The impact of institutional quality on economic growth and carbon emissions: Evidence from Indonesia, South Korea and Thailand

Journal of Cleaner Production 241 (2019) 118331

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The impact of institutional quality on economic growth and carbon emissions: Evidence from Indonesia, South Korea and Thailand Muhammad Salman a, *, Xingle Long a, Lamini Dauda a, Claudia Nyarko Mensah a a

School of Management, Jiangsu University, Zhenjiang, 212013, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 April 2019 Received in revised form 4 September 2019 Accepted 7 September 2019 Available online 11 September 2019

The main purpose of this paper is to explore the impact of institutional quality on growth-emissions nexus in a panel of three East Asian countries over the period from 1990 to 2016. We also incorporated energy consumption and trade openness as important indicators in the model to avoid variable bias. The results of unit root tests show that the variables are stationary at first difference. The panel cointegration test confirmed that all the variables are cointegrated. We employed Fully Modified Ordinary Least Squares and Dynamic Ordinary Least Squares methods to estimate the long-run effects of explanatory variables on economic growth. The main findings are: (i) a positive significant interaction variable between carbon emission and institutional quality indicates that efficient and impartial domestic institutions are very important to increase economic growth and decrease carbon emissions, simultaneously. (ii) Institutional quality, energy use and trade openness stimulate economic growth. (iii) The VECM granger causality test results show that there is a one-way causality runs from institutional quality to economic growth, carbon emission and energy consumption, from trade openness to carbon emissions in both short-run and long-run, from energy use to trade openness, from energy use to carbon emission in both short-run and long-run. Moreover, feedback effect is present between economic growth and carbon emission, and between economic growth and energy use in both short-run and long-run. It is therefore, necessary to regulate and strengthen the role and effectiveness of local institutions with the aim of lowering carbon emissions in the course of economic development. © 2019 Elsevier Ltd. All rights reserved.

Handling editor. Prof. Jiri Jaromir Klemes Keywords: Economic growth Carbon emissions Institutional quality Panel granger causality

1. Introduction Since the seminal works by Williamson (1989) and North (1990), the institutional economics literature has arguably acknowledged that institutional quality is one of the most significant elements of GDP growth. Institutions formulate and regulate rules and regulations in public by imposing contextual controls (Acemoglu and Robinson, 2010). Generally speaking, institutional quality is correlated to the policies implemented by domestic institutions in order to set legal and cultural framework, in which socio-economic activities take place. Hence, indicating the capability of the government to articulate and impose policies and regulations that encourage private sector, improve quality of contract execution, property rights protection, strong rule of law and

* Corresponding author. E-mail addresses: [email protected] (M. Salman), [email protected] (X. Long), [email protected] (L. Dauda), [email protected] (C.N. Mensah). https://doi.org/10.1016/j.jclepro.2019.118331 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

the impartiality of the institutions from political influence (Canh et al., 2019). On the other hand, weak institutions inefficiently support private sector, which leads to corruption, ineffective bureaucratic system and weak environmental regulations (Asoni, 2008). Recently, institutional quality has gained attention of economists, scientists and policy makers in the context of environment. Indeed, the government can directly and indirectly influence the environmental quality. Among various dimensions of governance, rule of law is one of the most widely adopted element, which represents an effective and well-functioning constitutional system. Moreover, strong rule of law can reduce the effects of market failures. In addition, Olson (1996) stated that the effective and impartial government institutions can play a vital role in enhancing productive cooperation among market players. Therefore, rule of law becomes a crucial element in tackling the environmental issues. Thus, strong rule of law is imperative to impose carbon dioxide (CO2) control procedures and firms would not hesitate to comply. On contrary, if flaws exist in institutional quality, the firms

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would easily overlook the carbon dioxide (CO2) control procedures by ignoring the environmental externalities and consequences related to the growth process (Welsch, 2004). This study consists on a panel of three East Asian countries namely, Indonesia, South Korea and Thailand. The reasons behind considering these three countries as an appropriate for our study are as follow: First, these countries are among the emerging economies across East Asian countries. The annual average economic growth of Indonesia, South Korea and Thailand is 3.36%, 3.44% and 4.46%, respectively (Zafar et al., 2019). Second, these countries have enormously contributed to carbon emissions due to the combustion of fossil fuel over the last few decades. In terms of fossil fuel CO2 emissions (measured as kiloton) in 2015, South Korea ranked 8th (617,285 kt), Indonesia ranked 11th (502,961 kt) and Thailand ranked 21st (279,253 kt), globally (NEAA, 2017). Third, with regard to energy consumption, 70% of primary energy demands are fulfilled by coal combustion in Indonesia and South Korea ranks eight (8) on top ten total energy consumption countries accounted for 218.6 (Mtoe) in 2015 (Shahbaz et al., 2018). Similarly, in 2015, final energy consumption raised by 4% due to increased economic growth in Thailand (Kyophilavong et al., 2015). Last, among the East Asian countries, particularly, these three countries hit hard by the East Asian recession that occurred in 1997, which caused severe economic depression and political instability in the selected countries. For instance, in Indonesia, the current account debit went higher to 3.4% of gross domestic product in 1996 from average current deficit to 2.5% of GDP during 1993e1996. In South Korea, the gap widened from an average of 1.9% of gross domestic product to 4.9% of GDP during the crisis years. While, in Thailand, the current account deficit rose from an average of 6.7% of GDP to 7.9% of GDP during the recession years (Ghosh, 2006). Therefore, this makes sense to investigate the growth-emissions model by incorporating institutional quality, trade openness and energy use in the investigated countries. Hosseini and Kaneko (2013) argued that institutional quality not only affects the country's own environmental quality but the environmental quality of countries can be spread to its neighboring countries simultaneously through channel of spatial institutional spillover. Neutral and effective

Fig. 1. GDP growth (measured as constant US$, 2010). Data source: World Development Indicators.

domestic institutions play a significant role in abating CO2 emissions during the phase of economic growth (Lau et al., 2014). In contrary, weak institutional system is the major restriction on country's capability to build up productive elements such as physical and human capital, hinders the innovation and adaptation of new technologies, promotes expropriation activities and jurisdictional manipulation, thus, worsen the environmental quality by overlooking the environmental externalities and consequences related to the process of economic development (Slesman et al., 2015). Poor institutional quality is the major determinant of a country's low income trap (Kar et al., 2019). Therefore, keeping in view the controversial ideas of scholars and conflicting empirical results of previous studies on the role of quality of institutions on growth-emissions nexus, this study attempts to answer the following research question. Does well developed and effective institutional system simultaneously enhance economic growth and reduce carbon emissions in the selected countries? The novelties of this paper are as follow: (1) First, we believe that this is the first empirical paper of its kind that takes three East Asian countries namely, Indonesia, South Korea and Thailand into account over the period 1990e2016. (2) we examined the role of institutional quality into the process of economic growth (3) Moreover, we incorporated energy consumption and trade openness as important factors in growth-emissions model, this might assist us to reduce omitted variable bias (4) we also incorporated the interaction variable between carbon emissions and institutional quality to examined the relative importance of domestic institutions in stimulating economic growth and mitigating carbon emissions, simultaneously. (5) we addressed various panel data problems such as cross-sectional dependency, unit root, cointegration, endogeneity and serial correlation by adopting robust econometric techniques. Fig. 1 shows a time series increasing trend in GDP growth over the period of 1990e2016 in the selected countries. From Fig. 1 it can clearly be seen a sharp decline in GDP growth during 1997e1999 due to the East Asian crisis of 1997. Fig. 2 displays a time series rising tendency in carbon emissions, South Korea has the highest increasing trend in carbon emissions followed by Indonesia and

M. Salman et al. / Journal of Cleaner Production 241 (2019) 118331

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Fig. 2. CO2 emissions (measured as kiloton) Data source: World Development Indicators. .

