Green economic growth, cleaner energy and militarization: Evidence from Turkey

Resources Policy 63 (2019) 101407

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Green economic growth, cleaner energy and militarization: Evidence from Turkey

T

Kazi Sohaga,b,c,**, F. Dilvin Taşkınd,∗, Muhammad Nasir Malike a

Graduate School of Economics and Management, Ural Federal University, Russia School of Commerce, University of Southern Queensland, Australia c Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, Saudi Arabia d Yasar University, Faculty of Business, Selcuk Yasar Campus, Universite Cd. No:37-39, Agacliyol, Bornova, Izmir, Turkey e University of Central Punjab, Lahore, Pakistan b

ARTICLE INFO

ABSTRACT

Keywords: Green economic growth Cleaner energy Innovation Militarization Asymmetric ARDLJEL classification: O44 O47 Q20 Q55

This study examines the role of cleaner energy, technological innovation and militarization on green economic growth (GEG) under different economic conditions in the context of Turkey. To this end, we apply Autoregressive Distributed Lags (ARDL) under the assumption of symmetric and asymmetric adjustment approaches to analyse a time series data over the period 1980–2017. We initially examine GEG by adding merit goods and deducting natural resources depletion, the damage of carbon emissions and other particulate emissions impairments from gross domestic product (GDP). Our analysis demonstrates that cleaner energy and technological innovation are driving factors in promoting GEG in the long-term. Militarization is found to be detrimental for GEG in the Turkish economy in the long run. The research further finds that the impacts of cleaner energy, technological innovation, militarization and population density on GEG follow an asymmetric adjustment in the long run. Our findings provide important policy implications for promoting GEG in Turkey.

1. Introduction Environmental challenges and enduring energy demand have triggered the production and use of cleaner energy in both developed and emerging countries. Prior literature argues that the production and usage of cleaner energy is the optimum choice to ensure intertemporal energy security, reducing dependency on exhaustible resources (such as crude oil, gas, coal and so on) and curbing carbon emissions (Alper and Oğuz, 2016; Kaygusuz et al., 2007; Bhattacharya et al., 2016). Most importantly, replacing fossil fuels with cleaner energy in the production process can significantly lower the negative externality of the economic growth process. Although existing literature focusses on assessing the role of cleaner energy in promoting economic growth in developing economies (Apergis and Payne, 2010a; Apergis and Payne, 2010b; Inglesi-Lotz, 2016; Shahbaz et al., 2015), we argue cleaner energy promotes green growth in a number of ways. Firstly, cleaner energy is produced from hydro, wind, solar, biomass and geothermal sources. Many studies document that renewable sources of energies are environmentally desirable as they are regarded as zero or reduced emitters of carbon emissions into the atmosphere.

∗

The production of cleaner energy sources (biomass renewable energy, non biomass renewable energy, and total renewable energy) is expected to increase sustainable economic development since cleaner energy has less externalities in the production process. In line with our argument, Schmalensee (2012) states that unchecked environmental degradation and the shortening of natural capital stocks due to the usage of nonrenewable energy make it impossible to sustain economic growth. There are also legally binding agreements, such as the Kyoto Agreement, which encourage countries to cut back their CO2 emissions, which makes the use of nuclear and cleaner or renewable energy more appealing (Becker and Posner, 2005). Secondly, production of cleaner energy reduces dependency on imported non-renewable energies, including crude oil, gas, coal, etc. In addition, the consumption of cleaner energy from domestic production also stabilizes macroeconomic performance through minimizing the negative shocks induced from imported oil price volatility (Menyah and Wolde-Rufael, 2010). Further, the production of cleaner energy within the local economy reduces pressure on the balance of payments, enhancing sustainable economic growth. Sadorsky (2009) and Apergis and Payne (2010a) present a positive association between real per

Corresponding author. Corresponding author. Graduate School of Economics and Management, Ural Federal University, Russia. E-mail addresses: [email protected], [email protected] (K. Sohag), [email protected] (F.D. Taşkın), [email protected] (M.N. Malik).

∗∗

https://doi.org/10.1016/j.resourpol.2019.101407 Received 1 December 2018; Received in revised form 10 May 2019; Accepted 14 May 2019 0301-4207/ © 2019 Elsevier Ltd. All rights reserved.

