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Faculty of Economics & Business

MSc in International Economics & Business

Master Thesis:

The impact of renewable energy sources on economic growth of

Mediterranean countries

Author: Xenia Masoura (s3169952) Email: x.masoura@student.rug.nl Supervisor: Jepma C.J.

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2 Abstract

The purpose of this paper was to assess the effect that renewable energy consumption has on the economic growth of the following eight Mediterranean countries; Albania, Bosnia & Herzegovina, Croatia, Greece, Italy, Portugal, Slovenia, and Spain for the time period of 1996 to 2016. The sample of countries shares similar economic state (GDP annual growth %), renewable energy consumption levels (World Bank, 2018), as well as geographical position (weather and geopolitical factors) and culture. The above-mentioned set of countries are unique because; one they have not been assessed before by other economists and two they could qualify as a rather homogenous sample, and three no other literature in the field has undertaken this analysis on such a recent data. I used panel estimation techniques and identified cross-sectional dependence and heterogeneity across the countries. I confirmed the presence of long-run dynamics between economic growth and renewable energy consumption as well as with the rest of the variables (traditional inputs and non-renewable energy). Based on the dynamic OLS and fully modified OLS long-run output elasticity models, renewable energy consumption has a positive effect on economic output (growth hypothesis). Therefore, policymakers should promote the use and generation of renewable energy focusing on future sustainable economic development. I have also conducted further analysis concerning the direction of causality in the short-run. I could not establish any unidirectional or bidirectional causality between output and renewable energy consumption in the short-run. This is a clear indication for the existence of the neutrality hypothesis. Finally, the time-series analysis of long-run output elasticities for each individual country suggested the significant and positive impact of renewable energy consumption on economic growth. For seven out of eight countries, the findings demonstrated a sustainable economic growth outcome in the long-run, which could be indicative for policy implications.

Keywords: renewable energy consumption, economic growth, neutrality hypothesis, growth hypothesis

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Table of Contents

1. Introduction ... 4

2. Literature Review ... 5

2.1 Economic growth and renewable energy consumption ... 6

2.2 Economic models used in the existing literature ... 10

2.3 Renewable energy sources ... 11

2.4 Renewable energy (RE) in the current sample countries ... 12

3. Methodology ... 14 3.1 Hypotheses ... 14 3.2 Method ... 15 3.3 Data ... 15 3.4 Descriptive statistics ... 17 4. Empirical Analysis ... 18

4.1 Issues related to heterogeneity and cross-sectional dependence ... 18

4.2 Estimation steps and empirical findings ... 19

4.2.1 Test for cross-sectional dependence... 19

4.2.2 Panel unit root test... 20

4.2.3 Panel cointegration test ... 20

4.2.4 Panel data analysis of long-run output elasticities ... 22

4.2.5 Heterogeneous panel causality test ... 23

4.2.6 Time series analysis of long-run output elasticities ... 24

4.3 Policies on RES ... 26 5. Conclusions ... 29 References ... 32 Online Sources ... 35 Appendix ... 37 4.1. Heteroskedasticity tests ... 37

4.2.1. Tests for cross-sectional dependence ... 37

4.2.2. Unit root tests ... 39

4.2.3 Cointegration Tests ... 45

4.2.5 Dumitrescu & Hurlin (2012) Granger non-causality test ... 47

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1. Introduction

Fossil fuels – in the form of oil, coal, and gas are the main sources of energy for many of the countries on this earth and this is especially the case for European countries. The threats of climate change, environmental degradation, and the increasing demand for energy are forcing countries to promote and invest in alternative energy sources. Countries which are moving away from fossil fuels and towards greener energy sources such as; hydro, wind, solar, biomass, marine and geothermal power are concerned about the impact “new” sources of energy can have on economic growth. Advanced nations use the Maji et al. 2015 findings that an increase in efficient energy use is a driver for sustainable economic growth, as a rationale for transitioning to renewable sources of energy.

The energy consumption-economic growth nexus is a piece of research that has been extensively explored, however not on the fronts of renewable energy. The growing significance of renewables as a source of energy has created wide interest (Bhattacharya et al., 2016). The objective of this paper is to review present literature for the effect that renewable energy consumption has on sustainable economic growth and to provide an in-depth analysis of the effect that it may have on the economic growth of Mediterranean European countries. The existing literature suggests that renewables are typically found to have a positive affect the economic growth of countries (especially in the long-run). Researchers, however, have mostly focused on developed economies such as the Netherlands, Germany (Rafindadi, A. A., & Ozturk, I., 2017 - Inglesi-Lotz, R., 2016) or emerging nations such as Nigeria and Mexico (Fang, Y., 2011 - Pao, H. T. et al., 2014). There is a general lack for literature in which a larger sample of countries are treated. This is in part due to the scarce availability of environmental data (Bhattacharya, M. et al., 2016). This thesis will provide an in-depth analysis of a new set of countries supported by a new set of observations (1996 – 2016).

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The literature indicates four hypotheses which illustrate the causal relationship between renewable energy consumption and economic growth: feedback hypothesis, conservation hypothesis, neutrality hypothesis, and growth hypothesis. Most studies indicate feedback hypothesis as the explanatory one for the energy consumption growth nexus which implies a bi-directional relation between renewable energy consumption and growth. This paper will close the literature gap by analyzing countries that had not been included in past papers due to a lack of available data. Hence, I will examine the following research question:

What is the effect of renewable energy consumption on the economic growth of Mediterranean European countries?

I will follow the methods of Bhattacharya, M. et al, 2016 and enhance my analysis with the addition of various tests to verify the credibility of results and methods. I use a strong balanced panel of the 8 countries covering the period between 1996 and 2016 and conduct various panel estimation techniques to identify cross-sectional dependence and heterogeneity across the countries. I confirm the presence of long-run dynamics between economic growth and renewable energy consumption. Based on the DOLS (dynamic ordinary least-squares) and FMOLS (fully modified OLS) long-run output elasticities models, renewable energy consumption has a significant and positive impact on economic output, which agrees with the growth hypothesis and the unidirectional causality (RE > GDP). Based on these findings, policies should focus on long-term sustainable development based on promoting renewable energy deployment. Further analysis concerning the direction of causality in the short-run is conducted with the use of Dumitrescu & Hurlin (2012) Granger non-causality tests. I could not establish any unidirectional or bidirectional causality between output and renewable energy consumption in the short-run. Based on the literature review, my findings support the neutrality hypothesis in the short-run which implies that economic growth and energy consumption are independent and do not affect each other. Last, I conduct a time-series analysis of long-run output elasticities for each individual country to identify the dynamic impact of renewable energy consumption on output across countries. The empirical findings suggest that the impact of renewable energy consumption on economic growth is positive and significant for the sample countries (except for Bosnia & Herzegovina) in the long-run. These findings demonstrate that higher renewable energy consumption in these countries will generate greater economic output (growth hypothesis). From these results, I could infer that these countries are moving towards sustainable economic growth in the long-run.

