. International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 201940 International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2019, 9(2), 40-50. Globalization, Financial Development, and Environmental Degradation in the Presence of Environmental Kuznets Curve: Evidence from ASEAN-5 Countries Le Hoang Phong School of Public Finance, University of Economics Ho Chi Minh City, Vietnam. Email: lhphong@hcmulaw.edu.vn Received: 25 October 2018 Accepted: 29 January 2019 DOI: https://doi.org/10.32479/ijeep.7290 ABSTRACT ASEAN is regarded as an economically dynamic region with notable policies towards economic openness, implying the encouragement of globalization and trade liberalization. Considerable globalization and financial development processes, together with the incremental energy demand, necessitated the issue of controlling environmental damage. The main objective of this research is to evaluate the impacts of globalization and financial development, incorporating energy consumption, urbanization and GDP per capita, on carbon dioxide emissions with the presence of Environmental Kuznets Curve (EKC) model for selected ASEAN countries. From the author’s best knowledge and review of literature, there has been no study that only focuses on ASEAN region, and this paper serves as the first one in the discipline. This research approaches the heterogeneity in the panel data over the 1971-2014 period by utilizing the fixed and random effects regression models. The author uses the tests based on Durbin–Hausman–Wu statistic to determine the appropriate models. The findings indicate that (i) financial development, energy consumption and urbanization boost the carbon dioxide emissions; (ii) globalization as an aggregate measure significantly increases carbon dioxide emissions and the effect mainly comes from the economic globalization facet; (iii) the EKC hypothesis is underpinned in ASEAN-5 countries. Hence, this suggests crucial implications for policy-makers. Keywords: Globalization, Financial development, Carbon dioxide emission JEL Classifications: F64, O44, Q56 1. INTRODUCTION In recent years, the challenging concern of worsening global environmental quality has strongly manifested, which is clearly illustrated by the upward trend of CO2 (Carbon Dioxide - one of the main components of the greenhouse effect) in the atmosphere (as displayed in Figure 1). Albeit economists as well as policy-makers endeavored to explore and scrutinize the determinants of CO2 emissions such as energy consumption, economic growth, financial development and urbanization by various national and international researches in order to support sustainable development policies, the results regarding the relationship between the aforementioned factors and environmental damage remain controversial (Omri, 2013; Stern, 2004; Dinda, 2004; Omri et al. 2015; Shahbaz et al., 2015b; Shahbaz et al., 2016b; Dar and Asif, 2017; Phong et al., 2018). As an economically open and dynamic region, ASEAN experiences rapid globalization process, especially economic globalization through trade and investment activities. The role of finance, namely the importance of credit in private sector, enhances economic activities of ASEAN countries. Lately, ASEAN has been recognized as one of the top regions in the world by economic growth, which increases the incomes of member countries, fosters the living standards of their residents, and facilitates urbanization. This Journal is licensed under a Creative Commons Attribution 4.0 International License Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 2019 41 The intense globalization and financial development progresses, along with the upsurge in energy demand for economic activities, induce substantially higher CO2 emissions. Thus, the scrutiny of environmental quality with the presence of financial development, globalization, and urbanization necessitates special attentions. Moreover, it is essential that variable omission is avoided so as to gain accurate findings when investigating the existence of EKC hypothesis (Pata, 2018). The major goal of this paper is to scrutinize the effects of globalization, financial development, energy consumption, urbanization and GDP per capita on carbon dioxide emissions under a multivariate framework with the inclusion of Environmental Kuznets Curve (EKC) model for selected ASEAN countries. To the best of the author’s knowledge, this is the first research to examine the dynamic connections between energy consumption, GDP per capita, urbanization, and carbon dioxide emissions when incorporating globalization and financial development in case of ASEAN countries, with the presence of EKC hypothesis. The rest of this article consists of 5 parts and is organized in the following order: The “Literature Review” part stresses the EKC hypothesis as well as relevant researches that form the basis for subsequent analyses; “Materials and Methods” illuminates the variables, estimation model and econometric methodology utilized in this study; “Results” gives explanation to the findings; “Discussion” provides arguments and further information; and finally, the “Conclusions” part contains important summary of this paper with the inclusion of policy implications drawn from the empirical results. 