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capita income and per capita renewable energy consumption for emerging economies and OECD countries, respectively. Chiua and Chang (2009) note that cleaner energy sources are sustainable since the economic growth achieved from renewable sources is dissociated from the depletion of resources. Cleaner energy substitutes for fossil fuels lead to less carbon emissions and ensure a cleaner energy consumption structure (Bilgili et al., 2016). Additionally, producing cleaner energy from renewable sources is of paramount importance for long-term sustainability given geopolitical turbulence in OPEC countries. Menegaki (2011), Elliot (2007), and Ferguson (2007) also suggest that renewable energy sources can be the optimal solution to global warming and energy security. Besides clean energy, green technologies or technological innovation is an important driver for promoting GEG. Yin et al. (2015) suggest that technical progress, especially the implementation of clean technologies, can reduce carbon emission levels significantly. Shafik and Bandyopadhyay (1992) document that technological progress improves environmental quality. Kaygusuz (2007) and Kaygusuz et al. (2007) assert that the production of renewable energies leads to the modernization of the energy sector and eventually promotes economic development and sustainability. Pertaining to this fact, we incorporate technological innovation in explaining long-term GEG. Prior studies argue strongly that militarization appears to be detrimental to the growth of the green economy because many sophisticated military weapons depend heavily on fossil fuels (Podopnik, 2006). Statistics show that the use of fossil fuels in the military sector is increasing due to significant competition among many countries to maintain military power in the name of national security. This situation is aggravated by attempts by countries to increase geopolitical influence or political hegemony, and by the existence of domestic problems in some countries (Hooks and Smith, 2004; Jorgenson, 2005). Many studies also highlight that greater military expansion results in an upsurge in access to and use of natural resources (Chase-Dunn,1998; Dalby, 2004; Conca, 2004; Kentor, 2000; Magdoff, 1978; McNeill, 1982; Podopnik, 2006). Though there are differences in the scale of environmental damage across nations, affluent countries undoubtedly continue to pose a greater threat to the global ecosystem compared to less affluent ones (Roberts and Parks, 2007; Srinivasan et al., 2008). For instance, the Pentagon is the world's leading consumer of non-renewable resources, particularly in the form of fossil fuels (Santana, 2002; Hynes, 1999). The gradual adoption of advanced technology has expedited the military costs associated with inflated operating expenses (Randall & Collins, 1981). These developments are undeniably having a deteriorating impact on natural resources. In the light of the scarcity of resources, military investments should be detrimental to an economy which cannot produce its own resources to support its military infrastructure. The above discussion motivated us to analyse the impact of cleaner energy and technological innovation, coupled with militarization, on GEG in the context of Turkey. We defined GEG by deducting all possible negative externalities occurring from the growth process due to the utilization of non-renewable energy. We use a sample country like Turkey for several reasons. Turkey is an emerging market, being the 17th largest economy in the world and the 6th largest in Europe. Turkey witnessed steady economic growth from 2002 and, parallel to this, energy needs surged concomitantly. As the demand for energy grew, Turkey's most recent tendency, as characterised in all emerging economies, has been to promote the usage and production of cleaner energy sources through government initiatives. As Bhattacharya et al. (2016) note, more than $1 billion was invested in the infrastructure of renewable energy in Turkey between 2004 and 2014. Turkey ranks fourth in Europe in terms of the amount of investments allocated to cleaner energy sources. Moreover, fragile relationships with its neighbours has prompted the country to try to limit its dependency on imported oil and natural gas from Russia. Consequently, Turkey finds itself in the inevitable position of trying to use natural energy resources