The remainder of the paper is organized as follows: Section 2 provides an overview of the literature review, which discusses the different studies of explaining the dynamics of renewable energy consumption and economic growth as well as the different sources of clean energy used. Section 3 discusses the indicators, methods, and data used. Section 4 comments on the empirical findings and policies and finally, Section 5 presents conclusions and provides relevant policy suggestions.

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In the literature related to renewable energy consumption, the focus is on the direction of causality and the results that researchers have come across in past papers. These points will be discussed extensively. Furthermore, I will present and explain all relevant economic models. Last, I will examine the renewable energy sources and the current status of the countries in my sample.

2.1 Economic growth and renewable energy consumption

The literature includes four hypotheses which describe the causal relation between economic growth and renewable energy consumption. The feedback hypothesis suggests a bi-directional relationship between economic growth and energy consumption. This hypothesis indicates that any change in energy consumption will have an impact on economic growth with a reverse effect. The conservation hypothesis suggests that economic growth causes the consumption of energy. In this case, conservation policies will not affect economic growth. The neutrality hypothesis implies that economic growth and energy consumption are independent and do not affect each other. Last, the growth hypothesis “sees” energy as a major source of input into the growth process and indicates a unidirectional causality from energy consumption to economic growth. Researches over the years have concluded in a wide range of findings across countries under each of these hypotheses (Bhattacharya et al., 2016).

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(2017) use a panel cointegration and heterogeneous causality model for nine Black Sea and Balkan countries (Albania and Greece included), which proves the two-way causality relation between renewable energy consumption and economic growth in 1990–2012 period. According to this, renewable energy consumption encourages economic growth and economic growth encourages renewable energy consumption for Greece and Albania. These results suggest that renewable energy sources are an important aspect for the delivery of sustainable development in these countries.

The conservation hypothesis is not as popular. Armeanu et al. (2017) address the EU-28 countries for the period of 2003-2014 by PVECM method and Granger causality and conclude that biomass energy has the highest influence on economic growth and generally acknowledge the hypothesis to stand both in the short-run and the long-run. Lise and Van Montfort (2007) and Ocal and Aslan (2013) provide evidence for the case of Turkey for the periods of 1970-2003 and 1990-2010 respectively and reveal the link between energy consumption and economic growth. Similarly, Fang (2011) with an OLS approach suggests the same results for China for the period of 1978-2008. For India (1960-2009), Tiwari (2011) uses structural vector autoregressions (VAR) and reveals once again the conservation hypothesis linkages. For the US case for the period of 1960 to 2007, Menyah and Wolde-Rufael (2010) indicate the uni-directionality from GDP to renewable energy consumption. Last, in Sadorky (2009) we see that growth (real per capita income) displays a positive and statistically significant effect on (per capita) renewable energy consumption.

Vaona (2012) explores the case of Italy for the period 1861-2000 using a Granger non-causality method, indicating the independence of the two indicators, supporting the neutrality

hypothesis. At the same token, Menegaki (2011) estimates a panel error correction model which

revealed the lack of short-run and long-run Granger causality from renewable energy consumption to economic growth within 27 European countries between 1997 and 2007. Last, Payne (2009) uses Toda – Yamamoto causality tests for the case of US from 1949 to 2006 and claims the neutrality hypothesis, since evidence shows a lack of Granger causality between renewable and non-renewable energy consumption and real GDP.

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growth of those countries. Inglesi-Lotz (2016) uses a Pedroni cointegration and shows a positive and significant influence of renewable energy consumption on economic growth for 34 OECD countries for 1990-2010. The estimations indicate that a 1% increase of renewable energy consumption will increase GDP by 0.105% and GDP per capita by 0.10% while a 1% increase of the share of renewable energy to the energy mix of the countries will increase GDP by 0.089% and GDP per capita by 0.09%. Last, Ozturk and Bilgili (2015), this time for 51 Sub-Saharan African countries, claim significance of the biomass consumption over the economic growth for the set of countries, bracing the growth hypothesis once more.

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Table 1. Researches related to the renewable energy consumption-economic growth nexus

Research Methodology Period Country Outcome

Amri Two-step GMM 1990-2010 72 countries Apergis & Payne PVECM 1985-2005 20 OECD countries

Apergis & Payne PVECM 1980-2006 6 Central American countries Apergis & Payne PVECM 1990-2007 80 countries

Apergis & Payne PVECM 1992-2007 13 Eurasian countries

Kahia et. al PVECM 1980-2012

MENA net oil importing countries

Koçak & Şarkgüneşi,

Panel cointegration and

heterogeneous causality 1990-2012

9 Black Sea and Balkan countries

Lin & Moubarak

ARDL, Johansen cointegration,

Granger causality 1977-2011 China

Sebri & Ben-Salha ARDL, VECM 1971-2010 BRICS countries Shahbaz et al. ARDL, VECM 1972Q1-2011Q4 Pakistan Shahbaz et al. PVECM 1991Q1-2015Q4 BRICS countries Armeanu et. al PVECM, Granger causality 2003-2014 EU-28 countries

Fang OLS 1978-2008 China

Lise & Van Montfort ECM 1970-2003 Turkey Menyah & Wolde-Rufael Toda-Yamamoto causality 1960-2007 US Ocal and Aslan ARDL, Toda-Yamamoto causality 1990-2010 Turkey

Sadorsky Panel cointegration 1994-2003 18 emerging countries Tiwari Structural VAR 1960-2009 India

Bhattacharya et al. Heterogeneous panel estimations 1991-2012

38 top renewable energy consuming countries

Bilgili & Ozturk

Panel cointegration, Conventional

OLS, DOLS 1980-2009 G7 countries Inglesi-Lotz Pedroni cointegration 1990-2010 34 OECD countries

Ozturk & Bilgili

Panel cointegration, Conventional

OLS, DOLS 1980-2010 51 Sub-Saharan countries Menegaki Panel error correction model 1997-2007 27 European countries Payne Toda-Yamamoto causality 1949-2006 US

Vaona Granger non-causality 1861-2000 Italy Ai-mulali et al FMOLS 1980-2009 108 countries

Apergis et al.

Nonlinear panel smooth transition

VECM 1965-2012

10 major hydroelectricity consuming countries

Bildirici ADRL, Dynamic ECM 1980-2009

10 developing and emerging countries

Cho et al. PVECM 1990-2010 31 OECD, 49 non-OECD countries

Gaspar et al.

Panel-corrected standard errors and fixed effects vecotr

decomposition estimators 1995-2014 20 European states Huang et al. System GMM and PVAR 1972-2002 82 countries

Menegaki & Tiwari

Quantile regression, fixed effects

model, PVECM 1990-2013 20 American countries Menegaki & Tugcu ARDL 1995-2013 G7 countries

Menegaki & Tugcu

Panel cointegration, Granger

causality 1985-2013 42 Sub-Saharan African countries

Menegaki & Tugcu

Granger causality and seemingly

unrelated regression 1995-2013 15 emerging economies

Ozturk & Acaravaci ARDL, VECM 1980-2006 Albania, Bulgaria, Hugary, Romania Rafiq et al. Panel, FMOLS 1980-2006 6 major emerging countries Solarin & Ozturk VECM 1970-2012 7 Latin America countries Tugcu et al. ARDL, Hatemi J causality 1980-2009 G7 countries

Yildirim et al. Toda-Yamamoto and Bootstap-connected causality1949-2010 US

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2.2 Economic models used in the existing literature

The neoclassical Cobb-Douglas production function constitutes the base of empirically testing the renewable energy consumption-economic growth nexus. It represents the relationship between output (production) and inputs (capital, labor). Throughout the literature either the original Cobb-Douglas production function or its transformations are being used.