Thailand. Fig. 3 shows that South Korea is the highest energy consumer followed by Indonesia and Thailand. Fig. 4 exhibits that both South Korea and Thailand have an increasing trend while Indonesia experiences a decreasing trend in trade openness during 2014e2016. Fig. 5 shows the institutional quality (measured as law and order) in the selected countries during 1990e2016. Clearly, a sharp fall can be seen in the quality of institutions in Indonesia and South Korea during the East Asian crisis period (1997e1999) and a sharp decline in institutional quality in Thailand during the period 2003e2006. The main purpose of this study is to empirically estimate the link between GDP growth and CO2 emissions and to explore the

Fig. 3. Energy consumption (measured as kiloton). Data source: World Development Indicators.

role of institutional quality on growth-emissions nexus given the background of three East Asian economies, namely Indonesia, South Korea and Thailand over the time period of 1990e2016. This paper is different from other studies with regard to the dataset it takes into consideration and the set of countries it considers. Notwithstanding, many studies have been carried out to examine the factors affecting growth-emissions nexus using different datasets and different set of explanatory variables. However, the current empirical work is absolutely in contrast with the previous studies on factors affecting growth-emissions nexus in these three East Asian countries. Consequently, this study will contribute to the growth-emissions literature on the subject matter, chiefly with

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Fig. 4. Trade openness (export þ imports/GDP, constant US$,2010). Data source: World Development Indicators.

reference to East Asian economies. The rest of this study is structured as follow. Section 2 offers a brief review of past studies. Section 3 debates the data sources and estimation techniques. Section 4 sheds light on the findings and section 5 provides comprehensive discussion. Section 6 reports final remarks and policy recommendations. 2. Literature review 2.1. Growth-emission nexus In the first strand of research, we would discuss two ways that

Fig. 5. Institutional quality (measured as Law & Order). Data source: International Country Risk Guide.

empirically investigate the association between carbon emission and economic growth. The first way pays attention to confirm the presence of Environmental Kuznets Curve (EKC) theory, which argues that the link between per capita income and carbon emission is a bell-shaped curve implying that income per capita and CO2 emissions increase monotonically at lower economic development stage until a threshold income level is attained after, which carbon emission decreases with further economic development (Grossman and Krueger, 1991, 1995). In addition, Panayotou (1993) argued that the bell-shaped link between per capita income and carbon emission is due to the scale effect, which infers that at low level of economic development, environmental deterioration

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becomes more severe, if the scale effect exceeds the composition and technique effects. However, at higher stage of economic development, the economy matures, enforces strict environmental regulations and implements clean and eco-friendly technologies consequently, improving the environmental quality. Previously, numerous cross-country and country-specific studies have been carried out to explore this hypothesis. The empirical findings regarding testing this hypothesis have always been controversial. For example, Ahmad et al. (2017) examined the non-linear association between income per capita and carbon emissions. Applying auto regressive distributive lag method, the findings validate the presence of EKC in Croatia. Similarly, Solarin et al. (2017) explored the presence of EKC hypothesis in case of China and India. Their results confirmed that EKC theory holds in the investigated countries. Sinha and Shahbaz (2018) carried out a country-specific study to explore the bell-shaped curve between income per capita and CO2 emissions where they applied auto regressive distributive lag bounds test. The findings validate the bell-shaped curve between income per capita and carbon emissions with the turning point at USD 2937.77 in India. Moreover, the study of Ulucak and Bilgili (2018) found the presence of EKC hypothesis in high, middle and low income economies using the dataset of 1961e2013. Salman et al. (2019) estimated the validity of EKC hypothesis in a panel of seven ASEAN countries over the time period of 1990e2017 adopting panel quantile regression approach. The results showed that EKC is valid in a panel of seven ASEAN countries. However, the studies of Begum et al. (2015) for Malaysia, Alshehry and Belloumi (2017) for Saudi Arabia and Zhu et al. (2016) for ASEAN-5 found that this assumption is not valid in these countries. The second way discusses the impact of carbon emissions on economic growth. For instance, the empirical works of Pearson (1994), Stern et al. (1996) and Dinda (2009) added carbon emissions into the growth models to accompaniment the conventional determinants suggested in the neoclassical and endogenous growth models. In other words, carbon emissions can act as a driving factor for economic development. Furthermore, the studies of Porter and Claas Van der Linde. (1995), Hung and Shaw (2006) and Lau et al. (2014) confirmed a negative impact of CO2 emission on economic development implying that the better environmental quality leads to increase economic development mainly in advanced economies. 2.2. Growth-institutions nexus The second research stream focuses on the growth-institutions relationship in the context of environment. As a general perception, institutions can be categorized as both informal constitutional constraints (i.e. authorizations, societies, customs and protocols) and formal procedures (rule and law, bureaucracy, property rights, and constitutions). In this study, we focus on the latter: formal rules and civil service institutes. This strand of literature argues that the quality of institutes for instance, rule of law, good bureaucratic system and corruption plays a vital role in analyzing growth-emission nexus. In addition, Panayotou (1997) stated that institutional quality plays a crucial role in improving the environmental quality in a country, even if the economic level of a country is low. This means that the efficient institutions can help decrease the environmental cost of increased economic development thus, enable countries to mitigate the environmental pollution. Moreover, Gagliardi (2008) argued that better institutional quality can help discourage exploitation, improve collaborative relationship among the agents thus, encourage agents to incorporate the externalities. Consequently, the better institutional quality can provide comprehensive solutions to be adopted for the enhancement of economic development and betterment of

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environmental quality (Subramanian, 2007). Previously, many empirical works have studied the role of institutional quality on growth-emissions nexus. For instance, Tamazian and Rao (2010) investigated the importance of institutional quality for environmental quality for 24 transition countries over the period of 1993e2004 using GMM estimation technique. The empirical findings of the study provided evidence of the importance of institutional quality for the improvement of environmental quality in the selected countries. Moreover, Lau et al. (2014) explored the role of institutional quality on growth-emissions nexus in Malaysia during 1984e2008. The results of auto regressive distributive lag (ARDL) bounds test confirmed that impartial and effective domestic institutions are very important to mitigate carbon emissions in the course of economic development. In addition, adopting GMM estimation method, Abid (2017) incorporated institutional quality in growth-emissions model for 41 EU and 58 Middle East & African (MEA) countries using dataset of 1990e2011. He argued that institutional quality matters for enhancing economic growth and reducing carbon emissions at the same time in the selected countries. Bhattacharya et al. (2017) explored the role of institutional quality on enhancing economic growth and lowering CO2 emission in 85 developed and emerging economies over the period from 1991 to 2012 using system-GMM and fully modified OLS methods for empirical analysis. Overall, the results show that institutions are significant in improving economic progress and combating carbon emission in the investigated economies. Furthermore, the study of Sarkodie and Adams (2018) found that institutional quality reduces carbon emissions by 0.1% in case of South Africa.