and deploy technology to produce cleaner energy. Moreover, the country has a geopolitically strategic position in the world and faces security concerns from the Middle East, which forces the country into significant military expenditure. Currently (2016) military expenditure amounts to 3% of total GDP, and the absolute value is increasing over time as a result of exchange rate pressure. Another significant point about military expenditure is that most equipment and know-how is imported from the US. Only in recent years have there been policies to develop import-substitute products. The characteristics of the country make it an attractive sample and our findings are important not only for the country itself but also for the other energy-importing emerging economies. We contribute to the energy economics literature in several ways. First, despite the existence of numerous studies focusing on the renewable energy and economic growth relationship, none of them have considered the role of cleaner energy on GEG. To the best of our knowledge, this is the first study that synthesizes the role of cleaner energy, technological innovation and militarization in explaining GEG. Second, our analysis is made robust by incorporating the assumption of symmetric and asymmetric adjustment under an Autoregressive Distributed Lags (ARDL) framework. Our findings also demonstrate that the production and consumption of cleaner energy are positively and asymmetrically associated with GEG. In contrast, militarization is negatively and asymmetrically associated with GEG for Turkey. Our key findings provide several policy implications for promoting GEG in Turkey. The remainder of the paper is as follows: Section 2 presents a brief literature review and hypothesis development; Section 3 discusses the data and the methodology; Section 4 presents the results; and Section 5 Concludes. 2. Review of literature and hypothesis development According to classical growth theory, any production requires technology, human capital, physical capital and labour (Solow, 1956). Despite the contribution of core macroeconomic theory, negative externalities are overlooked. This theory considers natural possessions and the environment as depleting resources (Hallegatte et al., 2011), and requires factors like renewable energies to be uncontextualized for a sustainable economic growth paradigm. With these suggested modifications, the environment becomes a natural capital which is essential for economic growth. Nevertheless, only a few studies have shed light on the concept of green growth e.g., Malthus (1798), and more recently Nordhaus (1974), and Dasgupta and Maler (1974). OECD (2011, p.4) defines green growth as a way of fostering economic growth and development while ensuring the sustainable use of natural resources without depriving future generations. In addition, OECD (2011) also argues that technological innovation is an indispensable element for ensuring GEG. Jänicke (2012) adds that green growth refers to minimizing the cost of economic activities and emphasizing the quality of economic growth. To ensure green growth, it is essential to protect natural resources whilst fostering economic growth. An ample number of studies focus on scrutinizing the impact of renewable energy on economic growth or carbon emissions. A closer look at the existing literature enables us to divide them into three strands: renewable energy and economic growth; technological innovation and green growth; and lastly the relationship between militarization and green growth. 2.1. The cleaner energy and GEG relationship Various papers investigate the relationship between the usage of renewable energy and economic growth. The first strand of research highlights the positive impact of renewable energy on economic growth. Apergis and Payne (2010a) demonstrate a long-term equilibrium relationship between real GDP and renewable energy 2

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consumption for twenty OECD countries over the period 1985–2005. Sadorsky (2009) makes a similar finding in the context of emerging economies. More particularly, a 1% increase in real income per capita augments the consumption of renewable energy per capita by approximately 3.5% in the long run. Several studies have obtained similar results. Fang (2011), for example, notes a positive correlation between renewable energy sources and GDP growth for China for the period between 1978 and 2008. Alper and Oğuz (2016) assess the relationship between economic growth, renewable energy consumption, capital and labour for new EU member countries for the period of 1990–2009 using an ARDL approach. Their findings reveal that the use of renewable energy is positively associated with economic growth for Bulgaria, Estonia, Poland and Slovenia. Mamun et al. (2018) document that the development of financial markets facilitates biomass and non-biomass renewable energy production for OECD countries. Many other studies investigate the impacts of renewable energy on growth in various settings and establish a positive relationship between renewable energy and growth, e.g., Frondel et al. (2010) and Lehret al. (2012) for Germany; Sbia et al. (2014) for UAE; Yildirim et al. (2012) for USA; Pao and Fu (2013) for Brazil; Apergis and Payne (2010b) for Eurasian countries; Apergis and Payne (2011) for Central American countries; and Salim and Rafiq (2012) for 6 emerging countries. Apergis and Payne (2012) analyse the impact of renewable and non-renewable energy on economic growth in the context of 80 countries over the period 1990–2007. Their findings suggest that both energy sources are crucial for economic growth. However, they added that the consumption of non-renewable energy sources significantly elevates the emission of greenhouse gases. Despite the evidence of a positive relationship in the first strand of literature, some papers argue that the use of clean energy or renewable sources has a negative effect on economic development as well. These studies suggest that the establishment of facilities to promote clean energy is very costly and hence likely to impede economic growth (Brundtland, 1987; Beck and Martinot, 2004). Applying the ARDL testing approach, Ocal and Aslan (2013) document that renewable energy consumption has a negative impact on economic growth in Turkey. Ocal and Aslan (2013) further argue that the findings from country-specific and multi-country studies are inconsistent in terms of the direction of causality between renewable energy and economic growth. Bhattacharya et al. (2016) argue that the impact of renewable energy on growth is conditional on the stages of economic growth, on differences in credit disbursement for renewable energies projects, and on the estimation framework. In summary, prior literature concentrates on the relationship between renewable energy and economic growth, whereas the concept of green growth remains ignored. Therefore, the current study aims to measure the impact of cleaner energy production and consumption on GEG.