The standard form is presented in equation 1, where Yit is the total output, A is the total factor productivity (technological efficiency), Lit is the labor input, Kit is the capital input and α and β are the output elasticities of capital and labor respectively.

(1) Yit = AKitαLitβ α, β > 0

Multiple unrealistic assumptions are included in this function. First, the consideration of only two inputs. Second, the assumption of substitutability of factors neglecting the complementarity of factors. Last, the assumption that A changes with technological progress, but the balance of the inputs remains unaffected. This implies that the marginal effect of all input changes in equal proportions.

The next variable to be included as an input in the Cobb Douglas production function is the first step to integrate energy and the ecological effects of using energy into economics. This step does not yet consider the different types of energy resources (Steve Keen, 2016). Therefore, equation (2) is transformed with the addition of an energy element:

(2) Yit = AKitαLitβEitθ α, β, θ > 0

where 𝐸𝑖𝑡 is the energy variable and 𝜃 is the elasticity of output with respect to energy. Equation (3) which is fit for linear regression derives by taking natural logarithms:

(3) lnYιτ = lnA + αlnKit + βlnLit + θlnEit α, β, θ > 0

The above equation is fundamental to relate economic growth and the consumption of energy. The benefit of using this model is, that it is not only able to capture the impact of total energy consumption on economic growth (Lee et al. 2008), but also the impact of renewable energy consumption on economic growth (Apergis and Payne, 2010; and Lotz 2016). Inglesi-Lotz (2016) find the elasticity of output to REC to be 0.1 while Apergis and Payne (2010) 0.76. This quite big difference lies on the fact that they use different estimators in the model. Apergis and Payne base their results on (3) using FMOLS while Inglesi-Lotz use a fixed effect estimator (FE) and includes R&D expenditures to express the technological shift.

Other models such as from Menegaki (2011) include the share of renewables in total energy consumption. A benefit of this method is that it enables the authors to observe the effect an increase or decrease in the share of renewable energy of total energy consumption has on economic growth. This model is described as:

(4) lnYit = lnA + αlnKit + βlnLit + φlnωREN α, β > 0

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results display a strong coefficient for the share of renewables in the total energy consumption mix. This is another indicator that suggests that the transition from non-renewable energy to renewable energy has a positive impact on economic growth. Although Inglesi-Lotz results show a positive and significant positive coefficient, his model does have limitations. The share of renewable energy consumption will increase if non-renewable energy consumption will decrease. This is a significant drawback since the share of renewable energy consumption can increase, without the total renewable energy production increasing. Furthermore, renewables can increase while non-renewables stay constant. Last, the numerator is the only variable that can increase if non-renewables are replaced by renewables Inglesi-Lotz’ s model might be robust; however, I cannot use it for this analysis due to its limitations over the non-conclusive origination of the increasing share.

An alternative to using Inglesi-Lotz’ s model is to instead include renewable energy and non-renewable energy consumption (Apergis and Payne, 2012; Salim et al., 2014; and Bhattacharya et al., 2016). This approach can be visualized in the equation below:

(5) lnYit = lnA + αlnKit + βlnLit + γlnRENit + δlnNRENit α, β, γ, δ > 0

where 𝑅𝐸𝑁𝑖𝑡 as renewable energy consumption, 𝑁𝑅𝐸𝑁𝑖𝑡 as non-renewable energy consumption, 𝛾 and 𝛿 as the elasticities of output with respect to renewables and non-renewables. The research question will be answered through the above calculated elasticities, since they are useful in showing the marginal effect of renewable and non-renewable consumption over economic growth and revealing the kind of (non) - causality relationship underneath.

The renewable and non-renewable energy consumption is the backbone of the model and of my research. The focus of the paper is to assess the effect of renewable and non-renewable energy inputs on economic growth (Bhattacharya et al. 2016). There are some limitations in the chosen model, that focuses on renewable energy as a main goal for future economic progress. The deployment of renewables is a long-term process which depends on a variety of factors such as costs, regulation, efficiency and the national institutional structure that may indirectly or directly affect economic growth (Bhattacharya et al. 2016). Although these factors are not included in the model, the chosen countries are all developed and most of them share energy regulation and institutional structure, thus I will assume that these factors do not weight in as a significant change or barrier to the results.

2.3 Renewable energy sources

Renewable energy is collected from “renewable resources, which are naturally replenished on a human timescale, such as sunlight, tides, wind, rain, waves, and geothermal heat” (Ellabban et al., 2014). It provides energy in four important areas: electricity generation, air and water heating/cooling, transportation, and rural (off-grid) energy services (RNE21, 2010).

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resources. This path leads to energy efficiency along with energy security (less dependency from oil and gas producing countries), environmental sustainability and potential economic growth (IEA, 2012). This is a chance for developing countries where electricity is crucial for human development.

The efficiency and affordability of renewable energy systems are directly correlated to consumption, if the consumption of RE increases the price of RE decreases (economies of scale). This can already be observed in Germany, where some wind parks no longer need subsidies from the state to run at a profit. Renewables constituted 19.3% of global energy consumption in 2015 and 24.5% of electricity generation in 2016 (REN21, 2017). China and United States have been investing in solar, wind, hydro, biofuels pushing the worldwide renewable technology investments to more than 286$ dollars in 2015 (REN, 2016). 7.7 million jobs can be associated to industries involved in renewable energy resources (IRENA,2015) which clearly reflects the creation of new opportunities.

2.4 Renewable energy (RE) in the current sample countries

Following the REN21 (Renewable Energy Policy Network for the 21st Century) reports and the data of the International Energy Agency for the years 1990 to 2016 and specifically the Total Primary Energy Supply (TPES) by source, I present below the most common resources used in each country of the current sample and a brief summary of their renewable energy generation and consumption to observe their current RES (renewable energy sources) status, which will be included in the analysis and the explanation of the results:

• Hydropower – Bosnia, Albania, Spain, Italy, Portugal, Slovenia, Croatia

• Geothermal, Solar - Italy, Greece, Bosnia, Spain, Portugal, Albania, Slovenia, Croatia • Wind power – Spain, Greece, Italy

• Biofuels and waste - Greece, Bosnia, Albania, Italy, Portugal, Slovenia, Croatia • Nuclear - Spain, Slovenia

• Wave power - Portugal

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Figure 1 Renewable energy consumption (% total energy consumption) from 1990-2015, Data extract from the

World Development Indicators (last updated 18.10.2018)

Specifically, Bosnia & H. which seems to perform better than the rest of the sample (concerning RE consumption levels) is mostly producing and consuming hydropower (84% of its renewable energy consumption) and it’s a leading example in that domain, though it still has a lot to learn regarding the deployment of renewables and its expansion and inclusion of other resources.