2. LITERATURE REVIEW The last several decades witnessed the strong development of economic activities which raised concerns for their impacts on the environment at both national and international levels. The link between economic growth and environmental quality has drawn considerable attentions since Grossman and Krueger (1991) proposed the Environmental Kuznets Curve (EKC) hypothesis which assumes that economic growth positively influences CO2 emissions in the beginning stage, but the effect is negative in the subsequent stage after the CO2 emissions reaches the maximum level connected with a certain amount of income per capita. Such movement of CO2 emissions is described by the inverted U-shaped Environmental Kuznets Curve indicated in Figure 2. Following Grossman and Krueger (1991), many a research focused on testing the Environmental Kuznets Curve (EKC) hypothesis in different countries, and the results varied. The EKC hypothesis is underpinned by notable studies for a large number of countries including Lindmark (2002) for Sweden; Ang (2007) for France; Jalil and Mahmud (2009) for China; Ghosh (2010), Jayanthakumaran et al. (2012) for India and China; Nasir and Rehman (2011), Ahmed and Long (2012), Javid and Sharif (2016) for Pakistan; Saboori et al. (2012) for Malaysia; Alam et al. (2012) for Bangladesh; Baek and Kim (2013) for South Korea; Shahbaz et al. (2014) for Tunisia; Ahmed (2014) for Mongolia; Baek (2015) for Iceland; Shahbaz et al. (2015a) for Portugal; Tang and Tan (2015) for Vietnam; Zambrano-Monserrate et al. (2016) for Ecuador; Balaguer and Cantavella (2016) for Spain; Al-Mulali et al. (2016) for Kenya; Bento and Moutinho (2016) for Italy; Ahmad et al. (2017) for Croatia; Ozturk and Acaravci (2013), Yavuz (2014), Gokmenoglu and Taspinar (2016), Ozatac et al. (2017), Pata (2018) for Turkey; Cole et al. (1997) for 7 countries; Halkos (2003) for OECD and non-OECD countries; Apergis and Payne (2009) for Central America; Cho et al.(2014) for OECD; Pao and Tsai (2011), Sinha and Sen (2016) for BRICS; Farhani et al. (2014) for 10 MENA countries; Kasman and Duman (2015) for European countries; Zaman et al. (2016) for 34 developed and developing countries; Zhang et al. (2017) for 10 Newly Industrialized countries (NICs-10). On the contrary, the EKC hypothesis is not supported by Torras and Boyce (1998), Roca et al. (2001) for Spain; Day and Grafton (2003) for Canada; Chebbi (2009), Fodha and Zaghdoud (2010) for Tunisia; Pao et al. (2011) for Russia; Du et al. (2012) for China; Pal and Mitra (2017) India and China; Arouri et al. (2012) for 12 Middle East and North African countries; Giovanis (2013) for United Kingdom; Ozcan (2013) for 12 MENA countries; Wang et al. (2013) for 150 nations; Farhani and Ozturk (2015) for Tunisia; Lacheheb et al. (2015) for Algeria; Begum et al. (2015) for Malaysia; Mallick and Tandi (2015), Rehman and Rashid (2017) for SAARC countries; Bento and Moutinho (2016) for Italy; María and Jesús (2016) for 22 Latin American and Caribbean countries; Neve and Hamaide (2017) for 28 countries; Zoundi (2017) 25 African countries. The rapid economic growth process requires more energy consumption, hence damaging the environment (Islam et al., 2013; Zhang and Cheng, 2009; Shahbaz et al., 2016a; Shahbaz et al., 2017a). Manifold studies tested the Environmental Kuznets Curve (EKC) hypothesis with the influence of energy consumption. For instance, Pao and Tsai (2010) examined the impacts of energy consumption, economic growth on CO2 emissions and concurrently verified the EKC hypothesis in BRIC countries in the 1971-2005 period; and the outcomes confirmed the existence of the EKC hypothesis and denoted that energy consumption and economic growth were main factors raising CO2 emissions. Jaunky (2011) analyzed 36 high-income countries from 1980 to 2005 and found that energy consumption boosted CO2 emissions; also, EKC was evidenced in Greece, Malta, Portugal, Oman, and Figure 1: Atmospheric CO2 levels Source: Author’s calculations. Data is collected from https://climate. nasa.gov/vital-signs/carbon-dioxide. Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 201942 United Kingdom. Shahbaz et al. (2014) applied ARDL approach and VECM Granger causality tests and detected the occurrence of EKC in Tunisia in 1971-2010 period, together with the positive effects of energy consumption on CO2 emissions. Rehman and Rashid (2017) inspected the role of energy consumption on environmental damage under multivariate analysis in SAARC countries and indicated that energy consumption degraded the environment; also, the presence of EKC was affirmed. Recently, besides energy consumption, a large number of researchers have tested the EKC hypothesis based on the link between economic growth and environmental quality with the inclusion of some important factors such as financial development, globalization and urbanization. Tamazian et al. (2009) scrutinized the connections between financial development, economic development and CO2 emissions in BRIC countries during 1992 and 2004 and proved the evidence of EKC as well as the negative cause of financial development on CO2 emissions. Shahbaz et al. (2013b) employed ARDL and ECM approach over the 1965–2008 period to witness the occurrence of EKC; in addition, they found that financial development and economic development respectively reduced and stimulated CO2 emissions. Ozturk and Acaravci (2013) reported no link between financial development and CO2 emissions in Turkey from 1960 to 2007, but the proof of EKC was detected. Shahbaz et al. (2013a) included energy intensity, economic growth and globalization in their study using annual data of Turkey from 1970 to 2010 and applied ARDL and VECM Granger causality approach; they observed the presence of EKC and noted that energy intensity and economic growth made CO2 emissions rise but globalization had opposite effect. Boutabba (2014) studied the long-run equilibrium between CO2 emissions, financial development, economic growth, energy consumption and trade openness for the case of India and found the evidence of the long-run and causal relationships between CO2 emissions, financial development, income, energy use and trade openness in which financial development and energy use increased CO2 emissions; also, EKC was discovered. Shahbaz et al. (2015b) showed that globalization, energy consumption, financial development, and economic growth exacerbated the environmental quality of India from 1970 to 2012 and observed that EKC occurred in India. Farhani and Ozturk (2015) rejected the EKC hypothesis in Tunisia but concluded that all variables (real GDP, energy consumption, financial development, trade openness and urbanization) contributed to environmental pollution in the period 1971-2012. Al-Mulali et al. (2015) studied the connections of economic growth, urbanization, trade openness, financial development and renewable energy on pollution in 23 European countries during the period 1990-2013 and discovered the positive influence of GDP growth, urbanization and financial development on CO2 emissions, while trade openness has negative one. Shahbaz et al. (2016b) assessed the asymmetric impacts of financial development on environmental quality in Pakistan from the first quarter of 1985 to the last quarter of 2014 and concluded that ineffective use of energy aggravated the environmental quality; additionally, financial development based on banks worsened the environment. Javid and Sharif (2016) inspected the roles of financial development, energy consumption, economic growth in CO2 emissions in Pakistan employing ARDL method on 1972-2013 data and identified the EKC pattern as well as acknowledged that the higher levels of financial development, energy consumption and economic growth led to the greater CO2 emissions. Dogan and Turkekul (2016) found no sign of EKC in United States; moreover, they reported that trade activities promoted the environmental quality while energy consumption, urbanization damaged the environment and financial development had insignificant effect. Dogan and Seker (2016) spotted the EKC trace in OECD countries associated with the positive causal relationship between energy consumption and CO2 emissions, whereas openness and financial development decreased CO2 emissions. Solarin et al. (2017) pointed out the detrimental effects of financial development, urbanization, energy consumption and economic growth on the environmental quality in Ghana during 1980 and 2012. Saidi and Mbarek (2017) conducted the study for emerging countries and found no evidence of the inverted U-shaped EKC; rather, they observed that financial development and urbanization enhance environmental quality while income facilitates CO2 emissions. Xing et al. (2017) utilized