2.3. Growth-energy nexus The third section of literature review explains the energygrowth nexus. Theoretically, there are four assumptions available in existing energy-growth literature. The first hypothesis called “growth hypothesis” argues that energy use causes to GDP growth (Templet, 1999). The second assumption called “conservation hypothesis” which states that growth causes to energy use (Jalil and Feridun, 2014). The third assumption called “feedback hypothesis” which infers that energy use and GDP growth have a two-way causal association with each other. Fourth is the “neutrality hypothesis” which provides evidence of no causal association between energy utilization and financial development (Tiba and Omri, 2017). There exists a bulk of previous studies that explored these assumptions. Saidi et al. (2017) examined the causal relationship between economic development and energy utilization in a panel of 53 countries during 1990e2014 applying Vector Error Correction Model (VECM). The findings validate a two-way causal association between the two variables. Zafar et al. (2019) investigated the disaggregated effects of energy consumption on economic growth in Asia-Pacific Economic Cooperation (APEC) economies adopting a dataset from 1990 to 2015. The results of FMOLS approach show that energy use stimulate economic progress in APEC countries. In addition, Shahbaz et al. (2018) studied the energy-growth association in top ten energy emitter economies over the period from 1960 to 2015 where they implemented Quantile-on-Quantile method. Their empirical findings found a positive contribution of energy utilization to economic development in the investigated economies. Moreover, Bakirtas and Akpolat (2018) carried out a causality analysis between economic growth and energy consumption in Emerging-Market economies for the time period of 1970e2014. The results of Dumitrescu-Hurlin panel Granger causality test confirmed a panel Granger causality running from economic growth to energy use.

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2.4. Growth-trade nexus Theoretically, the association between GDP growth and trade openness can be explained by three hypotheses. The first hypothesis called “the trade-led growth hypothesis” demonstrates that trade openness is a crucial factor of economic development (Pradhan et al., 2018). Hence, there is a unidirectional causality running from trade openness to income growth. This assumption further postulates that trade openness stimulates financial development through resource expedition and technology transfer among the countries. The second assumption called “the growthled trade openness” states that the direction of causality runs from GDP growth to trade openness. In order words, trade openness plays a minor role to boost financial development because when a country develops, supplementary demand for goods and services arise in the economy. Therefore, the lack of trade openness in the emerging economies shows a shortage of demand for goods and services (Xu, 1996). The third mechanism known as “the feedback hypothesis” indicates a two-way causal association between openness of trade and GDP growth suggesting that trade openness is crucial to GDP growth whereas, financial development promotes trade openness through specialization (Sebri, 2014). Since past few decades, numerous studies attempted to explore this nexus. However, the empirical results are disputed regarding the influence of trade openness on economic development. For example, Zahonogo (2016) investigated the effect of trade openness on economic growth in a panel of 42 Sub-Sahara African countries for the period of 1980e2012. The results of Pooled Mean Group estimation technique show that trade openness has an inverted Ushaped association with economic growth. Pradhan et al. (2018) examined the growth-trade nexus for 25 ASEAN regional forum countries over the period 1961e2012 using Granger causality tests. The results confirmed a bidirectional link between the two variables. Sikder et al. (2019) explored the role of trade on economic progress for G-20 countries adopting a dataset of 1991e2013. The results showed that the effect of trade openness varies in these countries. The potential theoretical contributions of this study to the existing literature are as follow: First, we examined the role of institutional quality in the course of economic development to examine whether “hierarchy of institutions hypothesis” holds in the selected countries or not. Second, we also explored the causal links between trade openness and economic growth to testify whether “the trade-led growth hypothesis”, “the growth-led trade openness” or “the feedback hypothesis” exists in the investigated economies. Third, in order to validate “growth hypothesis”, “conservation hypothesis”, “feedback hypothesis” or “neutrality hypothesis”, we carried out causality analysis between economic development and energy utilization. Last, we examined the interactive influence of institutional quality and carbon emission on economic growth in order to confirm the assumption that natural and efficient domestic institutions matter for increasing economic growth and reducing carbon emission, simultaneously in the selected economies. The potential methodology contributions of this paper are as follow: First, we adopted two cross-sectional tests namely BreuschPagan LM test (1980) and Pesaran (2004) CD test to examine the cross-sectional dependency among the countries. Second, we implemented three panel unit root tests namely Breitung (2001), Im et al. (2003) and Pesaran (2007) CIPS to test the stationary properties of the data. Third, this paper adopted Westerlund (2007) Panel cointegration test to conduct the cointegration analysis. Fourth, we applied panel fully modified ordinary least squares approach that deals with endogeneity and serial correlation. Moreover, for robust analysis, we also used panel dynamic ordinary

least squares method. Last, to define the direction of causality among the variables, this paper implemented panel-based Granger causality (1987) approach. 3. Data, model and estimation techniques 3.1. Conceptual framework of the paper Before developing the econometric model, we construct the conceptual framework of this paper as this framework will assist us to decide upon the model indicators. The investigated countries under this study are categorized in terms of increased economic growth, and this increasing tendency in economic performance is highly correlated with the combustion of fossil fuel, which ultimately increases carbon emissions in the selected countries. Moreover, trade openness and energy consumption reinforce each other, and therefore, trade openness enhance environmental pollution through increased production of goods and services (Rahman, 2017). Similarly, efficient and independent domestic institutions are important to control the carbon emissions in the course of economic prosperity, concurrently (Lau et al., 2014). Keeping all this in view, we construct the theoretical structure of the study as Fig. 6. 3.2. Model specification Based on the theoretical arguments presented above and following Lau et al. (2014) and Bhattacharya et al. (2017), we develop an econometric model to study the long-run impacts of explanatory variables on economic growth as Formula 1. GDP ¼ f (CO2, EC, TO, IQ)

(1)

