Renewable and non-renewable energy use - economic growth nexus: The case of MENA Net Oil Importing Countries

Renewable and Sustainable Energy Reviews 71 (2017) 127–140

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Renewable and non-renewable energy use - economic growth nexus: The case of MENA Net Oil Importing Countries

MARK

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Montassar Kahiaa, , Mohamed Safouane Ben Aïssaa, Charfeddine Lanouarb a b

LAREQUAD & FSEGT, University of Tunis El Manar, Tunisia Department of Finance and Economics, College of Business and Economics, Qatar University, P.O.Box: 2713, Doha, Qatar

A R T I C L E I N F O

A BS T RAC T

Keywords: Renewable energy, Non-renewable energy Economic growth Panel cointegration MENA NOICs

This study examines the energy use – economic growth nexus by disaggregating energy use into two types of energy, renewable and non-renewable energy use. Our sample consists of eleven MENA Net Oil Importing Countries (NOICs) during the period 1980–2012. A multivariate panel framework was used to estimate the long run relationship and the panel Granger causality tests was employed to assess the causality direction among variables. The empirical results provide evidence for long-term equilibrium relationship between real Gross Domestic Product (GDP), renewable energy use, non-renewable energy use, real gross fixed capital formation and labor force. The results provide evidence also for positive and statistically significant elasticities. Moreover, the empirical findings from panel Error Correction Model confirm the existence of bidirectional causality between renewable energy use and economic growth, and between non-renewable energy use and economic growth, results that support the feedback hypothesis. Moreover, our empirical findings provide evidence for two way (bidirectional) causal association in both the short and long-run between renewable and non-renewable energy use which proves the substitutability and interdependence between these two types of energy sources. The policies implications of these results are also proposed and discussed.

JEL Classification: C33 Q43 O40

1. Introduction Motivated by the desire to keep the global worldwide rise in temperature below the critical level of 2 °C, many international environmental and energy experts met at the Paris COP21 climate change summit held in December 2015 have claimed that energy from renewable sources can play an essential role in improving environmental quality and reducing the effects of climate change on the nature. In the energy economic literature, several others factors have increased the interest of many countries on renewable energy sources including its positive effects on reducing their dependence on traditional energy sources, and in protecting their balance of trade. Consequently, several multilateral efforts in favor of development of renewable energy sources have been developed in the last few years. For instance, renewable energy is expected to have the strongest share in term of electricity generation by sources [1] and it will be “one of the basic indicators of economic and social development and improved quality of life” [2,3]. Moreover, many international environmental and energy organization such as the International Energy Agency (IEA) and the interna-

tional renewable energy agency (IRENA) among many others have claimed that renewable energy resources can offer a significant opportunity for the economic development and environmental quality improvement for many countries around the word.1 Among these countries, we found the MENA region which is characterized by a high potential of producing renewable energy due to the high levels of sunshine and wind that characterize this region compared to other regions in the word. For instance and according to the World Bank estimates, the MENA region receives approximately the quarter of all solar energy striking the earth.2 Thus, it is predicted that renewable energy generated from solar in the MENA region can meet the current global demand for electricity of the region. This interest of the MENA region in renewable energy can be explained by the particular vulnerability of these countries to the adverse consequences of spurring demand and the increasing volatility of energy prices. Additionally, several other factors have contributed to this vulnerability such as the rapid growth of population and economic activity, the specific weather of this group of countries, the inefficiency of energy use among many other factors. For instance, the energy demand in the region is

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Corresponding author. E-mail addresses: [email protected] (M. Kahia), [email protected] (M.S.B. Aïssa), [email protected] (C. Lanouar). Specifically for countries that benefit from geographical location in term of natural resources necessary for a vibrant renewable energy sector. 2 Following the World Bank, the renewable energy from solar sources generated per square kilometer per year is equivalent to the energy generated from 1 to 2 million barrels of oil. 1

http://dx.doi.org/10.1016/j.rser.2017.01.010 Received 10 June 2015; Received in revised form 17 December 2016; Accepted 5 January 2017 Available online 01 February 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.

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causality direction between energy use and economic growth. Four hypotheses have been developed and explored (growth hypothesis, conservation hypothesis, feedback hypothesis and neutrality hypothesis) through aggregating or disaggregating of energy use by sources (explicitly, non-renewable and renewable) and depending on the types of energy employed (specifically coal, electricity, nuclear energy and natural gas). So, to provide a broad review of the literature of the relationship linking energy use and economic growth, we divide this section into two sub-sections. The first subsection describes the four types of hypotheses that have defined the causal association between energy use and economic growth. Then in the second subsection, we present the three strands of the empirical literature that can summarize the energy use-economic growth nexus literature, respectively.

estimated to increase beyond the world average by approximately 3% yearly from 2010 to 2030 and the electricity demand is anticipated also to increase by 6% per year over the same period [4]. Hence, on the way to guarantee a sustainable long-term economic development in the MENA economies, this region is requested to branch out their energy sources in order to improve their energy security. However, in the empirical literature only few studies have investigated the causal association between renewable, non-renewable energy, capital, labor force and real GDP in the MENA region. Thus, in this study we try to fill this gap in the empirical literature by exploring possible existence of long-term relationship as well as determining the type of causality direction among variables. Empirically, the interest on energy use and its relationship with economic growth has growing rapidly in the last decades since the work of Kraft and Kraft [5]. This interest is primarily due to the fact that the causality direction between energy use and real GDP is in the heart of energy policy strategies of all countries around the world, e.g. each type of causation has its specific policies implications. However, at this level, it is worth noting that this relationship stills until now an unsettled and unconcluded issue [6– 11]. In previous empirical literature, several studies have shown that the relationship between aggregate energy use and economic growth lead in general mixed results in term of causal relationships [12–14]. Moreover, the empirical results can differ significantly between groups of countries and sometime for the same single country (e.g., [12–15]). More recent researches in the field emphasize that disaggregating energy use by sources (i.e., non-renewable and renewable energy) possibly will have diverse effects on economic growth and therefore it will be worth interesting to investigate the effects on economic growth of using both non-renewable and renewable energy simultaneously [16–19]. However, earlier studies, which have examined the case of the MENA countries, have investigated the energy use-economic growth nexus by means of common panel estimation methods that do not take into consideration possible presence of cross-sectional dependence in their estimation procedures [20–27]. These studies have not disaggregated the energy use into different type of energy sources. Hence, to fill this gap in the empirical literature, we focus our analysis in this study in the MENA region by allowing for heterogeneity amongst the MENA countries. We examine this by segmenting the region according to oil resources availability. In particular, we focus on the case of MENA Net Oil Importing Countries (NOICs), which a group of countries that is supposed to be the more affected by the increasing volatility of energy prices causing steep and unpredictable energy bills. Moreover, unlike the majority of the empirical literature that do not distinguish the impact of energy use by sources on the economic activity, we purpose in this study to use simultaneously both renewable and non-renewable energy as input in the production function in order to assess the exact effect of each type of energy input on the economic growth. The rest of the paper is structured as follows. Section 2 presents a review of the empirical energy economic studies that has examined the energy use-economic growth nexus as well as the four hypotheses that describe this relationship. Section 3 presents an overview of the MENA oil importing countries in term of energy use by type (renewable versus non-renewable) and in term of share evolution of total renewable energy in the total energy mix during the period 1980–2012. Section 4 introduces the model specifications, the panel unit root and panel cointegration tests under cross-sectional independence and dependence, the FMOLS estimation method and the Granger causality testing procedure. Section 5 discusses the empirical results. Finally, Section 6 concludes and propose policy implications.