the STIRPAT model and ARDL approach for the case of China and indicated that financial development could contribute to the escalation in CO2 emissions. Dar and Asif (2017) realized no relationship between economic growth and environmental quality in India but witnessed the harmful impacts of financial sector development and energy consumption on greenhouse gas emissions. Salahuddin et al. (2017) demonstrated the positive response of CO2 emissions in Kuwait during the period 1980-2013 under the effects of economic growth, electricity consumption, foreign direct investment and financial development by using ARDL approach and VECM Granger causality analysis. Recently, Shahbaz et al. (2017b) gauged the response of CO2 emissions to the changes of globalization level by incorporating energy consumption and economic growth in Japan from 1970 to 2014, which denoted that globalization, economic growth and energy consumption positively induced CO2 emissions. Twerefou et al. (2017) examined the EKC hypothesis in 36 Sub-Saharan Figure 2: Environmental Kuznets curve Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 2019 43 Africa and scrutinized the influences of economic growth and globalization on environmental quality from 1990 to 2013 with the application of GMM method for panel data, which demonstrated the beneficial effect and negative impact of economic growth and globalization on the environment respectively and concluded the occurrence of EKC. Zhang et al. (2017) tested the EKC hypothesis in 10 newly industrialized countries (NICs-10) from 1971 to 2013 and proved its existence; in addition, they found the trade openness substantially reduced CO2 emissions while real GDP and primary energy consumption stimulated the emissions. Haseeb et al. (2018) confirmed the existence of EKC phenomenon in BRICS countries and reported no significant causality between globalization, urbanization and CO2 emissions while energy consumption and financial development degraded the environment quality. Phong et al. (2018) scrutinized the roles of globalization in CO2 emissions of Vietnam from 1985 to 2015 by incorporating industrialization, urbanization, energy consumption and GDP per capita with the application of ARDL method, which indicated that energy consumption, industrialization and GDP per capita boosted CO2 emissions in the long run while globalization demonstrated negative effect. In general, it can be witnessed from the existing literature that the findings concerning CO2 emissions and its determinants are not uniform; rather, they depend on the unique characteristics of each country or region. In this study, the author inspects the dynamic connections between energy consumption, GDP per capita, urbanization and CO2 emissions in the presence of EKC hypothesis for ASEAN countries by including the analyses of globalization and financial development as important factors in economic openness. 3. DATA AND ECONOMETRIC METHODOLOGY 3.1. Data This paper employs balanced panel data from 1971 to 2014 for analyzing the impacts of financial development and globalization on environmental degradation as well as testing the EKC effect in some ASEAN countries including Myanmar, Malaysia, Philippines, Singapore and Thailand. The time range is limited by the availability of the data. There are 6 variables used in this study: CO2 emissions, financial development, globalization, GDP per capita, energy consumption and urbanization. The aforementioned variables (except globalization) are collected from World Development Indicators. Meanwhile, globalization is retrieved from KOF Globalisation Index provided by KOF Swiss Economic Institute. The KOF Globalisation Index was proposed by Dreher (2006) and revised by Dreher et al. (2008); it consists of three dimensions of globalization: Economic, social and political. The first dimension of globalization (economic) reflects flows of goods, capital and services as well as information and perceptions that accompany market exchanges. The second aspect of globalization (social) captures the spread of ideas, information, images and people. The final component of globalization (political) entails the diffusion of policies (Nye and Donahue, 2000). In this article, the author will utilize the aggregate measure of globalization together with each individual component measure. Besides, private sector credit is used as a proxy for gauging the level of financial development (Salahuddin et al., 2017). All variables are converted into natural logarithm to interpret elasticities of the coefficient estimates. Table 1 provides information regarding the variables and their sources. The descriptive statistics of variables are demonstrated in Table 2. 