Similarly, Albania relies mostly on hydroelectric resources. Therefore, it is challenging during drought periods where the country must depend on energy created by other countries even though Albania’s potential for solar energy, wind projects (climate and geo factors), geothermal energy (natural wells) and biomass is large.

Croatia uses hydro and thermal power plants for its electricity generation needs and partly nuclear power imported from Slovenia. The two countries have a joint venture nuclear power plant located in Slovenia. Apart from that, efforts to expand the current wind power generation through the construction of additional wind farms.

Renewable energy deployment in Portugal has outstanding potential and could be an example for the region. Renewable energies were the source of 27.2% of energy consumption in 2015, as mentioned above. The power production coming from renewables have been increasing and on February 2016 reached an equivalent to 95% of electricity consumed. Three months later, a big step towards sustainability was achieved. The country “ran” four days on renewable energy. The sources of the renewable energy produced in 2017 were solar, hydro and wind power, bio and geothermal energy and a small amount of wave power in the Azores. Portugal’s diverse use of RE sources could be the key to its success and development.

Slovenia’s renewable electricity generation is coming from nuclear power and hydroelectricity and some minor sources including solar power and biofuels. Although the country is a significant exporter of electricity, its import dependency on oil products and natural gas is very high which a renewable energy transition path could eliminate. Slovenia’s renewable energy

0 10 20 30 40 50 60

Renewable energy consumption (% total energy consumption)

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consumption has historically been high (20.88%), however it has plateaued since 2009. Policies might be needed to boost the RE direction even further.

Greece’s main renewable energy consumption is based on wind and solar power (photovoltaics - PVs) and secondly on biofuels and waste while hydroelectric power plants provide a fair amount of the country’s electricity. Despite the financial crisis, the wind power projects were stimulated leading to an increased generating capacity. Same applies for the solar PVs which flooded the market around 2013. Furthermore, the approval of a new renewable energy law (August 2016) introduced feed-in premiums, competitive tenders etc. in order to increase future investments.

Italy has been extensively developing its renewable energy engagement in the last decade to reduce the import dependency on fossil fuels. The renewable energy production is mainly solar which makes Italy the second highest solar energy contributor in the world after Germany. However, renewable energy is also largely produced by hydropower and to a big extent, wind, bioenergy and geothermal power. Although there was growth in the renewable energy sector, it is still depending highly on fuels. In 2008, the renewable energy consumption finally leveled above 10% and reached 16.5% in 2015 (World Bank, 2018). The sudden change could be because of the crisis and the slowdown of the economic growth or because EU countries have environmental goals which they are obliged to reach and have acted as incentives to develop its RE production.

Spain is one of the world leaders in wind power generation and had been one of the European leaders in solar power but has fallen behind in the last years in part due to the financial crisis. Nevertheless, Spain is using a variety of RES including wind, solar, geothermal and nuclear energy and is targeting for higher levels of RE generation to cover its future energy needs. As reflected in the literature review, all the countries in my sample have a large potential for development towards sustainability and it seems that, based on their status and goals, the development of renewable energy is their long-term objective.

3. Methodology

3.1 Hypotheses

My paper focuses on the above- mentioned Mediterranean countries because of their homogenous nature and lack of representation in past papers. The potential of those countries, the abundance of their renewable resources as well as their future goals share similarities. I intend to identify which of the four hypotheses of the renewable energy consumption – economic growth nexus describes them as illustrated in section 2. Below are the different hypotheses I test for:

Hypothesis 1: One-way (unidirectional) causality from RE consumption to economic growth (growth hypothesis)

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Hypothesis 3: Bidirectional causality between RE consumption and economic growth (feedback hypothesis)

Hypothesis 4: Absence of causality between RE consumption and economic growth (neutrality hypothesis)

Based on the existing literature, my findings should agree with the growth hypothesis (the unidirectional causal relationship). Increases in renewable or non-renewable energy consumption are expected to positively contribute to the economic output and growth (Apergis and Payne, 2012; Salim et al., 2014; and Bhattacharya et al. 2016). This suggests that the elasticities should be positive and significant as well as the output elasticities with respect to labor and capital. The latter would indicate the expected benefits of RE deployment.

3.2 Method

As mentioned in section 2.2, this research is based on a production function where capital, labor, renewable and non-renewable energy are used in the method. Capital, labor, and energy are addressed as separate inputs based on the neo-classical one-sector aggregate production technology. I do not consider other control variables as used in the literature to focus on the substitution process of energy and non-energy type inputs into economic growth (Bhattacharya et al. 2016).

(1) Yit = f (GFCFit ; LFit ; RECit ; NRECit)

where i and t of (1) indicate the country and time period respectively. Eq. (1) can be represented in terms of its parameters as follows: (2) Yit = GFCFitβ1i; LFitβ2i; RECitβ3i; NRECitβ4i)

Transforming data series into natural logarithms has the benefit that it avoids errors in the data that you would otherwise encounter with dynamic properties of the data series. Economists prefer to use the log transformation approach because it allows for each individual coefficient in a regression equation to be treated as an elasticity. This leads to the empirical equation: (3) lnYit = β1i lnGFCFit + β2i ln LFit +β3i lnRECit + β4i lnNRECit + φit

With β1i, β2i, β3i and β4i the elasticities of output with respect to gross fixed capital formation, labor force, renewable energy consumption, and non-renewable energy consumption respectively, and with φ the error term. Using the above equation, I proceed to my analysis.

3.3 Data

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growth. In Menegaki (2011), GDP per capita is used but it could alter the results based on population differences and most importantly it does not reflect the actual standard of living of people (due to the distribution of wealth issues) since it represents the average.

The real gross fixed capital formation (GFCF) in constant 2010 US dollars is used as a proxy for the growth of capital stock since it represents domestic improvements based on investments and can demonstrate the capital flow.

Total labor force (LF) reflects the available labor in the market (Bhattacharya et al., 2016). The total labor force index includes people who are under employment, people who are in search of a job, and first-time job-seekers. A visible limitation of the index is that it does not include the entire labor force. There are students, family workers or generally unpaid workers who are often excluded (World Bank Indicators, 2018). Most importantly, the annual numbers when split out by season or by month vary because of seasonal labor spikes. This is probably the case for the sample countries (as all of them experience the touristic summer season). The above annual data (GDP, GFCF, LF) were acquired from the World Development Indicators (WDI) online database published by the World Bank (last updated on the 28/8/2018).