2.1. Energy use and economic growth hypotheses The literature related to the energy use-economic growth association was the subject to several empirical investigations during the last three decades [5], [14,16,19,22,31–41]. However, overall, four hypotheses define the causality relationship between energy use and economic growth. These hypotheses are : the growth, conservation, feedback and neutrality hypotheses [7,34,42–46]. The main difference between these four hypotheses lies in the difference in terms of policy recommendations that implies each hypothesis compared to the other. The first hypothesis is called the growth hypothesis which asserts that energy use is a fundamental input of production, directly and/or indirectly, providing evidence for complementarities with capital and labor. The evidence of the growth hypothesis is found when one way (uni-directional) causality running from energy use to real GDP is detected. Then, energy conservation policies intended to decrease the amount of energy use in order to improve for example environmental quality will affect negatively economic growth. In addition, the harmful effects of energy use on real GDP can be due to over- utilization of energy in unproductive sectors, [11,44,47–50]. The second hypothesis is the conservation hypothesis which implies that economic growth causes energy use. Evidence for this hypothesis is proved if there is evidence for a one way causality running from economic growth to energy use. As a consequence of this hypothesis, energy conservation policies such as the reduction in the amount of CO2 emissions, measures to improve energy efficiency, and demand management policies, or policies designed to reduce energy use and waste, can be implemented and are recommended as they will have no negative impact on economic growth. Nevertheless, it is feasible that an economy increasingly compelled by politics, infrastructure or mismanagement of resources could lead to inefficiencies. If such is the case, a rise in economic growth will possibly have an adverse effect on energy use [12,44,51–53]. The third hypothesis is the feedback hypothesis which indicates that energy use and economic growth are interdependent and complementary. This hypothesis is supported when we found evidence for a two way (bi-directional) causality between energy use and economic growth. In this case, the increase /decrease in energy use leads to an increase / a decrease in economic growth, and similarly, the increase/ decrease in economic growth leads to an increase/decrease in energy use. Consequently, any energy policy that focuses on improving energy use efficiency possibly will not have negative impacts on economic growth [13,37,54–56]. The fourth hypothesis is the neutrality hypothesis which postulates that energy use is viewed as non-determinant of real GDP and for that reason that energy use will have insignificant effect on economic growth in view of its relatively negligible impact on economic growth. This hypothesis holds when there is no evidence of two way (bidirectional) causality between energy use and economic growth. For this case, reducing energy use through energy conservation policies will have no impact on economic growth [35,57–60].

2. An extensive literature review Recently, energy use was integrated as a fundamental factor of production [28–30]. Consequently, several studies have explored the 128

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provides strong evidence for the neutrality hypothesis. Alternatively, the results provide strong evidence for a two way causality direction between the commercial and residential non-renewable energy consumption, and real GDP, which supports the feedback hypothesis. In a recent study, Pao and Fu [83] have investigated the causal association between (dis)aggregate clean and non-clean energy use and economic growth during the period of 1980–2009 for the case of Brazil within a production function framework that includes also capital and labor. Their results revealed the presence of a long-term relationship between all variables. Moreover, the results from Granger causality analyses, at aggregated level, provide evidence for a one way causality relationship running from total renewable energy use to economic growth which support the growth hypothesis. Alternatively, there is absence for short-term causality between total non-renewable energy consumption and economic growth, supporting the neutrality hypothesis, with a bidirectional long-run causality from non-renewable energy consumption to economic growth, which proves evidence for the feedback hypothesis. At disaggregated level, the causality tests analyses proved the presence of mixture results. The second hypothesis, i.e. the conservation hypothesis, was also supported by many empirical studies among them we found the work of Pao and Fu [84] who explored the causal associations between economic growth and four types of energy use including the nonhydroelectric and the total renewable energy consumptions, and nonrenewable and total primary energy consumptions for the case of Brazil during the period 1980–2010 by using a multivariate framework. Their results confirm that there exists a long-term relationship among all variables in question. The findings from the vector error correction models showed that the growth hypothesis was supported for the nonhydroelectric renewable energy consumption, the feedback hypothesis between economic growth and total renewable energy, and the conservation hypothesis for the non-renewable energy consumption and total primary energy consumption toward economic growth. Recently, Salim et al. [85] have explored the relationship between energy use from renewable and non-renewable sources and two proxies of economic growth (industrial output and GDP growth) for the OECD countries during the period 1980–2011. Empirical results from panel cointegration method allowing for structural breaks show evidence for a long-term equilibrium between all variables. In addition, the panel causality testing approach show evidence for a feedback causal relationship in the short-run between GDP growth and non-renewable energy consumption. Evidence for a growth hypothesis was found between renewable energy consumption and GDP growth. More recently, Dogan [16] has examined the long- and short-term relationships between economic growth, electricity consumption from renewable and non-renewable sources for the case of Turkey over the period 1990–2012. His results show that in the long run, the Granger causality testing procedure show evidence for the growth hypothesis between renewable electricity consumption and economic growth. In the other hand, we found evidence for the feedback hypothesis between non-renewable electricity consumption and economic growth. Compared to the growth and conservation hypotheses, the third hypothesis, i.e. the feedback hypothesis, is the most supported in empirical energy economics studies. For instance, Apergis et al. [86] scrutinized the causal link between CO2 emissions energy consumption from nuclear and renewable sources, and economic growth conducted on a sample of 19 developed and developing economies over the period 1984–2007. Their results prove evidence for the existence of a long-run equilibrium between all variables. Their results show also that, while nuclear energy use has a significant negative impact on economic growth, in contrast to renewable energy consumption which has a positive effect. In addition, the short-run causality test analysis shows evidence for the feedback hypothesis between renewable and nuclear energy consumptions and economic growth. In the long, the results show evidence for the growth hypothesis and for both types of energy (nuclear energy use and renewable sources).

As mentioned above, these four hypotheses are associated to four types of causality direction that govern the energy use-economic growth nexus. It is worth noting that these four hypotheses are not energy type dependent which means that all these hypotheses are still valid whatever the type of energy used. However, the only difference that may exist when using different types of energy sources is that some types (renewable energy) are known to be less CO2 emitter and are more environmental friendly compared to other types of energy sources. These opportunities in terms of energy security and CO2 emissions reductions that can offer renewable energy have increased the interest in exploring the association between energy use and economic growth by considering renewable energy as a proxy of energy use. This importance of renewable energy as new energy sources is attributed to the fact that this type of energy has been target to be the main components of the total energy use of the majority of countries around the world. Another interesting feature of renewable energy is that its exploitation strengthens sustainable development. So, in this regard, many academic researchers, policymakers, and international public and private institutions become more concerned by determining the exact impact of renewable energy use on economic growth. 2.2. The renewable energy – economic growth nexus Another way to categorize the energy economic literature, is to classify previous studies into three principal strands based on the type of energy use. The first strand contains studies that have examined the (dis)aggregate energy use and economic growth relationship without providing qualitatively any discrimination (see Table A in Supplementary document of this paper). The second strand comprises studies that have focused in the renewable energy use-economic growth nexus (see Table 1 below). The third and last strand that correspond to the most recent studies in the field that have disaggregated energy into different sources (explicitly, non-renewable and renewable) and types (specifically, coal, electricity, nuclear energy and natural gas). The principle reason behind disaggregating the energy use is firstly to avoid the problem of possible variables omissions. Further, it helps to detect whether the parameter estimates of the short and long-term relationships and the causality direction for the type of energy sources employed are different from each other or not. This variety among research investigations in the energy use-economic growth literature is interesting as it highlights some attractive and motivating findings for policymakers as well as for governments that they are supposed to develop policies and strategies for each of energy sources and types to achieve sustainability in growth rates. In this section of literature review, we will focus our presentation and analysis in studies that are related to the main question of our empirical investigation which correspond to the third strand of literature presented above. In particular, we will classify the studies based on the four hypotheses previously presented in Section 2.1 and by following the same order as above. Precisely, we will be interested on the type of the causal relationship (type of hypothesis) that relies renewable energy and economic growth. Among the different studies that support the growth hypothesis, we found the study of Bowden and Payne [7] that has explored the relationship between the real GDP, capital, labor forces, and energy use from green energy (renewable) and traditional energy (non-renewable) sources using a multivariate model framework and the Toda– Yamamoto causality method over the period 1949–2006 for the US country. The empirical results of Bowden and Payne [7] show evidence for the existence of positive one way causality running from residential use of renewable energy to economic growth which supports the growth hypothesis. Their results have shown also the existence of one way causality direction running from industrial non-renewable energy use to economic growth which supports the growth hypothesis. However, no evidence for causal relationships was found between commercial and industrial renewable energy use, and economic growth. This result 129

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Table 1 Summary of literature review for renewable energy consumption and economic growth. Study

Methodology

Period

Country

Confirmed hypothesis

Chien and Hu [61] Payne [62] Fang [63] Tiwari [64] Zeb et al. [48]

Data envelopment analysis (DEA). Toda–Yamamoto procedure. OLS. Structural VAR. Panel cointegration tests; Fully Modified OLS (FMOLS); VECM Granger causality. Nonparametric techniques. Panel cointegration methodology; bootstrapcorrected Granger causality test. ARDL bounds testing approach. Multivariate Johansen cointegration test; Toda– Yamamoto procedure. Panel cointegration and Panel Causality Tests. Panel cointegration and Panel Causality Tests. Panel cointegration and Panel Causality Tests. Panel cointegration tests; Holtz Eakin causality test. Westerlund cointegration; Fully modified OLS; Dynamic OLS; ARDL model. ARDL model; Rolling Window Approach (RWA) for cointegration; VECM Granger causality. Nonlinear panel cointegration; Nonlinear panel smooth transition VECM. Panel cointegration tests; the Canning and Pedroni (2008) long-run causality test. Panel cointegration; Fully modified OLS and Panel Causality Tests. ARDL bounds testing approach; VECM Granger causality. ARDL bounds testing approach; Johansen cointegration techniques. ARDL model; Rolling Window Approach (RWA) for cointegration; VECM Granger causality. Ordinary least squares (OLS); Fully modified OLS; Dynamic OLS. Panel cointegration; Fully modified OLS; Dynamic OLS; Seemingly Unrelated Regression (SUR). ARDL approach; Toda–Yamamoto procedure. Pedroni cointegration; Dynamic OLS and VECM Granger causality. One-way random effect model; Panel Causality Tests. Toda–Yamamoto procedure. Toda–Yamamoto Granger causality. ARDL approach; VECM Granger causality.