Eq. (1). implies that GDP is a function of carbon emission (CO2), energy consumption (EC), trade openness (TO) and institutional quality (IQ). Transforming Eq. (1) into natural logarithm form and time series specification as Eq. (2). lnGDPt ¼ ai þ b1lnCO2t þ b2lnECt þ b3lnTOt þ b4lnIQt þ εt

(2)

Following Lau et al. (2014), we incorporate the interaction variable between carbon emissions and institutional quality (CO2IQ) in order to validate the assumption that effective and impartial domestic institutions would help to simultaneously improve economic growth and decrease carbon emissions in the investigated countries. Furthermore, we also add the dummy variable that captures the impacts of East Asian Crisis of 1997 on economic growth and institutional quality. The dummy variable is assigned the value of zero for each year from 1990 to 1996, and 2004 to 2016, whereas it takes the value of one from 1997 to 2003 indicating the ineffectiveness of institutions and political instability in the selected countries. Hence, rewriting Eq. (2) as follow: lnGDPt ¼ ai þ b1lnCO2t þ b2lnECt þ b3lnTOt þ b4lnIQt þb5lnCO2IQt þ b6Dumt þ εt (3) Transforming Eq. (3) into panel data model as follow: lnGDPit ¼ ait þ b1lnCO2it þ b2lnECit þ b3lnTOit þ b4lnIQit þb5lnCO2IQit þ b6Dumit þ εit

(4)

where i and t indicates country and time dimensions, respectively. GDP shows economic growth, CO2 indicates carbon emissions, EC represents energy consumption, TO is the trade openness, IQ shows institutional quality. Consistent with Lau et al. (2014), this study

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Fig. 6. Conceptual framework of the study.

adopts the variable “Law and order” as a proxy for institutional quality. The variable “Law and order” consists of two components. The “law” subcomponent is used to measure the effectiveness and independence of domestic institutions, whereas, the “order” subcomponent indicates the strength of the law. Both subcomponents (i.e. “law” and “order”) are assigned equal weights consist of 0e3 points. The coefficient of law and order ranges from zero to six implying that the higher the value, the strong and effective the government institutions are. The expected signs of CO2 and the interaction variable between carbon emissions and institutional quality (CO2IQ) are negative and positive, respectively which suggests that effective and impartial domestic institutions play a significant role to mitigate carbon emissions without affecting the economic growth (Lau et al., 2014). The expected sign of energy consumption is positive. The expected value of institutional quality is positive implying that effective and independent institutions improve economic growth. The expected coefficient of trade openness is either positive or negative depends on the trade balance position. If the volume of imports exceed the volume of exports and exchange rate deflates, it shows that trade openness has worsen impact on economic development and vice versa. We transformed the variables into natural logarithm to smoothen the data.

3.3. Data We derived the data over the period of 1990e2016 from the World Bank Development Indicators (WDI) for gross domestic product (measured as constant US$, following base year 2010), carbon emissions (measured as kiloton), energy consumption (measured as kiloton), trade openness (calculated as export þ imports/GDP, constant US$, 2010) for three East Asian countries namely, Thailand South Korea and Indonesia.1 The data for institutional quality is extracted from the International Country Risk Guide (ICRG), 2018, most widely adopted institutional data source (Lau et al., 2014).2

1 2

https://data.worldbank.org.cn/. https://guides.tricolib.brynmawr.edu/icrg.

3.4. Correlation analysis Correlation analysis is used to check the mutual relationship between two variables and can be estimated as formula.5.

CovðX; YÞ CorrðX; YÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi VarðXÞVarðYÞ

(5)

where Cov (X, Y) indicates covariance between variables Y and X. Var(X) and Var(Y) are respectively variable values of (X) and (Y). The coefficient of correlation ranges between 0 and 1. If the value is closer to 1, it shows that the variables are highly correlated and vice versa. 3.5. Cross-sectional dependence tests Cross-countries have a strong tendency of interrelationship due to the growing economic assimilation of the countries (De Hoyos and Sarafidis, 2006). In panel data analysis, cross-sectional dependence is a serious problem and neglecting this issue may lead to bogus results. Keeping this in view, this study adopts two cross-sectional dependence tests namely the Breusch-Pagan LM (1980) and Pesaran (2004) CD tests. Breusch-Pagan LM (1980) developed cross-section dependence test and is widely adopted analytical tool to detect the issue of cross sectional dependency in the panel data. The motivation to apply the Breusch-Pagan LM (1980) test is that this test is more valid for small sample size and can be applicable to check the cross-sectional dependency in heterogeneous panels. Figs. 1e5 indicate substantial variations in terms of economic growth, CO2 emission, energy use, trade openness and institutional quality in the selected countries. Breusch-Pagan LM (1980) test can be estimated as formula 6.

LM ¼

N 1 X

N X

i¼1 j¼iþ1

2

Tij b r ij /c2

NðN  1Þ 2

(6)

where N and T represent cross-section and time dimensions, respectively. In this study, N ¼ 3, T ¼ 27. b r 2ij is the correlation parameter of the errors, this is asymptotically distributed under the null as a c2 with N(N-1)/2 degrees of freedom. This test is based on

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the average of the squared pair-wise sample correlation parameters of the residuals and is implemented when N is fixed and Tij ∞ (i.e. when N is small comparative to large T). The null hypothesis of cross-sectional independence under this test can be stated as follow: H0:b r ij ¼ 0 for i s j against the alternative hypothesis of crosssectional dependence Ha:b r ij s 0 for i s j. However, the disadvantage of this test is that this test can suffer from size distortion and cannot be applied when N ∞. In order to facilitate the problem of size bias of Breusch-Pagan LM (1980) test, Pesaran (2004) CD proposed an alternative approach to test the cross sectional dependency in the panel data, and can be expressed as follow:

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N1 N X X 2 CD ¼ T b r 2 /Nð0; 1Þ NðN  1Þ i¼1 j¼iþ1 ij ij

(7)

The term b r ij is the correlation statistics obtained from the residuals of the aforementioned model. CD is asymptotically distributed as N (0,1), under the null, with Tij ∞, and then N ∞. N and T are the respectively cross-section and time dimensions. The Pesaran (2004) CD test assumes the same null and alternative hypothesis as of Breusch-Pagan LM (1980) test.

avoiding the issue of cross sectional dependency, Pesaran (2007) developed a second generation unit root test called Pesaran (2007) CIPS that is more valid in the presence of cross-sectional dependency. Pasaran improved the ADF regressions with the cross-section averages of lagged levels and first differences for each unit. The cross-sectionally augmented Dickey-Fuller (CADF) statistics can be calculated as formula 10.

Dyit ¼ ai þ bi yi;t1 þ ci yt1 þ

p X j¼0

dij Dytj þ

p X

dij Dyi;tj þ eit

j¼1

(10) where yt represents the average at time T of all N cross-sections. After running the CADF statistics, the CIPS statistics can be expressed as formula 11.