growth is influenced by technological-innovation-driven environmental regulation, but the effect of environmental regulation in isolation is uncertain. Padilla-Pérez and Gaudin (2014) examine the relationship between science, technology and innovation, and sustainable economic growth for Central American countries through questionnaires. They find a strong correlation between those innovations and the level of development. Zafar et al. (2019) document that investment increases economic growth along with renewable energy consumption for APEC countries. Considering the prior literature, we expect that technological innovations have a positive impact on GEG. 2.3. Militarization and GEG The last strand of literature highlights the relationship between military expenditure, the use of natural resources, and environmental quality. The fierce competition among developed countries to expand military power is driven by issues of national security, geopolitical influence, greater political ambition, and domestic political turmoil (Hooks and Smith, 2004; Jorgenson, 2005). This competition poses a significant challenge to environmental quality and the global ecosystem (Roberts and Parks, 2007; Srinivasan et al., 2008). With the pace of militarization, the use of global natural resources is increasing at an accelerated rate (Chase-Dunn, 1998; Dalby, 2004; Conca, 2004; Kentor, 2000; Magdoff, 1978; McNeill, 1982; Podopnik, 2006). This substantial consumption of fossil fuels and its related emissions, coupled with other greenhouse gases (GHG) and carbon emissions, has become the prime driver for climate change. In addition, the armed forces depend on resource-intensive and waste-generative equipment that relies on nonrenewable energy (Shaw, 1988). Moreover, military bases located all around the world require maintenance, which also relies on the consumption of natural resources (Blaker, 1990; Foster, 2006). Supporting the deployment and sustainability of armed forces around the world results in excessive use of thinners, solvents, lubricants, degreasers, fuels, and pesticides, which further increase environmental degradation (Singer and Keating, 1999). Lastly, the literature presents evidence that military expenditures are constantly damaging for the environment because of their non-renewable energy dependence, even in times of peace (Dycus, 1996; Hooks and Smith, 2004). Nevertheless, military expenditure does stimulate technological innovation that may enhance economic growth. Several other studies (see for example Babin (1990); Chowdhury (1991); Kick et al. (1998)), posit that militarization fosters economic expansion and labour force opportunities, especially in developing countries. More recently, Bildirici (2017) reported cointegration between militarization, carbon dioxide emissions, energy consumption and the economic growth of G7 countries for the 1985–2015 period. With this backdrop, despite some evidence that militarization can positively impact economic growth generally, we make the assumption that it nevertheless consumes natural resources which have a negative impact on GEG.

2.2. Technological innovation and GEG

3. Model specification, data and methodology

The second strand of literature focuses on the impact of technological innovation on renewable energy, carbon dioxide emissions, and economic growth. Many empirical studies document that technological innovation is a crucial cornerstone in reducing carbon emissions through increasing efficiency and factor productivities (Hascic et al., 2009; Martins et al., 2011; Liu and Liang, 2013; Shi and Lai, 2013; Sohag et al., 2015). Klewitz and Hansen (2014) suggest that technological innovations are the best way to ensure the efficient, clean and optimal use of limited resources. By examining the relationship between environmental regulations, green product innovation and performance at the firm level in China, Chan et al. (2016) conclude that regulations have a positive impact on innovation, which in turn increases firm profitability. Guo et al. (2017) analyse the effects of environmental regulation and technological innovation on GEG in an integrated methodological framework. Their analysis reveals that green

3.1. Model specification We define GEG as positive sustainable economic growth that is realized by ensuring the efficient use of renewable resources after deducting the damage due to greenhouse gases, the exploitation of natural resources and other negative externalities. The formation of green growth is as follows:

GEGt = GDPt + EEt

NRPt

NFDt

CO2, t

Where GEG indicates green economic growth; GDP is gross domestic product; EE is education expenditure; NRP is the monetary value of depleted coal, crude oil, natural gas and other minerals; NFD indicates the monetary value of forest depletion; CO2 is the monetary value of carbon dioxide, particulate emissions damage; and t represents time. 3

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Table 1 Data and their definition. Variable

Definition

Green Economic Growth (GEG)

Economic growth decoupled from negative externalities like carbon dioxide damage, natural resource depletion, net forest depletion and emission damage Patent applications of residents Share of electricity generated by renewable power plants in total electricity generated by all types of plants Share of renewable energy in total final energy consumption Number of people per kilometre square Share of military expenditure in total GDP

Technological Innovation (TI) Cleaner Energy Production (CEP) Cleaner Energy Consumption (CEC) Population Density (PD) Militarization (ME)

Based on our hypothesis and review of the literature, we develop two different models focusing on the supply side and demand side of cleaner energy in explaining green growth by incorporating the role of technology, population and militarization. The model selection follows a general to specific approach. The supply side of cleaner energy comprises the total production of cleaner energy (M1).

useful to apply non-linear approaches. In this paper, we apply Shin, Yu and Greenwood-Nimmo's non-linear ARDL framework that integrates an error correction mechanism. In this approach, parameters are estimated by using OLS through the integration of persistent and stationary variables in a coherent pattern. The framework also assumes asymmetry in the long run relation.