2001–2002 1949–2007 1978–2008 1960–2009 1975–2010

45 economies US China India SAARC

Growth

1990–2011 1971–2007 1961–2011 1971–2011

36 countries 11 sub-Saharan African countries United States Vietnam

1985–2005 1992–2007 1980–2006 1980–2007

20 OECD countries 13 countries within Eurasia 6 Central American countries OECD countries

1980–2006

6 major emerging economies

1972Q1– 2011Q4 1980–2010

Pakistan

1990–2012

80 countries

1980–2010

11 South American countries

1971–2010

BRICS countries

1977–2011

China

1972Q1– 2011Q4 1994–2003

Pakistan

1980–2005

G-7 countries

1990 –2010 1980–2008

Turkey OECD

1997–2007

27 European countries

1949–2009 1972–2012 1980–2009

US Denmark 10 developing and emerging countries Lithuania

Halkos and Tzeremes [49] Hamit-Haggar [11] Aslan [50] Tang et al. [10] Apergis and Payne [65] Apergis and Payne [66] Apergis and Payne [67] Bayraktutan et al. [68] Salim and Rafiq [37] Shahbaz et al. [54] Apergis and Payne [69] Apergis and Danuletiu [55] Apergis and Payne [70] Sebri and Ben-Salha [71] Lin and Moubarak [72] Shahbaz et al. [56] Sadorsky [73] Sadorsky [51] Ocal and Aslan [74] Kula [53] Menegaki [58] Payne [59] Kulionis [60] Bildirici [75] Bobinaite et al. [76] Yildirim et al. [77]

Ben Aïssa et al. [38] Lin [78] Farhani [22] Ben jebli and Ben Youssef [24] Alper and Oguz [79]

Lin et al. [80] Destek [81]

Tsou and Huang [82] Chang et al. [40]

Johansen cointegration test; Granger causality test. Toda–Yamamoto procedure; Bootstrap-corrected causality test.

1990–2009

7 Central American countries

18 emerging countries

1949–2010 ; 1960–2010 ; 1970–2010 1980–2008

US

1982–2011

9 OECD countries

1975–2008

12 MENA countries

1975–2008

11 MENA countries

1990–2009

EU member countries

The Granger linear causality test; the Hiemstra and Jones nonlinear causality test. The asymmetric causality approach.

1989–2008

US

1971–2011

Toda–Yamamoto procedure. The Emirmahmutoglu and Kose causality methodology.

1984–2008 1990–2011

The newly industrialized countries: Brazil, India, Turkey, South Africa, Mexico and Malaysia 13 OECD countries G-7 countries

Panel cointegration tests; Panel OLS; FMOLS and DOLS tests; panel ECM. ARDL approach; VECM Granger causality. Panel cointegration tests; Panel OLS; FMOLS and DOLS tests; panel ECM. Panel cointegration tests; Panel FMOLS and DOLS tests; Panel Granger causality test, panel ECM. ARDL approach; asymmetric causality test approach of Hatemi-J.

Source: Authors’ tabulation.

130

Feedback

Conservation

Neutrality

Growth and the feedback Growth and the neutrality

11 African countries Growth and the conservation Conversation and the neutrality

Growth, the conversation and the neutrality Feedback, the conversation and the neutrality

Growth, the feedback, the conservation and the neutrality

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explored the effect of electricity consumption from renewable and nonrenewable sources on economic growth in 18 Latin American countries over the period 1980–2010 has been proposed by Al-Mulali et al. [92]. Their panel cointegration test analysis confirmed that there exists a long-term equilibrium among all series. The Granger causality results revealed a -two way causal association between renewable electricity consumption and economic growth, supporting the feedback hypothesis, while a one way causal relationship was found from non-renewable energy consumption to GDP growth, indicating the existence of the growth hypothesis. In a very recent study, Kahia et al. [41] investigates the economic growth – disaggregated energy consumption association for two samples of MENA Net Oil Exporting Countries (NOECs) over the period of 1980–2012 by means of panel cointegration approach and panel error correction model in multivariate framework with the inclusion of both capital and labor force as additional variables. The findings prove evidence for the existence of a long-run association between the candidate variables. In the short run, the results prove evidence for a unidirectional causality running from economic growth to renewable energy consumption for the entire group of MENA NOECs while bidirectional causality between renewable energy and economic growth is found for the 5 selected MENA NOECs sample, supporting the conservation and the feedback hypotheses, respectively. In the long-term, the results confirm the existence of bidirectional causal association for both samples. The findings further provide evidence for two way (bidirectional) causal relationship between renewable and non-renewable energy consumption supporting the feedback hypothesis with negative and significant coefficient in the short-run which indicate the substitutability between the two energy sources. The fourth and last hypothesis is the neutrality hypothesis which is the less supported by empirical studies. For instance, using the Toda– Yamamoto technique within a framework of a multivariate model by including both capital and employment as measures, Payne [93] has examined the causal link between energy consumption from both renewable and non-renewable sources, and real GDP in the U.S. from 1949 to 2006. He found no evidence for causal association between renewable and non-renewable energy consumption and economic growth which support the neutrality hypothesis. The neutrality hypothesis was also supported in the study of Shafiei and Salim [94]. The authors have employed the Generalized Method of Moments (GMM) method to study the short and long-term Granger causalities between CO2 emissions, total population, population density, GDP per capita, urbanization, industrialization, the contribution of services to GDP and renewable and non-renewable energy consumption using panel data from 1980 to 2011 for a set of 29 OECD countries. The outcomes from Granger causality test confirmed a uni-directional causal relationship running from GDP per capita to energy consumption from nonrenewable energy sources in the short-term, supporting the conservation hypothesis and bidirectional causal association in the long-term, indicative of the existence of feedback assumption. Alternatively, results provided evidence for no causality running in any direction between renewable energy consumption and GDP per capita, supporting the neutrality hypothesis. Recently, Dogan and Seker [19] study has also supported the neutrality hypothesis by employing the DumitrescuHurlin non-causality method to explore the mutual impacts between renewable and non-renewable energy, real income, trade openness and CO2 emissions for the European Union over the period 1980–2012. Their findings confirmed that there is no causality between real income and renewable energy consumption, supporting the neutrality hypothesis. Conversely, a one way causal relationship running from real income to non-renewable energy consumption was found, representing the existence of the conservation hypothesis. To summarize, there is until now no consensus between researchers regarding which hypothesis is more valid in describing the association between energy use and economic growth. In particular, the outcomes