CIPS ¼

N 1 X ti ðN; TÞ N

(11)

i¼1

where ti (N,T) is the t-statistics in the CADF regression for each individual i of the panel given by formula 10. 3.7. Panel cointegration test

3.6. Panel unit root tests In economics data estimation, stationary analysis is very crucial to avoid spurious regression results. Numerous panel unit root tests exist in the literature. Since, every unit root test has some merits and demerits based on the characteristics of sample size and strength of the test (Narayan and Narayan, 2010). This study selects two first generation panel unit root tests, namely Breitung (2001), Im et al. (2003), and 1 s generation unit root test namely Pesaran (2007) CIPS to obtain more robust findings. The null hypothesis of Breitung (2001) unit root test suggests that the data is not stationary, whereas, the alternative hypothesis postulates that the data contain no unit root. Breitung (2001) adopts the formula 8 as follow:

yit ¼ ait

pþ1 X

bik Xitt þ εit

(8)

k¼1

The benefit of using Breitung (2001) panel unit root test is that it has higher test power and provides more robust results when the sample size is small. The demerit of Breitung (2001) is that, both null and alternative assumptions restrict the autoregressive parameter to be identical across countries (Kashman and Duman, 2015). In order to compensate the drawbacks of Breitung (2001), we conduct Im et al. (2003) panel unit root test expressed as formula 9.

t  bar ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffi Nðta  kt vt

(9)

Once, it is confirmed that all the variables are stationary at first difference (i.e. I (1)), we can estimate the long-run relationship among the indicators. If there is an evidence of the presence of cross-sectional dependency among the variables, ordinary cointegration tests such as Johansen (1988) and Kao and Chiang (2001) might provide biased estimates, therefore, Westerlund (2007) cointegration test is recommended (Dauda et al., 2019). Westerlund (2007) cointegration test comprised of four normally distributed tests namely Gt (between groups), Ga (among groups), Pt (between panels), and Pa (among panels). Gt and Ga indicate group mean tests depend on t-statistics and calculate coefficient for individual country. The alternative hypothesis for Gt and Ga is that, at least one cointegrated unit has cointegration. Pt and Pa represent cointegration for the panel as a whole. The alternative hypothesis for Pt and Pa supports cointegration relationship for the panel as a whole. Moreover, Westerlund (2007) augmented the test procedures by developing a bootstrap technique. After having confirmed the cointergration in the data, we then proceed to estimate the long-run cointegration vector using panel fully modified ordinary least squares and panel dynamic ordinary least squares. Phillips and Moon (1999) stated that Ordinary Least Squares (OLS) provides bias estimates in small sample size. The merit of using FMOLS technique is that it takes into account the problems of serial correlation and simultaneous bias (Narayan and Narayan, 2010). This study also uses panel DOLS estimator for robustness of results. The panel DOLS method provides better estimation results under small sample size (Kasman and Duman, 2015). 3.8. Panel granger causality test

where N represents sample size, ta denotes the average of the single augmented Dickey-Fuller (ADF) t-statistics for the crosssectional unit, with and without a trend, and kt and vt represent the mean and covariance of each tai statistics, respectively. The merit of using Im et al. (2003) unit root test is that it is less preventive because under null assumption, it does not allow that all the countries move towards the common drift at the same rate. However, this test does not take cross-sectional dependency into account. The Im et al. (2003) unit root test assumes the same null and alternative hypothesis as of Breitung (2001). With the aim of

The cointegration test provides only the evidence of long-run association among the variables. However, it fails to define the direction of causality between the variables. Engle and Granger (1987) argued that there should be at least one-way causal association if two or more variables are co-integrated. Therefore, we adopt the panel-based error correction model put forward by Engle and Granger (1987) to explore the causal direction among the variables, this method is based on two steps. Step 1 involves estimating the long-run factors in Eq. (4) employing FMOLS method to

M. Salman et al. / Journal of Cleaner Production 241 (2019) 118331

find the residuals. In step 2, we reckon the short-term error correction model by adding the residuals calculated in step 1. This study considers the Granger causality method including the error correction term (ECT) as follow:

Dln GDPit ¼ a1 þ

q X

b11;p Dln CO2itl þ

p¼1

þ

q X

b13;p Dln TOitl þ

p¼1

þ

q X

q X

b12;p Dln ECitl

p¼1 q X

b14;p Dln IQitl

p¼1

b15;p DCO2 IQitl þ c1 ECTitl þ m1it

(12)

p¼1

Dln CO2it ¼ a2 þ

q X

b21;p Dln GDPitl þ

p¼1

þ

q X

b23;p Dln TOitl þ

q X

q X

b24;p Dln IQitl

p¼1

b25;p DCO2 IQitl þ c2 ECTitl þ m2it

(13)

p¼1

Dln ECit ¼ a3 þ

q X

b31;p Dln GDPitl þ

p¼1

þ

q X

þ

b32;p Dln CO2itl

p¼1

b33;p Dln TOitl þ

p¼1 q X

q X

q X

b35;p DCO2 IQitl þ c3 ECTitl þ m3it

b41;p DlnGDPit l þ

p¼1

þ

q X

b43;p DlnECit l þ

(14)

p¼1

þ

q X

q X

b42;p DlnCO2it l

p¼1 q X

The descriptive analysis of the main variables is reported in Table 1. The highest mean GDP is recorded for South Korea followed by Indonesia and Thailand. In terms of carbon emissions, South Korea is the highest carbon emitter country while Thailand has the lowest mean carbon emissions. The highest mean energy use is recorded for South Korea followed by Indonesia and Thailand. With regard to institutional quality, South Korea has the best institutional quality followed by Thailand and Indonesia. Regarding trade openness, Thailand has the highest mean trade openness followed by South Korea and Indonesia.

Table 2 shows the correlation analysis of the variables. Not surprisingly, GDP is positively associated with CO2 emission, energy use, institutional quality and trade openness. The results are consistent with the existing literature that all these variables contribute to the economic development in a panel of three East Asian countries. As expected, CO2 emissions and energy use are highly and significantly correlated with GDP growth whereas, energy use is highly correlated with carbon emissions, which is according to the intuition that increased energy use not only causes higher economic growth but also enhances carbon emissions. Moreover, institutional quality and trade openness appeared to be positively correlated with carbon emissions and energy use.