3.2. Econometric Methodology In order to assess the impacts of globalization and financial development as well as verify the occurrence of EKC for some ASEAN countries, the author employs the CO2 emissions functions based on Shahbaz et al. (2015b), Phong et al. (2018), and Haseeb et al. (2018) as follows: CO2=f(GDP, GDP2, EC, FD, URB, KOF) (1) CO2=f(GDP, GDP2, EC, FD, URB, KOFe) (2) CO2=f(GDP, GDP2, EC, FD, URB, KOFs) (3) CO2=f(GDP, GDP2, EC, FD, URB, KOFp) (4) Where, CO2 stands for CO2 emissions per capita; GDP denotes GDP per capita computed at the constant price (2010US$); GDP2 means the square of GDP; EC reflects primary energy consumption per capita; FD demonstrates financial development; URB is the urban population share of the total population (%); KOF represents the overall globalization index; KOFe stands for the economic dimension of globalization; KOFs symbolizes the social aspect of globalization; and KOFp is the political component of globalization. According to Shahbaz et al. (2016b), Dar and Asif (2018), when all variables are transformed to natural logarithm, the log-linear regression equation can smooth out the dynamics of time-series and produce reliable estimations. The equations (1), (2), (3) and (4) can be converted into the log-linear form as follows: LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + α α α α α α α 0 1 2 2 3 4 5 6 OOFit it+ε (5) LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + β β β β β β β 0 1 2 2 3 4 5 6 OOFeit it+ µ (6) LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + χ χ χ χ χ χ χ 0 1 2 2 3 4 5 6 OOFsit it+ν (7) LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + δ δ δ δ δ δ δ 0 1 2 2 3 4 5 6 OOFpit it+π (8) Where L stands for the natural logarithm; i indicates the number of countries; t represents the number of periods; α0; β0; χ0; δ0 are Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 201944 the intercepts; α β χ δk k k k k k k k= = = =( ) ( ) ( ) ( )16 16 16 16, , , ,; ; ; are regression coefficients of the explanatory variables; and εit; µit; vit; πit illustrate the error terms. Under the EKC hypothesis, the signs of α1 and α2 are expected to be positive and negative respectively in order to reflect the inverted U-shaped pattern. Similarly, the former coefficient in each of the three pairs β1 and β2, χ1 and χ2, δ1 and δ2 is expected to be positive while the latter’s is negative. To estimate the above regression models, the author considers the following general panel data regression model: Y Xit it it= + +ρ ρ ω0 1 (9) The error terms (ωit ) in equation (9) involves all unobserved factors possibly affect the dependent variable over time and cross- sectionally. When the unobserved effect equals zero, both unique characteristics between entities and general effects over time are absent, thus enabling the application of Pooled OLS estimation method. Nevertheless, if it is different from 0 or there exists heterogeneity, the OLS estimator is no longer best linear unbiased estimator for equation (9), and therefore, fixed effects model and random effect model are considered to be used. Consequently, equation (9) can be rewritten as follows: Y X uit it i it= + + +ρ ρ λ0 1 (10) Where λi represents the unobservable time-invariant factors; µit is the remainder error changing over time and entities. It is of vital importance that one must identify whether λi correlates with the regressors in the model (Mundlak, 1978). In case the time-invariant factors correlate with the regressors, they must be treated as independent variables and cannot be considered as error term; accordingly, the fixed effects model is appropriately utilized and equation (10) becomes fixed effects model where λi is the intercept indicating the unique characteristics of the countries (Stock and Watson, 2014). If the time-invariant factors do not correlate with the regressors, they can be regarded as composite error (Maki, 2011). In this article, the author will estimate 04 fixed effects models denoted as (I), (II), (III) and (IV) as well as 04 random effect models denoted as (V), (VI), (VII) and (VIII). They are listed as follows: Model (I): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + α α α α α α α 0 1 2 2 3 4 5 6 OOFit i i it+ +α ε ε ' (11) Model (II): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + β β β β β β β 0 1 2 2 3 4 5 6 OOFeit i i it+ +β µ µ ' (12) Model (III): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + χ χ χ χ χ χ χ 0 1 2 2 3 4 5 6 OOFsit i i it+ +χ ν ν ' (13) Model (IV): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + δ δ δ δ δ δ δ 0 1 2 2 3 4 5 6 OOFpit i i it+ +δ π π ' (14) Model (V): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + α α α α α α α 0 1 2 2 3 4 5 6 OOFit it it+ + +α τ ε ' (15) Model (VI): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + β β β β β β β 0 1 2 2 3 4 5 6 OOFeit it it+ + +β υ µ ' (16) Table 1: Variable description and sources Variable name Symbol Description Unit Data source Carbon dioxide emissions CO2 Carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. Metric tons per capita World development indicators Economic growth GDP The gross domestic product by the midyear population (GDP per capita) Constant 2010 US dollars World development Indicators Energy consumption EC It comprises petroleum products, natural gas, electricity, and combustible renewable and waste. Kg of oil equivalent per capita World development indicators Financial development FD The domestic credit to the private sector % of GDP World development indicators Urbanization URB Urban population refers to the number of people living in urban areas of a country Total urban population, % World development Indicators Globalization KOF Includes economic globalization (KOFe), social globalization (KOFs), and political globalization (KOFp) Index (from 0 to 100) KOF Swiss economic institute Source: Author’s collection https://www.kof.ethz.ch/en/ https://www.kof.ethz.ch/en/ Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 2019 45 Model (VII): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + χ χ χ χ χ χ χ 0 1 2 2 3 4 5 6 OOFsit it it+ + +χ φ ν ' (17) Model (VIII): LC LGDP LGDP LEC LFD LURB LK it it it it it it O2 = + + + + + + δ δ δ δ δ δ δ 0 1 2 2 3 4 5 6 OOFpit it it+ + +δ η π ' (18) Where α; β; χ; δ are the intercepts for all countries and τit; υit; ϕit; ηit are the within entity (country) error and ' ; ' ; ' ; 'it it it it    are the between entity (country error). The Durbin–Hausman–Wu test (also known as Hausman test) is necessary for verifying which of the fixed effects model or the random effect model is more effective (Hausman, 1978). The hypothesis H0 of Hausman test for judging the fixed effects model against the random effect model assumes that there is no correlation between the unobservable time-invariant factors and the explanatory variables. The alternative hypothesis H1 assumes that the aforesaid correlation occurs. If H0 is rejected, the fixed effects model is more effective and more pertinent than the random effects model. If H0 cannot be rejected, the latter model is preferred. The order of computing the two estimators can be reversed, and hence, the aforementioned hypotheses and conclusions about them can be inverted in the Hausman test. 4. RESULTS AND DISCUSSION 4.1. Results The author runs both fixed effects model (FE) and random effects model (RE) on balanced panel data. After that, the Hausman test is used to choose appropriate model as a basis for estimation. Next, the Durbin–Wu–Hausman test is implemented to determine whether fixed effects model (FE) or random effects model (RE) is more effective (Tables 3 and 4). When the null hypothesis is rejected, the FE model is more proper for further estimation and analysis. Nonetheless, the RE model should be selected if the null hypothesis cannot be rejected. Table 5 summarizes the Durbin– Wu–Hausman test results. From Table 5, regarding the two models (I) and (V), the null hypothesis is rejected (as evidenced by both p-value and chi- squared value), thus the RE model (I) is more effective. Concerning the pair of models (II) and (VI), H0 is also rejected and RE model (VI) is chosen. Next, similarly, the RE model (VII) is more preferable than the FE model (III). Finally, the FE model (IV) is better than the RE model (VIII). The model analysis result is indicated in Table 6 as follows.T ab le 2 : D es cr ip ti ve s ta ti st ic s of v ar ia bl es C ou nt ry St at is ti cs C O 2 G D P E C F D U R B K O F K O F e K O F s K O F p M ya nm ar M ea n± SD 0. 18 5± 0. 05 7 40 3. 38 8± 31 7. 66 2 28 5. 41 4± 20 .5 34 6. 35 1± 2. 80 6 26 .3 80 ±2 .9 96 26 .5 71 ±3 .8 48 36 .8 34 ±4 .0 83 12 .4 1± 5. 13 2 28 .8 63 ±6 .8 42 M al ay si a M ea n± SD 4. 32 6± 2. 26 7 56 10 .9 10 ±2 48 2. 24 4 16 09 .2 36 ±7 96 .2 41 90 .2 45 ±3 8. 36 6 53 .8 30 ±1 2. 38 6 65 .0 96 ±1 0. 39 8 62 .7 41 ±6 .2 74 64 .9 09 ±1 1. 19 0 67 .6 37 ±1 4. 17 3 Ph ili pp in es M ea n± SD 0. 79 8± 0. 12 3 16 67 .7 17 ±2 87 .5 01 45 5. 86 2± 25 .2 79 29 .5 04 ±8 .7 34 43 .6 00 ±4 .9 92 49 .4 94 ±1 1. 38 3 44 .9 44 ±9 .8 29 36 .1 67 ±1 1. 70 0 67 .3 70 ±1 4. 23 3 Si ng ap or e M ea n± SD 11 .4 67 ±2 .8 52 26 26 6. 35 0± 13 61 5. 26 0 38 38 .2 43 ±1 63 6. 90 3 85 .0 92 ±1 9. 80 2 10 0. 00 0± 0. 00 0 73 .6 96 ±7 .6 31 88 .1 48 ±4 .5 36 74 .7 42 ±7 .3 49 58 .2 65 ±1 1. 54 5 T ha ila nd M ea n± SD 2. 20 1± 1. 39 2 29 05 .4 41 ±1 48 7. 