For the case of G-7 countries, Tugcu et al. [45], by employing classical and augmented production functions, investigated the long– term and causal associations between energy consumption from renewable and non-renewable sources and economic growth and made a comparative analysis between renewable and non-renewable energy sources to figure out which sort of energy consumption is more essential for economic growth in G-7 countries over the period of 1980–2009. Their cointegration test analysis confirmed that the energy consumption from both renewable and non-renewable sources plays a crucial role for economic growth. and augmented production function Causality tests analysis showed that while a two causal relationship is obtained for the whole countries when employing the classical production function, supporting the feedback hypothesis, a mixed -findings are found for when using the augmented production function. For the case of Pakistan, Shahbaz et al. [87] have explored the association between energy (renewable and non-renewable) consumption and economic growth via Cobb–Douglas production function during 1972–2011. The authors combined the ARDL bounds testing procedure with the Gregory and Hansen [88] structural break cointegration approach. Their results provide evidence for cointegration relationship between disaggregated energy consumption (renewable and nonrenewable), economic growth, labor and capital in the case of Pakistan. In addition, the VECM Granger causality analyses provide evidence for the presence of feedback hypothesis between energy consumption from renewables and economic growth, energy consumption from non-renewables and economic growth, as well as between economic growth and capital. Pao et al. [89] explored the causal relationship between clean and non-clean energy consumption and economic growth in emerging countries of the MIST (Mexico, Indonesia, South Korea, and Turkey) within a multivariate panel framework that includes also labor and gross fixed capital formation for the period 1990–2010. Their cointegration test analysis confirmed the existence of a long-run equilibrium association between the used variables. The results from causality test analyses showed the presence of a positive unidirectional short-run causal relationship running from fossil fuel energy consumption to economic growth, supporting the growth hypothesis, with a bidirectional long-run causality, which indicates the presence of the feedback hypothesis. On the other hand, there is a positive two way short-run causality between renewable energy consumption and economic growth, supporting the feedback hypothesis, with a unidirectional long-run causality from renewable energy consumption to economic growth, which proves evidence for the growth hypothesis. Apergis and Payne [90,91] examined, by estimating a panel error correction model, the relationship between renewable and non-renewable energy consumption and economic growth for 16 emerging market economies and 9 South America countries, respectively; within a framework of multivariate panel that includes labor and gross fixed capital formation from 1990 to 2007. Their panel cointegration test analysis confirmed that there exists a long-run equilibrium relationship among variables in question. Using the results from causality test analysis, Apergis and Payne [90,91] showed the presence of two way causal relationships between the two types of energy consumption (renewable and non-renewable), and economic growth both in the short and the long–term, supporting the feedback hypothesis. Recently, Ohler and Fetters [39] examined the causal association between economic growth and electricity generation from non-renewable and renewable sources (biomass, geothermal, hydroelectric, solar, waste, and wind) across 20 OECD countries over 1990–2008 within a framework of multivariate panel that includes labor and gross fixed capital formation. The cointegration test analysis confirmed that there exists a long-term equilibrium relationship among variables in question. The results of causality tests provide strong evidence for a bidirectional relationship between (dis)aggregate renewable electricity generation, non-renewable electricity generation and real GDP, supporting the feedback hypothesis. Another empirical study that have 131

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seem to depend in many factors such as the economic structure of the country under study (oil importing versus oil exporting countries), the type of energy and proxy of energy used in the empirical study (renewable or no renewable energy; crude oil, gas nuclear, solar, wind etc…), the econometric approach used among many other factors. As the importance of renewable energy in term of its share in the total mix or in term of its contribution to both economic growth and preserving the environment have growing rapidly in the last few decades, then it appears interesting to analyze the potential of the market of renewable energy in the MENA NOICs. Precisely, in the following section, we will describe the actual situation of this market and also the future targets of some MENA NOICs.

Renewable vers non-renewable energy consumption

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1980

1985

1990

REC % of total mix

1995

2000

2005

2010

NREC % of total mix

Fig. 1. Total renewable versus Non-Renewable Energy Production in the MENA NOICs in Kilowatt hour (kWh), 1980 – 2012. Sources: British Petroleum [103].

3. Market potential of renewable energy in the MENA NOICs In the last decades, the market of renewable energy in the MENA NOICs has growing rapidly due to the abundance of these countries in natural resources that are necessary to generate this type of energy. Moreover, motivated by the desire to enhance their energy security, spur their economic growth, and conscious about the threats of possible conventional energy scarcity in the future, the majority of the MENA NOICs have launched renewable energy targets to diversify their energy mix and boost up industrial sector. To better understand both the current situation and the potential of renewable energy for the MENA NOICs, we propose to present in the following two subsections the achievements of this region in terms of renewable energy capacities and growth, and in terms of future targets for enhancing renewable energy developments.

[99], all importing countries use solar PV with the intention of meeting a part of their electricity demand. The countries leaders in term of solar PV installed capacity are Israel and Morocco with almost 270 MW and 15 MW at the end of 2012, respectively.3 It is attractive to note that Morocco, in the Programmed’ Electrification Rural Global (PERG) framework, substitutes expensive, inefficient, and polluting off-grid diesel generators by the solar PV. In mid-2016, Morocco was inaugurated the Noor-1 concentrated solar power (CSP) plant as a first phase of the Noor complex in Ouarzazate, Morocco. This solar plant is intended to generate a total of 2 GW of electricity production capacity from solar by 2020, providing 38% of the annual electricity needs of Morocco, and reducing carbon emissions in the country by 240,000 tons yearly [101]. The subsequent two phases of the project Noor totaling of 350 MW are expected to enter into effective production by 2018. This will make Noor to become the largest CSP complex in the whole world [101]. Besides, Morocco is the only importing country currently operating Concentrating Solar Power (CSP) plants with 20 MW in 2012, which contributes appreciably to the increasing of its share of solar energy [102]. Modern biomass and geothermal are the slightest utilized renewable energy sources in the importing countries. Israel and Jordan are the unique countries currently generating electrical energy from modern biomass with 27 MW and 3.5 MW of installed capacity, respectively [97,100]. The geothermal is not yet exploited by the importing countries due to the fact that most projects are not undertaken [99]. In terms of shares, the renewable energy for the total MENA NOICs lies between 25% and 30% during all the period of study, see Fig. 1 below. The rapid growth rate of the renewable energy for the MENA NOICs is mainly aimed at maintaining energy security and reducing dependence on expensive imported oil [99]. To be more precise, in Fig. 2 below, we report the share evolution of renewable energy in the total energy mix by country during the same period. It shows that the share is more volatile between countries and over the period of study compared to the total share reported in Fig. 1. In particular, the results, reported in Fig. 2, show that five countries over of eleven have a share that vary between 20% and 56% during all the period 1980–2012. These countries are Georgia, Armenia, Turkey, Morocco and Lebanon. Fig. 2 shows also that four countries out of the sample have a share that is overall significant during the period of study; their share is above 4–5%. These countries are Israel, Jordan, Tunisia and Mauritania. The last group is formed by only two countries, Malta and Cyprus that are characterized by a share that is close to zero until recently and become highly important during the last decade. In a nutshell, Figs. 1 and 2, show that renewable energy play a crucial role in the MENA NOICs since 1980 as it is shown by the share

3.1. Actual situation and renewable energy share growth The rapid increase of energy demand in the oil importing countries in the last few years have make a real pressing on a new supplementary energy supplies in these countries. As we have already mentioned previously, different reasons have contributed significantly to this increase of demand and in particular the demand for electricity such as the rapid economic expansion, the speedily growing population [95] and the energy intensive nature of the importing region’s extractive industries. In addition, the increasing volatility of energy prices has also resulted in unpredictable energy bills for the importing countries. However, as these countries was blessed with many natural resources that are necessary for producing renewable energy, for instance, biomass, hydropower, wind and solar, the majority of MENA NOICs governments have set an energy strategies that are based on promoting clean energy and rising the renewable energy share in their total energy mix. A lot of them have already implemented some regulatory reforms that promote the setting up of renewable energy infrastructure that help to achieve these targets. For instance, the Total Primary Energy Supply (TPES) in the MENA NOICs was increased by 10.5% between 2007 and 2010 which is equivalent to an average growth of 4.7% per year during the considered period [96]. In the MENA NOICs region, the Hydropower and wind energy remain the most common sources of renewable energy for power. Morocco and Tunisia are considered the leaders in hydropower installed capacity with over 1 Gigawatt (GW) and 66 Megawatt (MW) in 2012, respectively [97]. With regard to wind power capacity, Morocco and Tunisia are also the leaders with 291 MW and 154 MW of installed capacity in 2012, respectively [98]. It is interesting to note that Tunisia has experienced considerable growth over the period of 2008–2012, with wind power capacity increasing eightfold during the aforementioned period [98]. In the MENA NOICs region, the second potential source of renewable energy is the solar photovoltaic (PV) which also has experienced a speedy expansion due to its remarkable potential and incessantly declining technology costs. Moreover, the solar PV has also a major role to play in the electrification of rural areas. According to REN21

3 Data of both Morocco and Israel for 2012 are obtained from RCREEE [97] and Union for the Mediterranean (UfM, [100]), respectively.

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0.6

0.3

the capital which is defined as the real gross fixed capital formation and calculated in millions of constant U.S. dollars. Finally, the last independent variable is the labor force which is defined as the active population using the WDI definition which includes all individuals that have supplied labor and contributed to the production for a given period. The total labor force is measured in millions of people.

