increase levels of renewable energy production under several risks and obstacles,
	including technological and financial severe constraints. First, it is urgent to priorities
	technologies that can supply renewable energy at a low cost. Second, there a need to
	design and implement appropriate policies to attract private investment in the renewable
	energy sector. This is an ambitious vision that will require solutions to complex technical
	problems, including high-voltage interconnection, renewable energy integration, smart
	grids and storage. Additionally, policy makers should encourage and improve region-
	These findings suggest that increasing the supply of renewable energy would allow the wide Mediterranean co-operation, e.g. developing mutually R&D research centers,
	replacement of carbon-intensive energy sources and significantly reduce global warming unified standards, and free trade zones across the Region.
	P a g e 34 | 38 P a g e 35 | 38

Table 1

 1 Summary of the existing empirical studies on the relationships between CO2 emissions, energy consumption and GDP

	Authors	Year Country and period	Variables	Methodology	Results
	Ang	2007 France 1960-2000	GDP, CO2, EC	EKC, VECM, ARDL	GDP→EC
	Ang	2007 Malaysia 1971-1999	GDP, CO2, EC	VAR	EC→ CO2
	Payne	2009 6 central American countries	GDP, CO2, EC	EKC, panel VECM	CO2 ↔GDP; EC→ CO2
			1971-2004
	Haliacioglu	2009 Turkey 1960-2005	GDP, CO2, EC	VECM, ARDL	CO2 →GDP
	Soytas and Sari	2009 Turkey 1960-2000	GDP, CO2, EC, K, L	VAR	CO2 →EC
	Zhang and Cheng	2009 China 1960-2007	GDP, CO2, EC, K,	Toda and Yamamoto	GDP→EC
				Urban Population	procedure
						EC→ CO2
	Lean and Smyth	2010 5 Asian countries 1980-2006 GDP, CO2, ELEC	EKC, VECM	CO2 →EC
	Lotfalipour et al.	2010 Iran 1967-2007	GDP, CO2, EC	Toda-Yamamoto	GDP→ CO2
	Ozturk and Acaravci	2010 Turkey 1965-2006	GDP, CO2, EC, L	VECM, ARDL	CO2 →GDP
	Shahbaz et al.	2011	Pakistan, 1971-2009	GDP, CO2, EC, trade	EKC
				openness
	Payne	2012	US 1949-2009
				GDP, CO2, REC
	Arouri et al.	2012 MENA 1981-2005	GDP, CO2, EC	Panel unit root tests and	EC→ CO2
					cointegration
	Omri	2013 MENA 1990-2011	GDP, CO2, EC, K, L	GMM	EC→ CO2 CO2 ↔GDP
	Ozcan	2013 12 Middle East countries	GDP, CO2, EC	Panel unit root test, panel	GDP→EC ST
			1990-2008		cointegration method and
					panel causality tests	GDP→ CO2 LT
						EC→CO2 LT
	Cowan et al.	2014 BRICs 1990-2010	GDP, CO2, ELEC	Panel causality analysis	GDP↔ELEC Russia
						No causality for Brazil
						GDP↔CO2 Russia
						GDP→ CO2 South Africa
						CO2→GDP Brazil
						EC→ CO2 India
	Kasman and Duman	2015 15 European countries 1992-	GDP, CO2, EC, trade	Panel unit root test, panel	EC→ CO2 ST
			2010	openness, urban	cointegration method and
				population	panel causality tests	GDP→EC ST
						GDP↔EC LT GDP↔CO2
						LT EC↔CO2 LT
	Alshehry and Belloumi 2015 Saudi Arabia 1971-2010	EC, CO2, GDP, energy	Johansen multivariate	EC→ CO2
				price	cointegration technique
						EC →GDP

GDP↔CO2

Note: EC, CO2 and GDP indicate Energy Consumption, CO2 emissions and Gross Domestic Product, →, ↔ indicate unidirectional causality and feedback hypothesis, respectively.

Table 2 .

 2 Summary of the existing empirical studies on the relationships between CO2 emissions, renewable/nonrenewable energy consumption and GDP REc, CO2 and GDP indicate Non-renewable and Renewable Energy Consumption, CO2 emissions and Gross Domestic Product, →, ↔ indicate unidirectional causality and feedback hypothesis, respectively.