51 9 95 7. 86 8± 52 2. 85 5 84 .4 98 ±4 1. 66 0 31 .7 87 ±7 .3 08 50 .7 44 ±1 2. 73 6 46 .0 57 ±1 2. 16 3 40 .6 90 ±1 3. 14 1 65 .4 83 ±1 3. 22 2 So ur ce : A ut ho r’ s ca lc ul at io ns Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 201946 4.2. Discussion From Table 6, financial development positively impacts CO2 emissions, which is analogous to the findings of Boutabba (2014) and Shahbaz et al. (2015b) for India; Farhani and Ozturk (2015) for Tunisia; Al-Mulali et al. (2015) for Europe; Javid and Sharif (2016), Shahbaz et al. (2016b) for Pakistan; Salahuddin et al. (2017) for Kuwait; Solarin et al. (2017) for Ghana; Xing et al. (2017) for China; and Haseeb et al. (2018) for BRICS economies. This implies that financial development probably promotes the development of new projects and activities which in turn boost energy consumption, hence increasing CO2 emissions (Javid and Sharif, 2016). Accordingly, governments of ASEAN countries should discourage money lent to inefficient- energy-consuming activities or projects that potentially harm the environment. Also, financial institutions are recommended to allocate more financial resources to “green” or environmentally friendly projects. The aggregate measure of globalization accelerates CO2 emissions in ASEAN countries when 1% increase in the overall LKOF causes around 0.335% rise in LCO2. The economic dimension of globalization (LKOFe) also raises LCO2 by nearly 0.344% for each 1% increase, which signifies that economic activities under globalization exacerbates the environmental quality. The social aspect (LKOFs) and political facet (LKOFp) of globalization respectively had negative (–0.0990) and positive (0.0751) coefficients, yet their impacts on CO2 emissions are small and statistically insignificant. It can be argued that globalization, especially the economic dimension, reduces trade and investment barriers, which in turn expands economic activities and aggravates the environmental quality. This is in line with the findings of Cole (2004), Shandra et al. (2009), Shahbaz et al. (2015b), Farhani and Ozturk (2015), Ertugrul et al. (2016), and Shahbaz et al. (2017a; 2017b) when incremental trade activities produce the scale effect that precipitates pollution. As a consequence, the governments play a vital role in improving Table 3: Regression results with fixed effects Model (I) (II) (III) (IV) Dependent variable LCO2 LCO2 LCO2 LCO2 LGDP 2.246*** (12.95) 2.287*** (12.98) 2.086*** (11.55) 2.238*** (12.69) LGDP2 –0.166*** (–13.76) –0.164*** (–13.62) –0.158*** (–13.20) –0.164*** (–12.79) LEC 1.133*** (13.39) 1.091*** (12.96) 1.152*** (13.47) 1.115*** (12.48) LFD 0.180*** (4.34) 0.178*** (4.19) 0.195*** (4.83) 0.202*** (4.91) LURB 0.201 (1.19) 0.293* (1.85) 0.238 (1.50) 0.384** (2.27) LKOF 0.335** (2.55) LKOFe 0.217** (2.09) LKOFs 0.240*** (2.77) LKOFp 0.0751 (0.51) Const –16.95*** (–22.96) –17.00*** (–22.48) –16.09*** (–21.81) –16.69*** (–21.34) N 220 220 220 220 R2 0.8738 0.9130 0.8842 0.8950 F (6, 209) 228.81 226.20 230.25 221.18 Prob>F 0.0000 0.0000 0.0000 0.0000 *P<0.10, **P<0.05, ***P<0.01. Source: Author’s calculation Table 4: Regression results with random effects Model (V) (VI) (VII) (VIII) Dependent variable LCO2 LCO2 LCO2 LCO2 LGDP 1.967*** (10.21) 1.711*** (9.13) 1.767*** (7.21) 2.347*** (14.73) LGDP2 –0.121*** (–9.64) –0.105*** (–8.50) –0.109*** (–7.30) –0.140*** (–13.54) LEC 1.007*** (11.51) 0.897*** (9.51) 0.969*** (10.61) 0.868*** (12.03) LFD 0.294*** (5.70) 0.179*** (3.52) 0.216*** (4.20) 0.359*** (8.62) LURB 0.921*** (8.77) 0.535*** (4.31) 0.738*** (7.09) 0.695*** (8.94) LKOF –0.725*** (–4.55) LKOFe 0.344** (2.27) LKOFs –0.0990 (–0.86) LKOFp –0.871*** (–11.44) Const –15.81*** (–17.76) –16.40*** (–17.05) –16.24*** (–15.07) –15.41*** (–20.99) N 220 220 220 220 R2 0.9704 0.9683 0.9677 0.9799 Wald χ2 (6) 6990.40 6511.26 6375.55 10390.09 Prob>χ2 0.0000 0.0000 0.0000 0.0000 *P<0.10, **P<0.05, ***P<0.01. Source: Author’s calculation Table 5: Results of Durbin – Wu – Hausman test Model (I) (V) (II) (VI) (III) (VII) (IV) (VIII) FE RE FE RE FE RE FE RE Hausman test1 FE and RE RE and FE RE and FE FE and RE χ2 1394.26 2687.36 349.94 80.63 Prob>χ2 0.0000 0.0000 0.0000 0.0000 FE is the fixed effects model; RE is the random effects model. 