0.2

4.2. Model specification

0.1

Our starting point is to introduce the econometric model of interest to be used to investigate the relationship between renewable, nonrenewable energy use and economic growth. In this study, we start by using a production model function where energy use is introduced as additional factor of production with the traditional factors, i.e., the capital and labor force [28–30,106,107]. Additionally, we divide the energy input into two types of energy, renewable and non-renewable energy. This will enable us to examine and distinguishing the effect of each type of energy on the economic activity [7,41,86,91] among others. The capital and labor factors are used to control for potential omitted variable bias. The general form of the production modeling function is given by,

Evolution of the share of renewable energy on the total energy mix by country

0.5 0.4

0

1980 Lebanon Turkey

1985

1990 Israel Armenia

1995 Jordan Cyprus

2000 Malta Georgia

2005 Morocco Mauritania

2010 Tunisia

Fig. 2. Share of renewable Energy Production in the total energy mix for the MENA NOICs in Kilowatt hour (kWh), 1980–2012. Sources: British Petroleum [103].

of renewable energy in the total energy which remains significant during all the period of study despite the relative decreasing of nonrenewable energy prices during some periods. So, it is clear that renewable energy is actually an important determinant of both economic growth and energy security of the MENA NOICs.

Yit =f (RECit ,

NRECit ,

Kit ,

(1)

LFit )

where Yit denotes real GDP; RECit is total renewable energy use; NRECit is total non-renewable energy use, Kit represents the capital and LFit is the total labor force. All variables are defined and measured as described above. In Eq. (1), i lies in [1, …, N ] and t lies in [1, …, T ], N = 11 and T = 23 are the number of countries and the number of years, respectively. Eq. (1) can be written as follows:

3.2. Renewable energy objective in the future The importing countries saw significant developments that consist in the introduction of new renewable energy projects. For example, Morocco set a target of 42% renewable share of installed capacity by 2020. It targeted 2 GW of solar, wind and hydro capacity by 2020; Lebanon, with 12% renewable share of electrical and thermal energy, targeted 40 MW of hydro, 15–25 MW of biogas, and 60–100 MW of wind capacity by 2015, and Tunisia declared a target of 11% and 25% renewable share of electricity production by 2016 and 2030, respectively. The country also announced a target of 16% and 40% renewable share of installed power capacity by 2016 and 2030, respectively. In fact, it targeted 140 MW and 2 GW of solar installed capacity, 430 MW and 1.7 GW of wind capacity, as well as 40 MW and 300 MW of solid biomass by 2016 and 2030 respectively [99]. It is worth noting that with the exception of Tunisia, the MENA NOICs have not set targets for renewable energy capacity outside 2020. This can be mainly attributed in part to the fact that NOICs are not in a favorable position to finance renewable energy projects.

β

Yit =ARECitβ1i NRECitβ2i Kitβ3i LFit 4i

(2)

Where A represents the total factor of productivity, β1i=(β1i , β2i , β3i β4i ) is the vector of the output elasticity to renewable, non-renewable energy, capital and labor force, respectively. Taking the logarithm of Eq. (2), and adding the trend and error term, we get,

LYit =αi+δi t +β1i LRECit +β2i LNRECit +β3i LKit +

β4i LLFit +εit

(3)

where αi and δi are the intercept and the parameter associated to the trend, respectively. The use of the logarithm permits to remove heteroscedasticity from the regression model and also to interpret the coefficients as long-term elasticities.

4. Empirical methodology

4.3. Panel unit root and panel cointegration tests

4.1. Data

4.3.1. Panel unit root tests Examining the statistical properties in term of stationarity is an important step when using cointegration. Evidence for the presence of unit root is a crucial precondition when testing for the presence of a long-term association between variables. Following the cointegration theory, all the variables employed should be integrated with first order, I(1). The literature in unit root tests is also rich and several tests have been propose din the empirical literature [108–114]. However and since the seminal paper of Dickey and Fuller [108] that have introduced the unit root tests in time series context, a large number of unit root tests have been developed in the last three decades both in the time series and the panel data contexts. In the latter context, i.e., the panel data, many panel unit root tests have been developed and proposed in the empirical literature such as Breitung [109], Hadri [110], Im et al. (IPS, [111]), Levin et al. (LLC, [112]), Maddala and Wu [113] and Pesaran [114] among many others. While the five first tests previously presented assume cross-section independence in the dynamics of the autoregressive coefficients, the Pesaran [114] test assumes cross-section dependence.

Yearly data that cover the period 1980–2012 are collected from the U.S. Energy Information Administration, the Penn World Table ([104], PWT8.0)4 and the World Bank Development Indicators online databases. The MENA NOICs include Lebanon, Israel, Jordan, Malta, Morocco, Tunisia, Turkey, Armenia, Cyprus, Georgia, and Mauritania.5 This provide us with T = 253 observations a sample size that is considered a large enough to conduct our empirical research. The variables used in this study are the Real Gross Domestic Product (RGDP) measured in millions of constant 2005 U.S which is the dependent variable of our model. The explicative variables include the renewable energy and non-renewable energy, the capital and the force labor variables. Renewable energy (net biomass geothermal, wind and solar energy) are defined in millions of kilowatt hours. Nonrenewable energy (total coal, natural gas, and petroleum) are measured in the same unit as renewable energy. The third explicative variable is 4 5

http://www.ggdc.net/pwt. Sources: REN21 [99] and U.S. EIA [105]

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dependence we perform the Westerlund [123] tests. These three panel cointegration tests are described briefly below.

In this empirical study, evidence for the presence of unit root among all variables used is investigated by using both cross-sectional independence and cross-sectional dependence tests. Thus in the following subsection, we will present briefly these two classes of tests.

i. Pedroni [120,121] cointegration tests Pedroni [120,121] has proposed several panel cointegration residuals based tests. Precisely, Pedroni [120] has developed two classes of panel cointegration tests: (1) the within dimension based tests statistics and (2) the between dimension based statistics. The first group, entitled panel cointegration tests, includes four basic tests statistics: the panel v-statistic (Z v ), panel ρ-statistic (Zρ ), panel PP- statistic (Z pp ), and panel ADF-statistic (ZADF ) all these tests statistics are derived from the within dimension. The aforementioned tests statistics allow pooling the AR coefficients across various individuals series for the unit root tests on the assessed residuals while assuming heterogeneity across individuals series and common time factors. The second set, named the group mean panel cointegration tests, which depends on the between measurement (dimension) approach. This group includes three sets of tests statistics6: group rho-statistic (Zρ ), group PP-statistic (Z pp ), and group ADF- statistic (ZADF ). The starting point of these tests is based on the following specification,

i. The cross-sectional independence panel unit root tests As previously discussed, several panel unit root tests have been proposed in the context of cross-sectional independence (see for instance [109–113]). This group of tests, cross-sectional independence tests, can be also subdivided into two subgroups: (a) homogenous and (b) heterogeneous cases. a. Homogenous case Three tests fall into this first subgroup namely the LLC [112], Breitung [109] and Hadri [110] tests. These three tests assume common unit root process which is identical across cross-sections. While, the null hypothesis for the LLC [112] and Breitung [109] tests is the unit root case, for the Hadri [110] test the null hypothesis corresponds to the stationarity case. b. Heterogeneous case The assumption of homogeneity in economic is very restrictive as it is hard to admit the existence of dynamic properties for all series of the same variable, independently of the country concerned. Neglecting the heterogeneity of the data can lead to spurious results. Thus, in addition to the homogeneity tests, we propose to use the ADF- and PP-Fischer tests of Maddala and Wu [113] and the Im et al. [111] tests.

m

Yit =

j =1

The second group of panel unit root tests that have been recently developed in the empirical literature assumes cross-sectional dependence among the individuals series included in the panel see for instance [114–117]. The assumption of cross sectional dependence have been motivated by several reasons such as the omission of common factors, the existence of spatial spillover, or remaining residual interdependence among many others factors. In this paper, we will use the cross-sectionally augmented IPS (CIPS) panel unit root test developed by Pesaran [114]. The CIPS test of Pesaran [114] is given by,

εit =ϕi εit −1+uit

i =1

(6)

Pedroni [120] shows that all the seven statistics have as a limit distribution the standard normal distribution which is given by,

(Z −ω N )/ λ ⎯⎯⎯⎯⎯⎯⎯⎯→N (0, 1)