4.3. Cross-sectional dependence tests results

p¼1

q X

4.1. Descriptive statistics

b34;p Dln IQitl

p¼1

DlnTOit ¼ a4 þ

4. Empirical results

4.2. Correlation results

b22;p Dln ECitl

p¼1

p¼1

þ

q X

9

b44;p DlnIQ it l

Table 3 presents the results of Pesaran (2004) CD and BreuschPagan LM (1980) cross sectional dependence tests. Regarding Pesaran test (2004) CD test, the results reject the null hypothesis of cross-section independence for all the variables at 1% significance level except institutional quality indicating that the variables are cross-sectional dependent. With regard to Breusch-Pagan LM (1980) test, the results reject the null hypothesis of no crosssectional dependence for all variables.

p¼1

b45;p DCO2 IQ it l þ c4 ECTit 1 þ m4it

4.4. Panel unit root results

p¼1

(15)

Dln IQit ¼ a5 þ

q X

b51;p Dln GDPitl þ

p¼1

þ

q X p¼1

þ

q X

b53;p Dln TOitl þ

q X

b52;p Dln CO2itl

p¼1 q X

b54;p Dln IQitl

Table 4 provides the unit root results of the variables used in this study. The results obtained from first generation unit root tests namely Breitung (2001) and Im et al. (2003) accept the null hypothesis that the series are not stationary at level. However, the series are stationary at first difference. Regarding second generation unit root test Pesaran (2007) CIPS, the results confirmed that all the variables are I (1).

p¼1

4.5. Panel cointegration results

b55;p DCO2 IQitl þ c5 ECTitl þ m5it

(16)

p¼1

where “D” denotes the first-difference operator, i indicates country and t represents time. a, b and c are the respectively fixed country effect, the factor of short-run causal effect on GDP growth, and the long-run correction factor, p represents the auto-regression lag length. ECT and m are the respectively error correction and random disturbance terms. The whole estimation process is depicted in Fig. 7.

Table 5 reveals the results of Westerlund (2007) cointegration test. The results for group mean tests (Gt and Ga) accept the alternative hypothesis indicating that there is a long-run association in at least one cointegrated unit. Regarding Pt and Pa, the results reject the null hypothesis at 1% significance level implying that there exists a cointegration for a panel as a whole. Generally, the results confirmed the long-run association among the variables. Therefore, we can proceed for long-run estimation using panel FMOLS and panel DOLS methods.

10

M. Salman et al. / Journal of Cleaner Production 241 (2019) 118331

Fig. 7. Estimation process for investigating the relationship between economic growth, CO2 emissions and institutional quality.

4.6. Panel fully modified ordinary least squares regression results After having confirmed the presence of cointegration in the data, we then implement panel FMOLS approach developed by Pedroni (2001) for miscellaneous cointegrated vectors. The FMOLS takes into account the issues of serial correlation and endogeneity (Ozcan, 2013), and the most appropriate estimation method to be used under the situation, when cointegrated panels exist (HamitHaggar, 2012). Table 6 reports the results of panel FMOLS test. Clearly, the coefficient of institutional quality is positive and significant at 1%, which suggests that efficient and neutral domestic institutions strengthen the process of economic growth thus supporting the validity of “hierarchy of institutions hypothesis” in

these three East Asian countries. The results are supported by (Bhattacharya et al., 2017). The coefficients of carbon emissions and the interaction variable (CO2IQ) are respectively negative and positive, which strongly supports the hypothesis that the environmental pollution can be reduced in the selected countries, if the institutions achieved a particular stage of development. In other words, these three countries may enjoy higher growth rate and reduce the deteriorating impact of carbon emissions under the presence of efficient and neutral institutions, simultaneously. The results are consistent with (Lau et al., 2014). The effect of energy use on GDP growth is positive significant at 1% implying that 1% upsurge in energy use contributes to 167.4% of GDP growth. The results are in line with (Shahbaz et al., 2018). The value of trade

M. Salman et al. / Journal of Cleaner Production 241 (2019) 118331

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Table 1 Summary of descriptive statistics. Country Indonesia

Mean Stdv Min Max Mean Stdv Min Max Mean Stdv Min Max

Thailand

South Korea

GDP

CO2

ENE

IQ

CO2IQ

TO

5.92eþ11 2.12eþ11 3.10eþ11 1.04eþ12 2.68eþ11 7.89eþ10 1.42eþ11 3.97eþ11 8.27eþ11 2.93eþ11 3.63eþ11 1.31eþ12

343092.1 130920.3 149565.9 637078.9 219570.6 73814.59 90805.92 361053.6 458019.6 109791.7 246943.1 642482.1

168245.1 41996.7 98648.09 244056.6 90469.63 31767.39 41943.87 150334.6 197332.9 56738.2 92912.3 291363.2

2.955 0.7874 2 4.75 3.625 1.188273 2.5 5 4.5679 0.7644 2 5

1003453 411221.3 299131.8 1911237 730018.3 166811.3 363223.7 1041611 2138930 698686.1 493886.2 3212410

0.4308 0.0551 0.3320 0.5318 1.07731 0.2349 0.6851 1.3993 0.7374 0.2380 0.3907 1.0758

Notes:aAll the variables are without logarithms form. bStdv indicates standard deviation.

Table 2 Correlation results.

Table 5 Westerluind (2007) cointegration test results.

GDP GDP CO2 ENE IQ TO

CO2

1 0.9508 0.9736 0.2459 0.0670

ENE

1 0.9543 0.1633 0.0111

1 0.0889 0.0887

IQ

TO

1 0.0791

1

Table 3 Results of cross-sectional dependence results.

Statistics

Value

Robust p-value

Cointegration

Gt Ga Pt Pa

1.561*** 2.407 1.841*** 2.654***

(0.000) (1.000) (0.000) (0.000)

Yes No Yes Yes

Notes:aCE(s) are the cointegration equations. bP indicates probability.

Table 6 Panel FMOLS and panel DOLS results.

Cross-sectional dependence test

Pesaran (2004) CD test

Breusch-Pagan LM (1980) test

Variables

CD-test

P-value

Statistic

P-value

lnGDP lnCO2 lnENE lnIQ lnTO

8.794*** 8.628*** 8.893*** 0.496 6.817***

(0.000) (0.000) (0.000) (0.624) (0.000)

77.397*** 71.101*** 78.981*** 11.710** 47.135***

(0.000) (0.000) (0.000) (0.008) (0.000)

Notes:a** and ***are respectively the significant levels at 5% and 1%. bP-values are in parenthesis.

Dependent variable: lnGDP Method

Panel FMOLS

Panel DOLS

lnCO2 lnENE lnIQ lnCO2IQ lnTO Dum Constant R-squared

0.526*** (0.068) 1.674*** (0.067) 0.422*** (0.024) 0.546*** (0.015) 0.065** (0.019) 0.094*** (.017) 13.24*** (0.218) 0.783

0.328* (0.175) 1.165*** (0.192) 0.42*** (0.0317) 0.387*** (0.048) 0.059** (0.035) 0.061* (0.029) 11.9*** (0.336) 0.971

Notes:a*, **, and ***are the respectively significant levels at 10%, 5% and 1%. bValues in parenthesis are standard errors.

openness is positive and significant indicating that 1% growth in trade stimulates GDP growth by 6.5% in the selected countries. The results are supported by (Zafar et al., 2019). Predictably, the coefficient of dummy variable is negatively associated with economic growth, which shows the worsen effects of East Asian crisis of 1997, that caused negative economic growth (i.e. 9.4%) in the selected countries. Moreover, for robustness of results, we also applied panel DOLS and the results are same with the FMOLS results with respect to sign and significance. Table 7 reveals the FMOLS results for individual country. According to Ozcan (2013), miscellaneous long-run equilibrium must be specified for individual country due to the existence of variations in long-run factors. The findings reveal that institutional quality has

a positive influence on economic growth in the selected countries implying that institutional quality is a significant driver of economic growth in these three countries. The results are in line with (Bhattacharya et al., 2017). Moreover, the results obtained validate the assumption that effective and independent institutions not only enhance economic growth but also at the same time reduce carbon emissions as the interaction term (CO2IQ) has a positive effect on GDP growth in the selected countries. This indicates that the links between economic development and CO2 emission depend on the country's absorptive capability for instance, quality of the institutions. The results are similar with (Lau et al., 2014).