Renewable energy consumption (REC), and non-renewable energy consumption (NREC) are used as the energy factors of the production function. I use renewable energy consumption as electricity consumption generated from various renewable energy sources (Bhattacharya et al., 2016). Specifically, nuclear energy, hydroelectricity and non-hydroelectric renewables. The latter refers to geothermal, wind, solar, tide, wave, fuel cell, biomass and waste. Nuclear energy is controversial concerning its characterization as a renewable or non-renewable source. In this study, I consider it part of the REC sources since its emissions are considerably lower than the NREC sources and substituting them with nuclear energy would still be beneficial. Moreover, based on my acquired data, the sample countries which use nuclear energy are Slovenia and Spain and the rest of the sample relies on hydroelectricity and non-hydroelectric renewables only. Non-renewable energy consumption (NREC) includes sources of coal (anthracite, metallurgical coal, bituminous, sub-bituminous and lignite), dry natural gas, petroleum and other liquids (motor gasoline, jet fuel, kerosene, distillate fuel oil, residual fuel oil, liquified petroleum, other products). The required annual data on renewable and non-renewable energy consumption were collected from the U.S. Energy Information Administration (EIA). In this study, all the renewable, as well as the non-renewable energy consumption data, are used as the aggregate of their energy sources respectively, in quadrillion Btu units. It is important to normalize the data series due to the differences in units - the output and GFCF are measured in monetary units while labor is measured in numbers - (use of equation (3)).

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strong balanced panel with missing values only for the GFCF of Bosnia & H. for the period of 1996 – 2005. For this reason, I interpolated the missing values to enhance my results’ credibility before transforming my data in natural logarithms.

3.4 Descriptive statistics

I conduct summary statistics focused on the average annual growth rates for each of the variables of the model for the time period 1996-2016 (Bhattacharya et al., 2016). Table 2 demonstrates that there is heterogeneity across countries for these statistics but not for all variables. Specifically, the average annual real GDP growth for the time period was 5.9% for Bosnia & H. (the highest), followed by Albania (4.1%), Slovenia (2.5%), Spain (2.13%), Croatia (1.96%), Portugal (1.08%) and last, Greece with 0.7% and Italy with 0.5%. The last pair with the lowest rates could also reflect the hard hit of the crisis of the last decade. Looking at the negative outcome of average annual growth of GFCF of Greece and Portugal, this thought is enforced since it shows the absence of investments and capital flow. On the other hand, Bosnia & H. and Albania seem to be performing better than the rest of the sample but it could also show their “late blooming”, thus, the higher average annual growth rates. Concerning the renewable energy consumption, the highest average annual consumption of renewables is seen in Portugal with 9.28%, Greece (8.96%), Albania (4.8%) and Italy (4.37%). Still the rest of the countries are performing positively (Croatia – 3.46%, Bosnia & H. – 2.86%, Spain – 2.82%) with the lowest rate the one of Slovenia with 1.56%. Comparing the above with the annual growth rate of non-renewable energy consumption, I find that only in Bosnia & H. (8%) the average annual growth of NREC is significantly higher than the REC. In the case of Albania (4.39%), it is very close to the average annual growth rates of REC. However, for the rest of the sample, the annual growth rate of NREC is lower than the REC (Portugal, Spain) and even negative (Croatia, Greece, Italy, Slovenia). This could be indicative of the sustainability goals that countries started introducing and achieving around the 2000s worldwide, but it also shows the gap between non-EU and EU countries’ policies concerning non-renewable and renewable energy over this time period as we see only Albania and Bosnia & H. with high average annual growth rates of NREC. Last, the time period addressed is the period of the flourishing of renewable energy investments and technologies in order to actively engage the transition.

Table 2. Average annual growth* of each variable in the model: 1996 – 2016

Country GDP GFCF LF REC NREC

Albania 0.0411 0.0656 -0.0006 0.048 0.0439

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*growth rates are calculated using original data after interpolating GFCF for Bosnia & H.

In sequence, and after transforming my panel data into natural logarithms to normalize them, I explore the relationship between the variables. Table 3 shows the pairwise correlations among the variables for the given balanced panel data set (Bhattacharya et al., 2016). Through these statistics, we can identify the interrelation of the variables. Specifically, the table suggests that the output has higher correlation with capital and non-renewable energy consumption, and the lowest correlation with renewable energy consumption but nonetheless, significant. This suggests that non-renewable energy consumption and capital have had an important role in the economic growth and investing activities across countries. Renewable energy consumption plays a significant role in economic growth, but it seems to not have a primary role over this time period. However, results also suggest that renewable energy consumption had the highest correlations with labor force and capital which indicates that employment and investment play an important role in the expansion of the renewable energy sector and the creation of jobs.

Table 3. Pairwise correlations for the panel data set**

Variables (1) (2) (3) (4) (5) (1) GDP 1.000 (2) GFCF 0.994* 1.000 (3) LF 0.947* 0.944* 1.000 (4) REC 0.849* 0.854* 0.874* 1.000 (5) NREC 0.985* 0.977* 0.947* 0.830* 1.000 *statistical significance at .01 level **variables are in natural logarithms

4. Empirical Analysis

4.1 Issues related to heterogeneity and cross-sectional dependence

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For this reason, I should consider heterogeneous panel models. Firstly, because it is possible to approach each individual country separately. This will also allow for the parameters to be tested for their equality rather than assuming it like in the t-test. Large differences in the estimates between countries is common since the homogeneity hypothesis is often rejected (Bhattacharya et al., 2016). Due to the potentially large differences in the estimations between countries, researchers should use specific techniques for when they estimate a heterogeneous panel. Furthermore, I believe that my sample countries could be affected by cross-sectional dependence. Cross-sectional dependence can originate from common global shocks like the financial crisis, single trade, and EU energy and fiscal policies. My sample countries could be affected by spill-over effects due to their proximity to each other (regional effects), trade and common energy policies and regulations.Cross sectional dependence can either be large or small. If it turns out to be significant, then proper estimation techniques are needed in order to reach efficient results. Last, cross sectional dependence allows to observe common factors, such as regional effects which would not have been addressed otherwise (Bhattacharya et al., 2016).

4.2 Estimation steps and empirical findings

4.2.1 Test for cross-sectional dependence

The Im–Pesaran–Shin (IPS) test and other similar first-generation tests assume cross-sectional independent distribution which could violate my results (Pesaran, 2007). Therefore, I run a general diagnostic test to discover if my panel variables are cross-section dependent (Pesaran, 2004). The Pesaran CD test is based on “average pair-wise correlation coefficients of the OLS residuals from individual regressions in the panel” (Pesaran, 2004). It uses the correlation coefficients between the time-series for each panel country. The null hypothesis assumes cross-sectional independence over cross-cross-sectional dependence (Bhattacharya et al., 2016). Table 4 demonstrates the findings of the Pesaran CD test for the model as well as the strong rejection of the null hypothesis of cross-sectional independence for all the variables. Hence, the test results have confirmed my earlier suspicion of the presence of cross-sectional dependence. This is probably due to the common EU energy policies and the worldwide goals towards sustainable development which attribute to the dependency between countries, along with the regional energy dependency and the crisis which severely affected most of the sample countries.