N

∑ CADFi

(5)

where N is the number of countries, T is the number of observations and m regressors ( Xm ). It is important to note that Yit and Xji, t are assumed to be integrated of order one in levels, e.g. I(1). These statistics are based on averages of the individual autoregressive coefficients linked to the residuals’ unit root tests for each country. The null hypothesis of absence of cointegration assumed that all seven tests specify the nonexistence of cointegration. H0 : ϕi=1 for all i , while the alternative hypothesis is defined as H1 : ϕi <1 for all i , where ϕi is the autoregressive term of the estimated residuals of Eq. (5), that is,

i. The cross-sectional dependence panel unit root tests

CIPS =N−1

μi +θi t + ∑ αji Xji, t +εi, t

N , T →∞

(7)

where Z is normalized statistics, ω and λ are as tabulated in Pedroni [120] (see [25,120,121,124]). ii. Kao [122] cointegration test The second test of panel cointegration used in this paper is the Kao [122] test. This test that have been proposed by Kao [122] estimates the homogeneous cointegration relationship through pooled regression allowing for individual fixed effects. Kao [122] proposes an Augmented Dickey-Fuller (ADF) panel cointegration test where the vectors of cointegration are homogeneous. Let us consider εˆit being the estimated residual obtained from Eq. (8),

(4)

where CADFi is the individual cross-sectionally augmented DickeyFuller statistic for the time series or individual i [114,118]. 4.3.2. Panel cointegration tests analysis The cointegration concept corresponds to the systematic co-movement among two or more variables over the long-term [119]. The cointegration theory allows the study of non-stationary variables but whose linear combination is stationary. The main feature of cointegration theory is that it allows examining the existence of stable relationship in the long-term between several variables . Testing for cointegration is an important step in anon-stationary panel data. In this context, several tests have been used where some of them are based on group-mean estimates and some others on pooled estimates. In this subsection, we follow the same categorization as in the panel unit root tests by classifying these tests into two categories: (1) the first category includes tests that assume cross-sectional independence, and (2) the second category includes tests that assume cross-sectional dependence. Overall, three tests of panel cointegration will be used in this paper. As panel cointegration tests that assume cross-sectional dependence we will employ the well-known Pedroni [120,121] and Kao [122] tests, then as panel cointegration that assumes cross-sectional

Yit =αi+βXit +εit

(8)

For all t = 1, …, T and i = 1, …, N . Where αi and β are the parameters of the model. The ADF test is obtained by the estimated the following regression, p

εit =λεit −1+ ∑ ρj ∆εˆi, t − j +vit j =1

(9)

6 The between dimension tests are known to be less restrictive because they allow for heterogeneity of the parameters across countries [51].

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where λ is chosen so that the residuals vit are serially uncorrelated assuming the null hypothesis of absence of cointegration. Kao [122] proves that the ADF test normally converges to a standard normal distribution N (0,1). iii. Westerlund [123] cointegration tests

q

∆Yit =γ1j + ∑ θ11ik

q

∆Yit − k + ∑ θ12ik

k =1

k =1

q

∆Kit − k + ∑ θ15ik

k =1

∆Yit =δi′ dt +αi Yi, t −1+λi′ Xi,

t −1+

q

∆RECit =γ2j + ∑ θ21ik

∑ αij ∆Yi,t −j+ ∑ j =−qi

γi, j Xi,

q

∆Kit − k + ∑ θ25ik

k =1

∆LFit − k +λ2i ECTit −1+v2it

k =1

(12b) q

q

∆NRECit =γ3j + ∑ θ31ik

q

∆Yit − k + ∑ θ32ik

k =1

∆RECit − k + ∑ θ33ik

k =1

∆N

k =1

q

q

RECit − k + ∑ θ34ik

∆Kit − k + ∑ θ35ik

k =1

∆LFit − k +λ3i ECTit −1+v3it

k =1

(12c) q

q

∆NRECit − k +

k =1

q

∑ θ44ik

∆Kit − k + ∑ θ45ik

∆LFit − k +λ 4i ECTit −1+v4it

k =1 q

q

∆LFit =γ5j + ∑ θ51ik

∆Yit − k + ∑ θ52ik

k =1 q

∑ θ54ik k =1

∆RECit − k + ∑ θ43ik

k =1

q

(10)

q

∆Yit − k + ∑ θ42ik

k =1

where N refers to country numbers and T refers to observation numbers. dt represents the deterministic components, dt =0 (no deterministic components), dt =1 (with constant), and dt =(1,t )′(with constant and trend). αi determines the speed at which the system corrects back to the equilibrium after an unpredicted shock. Two tests are considered to assess the alternative hypothesis that the whole panel is cointegrated (Gt , Ga ), while the further two tests (Pt , Pa ) the alternative that as a minimum one unit is cointegrated. The null hypothesis for all the tests proposed by Westerlund [123] is the same where, H0 : αi=0 for all i . In contrast, the alternative hypothesis is that H1g : αi <0 for at least one i for the group-mean tests (Gt , Ga ) and H1p : αi=α <0 for all i which means that the αi ’s are equal for all i under the panels (Pt , Pa ) tests.

(12d)

q

∆RECit − k + ∑ θ53ik

k =1

∆NRECit − k +

k =1

q

∆Kit − k + ∑ θ55ik

∆LFit − k +λ5i ECTit −1+vit

k =1

(12e)

where ∆ denotes the first differences; θ represents the fixed country effect; k (k =1, …, q ) represents the optimal lag length from the Schwarz Information Criterion (SIC). ECTit refers to the error correction terms at time t , λ is the coefficient of adjustment toward the long-term equilibrium and v is the error term supposed to be uncorrelated with E (v )=0 . The short-run causal relationship is examined by testing jointly the significance of the coefficients associated to the variables in first difference variables including their lags. The partial F-statistic is used for all Eqs. (12a)–(12e). A positive (negative) and statistically significant result for each coefficient θij , where i , j = 1, …, 5 with i ≠ j , indicates that the variable has a positive (negative) short-run causal impact on the dependent variable [137]. For example, in the real GDP Eq. (12a), short-term causal relationship from renewable energy use, non-renewable energy use, capital, and the labor to real GDP are tested using the Wald test based on the following null hypotheses H0 : θ12ik =0 ∀ ik ,H0 : θ13ik =0 ∀ ik , H0 : θ14ik =0 ∀ ik and H0 : θ15ik =0 ∀ ik , respectively. For the renewable energy use Eq. (12b), evidence for short-run causality running from real GDP, non-renewable energy use, capital and the labor force to renewable energy use are tested based onH0 : θ 21ik =0 ∀ ik ,H0 : θ 23ik =0 ∀ ik , H0 : θ24ik =0 ∀ ik and H0 : θ25ik =0 ∀ ik , respectively. The long-term causality direction is examined by testing the significance of the coefficient associated to each ECT using a t-test statistic. The number of years required to return back to the long-term equilibrium can be obtained by taking the inverse of the absolute value of the coefficient associated to the ECT, [46,51].

4.4. Panel FMLOS estimates and Granger causality test In the econometric literature related to panel cointegration, several estimation methods have been proposed to estimate the long and short-run relationships [120] and [125–130]. Two methods have been largely used in the empirical literature: (1) the Fully Modified OLS (FMOLS) estimator initially proposed by [131] and (2) the Dynamic OLS (DOLS) estimator of [132,133]. In this paper, we propose to use the FOLS (FMOLS) procedure. This approach helps in solving possible endogeneity problem between regressors [126]. It allows also to account for the heterogeneity in the cointegrated vectors. The use of the FMOLS approach is motivated by the empirical findings of Banerjee [134] who shows that the FMOLS or DOLS estimates are asymptotically equivalent for a sample size higher than 60 observations (in this paper the panel data set comprises 429 observations). Subsequently, a panel vector error correction model (VECM) is estimated to carry out the Granger-causality tests [135]. The two-step technique of Engle and Granger [136] is performed by estimating the long-term relationship developed in Eq. (3) above. From that equation, we recuperate the estimated residuals called the error correction term (ECT) given by,

βˆ4i LLFit

∆N

k =1

q

k =1

ECTit =LYit − αˆ i− δˆi t − βˆ1i LRECit −βˆ2i LNRECit −βˆ3i LKit −

∆RECit − k + ∑ θ23ik

k =1

RECit − k + ∑ θ24ik

t −1+ eit

j =1

(12a)

q

∆Yit − k + ∑ θ22ik

k =1

∆Kit =γ4j + ∑ θ41ik pi

∆LFit − k +λ1i ECTit −1+v1it

k =1 q

pi

∆NRECit − k +

k =1

q

∑ θ14ik

The third and last panel cointegration test that we will employ is bootstrap based panel cointegration tests which have been proposed by Westerlund [123]. Westerlund [123] proposes four basic panel cointegration tests (Gt ,Ga ,Pt ,Pa ) that do not require any common-factor limitation. The null hypothesis under this test corresponds to the hypothesis of no cointegration. The test can be carried out by testing the significance of the error-correction term in a restricted panel errorcorrection model. The author shows that all the four tests are normally distributed. Moreover, Westerlund [123] suggests that these tests offer p-values that are robust against cross-sectional dependencies passing through bootstrapping. The Westerlund [123] considers the following error-correction model given by,

∆RECit − k + ∑ θ13ik

5. Results and discussion The results of panel unit root tests reported in Table 27 show that all variables are integrated with first order independently on the type of the tests used; cross-sectional independence or cross-sectional dependence. This evidence of first order integration of all variables enable us to examine the hypothesis of cointegration among real GDP, renewable

(11)

then, the lagged error correction term ECTit−1 is used in the following models,

7

135

All the results are obtained using Eviews 9 software.