Table 4 Panel unit root results. Breitung (2001)

IPS Im et al. (2003)

Pesaran (2007) CIPS

Variables

level

First difference

Level

First difference

Level

First difference

lnGDP lnCO2 lnENE lnIQ lnTO

5.158 3.287 4.808 0.236 1.952

3.328*** 4.272*** 5.003*** 2.151* 5.493***

1.650 2.103 2.040 2.748* 2.164

3.976*** 4.498*** 5.041*** 3.347** 5.588***

0.848 2.374 2.607 2.376 1.648

3.389*** 4.515*** 5.185*** 3.426** 4.804***

Notes:a*, **, and ***are respectively the significant levels at 10%, 5% and 1%. bSchwarz information criteria was used to select the optimal lag lengths.

Decision

I(1) I(1) I(1) I(1) I(1)

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M. Salman et al. / Journal of Cleaner Production 241 (2019) 118331

Table 7 Panel FMOLS individual country results. Dependent variable: lnGDP Variables

Indonesia

Thailand

South Korea

lnCO2 lnENE lnIQ lnCO2IQ lnTO Dum Constant R-squared

0.675*** (0.022) 2.839*** (0.026) 0.685*** (0.020) 0.583*** (0.012) 1.24*** (0.020) 0.110*** (0.006) 0.494** (0.172) 0.373

0.704*** (0.014) 1.473*** (0.014) 0.492*** (0.004) 0.542*** (0.004) 0.582*** (0.008) 0.172*** (0.001) 17.57*** (0.057) 0.881

0.552*** (0.006) 1.504*** (0.008) 0.015*** (0.001) 0.0225*** (0.001) 0.149*** (0.004) 0.062*** (0.000) 16.32*** (0.047) 0.995

Notes:a** and ***are the respectively significant levels at 5% and 1%. bValues in parenthesis are standard errors.

Furthermore, energy use has a positive impact on GDP growth, which means that energy use is an important element of economic growth in the selected countries. The results are in line with (Shahbaz et al., 2018). Regarding trade openness, the impact is positive and significant in South Korea and Thailand. For instance, 1% increase in trade openness leads to 14.9% and 58.2% increase in GDP growth in South Korea and Thailand, respectively. However, trade openness is negatively linked with GDP growth in Indonesia. The reason might be the negative trade balance position, with the volume of import exceed the volume of export. The other reason might be the exchange rate deflation. The results are similar with (Zafar et al., 2019). In addition, the coefficient of dummy variable is negative in all the three countries, which provides evidence that these three countries suffered from economic depression due to East Asian crisis of 1997.

4.7. Panel granger causality results Table 8 reports the results of both short-run and long-run panel causality tests. With regard to short-run granger causality test, the results found a one-way short-run granger causality running from institutional quality to GDP growth, carbon emission and energy utilization in both short-run and long-run, from trade openness to carbon emission in both short-run and long-run, from energy use to trade openness and from energy use to carbon emission in both short-run and long-run. The results also found a two-way granger causal link between GDP growth and CO2 emission, and between GDP growth and energy use in both short-run and long-run, a twoway short-run causal relationship also detected between economic growth and trade openness. Moreover, the estimated error correction term appeared to be negative and statistically significant in economic growth, CO2 emission, energy use and institutional quality implying that these variables are crucial factors for the speed of adjustment as the system deviates from the long-run equilibrium. Consequently, these variables have a two-way granger causality association. Fig. 8 graphically displays the short-

Fig. 8. shows short-run causal relationship between the variables used in the study. indicates unidirectional short-run Granger causality. indicates bidirectional short-run Granger causality. represents unidirectional long-run causality. shows bidirectional long-run Granger causality.

run and long-run association between the variables for the investigated countries.

5. Discussion At panel level, the empirical results obtained for the three East Asian countries namely, Indonesia, South Korea and Thailand indicate a positive contribution of the quality of domestic institutions for economic growth hence, confirmed the presence of “hierarchy of institutions hypothesis” in these three countries. Moreover, the interactive impact of institutional quality and carbon emissions on economic growth testified that domestic institutions are efficient enough to increase economic growth and reduce carbon emissions, simultaneously. The results further revealed that energy consumption and trade openness appeared to be significant drivers of economic growth in the investigated countries (see Table 6). In this paper, we also examined the influencing impacts of explanatory variables on economic growth for individual country. The institutional quality has a positive influence on economic growth in Indonesia. After the deteriorating impacts of East Asian crisis of 1997e1999, among other East Asian countries, Indonesia made several significant reforms in order to cope the aftermaths of the crisis such as Law of Decentralization (No. 22, 1999) and Law of Revenue Sharing between Regional and Central Governments (No. 22, 1999). The two laws indicate that administrative decentralization is subjected to transfer extensive amount of authorities to the provincial governments and empower the local regimes through revenue and power sharing with federal government. The coefficients of interaction variable (CO2IQ) and carbon emissions are respectively positive and negative implying that domestic institutions play a crucial role in improving economic progress and

Table 8 Results of panel Granger causality. Short-run causality (F-statistics)

Long-run

Independent variables Dependent variable

DlnGDP DlnCO2 DlnENE DlnTO DlnIQ

DlnGDP 5.095** (0.006) 2.49* (0.074) 2.995* (0.042) 1.834 (0.139)

DlnCO2

DlnENE

DlnTO

DlnIQ

DlnCO2IQ

ECT

3.931** (0.007)

5.594** (0.001) 5.388** (0.004)

2.867* (0.030) 4.614** (0.009) 1.737 (0.187)

3.369* (0.015) 2.803* (0.052) 2.30* (0.093) 1.145 (0.401)

1.41 (0.253) 2.802* (0.052) 2.43* (0.083) 1.025 (0.417) 0.357 (0.837)

0.553** (0.001) 0.518** (0.009) 1.264** (0.007) 0.013 (0.546) 0.356** (0.003)

1.397 (0.290) 1.645 (0.210) 0.357 (0.837)

2.431* (0.080) 0.986 (0.424)

0.243 (0.912)

Notes:a***, **, * represents 1%, 5% and 10% significance level, respectively. b p-values are in parenthesis.