Table 4. Tests for cross-sectional dependence

Variable GDP GFCF LF REC NREC

Pesaran CD-test 20.00*** 11.40*** 2.64*** 13.02*** 10.42***

P-value 0.000 0.000 0.008 0.000 0.000

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Notes: ***Indicates the rejection of null hypothesis of cross-sectional independence (CD test) at 1% significance level

4.2.2 Panel unit root test

In the panel unit root test framework two generations of tests have been developed. There are three first generation tests notably by Levin, Lin and Chu (2002), IPS (2003) and Fisher. The main limit of the first-generation tests is its assumption of cross-sectional independence across units, while the second generation of tests rejects the cross-sectional independence hypothesis (Barbieri, 2009). Therefore, in order to address the already established cross-sectional dependence while still being able to identify the order of integration of the variables in my panel, I applied the CIPS test (Pesaran, 2004). Pesaran (2004) developed a panel root t-statistic as cross-sectional augmented IPS test. A successfully developed, second-generation test that considers both heterogeneity and cross-sectional dependence. Table 5 indicates that all the variables have been integrated into the same order, I (1). Furthermore, the results show that the variables are stationary at the first order differentials. This means that the results of the CIPS test suggest the existence of a long-run equilibrium relationship among variables which will be further explored in the following section (Bhattacharya et al., 2016).

Table 5. The unit root test with cross-sectional dependence

Variable GDP GFCF LF REC NREC

CIPS test (level) -3.422*** -2.552 -3.327*** -3.512*** -2.856* CIPS test (first

difference)

-3.645*** -3.692*** -3.999*** -5.254*** -4.437*** Notes: * Indicates the rejection of the null hypothesis of unit root at 10% significance level. ***Indicates the rejection of the null hypothesis of unit root at 1% significance level. CIPS’ critical values at 10%, 5% and 1% are -2.74, -2.88, -3.15 respectively. CIPS test is estimated using constant and trend with 1 lag.

4.2.3 Panel cointegration test

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the tests have minor comparative advantages over different ranges of sample size (Pedroni, 2004).

The findings are presented in Table 6. Out of seven test statistics, five confirm the presence of cointegration among the variables by rejecting the null hypothesis (Ho = no cointegration) either at 10% or 1% significance level. The ADF – statistic (panel and group) and the group rho-statistic seem to indicate the most significant results indicating the cointegration. Based on the literature, the significance of the group-rho statistic should be enough to support the cointegration of the panels. For this reason, I conclude that real GDP, real gross fixed capital formation, labor force, renewable energy, and non-renewable energy consumption series share a long-run equilibrium relationship (Pesaran, 1999, 2004).

Table 6. Pedroni panel cointegration test results

Test Statistic panel p-value group p-value v - statistic 1.485* 0.0688 .

rho - statistic 1.576* 0.0575 2.621*** 0.0044 t - statistic -2.178 0.9853 1.859 0.9685 ADF - statistic 3.099*** 0.0009 4.492*** 0.0000 Notes: Indicates the rejection of the null hypothesis * at 10% significance level ***at 1% significance level

To account for cross-sectional dependence and robustness check, I applied the Kao test, the second generation Westerlund (2005) error-correction-based panel cointegration test, along with a separate Pedroni test approach. Incorporating all three above mentioned tests in the xtcointtest regression produces a test with a much higher power and thus increases the significance of the analysis.

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Statistic p-value Modified Dickey-Fuller t -1.7735** 0.0381 Dickey-Fuller t -2.5912*** 0.0048 Augmented Dickey-Fuller t -1.7567** 0.0395 Unadjusted modified Dickey-Fuller t -1.0234 0.1531 Unadjusted Dickey-Fuller t -2.294** 0.0109

Notes: Indicates the rejection of the null hypothesis **at 5% significance level ***at 1% significance level

Table 8 demonstrates the Philips – Perron unit root test which conducts a non-parametric correction to the t-test statistic establishing robustness concerning unidentified autocorrelation and heteroskedasticity and performs good in smaller samples (Phillips & Perron, 1988). The null hypothesis is again rejected at 1% significance level, supporting the cointegration of all panels (H1).

Table 8. Pedroni cointegration tests

Statistic p-value Modified Phillips-Perron t 2.5679*** 0.0051

Phillips-Perron t -4.0258*** 0.0000 Augmented Dickey-Fuller t -4.9376*** 0.0000

Notes: Indicates the rejection of the null hypothesis ***at 1% significance level

The Westerlund (2005) also demonstrates the rejection of null hypothesis at 10% significance level (see Appendix – 4.2.3) and the validation of cointegration in some panels. Finally, the three cointegration tests confirmed the existence of long-run equilibrium relationship among the variables which validates the next step of this research (the use of panel FMOLS model).

4.2.4 Panel data analysis of long-run output elasticities

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and FMOLS for my estimations. The empirical findings of these models are presented in Table 9. The results of the two models are similar regarding the sign and significance. However, the magnitude varies and especially for REC. In general, absolute values of the t-stat higher than 2.58 would be considered significant. In this case, the significance of the t-stat was computed in n-2 degrees of freedom at 1% level in order to validate the results. Based on the DOLS model, 1% increase in renewable energy consumption increased output by 0.31%, while the non-renewable energy consumption seems to be non-significant. The FMOLS results show that a 1% increase in renewable energy consumption increased output by 0.12%, while a 1% increase in non-renewable energy consumption decreased output by 0.2%. Both models suggest the positive and significant effect that capital, labor and renewable energy consumption have on the economic development of the selected countries. The panel data analysis of long-run output elasticities reveals that renewable energy consumption holds an important part in long-term economic growth which would be the first step in suggesting policies concerning the engagement, use and generation of RES. As the growth hypothesis (Hypothesis 1) suggests, higher renewable energy consumption would lead to higher level of economic output – unidirectional causality.

Table 9. Panel data analysis of long-run output elasticities

Variables DOLS FMOLS

Coefficient t-stat Coefficient t-stat GFCF 0.75 27.66*** 0.62 56.41*** LF 0.54 36.81*** 0.74 91.88*** REC 0.31 18.98*** 0.12 30.22*** NREC -0.44 -15.67 -0.20 -18.98***

Notes: DOLS and FMOLS are the ordinary least square, dynamic and fully modified ordinary least square methods, respectively (no constant). *** Denotes the significance level at 1%.

4.2.5 Heterogeneous panel causality test

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hypothesis for my panel - Hypothesis 4: Absence of causality between RE consumption and economic growth (neutrality hypothesis) – in the short-run, despite my expectations. The deployment of RES though is a long-term process based on sustainability and development and in most cases, we see results in the long-run (as demonstrated in Table 9 – positive and significant effect in the long-run).

Table 10. Dumitrescu & Hurlin (2012) Granger non-causality test

Null hypothesis Z-bar stat p-value GFCF does not Granger-cause GDP 4.3348*** 0.000

GDP does not Granger-cause GFCF 4.0311*** 0.0001 LF does not Granger-cause GDP. -0.2493 0.8032 GDP does not Granger-cause LF 8.4935*** 0.000

REC does not Granger-cause GDP -0.9331 0.3508 GDP does not Granger-cause REC 1.4101 0.1585 NREC does not Granger-cause GDP 0.9265 0.3542 GDP does not Granger-cause NREC 4.8122*** 0.000

Notes: Optimal number of lags (AIC): 1 (lags tested: 1 to 4). *** Indicates rejection of null hypothesis at 1% significance level.

4.2.6 Time series analysis of long-run output elasticities

I further aim to examine the time-series analysis of long-run output elasticities for each individual country to demonstrate the dynamic impact of renewable energy consumption on output across the sample countries. This differentiates the sample’s sustainable development since I have already established the significance of it. The long-run output elasticities are estimated using the FMOLS model for each individual country.