GEG =

y=

(TI , CEP , PD , MIL)

M1

(TI , CEC , PD , MIL)

M2

t

In order to measure the role of cleaner energy, technology, and militarization in explaining GEG in Turkey, we collected the relevant time series data over the years 1980–2016. We obtained cleaner energy production and consumption data from International Energy Statistics (EIA, 2017). The rest of the series was obtained from World Development Indicators (WDI, 2017). Table 1 presents the short form of each series and the definition. We used the logarithmic transformation of our series to reduce any abnormal size effect in our estimations.

h

z i lnTIt i

+

i=1

i=1

+ i=1

i lnMILt i

+

t

h

(1)

GEGt =

0

+

z i lnTIt i

+

i=1

i=1

+ i=1

i lnPDt i

m

+

i lnMILt i

+

t

i=1

j

xt

j

+

t

(4)

Table 2 Descriptive statistics.

r i lnCEPt i

xt+ j +

j=0

Table 2 presents the descriptive statistics of our variables. The descriptive statistics show that the change in the consumption of cleaner energy (CEC) is less than the production of cleaner energy (CEP). This suggests that there is still room for improvement for the distributional efficiency of cleaner energy in the production process. It is important to examine the order of integration for time series analysis, as the present value of any macro series is often influenced by the lag value. In order to examine the order of integration, we applied Generalized Least Squared- Dicky-Fuller (DF-GLS) and Phillips-Perron (P–P) approaches. DF-GLS procedure is an efficient approach in the case of a sample span of time. We also applied Phillips and Perron (P–P) (1988) that accounts for the issue of serial correlation in a series. The results of the unit root test affirm that all our variables are non-stationary at level but are stationary at first difference [I(1)] under both

i lnPDt i

i=1

+ j

4.1. Descriptive statistics

m

+

+

4. Results and discussion

r i lnCECt i

q j yt j

j=1

We employ the autoregressive distributed lag (ARDL) approach of Pesaran and Pesaran (1997) and Pesaran et al. (2001) to test for the existence of a relationship between GEG, technological innovation, cleaner energy and militarization. This approach can be applied to a series irrespective of whether they are I (0), I(1), or mutually cointegrated. After determining the appropriate time length using Schwarz Bayesian criterion, we estimated the ARDL model. The long-run relationships were estimated among the variables using the following ARDL model:

+

(3)

x t = vt

t

p

yt =

3.3. Estimation techniques

0

x t + ut ,

x t = j = 1 x j = j = 1 min( xj , 0) are partial sum processes of positive and negative changes in xt. Following Shin, Yu and Greenwood-Nimmo (2011), we assumed a single threshold value of zero to ensure a clear economic interpretation. The NARDL(p,q) in the levels framework embedding (1) is as follows:

3.2. Data and sources

GEGt =

x t+ +

where yt represents a scalar I(1) variable and xt is a kx1 vector of regressors defined such that x t = x 0 + xt+ + xt where x0 is the initial t t value and where and x t+ = j = 1 x+ max( x j , 0) j = j=1

Whereas the demand side of the model includes cleaner energy consumption (M2)

GEG =

+

(2)

In addition, we ran a cumulative sum of recursive residuals (CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ) tests in order to check the stability of the residuals.

Variable

Obs

Mean

Std. Dev.

Min

Max

GEG CEC CEP ME TI PD

38 38 38 38 38 38

22.910 2.947 3.480 3.156 7.398 4.372

0.332 0.318 0.299 0.796 0.800 0.175

22.246 2.434 2.853 1.847 6.028 4.045

23.661 3.405 3.861 4.267 8.983 4.653

Note: The table presents the descriptive variables of the statistics in the natural logarithm form for the period 1980 to 2016. TI, CEC, CEP, PD, ME refers to technological innovation, cleaner energy consumption, cleaner energy production, population density and militarization, respectively.

3.4. Asymmetric non-linear ARDL framework When the nature of relationships among variables is not linear, it is 4

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Table 3 Order of integration.

Table 5 GEG, cleaner energy consumption, population density and militarization.

DF-GLS

P–P

Level GEG CEC CEP ME TI PD

−1.938 −1.866 −1.655 −1.791 −2.528 −3.467

First Difference a

a

−4.038 −4.503 −3.489 −4.153 −3.605 −3.358

a a a a a

Level −0.575 −0.584 −1.787 −0.888 −0.567 1.421

Regressor Long Run CEC TI PD ME C Short Run ΔCEC ΔTI ΔPD ΔMIL ecm(-1)

First Difference a

−5.283 −10.371 a −8.176 a −6.401 a −4.878 a 1.149

TI, CEC, CEP, PD, ME refers to technological innovation, cleaner energy consumption, cleaner energy production, population density and militarization, respectively. “a” shows 10% significance level. ✗

approaches (Table 3). Nevertheless, population density (PD) is found to be stationary at level under the DF-GSL estimator, while the result from P–P nullifies the finding. The presence of a mixed order of integration endorses the suitability of the ARDL and NARDL methods to analyse our models.