	Authors	Year	Country and period	Variables	Methodology	Results
	Sadorsky	2009	G7 1980-2005	GDP, CO2, REC	Panel cointegration GDP, CO2 affect (+)
						REC
	Menyah and Wolde-	2010	South Africa 1965-2006	GDP, CO2, REC, Nuclear Energy	ARDL	CO2 ↔GDP
	Rufael			Consumption
						NEC→ CO2
						GDP→ REC
	Payne	2012	US 1949-2009	GDP, CO2, REC	TY procedure	No causality on REC
	Salim and Rafiq	2012	6 emerging countries 1980-	GDP, CO2, REC	OLS FMOLS	CO2 ↔GDP
			2006
						CO2 ↔REC
	Sebri and Ben-Salha	2014	BRICS countries 1971-2010 GDP, REC, NREC, CO2, Trade	VECM, ARDL	CO2 →GDP
						GDP↔REC
						CO2↔REC (LT)
	Shafiei and Salim	2014	OCDE countries 1980-2011 GDP, CO2, REC, NREC,	STIRPAT	CO2→REC
				urbanization, population size,
				industrialization, population		GDP→ CO2
				density
						CO2↔NREC
	Note: NREC,

Table 3 .

 3 [START_REF] Pesaran | General Diagnostic Tests for Cross Section Dependence in Panels[END_REF]), Frees (1995), Freidman (1937), and Breusch & Pagan cross-sectional dependence tests Notes: ***, ** and * indicate the test statistics are significant at 1%, 5%, and 10% levels, respectively.

	Test	Pesaran	Frees	Freidman	Breusch & Pagan
		CD test	CD(Q)	CD	Chi2
		2.365	2.786	62.758	399.51
	RE model
		(0.018)**	(0.000)***	(0.000)***	(0.000)***
	FE model	3.434	2.851	72.379	205.742
		(0.000)***	(0.000)***	(0.000)***	(0.000)***

Table 4

 4 Unit root test Notes: ***, ** and * indicate the test statistics are significant at 1%, 5%, and 10% levels, respectively.

		Level					First difference
	Variable LLC	Breitung IPS	Fisher-ADF	Fisher-PP	LLC	Breitung IPS	Fisher-ADF	Fisher-PP
	GDP	0.88 0.81	0.85 0.80	2.81 0.99	7.61 0.98	3.53 0.99	-7.79 0.00*** 0.00*** 0.00*** 0.00*** -6.30 -9.76 188.16	136.00 0.00***
	NRELC	-1.52 0.063*	2.82 0.99	-1.11 0.13	28.32 0.057*	16.53 0.55	-10.99 0.00*** 0.00*** 0.00*** 0.00*** -5.42 -10.95 148.741	517.64 0.00***
	PCO2	0.27 0.61	1.32 0.90	-1.37 0.08*	34.44 0.01***	40.25 0.001*** 0.00*** 0.00*** 0.00*** 0.00*** -16.52 -5.39 -15.45 218.08	345.19 0.00***
		-6.09	-5.80	-5.42	60.57	47.54
	ELECR
		0.00*** 0.00*** 0.00*** 0.00***	0.00***
	Table 5
	Westerlund cointegration test
	Statistic		Value	Z-value		P-value	Robust P-value
		Gt		-2.906	-2.141		0.016**	0.020**
		Ga		-11.769	-0.342		0.366	0.080*
		Pt		-9.141	-3.198	0.001***	0.000**
		Pa		-8.681	-0.558		0.288	0.141

Notes: *** and ** indicate the test statistics are significant at 1% and 5% levels, respectively.

Table 7

 7 Results of Granger causality test Note: p-value are given in parentheses. ***, ** and * indicate that the test statistics are significant at the 1%, 5% and 10% levels, respectively

	Dependent variables	Sources of causation (independent variable)
		Short-term				Long-term
		ΔCO2	ΔGDP	ΔNREC	ΔREC	ECT
	ΔCO2	_	.4135 (0.000) ***	-.00160 (0.000)***	.1205 (0.006)***	-.2329 (0.002)***
	ΔGDP	.1197 (0.033)**	_	.0093 (0.074)*	.0794 (0.079)*	-.0272 (0.143)
	ΔNREC	.2188 (0.114)	0.0213 (0.886)	_	-.0069 (0.058)*	-.1692 (0.017)**
	ΔREC	-1.0273 (0.022)**	8.0581 (0.005)**	-4.8901 (0.000)***	_	-.0035 (0.141)