Table 11 demonstrates the empirical findings which suggest that the long-run output elasticities with respect to renewable energy consumption seem to be inelastic and for most of the countries with a positive sign and significant at 1% level. Specifically, the REC long-run output elasticity is positive and significant and with the highest effect for Albania with 0.2, followed by Spain (0.12), Greece (0.11), Portugal (0.07). Slovenia (0.05), Italy (0.04) and last, Croatia with 0.03. These results reflect the significant effect of REC on economic growth and the sustainable economic growth that these countries could have in the future. This would indeed agree with my expectations concerning the growth hypothesis in the long-run since higher renewable energy consumption will generate greater economic output – unidirectional causality.

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Only Bosnia & Herzegovina seems to not align with the rest of the countries. The results show no significant effect of the renewable or non-renewable energy consumption on economic growth (neutrality hypothesis). This could be because Bosnia & H. have had very high renewable energy consumption established during the time period and is in need for more capital (3) and employment (2.4) in order to enforce its future economic growth process rather than RES. This could lead to more effective use of renewable energy sources in the production process to enhance the economic output (Bhattacharya et al., 2016).

Following Table 11, non-renewable energy consumption seems to be significant and positive for Greece (0.37) and Portugal (0.32) which suggest that these countries have depended on NREC for their economic growth and may continue to use non-renewable energy sources for future growth. Nevertheless, slowly shifting to the renewables can still attribute to their sustainable future development (based on the findings).

Table 11. Long-run output elasticities by country (FMOLS)

County Constant GFCF LF REC NREC R2 Adjuste d R2 Albania -10.66 0.82*** 1.09*** 0.2*** -0.34 0.843 .0801 Bosnia & Herzegovi na -75.9 3*** 2.4*** -0.00 0.03 0.982 0.977 Croatia 23.92 0.52*** -0.77 0.03*** -0.07 0.985 0.980 Greece -0.72 0.21*** 1.43*** 0.11*** 0.37*** 0.916 0.893 Italy 11.48 0.41*** 0.37*** 0.04*** -0.07 0.957 0.946 Portugal 9.779 -0.07 1.18*** 0.07*** 0.32*** 0.906 0.881 Slovenia -29.77 0.39*** 3.17*** 0.05** -1.01 0.907 0.883 Spain 9.292 0.33*** 0.58*** 0.12*** -0.07 0.982 0.977

Notes: *** Indicates the 1% significance level **Indicated the 5% significance level

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important issue for Albania also and energy mix policies by REC deployment could stimulate the job market. As it seems, the majority of the countries (except for Slovenia) would benefit by the RE deployment due to new job opportunities.

All things considered, the results could add value on important policy implications. Policies should focus on the efficiency of the RE deployment and the promotion of RES use and generation on the long-run. This could lead to a future sustainable economic development with low carbon emissions even if in the short-run the impact is not significant. Table 11 suggests that seven out of eight countries would be positively affected by that, resulting to higher economic output.

4.3 Policies on RES

Policies and measures concerning renewable energy resources vary between countries. A general European Union wide legal and institutional framework requires countries to abide to common laws regarding renewable energy and the liberalization of the energy sector. This though sets up certain limits while the national circumstances could differ. However, this does not apply to all sample countries. It would be interesting to mention the policies of my sample to identify their effectiveness towards sustainability and suggest new ones based on my findings. I should mention that policies towards public and social awareness regarding RES projects and their contribution to national level but also to the wider local community are important for all countries, especially for this region which is not as developed in that part. Citizens’ awareness towards sustainability and environmental issues encourage the transition and the realization of projects.

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Energy strategy which focuses on the security of supply. Competitive prices, inclusion of renewables.

As mentioned in the Lit. Rev. section, in Bosnia and Herzegovina, the RE consumption reached 40.57% of the total energy consumption in 2015 making the country one of the few which have already fulfilled its 2020 goal (40%) (World Bank, 2018). The regulation of renewable energy is based on general legislation with special provisions and is mostly stimulated through feed-in tariffs (RES Legal, 2018). The promotion and use of electricity from RES are one of the country’s priorities. The positive impact of efficient RES use on national level should be reflected from the creation of jobs and the contribution to the economic growth. The employment rates of the biomass segment suggest that the domestic workforce has been positively utilized. However, the administrative frameworks tend to hamper RE projects and end up reinforcing barriers for investment (Balkan Green Energy News, 2017). Based on my findings, labor force and capital are the important factors for the country’s growth process and the main benefits from the RE deployment. The already high levels of RE consumption do not suggest that there is no room for efficient policies towards sustainability but the opposite. The REC has not benefited the economic growth of the country for the selected time period and one of the reasons could be investment barriers. Policies should smoothen the bureaucratic barriers creating strategic planning in all governmental levels or harmonization of the strategic plans to attract investment (Balkan Green Energy News, 2017).

Renewable energy deployment in Portugal has outstanding potential and could be an example for the region. The government issued a new energy policy in 2001. The E4 Program (Energy Efficiency and Endogenous Energies) contributed in a steady plan for energy supply and demand. Market competition and environment preservation (reducing gas emissions) were the main goals of these energy efficiency measures. The previously high dependency on oil or natural gas is reducing the last years due to the increasing amount of renewable energy. The past year the government has introduced policies in order to lower the oil investments and focus on RE investment. For example, termination of the concession contracts for exploration, research and production of oil in the Algarve’s offshore areas and plans to de-carbonize the economy (growth of solar capacity). In general, the development policies have been focusing on solar photovoltaic plants and measures to reduce energy prices and the tariff deficit. On a second level approach, a legal framework is in force and a network of charging points of electric vehicles is in place (Energy 2019| Portugal, Laws and Regulations). Portugal’s target is to close all country's coal-producing facilities by 2030, making it partially dependent on renewable energy in the future. The RE steps are based on long-term development and innovation along with constant policy-making towards the future growth process. Nevertheless, based on my findings we see that the oil dependence is still high and a significant factor for growth (0.32). The new policies and measures should slowly shift to RES in order to substitute for any losses or costs produced by the oil sector. The long-term benefits from this transition would not only benefit the economic growth of country but the environment and the critical unemployment issues.

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small renewable energy power plants and customs (RES Legal, 2018). The constant improvements of law and policies concerning the RE deployment as well as the above measures show the important role of RES for the development of Albania. The National Energy Strategy (NES) was updated 2006-2020 with main concerns the energy safety, the environment and higher efficiency levels to reduce consumption costs. For this reason, this strategy aims in the diversification of renewable resources - which will be helpful when the water levels are low because of the high dependency on hydropower – and the construction of new plants. Social awareness is included as well to boost the local use of RES (REEGLE Policy Database, 2015). All efforts made would finally minimize the negative environmental impact and maximize the economic growth of the country. The current success of Albania is reflected by its achievement which has outdone the EU targets. It has set a target of 36% of energy production from RES by 2020 which it has already fulfilled. My finding agree with the country’s current status as REC seems to have the highest positive effect on its economic development which would most certainly lead to a sustainable future.