Table 4 Results of the bounds tests. F-value

Outcome

FGEG (GEG|TI , CEP , PD, MIL ) FTI (TI|GEG, CEP , PD , MIL) FCEP (CEP|GEG, TI , PD , MIL) FPD (PD|GEG, TI , CEP , MIL ) FMIL (MIL|GEG, CEP , TI , PD) FCEC (MIL|GEG , TI , PD , MIL)

2 1 1 2 2 1

5.0963a 1.7994 3.6816 1.9430 3.5789 3.6816

Cointegration No Cointegration Inconclusive No Cointegration Inconclusive Inconclusive

T-Ratio [Prob]

0.089 0.127 3.833 −0.129 3.793

0.019 0.070 0.860 0.043 3.758

4.694[0.000] 1.795[0.085] 4.457[0.000] −2.991[0.006] 1.009[0.323]

0.01198 0.00558 2.8406 0.03573 −0.74094

0.01442 0.05187 0.95179 0.04708 0.15568

0.83143[0.413] 0.10775[0.915] 2.9845[0.006] 0.75888[0.455] −4.7593[0.000]

2 Bound Test F-Stat = 5.0963a; ARDL(1,0,0,0,0) SC : = 2.266, F(1, 23) = 1.545; 2 2 2 2 2 (1) = 0.479 ; (1) : = 1.719, F(1, 23) = 1.153; n: hc : ff 2 (1) = 0.388,F (5, 19) = 2.0944; R2 = 0.967; R¯ 2 = 0.953.

growth. For instance, a number of studies empirically highlight that the process of cleaner energy production creates new jobs and new commercial opportunities through changing technologies (Dai et al., 2016; Akella et al., 2009). In addition, the production of cleaner energy within the local economy reduces the pressure on the balance of payments that enhances sustainable economic growth. Sadorsky (2009) and Apergis and Payne (2010a) present a positive association between real per capita income and per capita cleaner energy consumption for emerging economies and OECD countries, respectively. In the context of Turkey, it imports refined petroleum and spends significant amounts importing other mineral fuels, lubricants and related materials, all of which are negatively associated with the balance of payments and hence economic growth. Thus, the substitution of CEC reduces the burden on the balance of payments and fosters economic growth. We also found that technological innovation and population density positively and significantly promote GEG in the long run. Our finding regarding the impact of technological innovation on GEG corresponds with the findings of Klewitz and Hansen (2014), who document that technological innovations are the means for the efficient, clean and optimal use of scarce resources. Kaygusuz (2007), Ozturk and Acaravcı (2010) and Bulut (2017) also emphasize the need for technological innovation in order to increase the usage of renewable energy which leads to economic growth. Lastly, the negative and significant coefficient of military expenditure (ME) indicates it has a deteriorating effect on GEG. This finding is parallel with Jorgenson et al. (2010). Military expenditures is both resource-consuming and waste-generating. Population density (PD) also has a significant and positive relationship with green growth. This confirms our prior expectation that a higher population density will make it easier to disseminate technology, which is a necessary precursor to the development of cleaner energy technologies. Also, a higher population means a greater labour force and increased economic growth. When we consider the short run, only PD is found to have a significant relationship to GEG among the variables we analyzed. In addition, the coefficient of the error correction term is negative and statistically significant, which indicates the speed of adjustment towards long-run equilibrium per year after any economic shocks. Our estimated parameters are stable, consistent and robust as our diagnostic test found no issues in terms of heteroskedasticity, serial correlation, functional energy and normality conditions. The value of the CUSUM and CUSUMSQ plot is within a 5% statistical significance level, suggesting that the long-run coefficients and the short-run coefficients in the error correction model are stable (Fig. 1). We verified the robustness of Table 5 by considering cleaner energy