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Table 2 Results of unit root tests in panel for MENA NOICs, 1980–2012. Variables

LLC

IPS

ADF - Fisher

PP - Fisher

B

Hd (HOM)

Hd (HET)

Ps

Y

0.752 (0.77)

1.64 (0.95)

15.41 (0.84)

14.92 (0.86)

0.89 (0.81)

9.72*** (0.00)

9.74*** (0.00)

−2.44 (0.34)

ΔY

−4.34*** (0.00)

−6.04*** (0.00)

77.10*** (0.00)

231.34*** (0.00)

−5.28*** (0.00)

0.85 (0.19)

1.16 (0.12)

−2.83** (0.03)

REC

−1.60 (0.06)

0.06 (0.52)

23.79 (0.64)

24.51 (0.32)

−0.84 (0.50)

7.55*** (0.00)

5.42*** (0.00)

−2.14 (0.76)

ΔREC

−6.25*** (0.00)

−6.76*** (0.00)

86.05*** (0.00)

622.78*** (0.00)

−1.88*** (0.02)

1.14 (0.12)

1.38 (0.18)

−2.87** (0.04)

NREC

−1.36 (0.08)

0.58 (0.72)

25.80 (0.26)

25.42 (0.27)

−0.10 (0.45)

5.27*** (0.00)

6.66*** (0.00)

−1.68 (0.99)

ΔNREC

−4.85*** (0.00)

−6.21*** (0.00)

77.31*** (0.00)

160.06*** (0.00)

−4.32*** (0.00)

0.47 (0.31)

1.17 (0.11)

−2.79** (0.02)

K

1.36 (0.91)

1.89 (0.97)

28.71 (0.15)

15.63 (0.83)

2.11 (0.98)

5.36*** (0.00)

3.50*** (0.00)

−2.20 (0.69)

ΔK

−4.59*** (0.00)

−4.71*** (0.00)

75.44*** (0.00)

114.04*** (0.00)

−7.91*** (0.00)

0.57 (0.71)

0.09 (0.50)

−2.86** (0.04)

LF

−1.15 (0.12)

1.23 (0.89)

12.91 (0.93)

9.93 (0.98)

0.86 (0.80)

6.99*** (0.00)

7.41*** (0.00)

−2.37 (0.44)

ΔLF

−4.48*** (0.00)

−4.37*** (0.00)

63.33*** (0.00)

265.59*** (0.00)

−4.37*** (0.00)

0.64 (0.25)

1.49 (0.20)

−2.82** (0.02)

Notes: Δ=First difference operator. B, Hd and Ps denote the Breitung, the Hadri and the Pesaran unit root tests, respectively. HOM and HET designate homogeneous and heterogeneous, respectively. Panel unit root tests include intercept and trend exceptionally Hadri unit root test, which includes intercept only. *** and ** represent the significance at the 1% and 5% level, respectively.(.): Probabilities. Table 3 Pedroni [120,121] Cointegration tests for MENA NOICs, 1980–2012. Within-Dimension

Panel Panel Panel Panel

v-Statistic rho-Statistic PP-Statistic ADF-Statistic

Between-Dimension Statistic

Prob.

Statistic

Prob.

1.736 -2.105 -8.631 -5.513

0.0412** 0.0176** 0.0000*** 0.0000***

2.698 -1.988 -1.966 -1.742

0.0004*** 0.0234** 0.0246** 0.0407**

Statistic - 4.732 -7.336 -2.434

Group rho-Statistic Group PP-Statistic Group ADF-Statistic

Prob. 0.0000*** 0.0000*** 0.0075***

Notes: Null hypothesis: No cointegration. Trend assumption: Deterministic intercept and trend. Lag selection: Automatic SIC with a max lag of 5. *** designate the significance at the 1% significance level. ** designate the significance at the 5% significance level. Table 4 kao [122] Cointegration test for MENA NOICs, 1980–2012.

ADF Residual variance HAC variance

Table 5 Westerlund [123] ECM panel cointegration tests, 1980–2012.

t-Statistic

Prob.

Statistic

Value

Z-value

P-value

Robust P-value

-2.220 0.009404 0.009103

0.0132**

Gt Ga Pt Pa

-2.638 -9.077 -11.595 -7.340

-3.335 -1.578 -2.882 -2.829

0.001*** 0.048** 0.002*** 0.001***

0.020** 0.032** 0.010** 0.027**

Notes: Null hypothesis: No cointegration. Trend assumption: No deterministic trend. Automatic lag selection based on SIC with max lag of 6. ** designates the significance at the 5% significance level.

Notes: Optimal lag and lead length determined by Akaike Information Criterion with maximum lag and lead length of 2. We allow for a constant and deterministic trend in the cointegration relationship. Number of bootstraps to obtain bootstrapped p-values which are robust against cross-sectional dependencies set to 400. Results for H0: nocointegration. The Bartlett kernel window width set according to 4(T/100)2/9. *** denote the significance at the 1% significance level. ** denote the significance at the 5% significance level.

energy, non-renewable energy, capital and labor force variables by using panel cointegration tests [120–122]. In this paper, the hypothesis of cointegration between all variables is tested using Kao [122]’s residual cointegration tests, all seven panel cointegration tests of Pedroni [120,121] and the Westerlund [123] tests. The empirical results support the hypothesis of cointegration among all variables. This empirical finding proves evidence for the presence of a long-term equilibrium between real GDP, renewable energy use, non-renewable energy use, capital, and labor force (see Tables 3–5).

Once the hypothesis of the existence of a long-term relationship was supported, the following step consists on estimating this relationship. For this end, the FMOLS approach was used and the empirical findings are detailed in Table 6. The empirical findings show that all the 136

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is 5.21 years. Finally, the results obtained from Eq. (12e)8, show that only the renewable energy does not determine the labor force. Moreover, the results show that 14.71 years are required to return back to the long-term equilibrium. Consequently, the panel causality results provide strong evidence for two way causality relationships between the two types of energy sources (renewable and non-renewable) and economic growth in both the short and long-term. It is also worth mentioning that our findings confirm the importance of green energy on determining and stimulating economic growth for the MENA NOICs. Besides, our empirical findings confirm the empirical results of some previous studies such as [46,86,92,138], and contradict some others such as [25,90,140]. Besides, these results are not consistent with the outcomes obtained by [41] for the entire group of MENA NOECs but they are in accord with their results found for the 5 selected MENA NOECs sample. Moreover, the negative bidirectional causality between the two types of energy sources employed provides indication for their substitutability. This signifies that a rise in energy use from renewable sources can help in reducing the energy use from non-renewable sources and vice versa. In fact, renewable energy sources are looked as alternative in order to substitute non renewable sources of energy given that the main goal of these importing countries is to reduce their dependence on expensive imported oil to cut down their steep bills and to ensure energy security. As pointed out in the Economic Report of the president [141], rising volatility of oil prices will encourage households and businesses to reduce the consumption of traditional energy, pay for more resourceful goods and switch to renewable energy sources. Moreover, increasing volatility in energy prices will induce more additional barriers for oil importing economies in term of balancing annually their payments. Further, the existence of the feedback hypothesis between these two types of energy and economic growth provide evidence of their interdependence suggests that policies which boost the use of both renewable and non-renewable energy sources positively impact the economic activity. The results show also that the green (clean) energy source appears to be more suitable to be used as a fundamental energy source. The promotion of the renewable energy sector is more than recommended mainly due to : (1) in its positive effect on decreasing the level of GHG and (2) the substitutability between these two sources [46,90]. Furthermore, Chien and Hu [142] pointed out that when an economy is in great demand of energy, it will not only exploits more renewable energy but also imports more energy, such that the two energy sources tend to increase together. Besides, the two way causal link between renewable and non-renewable energy use, which is confirmation of their substitutability, it implies also that the expansion of the renewable energy sector can undoubtedly reduce the greenhouse gas emissions and toxic waste induced by non-renewable energy use, which can make the world a cleaner and protected place. From the results mentioned above, it is recommended that these importing countries should raise their investment in renewable energy project to enhance the growth rates of their energy consumption from renewable sources. Additionally, it is crucial that the investigated countries should put into practice promotional policies that encourage the development of renewable energy sector such as incentives in terms of fiscal (e.g., grant, rebate, tax credit), public finance (e.g., investment, guarantee, loan and public procurement), regulation (e.g., renewable portfolio standard, fixed payment feed in tariff) and access (e.g., net metering, guaranteed access to network), the creation of several research and educational institutions to develop local skills, and the creation of markets for renewable. By considering these recommendations, these

Table 6 Parameter estimation using FMOLS for MENA NOICs, 1980–2012. Variables

Coefficient

Prob.