In Slovenia, the energy demand is increasing due to the recovery of its economy over the recent years. The usage of nuclear power is increasing contributing up to 40% of all generating electricity in 2017 (Energy 2019 | Slovenia). At the same time, the generation of the rest of the RES is increasing since Slovenia must develop RES for energy consumption and electricity generation through its importance in the national and EU energy policy. Renewable energy projects either for electricity or heating are encouraged through feed-in tariffs, premium tariffs, subsidy applications and loans. Regarding the transport section, tax exempts and loan further advance the RE use. More policies aim in the development, installation and use of RES installations (RES Legal, 2018). However, the wind power projects are significantly limited, putting the country’s wind power goals for 2020 in one of the lowest in EU. Based on my findings, Slovenia should focus on the development of RES projects which would boost its economic growth. Nuclear energy plays an important role on the country’s development which is reflected by the significance of the results. Again, we see, that the goals of the policies are the promotion of RES, yet the efficiency of them is debatable since it has one of the lower REC effects on its economic growth.

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There are numerous policies to further advance the development, use of RES and governmental policy improvements. At least for the latter, Italy has implemented generous incentive schemes to encourage the development of renewable energy production. Specifically, a combination of premium tariffs, feed-in tariffs, tender schemes, tax regulation mechanisms, quota systems is used to attract consumers and investors. The RE deployments and installations are given priority but the national budgetary issues have restrained the investment and realization of projects. On the other hand, formation and training are being introduced at regional level which could advance the job market and open new opportunities with a trained workforce (RES Legal, 2018). This sets Italy’s potential back since its economic output would increase significantly by the deployment of renewables. The policies and measures chosen though, show the sustainability path that the country would like to follow and based on my findings, the country would be positively affected by this high inclusion of RES.

In Spain, the financial crisis led to a stagnation of the renewable energy development projects which had been expanded before and the suspension of support schemes. A new support scheme initiated on 2014 which aim to offer reimbursing in order to develop renewable energy projects. Specifically, tax credit, quota systems, priority against the grid are few of the new measures which seek to advance the RE deployment. Furthermore, training and certification, as in the case of Italy, has been initiated to contribute to labor market deficiencies. Last, new RD&D plan (2017 – 2020) focuses on supporting innovation and expansion project in various RE deployment projects. (RES Legal, 2018). Spain’s 2020 target is the generation of 20% of all its energy needs from renewable energy sources and an additional 0.8% for other EU countries under the cooperation mechanism. The policies and measures towards RES aim in regenerating the development projects and sustainable future which would more than benefit the Spanish economy based on my analysis. Spain seems to have a significant long-run output effect of renewable energy consumption on its economic growth. This suggests that the policies would revive its RES sector and in sequence its economic output.

Summarizing the above policies and measures, it seems that all countries are engaging in the environmental challenges and the “green” growth. All of them could be benefited economically and environmentally speaking with the efficient and constant improvement of policies and targets.

5. Conclusions

Climate changes and environmental awareness has increased renewable energy consumption the last years, for countries to lead a future of sustainable development. High oil dependency and volatile prices have pushed for RE development projects and policies which shapes a “new” future for every country (Bhattacharya M. et al, 2016).

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recent time period with data availability which was not possible before. At the same time, it represents the RE transition period, centered with numerous environmentally friendly changes and investment on RES.

Specifically, I conducted pairwise correlation tests which revealed that renewable energy consumption had the highest correlations with labor force and capital. This indicates that employment and investments play an important role in the expansion of the renewable energy sector. Furthermore, I used the Pesaran CD test for the considered variables and confirmed the presence of cross-sectional dependence across the countries which is only logical due to regional spill-overs. I concluded that real GDP, real gross fixed capital formation, labor force, renewable energy, and non-renewable energy consumption series shared a long-run equilibrium relationship based on various cointegration tests (Kao, Westerlund, Pedroni). Analyzing the long-run output elasticities with FMOLS and DOLS, the findings suggest that along with traditional inputs such as capital and labor, renewables played a significant role in the process of economic development in the selected countries. Once the long-run dynamics were established among the variables, I examined the direction of causality in the short-run using a pairwise panel causality test (Hurlin & Dumitrescu, 2012) which revealed the absence of causality between RE consumption and economic growth (neutrality hypothesis) – on the short-run.

Nevertheless, I continued by estimating the long-run output elasticities for each individual country in order to understand the dynamic impact of renewable energy consumption on output. My findings suggest that for seven out of the eight countries, renewable energy sources are a significant driver in economic growth in the long-run (growth hypothesis). These countries are Albania, Croatia, Greece, Italy, Portugal, Slovenia and Spain. In most of these countries, significant shift towards renewables occurred during the study period. For example, Spain experienced an excess demand of solar panels and Portugal has recently entered the wave power market. Out of these countries, a shift from non-renewables to renewables is very prominent in Bosnia & H., Croatia, Italy, Slovenia and Spain since NREC does not seem as a significant economic development driver. Bosnia & Herzegovina is the only country where I could not establish that renewable energy sources would be a significant driver of/or barrier, to economic growth in the long-run (neutrality hypothesis). One possible explanation for the result is that the use of renewable energy sources was not effective in the production process, and it therefore has almost no impact on the economic output. It is one of the few countries that its use of renewable energy is not diverse (mostly hydropower – while the rest of the sample countries use various RE sources) which could also reflect the inefficiency of investment or deployment. Therefore, policy advisers should focus on investing renewable energy effectively so the increase in demand for energy consumption from various economic activities can make use of renewable energy sources. Last, my findings demonstrate that the deployment of renewables could lead to the creation of new jobs for the economy in the long-run. Most importantly for Albania, Bosnia & H., Greece, Portugal, Slovenia since they seem to have the highest long-run output elasticities in respect to employment.

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Energy, either renewable or non-renewable depends on various elements which could affect the economic growth of a country. More specifically, the deployment of renewables depends on factors that are internal, external and/or across countries. Therefore, it is important to take a long‐term view of this process considering the spill-over effects, efficiency, regulation, costs and national institutional structure (IRENA, 2018). However, based on my research, the countries’ policies share similarities and they are headed and planned towards sustainability and higher renewable energy consumption. Hence, I do not account for these factors which may affect economic growth. I consider that my strong balanced panel data, the already existing development of the countries and their future goals could still provide the impact of REC on the economic growth of the countries. Nevertheless, future research could extend the factors included to account for the efficiency of the RE deployment process as an explanatory part of the sustainable economic development across countries. Furthermore, the sample size was a challenge in order to choose the proper tests which can provide valid results. More countries could be included to avoid this. Last, a numerous of methods have been used in previous researches as demonstrated in Table 1. With a bigger sample and more explanatory factors, a different method could approach this issue better.

To conclude, there has been a positive trend towards the deployment of renewables in the last two decades for a significant number of countries. The impact of renewable energy is different across countries due to many factors as discussed in various researches. My findings support heterogeneity across countries in the deployment process, regional dependence and the important role of renewable energy for a sustainable and profitable future. Policies across countries differ but effective RE deployment should be the main aspect of all to reach long-term economic development.

Acknowledgements

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