To examine the cointegrating relationship, we applied the Wald test or F-test for the joint significance of the coefficient on the lagged variables. Therefore, the null hypothesis is (Ho ) 1 = 2 = 3 = 4 = 5 = 0 indicating the existence of no cointegration against the 0 which does inalternative hypothesis of (Ha ) 1 2 3 4 5 dicate the existence of a cointegration relationship. The estimated F statistic FGG (GEG|TI , CEP , PD , MIL) = 5.1022, is greater than the upper bound critical value of Pesaran et al. (2001) at the1% significance level (Table 4). This finding implies that the null hypothesis of no cointegration is rejected. Thus, the finding further allowed us to proceed with an ARDL approach to measure the long run and short impact of regressors on the green growth model. In addition, when we normalized our model by CEP, MIL and CEC indicating an inconclusive result as calculated, the F-value fell in-between the upper and lower bounds of critical values of Pesaran et al., (2001). However, when we normalized our variable, TI and PD indicated no cointegration exists. At this stage, we estimated our Model 1 by applying a standard ARDL approach. In Model 1, we incorporated cleaner energy consumption (CEC), technological innovation (TI), population density (PD) and military expenditure (ME) to explain the dynamics of GEG. Table 5 presents the results. The coefficient of CEC is positive and significant, implying that the consumption of cleaner energy increases GEG in the long-term economy of Turkey. However, the role of CEC is found to be inconclusive in explaining GEG in the short-term. Our findings are consistent with many empirical studies e.g., Sadorsky (2009); Apergis and Payne (2010a); Apergis and Payne (2010b) and Apergis and Payne (2012). Prior literature documents different channels through which cleaner energy can promote economic

AIC

Standard Error

TI, CEC, PD, ME refers to technological innovation, cleaner energy consumption, population density and militarization, respectively.

4.2. GEG, cleaner energy, innovation and militarization under the assumption of symmetric adjustment

Dependent variable

Coefficient

Note: "a" shows a 10% significance level. 5

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Fig. 1. Stability of model 1.

our model is stable.

Table 6 GEG, cleaner energy production, population density and militarization. Regressor Long Run CEP TI PD ME C Short Run Δ CEP Δ TI Δ PD Δ MIL ecm(-1)

Coefficient

Standard Error

T-Ratio [Prob]

0.0149 0.2580 0.0142 −0.0846 19.7894

0.0040 0.0686 0.0046 0.0503 0.3277

3.6703[0.001] 3.7617[0.001] 3.0457[0.006] −1.6804[0.106] 60.3772[0.000]

.00303 .00675 .76687 .00533 -.86736

0.00370 0.05348 0.42474 0.05930 0.20615

0.8183[0.420] 0.1263[0.900] 1.8055[0.082] 0.0899[0.929] −4.2075[0.000]

4.3. GEG, cleaner energy, innovation and militarization under the assumption of asymmetric adjustment We re-estimated Model 1 and Model 2 by applying a non-linear ARDL approach to allow asymmetric adjustments. Table 7 reports the results. The long-run coefficients suggested for CEC− and CEC+ are 2.097 and −1.325 respectively, which are significant at a 1% significance level. Our findings imply that following an increase in cleaner energy consumption by 1%, GEG increases by 2.097%, while a 1% reduction of CEC results in a decrease in GEG of 1.325%. The impact of CEC is found to be asymmetric in the long run, while symmetric in the short run. A positive shock of TI significantly fosters GEG, but a negative shock of TI insignificantly explains GEG in the long run. Table 6 further indicates that the impact of TI is asymmetric in the long run but symmetric in the short run. Nevertheless, a positive shock in military expenditure negatively affects GEG whereas a negative shock promotes it in the long run. Interestingly, the coefficient is higher for positive shock than the coefficient for negative shock. Finally, the finding for PD is consistent with symmetric analysis as the coefficient of PD+ is positively significant, while PD− is negatively significant. Our findings are consistent and robust under all diagnostic tests. We present the results for the non-linear ARDL approach by allowing asymmetric adjustments in Table 8. The long run coefficients for CEP+ and CEP− are 0.024 and −0.322, respectively. A positive shock of CEP elevates GEG in the long run, while a negative shock of CEP diminishes GEG at a higher magnitude. TI+ significantly explains GEG, which is also parallel with our findings in Model 2. A positive shock to TI is conducive to augmenting GEG, whereas negative shocks are detrimental. In addition, both positive and negative shocks to military

2 Bound Test F-Stat = 4.1305a; ARDL(1,0,0,0,0) SC : = 1.3504, F(1, 22) = 2 : 0.8574; ff2 : 2 (1) = 0.6299, F(1, 22) = 0.391; n2 : 2 (1) = 0.164 ; hc 2 (1) = 0.8197 ,F (5, 19) = 0.7922; R2 = 0.968; R¯ 2 = 0.952 .