M. Salman et al. / Journal of Cleaner Production 241 (2019) 118331

reducing carbon emissions, simultaneously. Moreover, energy consumption induces economic growth. Whereas, trade openness has negative impact on economic growth. The possible explanation for the negative impact of trade openness on economic growth could be the trade deficit or exchange rate devaluation. The negative coefficient of dummy variable shows that Indonesia experienced great suffer due to the crisis of 1997e1999. As argued by Dutu (2016), East Asian crisis of 1997e1999 caused structural flaws in Indonesian fiscal sector, reduced the capital flights that led to economic growth by 13.7%, and the local currency devalued from about 2500/USD in 1997 to 12,000/USD a few months later. Regarding Thailand, the results validate positive contribution of domestic institutions to economic development. The results are according to our intuition because Thailand has thrived to overcome the political and economic depressions triggered by East Asian crisis of 1997 and has regained its earlier position as a fast growing nation by taking several measures such as recapitalization of financial institutions, shutting down bankrupt financial institutions, encouraging foreign banks through provision of bank licenses easily and specialization of domestic institutions. Moreover, the interaction variable (CO2IQ) and carbon emissions have respectively positive and negative effects on economic growth. This infers that domestic institutions can improve economic growth and reduce CO2 emission, simultaneously. In addition, energy consumption and trade openness induce economic growth. The coefficient of dummy variable is negative which captures the worsen impacts of East Asian crisis (1997e1999) on economic growth. In case of South Korea, the empirical findings are in line with our expectations. The positive influence of institutional quality on economic growth indicates that institutional quality is one of the important influencing factors of economic growth. As Lee (2005) argued that South Korea has undertaken several reforms in institutional system after the worsen impacts of financial crisis of 1997e1999 on country's economic growth and institutional quality. They included, monetary reforms, restructuring corporate sector and labor market reforms. The results further show that institutional quality not only enhances economic growth on one hand but also at the same time reduces the worsen effects of carbon emissions on environmental quality. Other variables, for instance, energy use and trade openness increase economic growth. Not surprisingly, the dummy variable has deteriorating influence on economic growth which provides evidence that South Korea suffered economic depression and politically instability due to the crisis of 1997e1999 (see Table 7). 6. Conclusions and policy implications The main purpose of this paper is to explore the impact of institutional quality on growth-emissions nexus in a panel of three East Asian countries over the period from 1990 to 2016. We also incorporated energy consumption and trade openness as important indicators in the model to avoid variable bias. Furthermore, we also incorporated the interaction variable between carbon emissions and institutional quality to validate the assumption that efficient and independent institutions are important to improve economic growth and at the same time reduce carbon emissions. To capture the deteriorating impact of East Asian crisis of 1997 on economic growth and political stability, we introduced a dummy variable. To explore the long-run impacts of the determinants of GDP growth, the panel FMOLS and panel DOLS estimation techniques are applied. The cross-sectional dependency tests show that the variables are cross-sectional dependent. The unit root tests confirmed that the variables are I (1). Moreover, the results of panel cointegration test show that the variables move towards the equilibrium point in the long-run. The FMOLS results reveal that institutional

13

quality has positive impact on economic growth implying that effective local institutions will ignite the process of economic growth. Furthermore, the results also supported the assumption that impartial domestic institutions are of great importance in improving economic growth and reducing carbon emissions at the same time. The other variables such as energy use and trade openness do contribute to enhance the economic growth in the selected countries. When it comes to time series analysis, the results indicate the importance of efficient institutions for GDP growth and carbon emissions in all the three countries. Energy use and trade openness are significant determinants of economic growth in South Korea and Thailand. However, trade openness is negatively associated with GDP growth in Indonesia. Regarding panel Granger causality test, the empirical findings confirmed a one-way panel causality running from (i) institutional quality to GDP growth, carbon emissions and energy use in both short-run and long-run, (ii) from trade openness to carbon emissions in both short-run and long-run, (iii) from energy utilization to openness of trade, from energy use to carbon emissions in both short-run and long-run. The results also found a two-way granger causal link between GDP growth and CO2 emissions, and between GDP growth and energy use in both short-run and long-run, a bidirectional short-run causal relationship also detected between economic growth and trade openness. The empirical results of this paper provide some important policy implications. First, since the institutional quality granger causes GDP growth, CO2 emissions and energy use. Therefore, policy makers should legalize and strengthen the role and effectiveness of local institutions in order to decrease the environmental pollution without affecting the economic growth in the selected countries. Second, panel granger causality test confirmed a “feedback hypothesis” between GDP growth and energy use indicating that both are inter-reliant and serve as complements to one another. Hence, as policy implication, the negative impacts of energy use on economic development should be taken into consideration, when formulating energy conservation policies. Therefore, energy saving and energy efficient policies are recommended. Third, the panel granger causality test validates the “feedback effect” between GDP growth and carbon emissions implying that economic growth can be achieved bearing the cost of polluting the environment. Regarding policy implications, low-carbon and green technologies should be promoted in the domestic production operations to proportionally reduce carbon emission and enhance financial development. Last, the two-way short-run causal association confirmed between GDP growth and trade openness hence, validating the “feedback hypothesis” which implies that both indicators are interdependent to one another. Therefore, the governments of the selected countries should come up with green trade policies and increase trade related activities for the sustainable economic development in these countries. Funding We appreciate the financial support provided by National Natural Science Foundation of China (No. 71603105); Natural Science Foundation of Jiangsu, China (No. SBK2016042936); Science Foundation of Ministry of Education of China (No.16YJC790067) and China Postdoctoral Science Foundation (Nos.2017M610051, 2018T110054). Any opinions, findings and conclusions or recommendations expressed in this paper are those of authors and do not necessarily reflect the views of the funding body. Acknowledgments We would like to thank Co-Editor-in-Chief of the journal, Dr. Jiri

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M. Salman et al. / Journal of Cleaner Production 241 (2019) 118331

Jaromir Klemes, and three anonymous reviewers for their constructive and valuable comments and suggestions in preparing this final version of the manuscript. Of course, all the errors and mistakes remain our own. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.118331. References Asoni, A., 2008. Protection of property rights and growth as political equilibria. J. Econ. Surv. 22 (5), 953e987. https://doi.org/10.1111/j.1467-6419.2008.00554.x. Acemoglu, D., Robinson, J., 2010. The role of institutions in growth and development. Rev. Econ.Inst. 1 (2). https://doi:10.5202/rei.v1i2.1. Abid, M., 2017. Does economic, financial and institutional developments matter for environmental quality? A comparative analysis of EU and MEA countries. J. Environ. Manag. 188, 183e194. https://doi.org/10.1016/j.jenvman.2016.12.007. Ahmad, N., Du, L., Lu, J., Wang, J., Li, H.Z., Hashmi, M.Z., 2017. 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