REC 0.570 [0.0000]*** NREC 0.387 [0.0000]*** K 0.517 [0.0000]*** LF 0.344 [0.0003]*** R2 = 0.979; Adj. R2 = 0.978; HE=2.96 [0.430]; RESET= 1.95 [0.121]; DW= 1.84 Note: Method: Fully-modified OLS (FMOLS). Panel method: Grouped estimation. Cointegrating regression contains constant and trend. HE is White’s Heteroscedasticity test. REST is Ramsey’s regression equation specification error test. DW is the DurbinWatson test for serial correlation. Null hypothesis: Ho: model has no omitted variables and Ho: Homoscedasticity for Ramsey RESET test and White’s Heteroscedasticity test, respectively. [.]: Probabilities. *** refers to the significance at 1% level.

coefficients of the long-term relationship have their expected sign, positive and statistically significant at the 1% significance level. In addition, since all series are in logarithms; then all estimated coefficients of the long-term relationship can be interpreted as long-run elasticities. The empirical results show that all the elasticities are lower than one. In particular, the results provide evidence that a 1 per cent increase in renewable energy use, non-renewable energy use, capital and the labor force rises real GDP by 0.570, 0.387, 0.517, and 0.344 per cent, respectively. Our empirical findings are similar to those reported by [41,46,91,92,138]. Moreover, earlier multi-country panel studies, [65–67,73,139], that are based on using only renewable energy consumption revealed evidence for the significance of the long-term elasticity with respect to renewable energy use. However, only few empirical studies does not found evidence for significance of the longterm coefficient associated to renewable energy use (see for instance [90,140]). Table 7 reports the results of the estimation of the panel error correction model. In particular, it reports the results of estimation of both the short- and the long-term dynamics as presented theoretically by Eqs. (12a)–(12e). The results show that all the explanatory variables have a positive and significant impacts on economic growth in the short-term. The ECT coefficient shows that the adjustment speed required to return back to the long-term equilibrium is approximately equal to 12.66 years. Besides, Eq. (12b) indicates, in the short-term, that economic growth, real gross fixed capital formation as well as the labor force have a positive and statistically significant effect on renewable energy use, the non-renewable energy use proxy has a negative and statistically significant effect on economic growth. The results provide also evidence for substitutability between the two types of energy sources (renewable and non-renewable) since nonrenewable energy use have a negative and significant impact on renewable energy. The significance of the ECT coefficient implies that renewable energy use responds to variations from long-term equilibrium with the speediest adjustment of 4.88 years. The results from Eq. (12c) show that each of the economic growth, the labor force and the capita variables have as expected a positive and significant effects on non-renewable energy use. Moreover, Eq. (12c) confirms the substitutability between these two types of energy sources. In fact, the negative and statistically significant causality effect running from renewable energy use to non-renewable energy use evidently proves the substitutability exposed in Eq. (12b). The ECT term is statistically significant with the slowest adjustment speed to the long-term equilibrium of 15.63 years. The main results from Eq. (12d) is the evidence of positive and significant effects of all the explanatory variables on the capital variable with the exception of renewable energy source which has an insignificant coefficient. Moreover, the result show that with the number of years required to return back to the long-term equilibrium

8 Apergis and Payne [46] point out that the two-way causality between capital and non-renewable energy use can be an indicator of the predominantly capital-intensive infrastructure that currently relies on non-renewable energy sources.

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Table 7 Panel causality tests for MENA NOICs, 1980–2012. Dependent Variable

Source Of Causation (Independent variables) Short-run

ΔY

ΔREC

∆Y (12a )

-

∆REC (12b )

5.114 [0.031]** (1.422) [0.000]*** 6.245 [0.012]** (0.421) [0.002]*** 13.678 [0.000]*** (0.441) [0.001]*** 4.210 [0.043]** (0.266) [0.005]***

∆NREC (12c )

∆K (12d )

∆LF (12e)

4.364 [0.036]** (0.170) [0.003]*** 4.369 [0.038]** (- 1.673) [0.000]*** 0.171 [0.679] (1.173) [0.759] 0.104 [0.747] (-1.112) [0.601]

Long -run

ΔNREC

ΔK

ΔLF

ECT

5.146 [0.021]** (0.115) [0.000]*** 5.016 [0.026]** (- 0.203) [0.000]*** -

13.004 [0.000]*** (0.059) [0.000]*** 5.896 [0.026]** (0.261) [0.001]*** 6.915 [0.010]** (0.046) [0.031]** -

4.198 [0.046]** (0.012) [0.000]*** 4.407 [0.036]** (0.498) [0.020]** 5.187 [0.031]** (0.253) [0.000]*** 4.223 [0.042]** (0.247) [0.000]*** -

-0.079 [0.000]***

6.601 [0.018]** (0.098) [0.007]*** 6.051 [0.014]** (0.034) [0.000]***

4.691 [0.031]** (0.232) [0.000]***

-0.205 [0.000]*** -0.064 [0.003]*** -0.192 [0.000]*** -0.068 [0.000]***

Notes: Partial F-statistics reported with respect to short-run changes in the independent variables. The sum of the lagged coefficients for the respective short-run changes is also performed and is denoted in parentheses. ECT denotes the estimated coefficient on the error correction term. The vector error correction model is estimated using panel regression techniques with fixed effects for cross section. Probability values, which represented the probability values of the partial F-statistic and the Wald chi-square tests, are in brackets [.] and reported next to the corresponding partial F-statistic and sum of the lagged coefficients, respectively. *** indicate the significance at the 1% significance level. ** indicate the significance at the 5% significance level.

Further, the authorities are supposed to remove the non economics barriers, for instance, grid access, administrative impediments, and the lack of training and information. Finally, the government should choose the most appropriate incentive policies based on their relevant objectives in terms of renewable energy, the availability of renewable energy technology and the budget constraints in order to promote optimally the exploitation of renewable energy.

importing countries would be able to mitigate global warming through controlling greenhouse gas emissions derived from the excessive use of non-renewable energy sources and reduce their dependence on fossil fuel to raise their energy security. Finally, it is important to state that successful and effective or efficient policies rely on expected, transparent, steady system conditions, and on high-quality design, which help developing the renewable energy market, encouraging investment, and promoting the development of renewable industries.

Appendix A. Supplementary material 6. Conclusions and policy implications Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.rser.2017.01.010.

This article try to fill the gap in the energy economic empirical literature investigating the short and long-term relationship as well as the Granger causality direction between energy use from non-renewable and renewable sources and economic growth in the MENA economies. Precisely, we will focus our analysis in the role played by the renewable energy sources type in MENA NOICs for the period 1980–2012 within a multivariate framework by including capital and labor as supplementary variables. The empirical findings provide proof for the existence of a longterm equilibrium between economic growth, energy use from renewable and non-renewable sources, labor and capital with elasticities estimated positive and statistically significant. The Granger causality results based on the panel vector error correction model provide evidence for the feedback hypothesis between the two types of energy employed and the economic growth. Further, a bidirectional causal relationship exists between renewable and non-renewable energy use providing proof for their interdependence and substitutability. This suggests that the two energy sources are vital for economic growth which encourages their exploitation mutually. In the case of the MENA NOICs economies, which have known a rapid phenomenal population growth in the last decades, their economies still have an intense dependence on non-renewable energy sources to satisfy the continuous increase in demand. Moreover, we found evidence for bidirectional causal association between economic growth and renewable energy use in the short-run, which is expected since these economies are seeking to protect themselves from price volatility of fossil fuels resulting in steep bills and to achieve their energy independence. Obviously, the transition towards a renewable energy supply requires some form of government intervention in an attempt to defeat market distortions favoring fossil fuels. In fact, the authorities ought to provide favorable environment for investors by enhancing macroeconomic stability, property rights, transparency, good governance and infrastructure, and openness to trade to reap the optimal benefits from the renewable energy use.

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