✗

TI, CEP, PD, ME refers to technological innovation, cleaner energy production, population density and militarization, respectively.

production (CEP). Table 6 highlights the results of Model 2. Interestingly, we still found the coefficient of CEP was positive and significant in promoting GEG in the long run. This finding affirms the robustness of the findings of Table 4. Similarly, the coefficient of TI is positive and significant in augmenting GEG in the long run. However, the impact of TI is inconclusive in promoting GEG. The positive and significant coefficient of PD affirms its conducive role in promoting GEG. Lastly, military expenditure is detrimental in augmenting GEG in the long-term economy of Turkey. The error correction term is also negative and significant and indicates the speed of adjustment (86%) towards longrun equilibrium per year after an economic shock. Fig. 2 confirms that 6

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Fig. 2. Stability of model 2. Table 7 GEG, cleaner energy consumption, innovation and militarization: Assumption of asymmetric adjustment. Exog. var.

CEC TI ME PD

Long-run effect [+]

Table 8 GEG, cleaner energy production, innovation and militarization: Assumption of asymmetric adjustment.

Long-run effect [-]

Long-run effect [+]

Long-run effect [-]

coef.

F-stat

P-Value

coef.

F-stat

P-Value

Exog. var.

coef.

F-stat

P-Value

coef.

F-stat

P-Value

2.097 0.264 −0.278 0.070

37.99 23.04 82.17 312.6

0.000 0.000 0.000 0.000

−1.325 0.048 0.244 −0.010

44.64 1.103 25.89 16.49

0.000 0.294 0.000 0.000

CEP TI ME PD

0.024 0.193 −0.214 0.057

0.083 11.700 49.210 136.400

0.772 0.001 0.000 0.000

−0.322 0.031 0.181 0.002

19.56 .4133 15.23 1.115

0.000 0.521 0.000 0.292

Long-run asymmetry CEC 10.42 0.001 TI 11.20 0.001 ME 55.17 0.000 PD 223.30 0.000 F-PSS = 40.5017 Portmanteau test up to lag 40 (chi2) Breusch/Pagan heteroskedasticity test (chi2) Ramsey RESET test (F) Jarque-Bera test on normality (chi2)

Short-run asymmetry 0.396 1.651 0.183 26.49 0.091 0.925 0.770 0.775

Long-run asymmetry

0.529 0.199 0.669 0.000

CEP 10.92 0.001 TI 6.266 0.013 ME 34.33 0.000 PD 108.3 0.000 F_PSS 29.2192 Portmanteau test up to lag 40 (chi2) Breusch/Pagan heteroskedasticity test (chi2) Ramsey RESET test (F) Jarque-Bera test on normality (chi2)

0.432 0.415 0.725 0.821

TI, CEC, PD, ME refers to technological innovation, cleaner energy consumption, population density and militarization, respectively.

Short-run asymmetry 1.76 7.358 0.413 0.013 0.207 0.679 0.792 0.322

0.185 0.007 0.521 0.908

0.821 0.560 0.632 0.231

TI, CEC, PD, ME refers to technological innovation, cleaner energy production, population density and militarization, respectively.

expenditures have significant impacts on GEG. An increase in military expenditure by 1% results in a reduction of GEG by 0.21%, and a decrease in military expenditure by 1% lowers GEG by 0.18%. Higher population density positively influences GEG and vice-versa.

5. Conclusion The production and use of cleaner energy and technological 7

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innovation are important drivers for green economic growth. Along with the significant commercial potential of clean energy, it can meet social needs by curbing carbon emissions and enhancing energy security. We have assessed the role of cleaner energy production and its use in augmenting GEG for Turkish economy using a robust methodology. We generate a figure for GEG by subtracting negative externalities like carbon dioxide damage, natural resource depletion, net forest depletion and emission damage from GDP. We highlight several interesting findings. First, both cleaner energy production and its use significantly stimulate GEG in the long run. Our analysis affirms that the coefficients of cleaner energy production and use are asymmetric in explaining GEG in the long run, implying the response to positive and negative shocks on cleaner energy production and use influence GEG differently. Second, a positive shock of technological innovation significantly promotes GEG, but a negative shock insignificantly explains GEG in the long run. Third, an increase in military expenditure is detrimental to GEG, while a reduction is conducive to promoting it. Finally, population density is found to be positive for GEG in the long run. Our findings have key policy implications. Firstly, production and use of cleaner energy promote GEG, which reemphasizes the importance of increasing the ratio of cleaner energy into the total energy mix for a sustainable economy in the long run. Secondly, as we show, cleaner energy is the driving factor for GEG, hence governments should subsidize cleaner energy production and encourage the private sector to increase production of cleaner energy. Thirdly, the positive and significant role of technological innovation in improving GEG implies that initiatives are needed to promote green technology by reforming financial markets. Lastly, the Turkish government should further regulate the use of fossil fuels so that the economy emphasises production and consumption of cleaner energy. This study provides future direction for similar studies to increase the amount of cross-border evidence with regard to GEG.

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