1 The order of computation and storage in Stata 15 for the Hausman test Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 2019 47 economic conditions, achieving globalization benefits and sustainably protecting the environment. Energy consumption (LEC) stimulates CO2 emissions by approximately 1.133% for each 1% rise. This is not dissimilar to Pao and Tsai (2010) and Haseeb et al. (2018) for BRICS countries; Jaunky (2011) for 36 high-income countries; Ozturk and Acaravci (2013) for Turkey; Shahbaz et al. (2014), Farhani and Ozturk (2015) for Tunisia; Boutabba (2014) and Shahbaz et al. (2015b) for India; Javid and Sharif (2016) for Pakistan; Dogan and Seker (2016) for OECD countries; Dogan and Turkekul (2016) for USA; Rehman and Rashid (2017) for SAARC countries; Solarin et al. (2017) for Ghana; Shahbaz et al. (2017b) for Japan; and Phong et al. (2018) for Vietnam. The aforementioned findings recommend that the governments of those countries necessitate some energy policies for sustainable development such as: promoting effective and efficient energy use, upgrading obsolete technology towards modernity and efficiency, researching and developing renewable energy and green energy sources and reducing the impacts of energy consumption on the environment. Finally, the evidence of EKC is confirmed for ASEAN-5 e c o n o m i e s . S p e c i f i c a l l y, G D P p e r c a p i t a m a k e s C O 2 emissions grow (as evidenced by the positive coefficient of LGDP exhibited in Table 6) while the square of GDP per capita decreases CO2 emissions (as denoted by the negative coefficient of LGDP2 displayed in Table 6), which implies that the movement of CO2 emissions follows the inverted U-shaped pattern of EKC hypothesis stating that CO2 amount rises and then declines after GDP per capita reaches a certain level. This is consistent with Cole et al. (1997) for 7 countries; Halkos (2003) for OECD and non-OECD countries; Apergis and Payne (2009) for Central America; Jaunky (2011) for Greece, Malta, Portugal, Oman, and United Kingdom; Shahbaz et al. (2013b) for South Africa; Farhani et al. (2014) for 10 MENA countries; Kasman and Duman (2015) for European countries; Cho et al. (2014), Dogan and Seker (2016) for OECD countries; Zaman et al. (2016) for 34 developed and developing countries; Twerefou et al. (2017) for 36 Sub-Saharan Africa countries; Zhang et al. (2017) for 10 Newly Industrialized countries (NICs-10); Pata (2018) for Turkey; Tamazian et al. (2009), Pao and Tsai (2010; 2011), Sinha and Sen (2016), Haseeb et al. (2018) for BRICS countries. 5. CONCLUSION The main objective of this study is to examine the relationship between globalization, financial development, energy consumption, economic growth, urbanization and CO2 emissions in some ASEAN countries with the presence of EKC hypothesis. The author employs panel data regression with the fixed effects and random effects models on annual data of 5 ASEAN countries (Myanmar, Malaysia, Philippines, Singapore and Thailand) over the period 1971-2014. The selection of pertinent models is implemented by Durbin–Wu–Hausman test. Empirical findings indicate several important results. First, financial development, energy consumption and urbanization have significantly positive connections with CO2 emissions in the long run. Second, globalization boosts CO2 emissions in some ASEAN countries, and the largest magnitude of impact comes from the economic dimension of globalization; meanwhile, social and political aspects of globalization insignificantly lowers and raises CO2 emissions respectively. Third, the evidence of EKC in ASEAN-5 economies is affirmed. Crucial implications can be recommended for the sampled ASEAN countries. The governments necessitate policies that encourage firms and industries to use energy effectively and efficiently, upgrade technology or adopt new or environmentally friendly energy. The development of energy infrastructure requires both energy security and environmental protection. Regarding the financial resources, the governments should promote the performance of projects that save energy and are harmless to the environment. A vital challenge for the governments is to sustainably control the environmental quality when the upsurge of globalization (especially economic dimension), trade and capital investment activities continues in this highly dynamic region of the world. Besides, the reforms of institution, corruption, legal system and financial security control remain essential issues that need great attentions of ASEAN policy-makers so as to foster globalization, financial development and energy security, which contributes to the sustainable development. Table 6: Results of empirical analysis Independent variable Dependent variable LCO2 LCO2 LCO2 LCO2 LGDP 2.246*** (12.95) 1.711*** (9.13) 1.767*** (7.21) 2.238*** (12.69) LGDP2 –0.166*** (–13.76) –0.105*** (–8.50) –0.109*** (–7.30) –0.164*** (–12.79) LEC 1.133*** (13.39) 0.897*** (9.51) 0.969*** (10.61) 1.115*** (12.48) LFD 0.180*** (4.34) 0.179*** (3.52) 0.216*** (4.20) 0.202*** (4.91) LURB 0.201 (1.19) 0.535*** (4.31) 0.738*** (7.09) 0.384** (2.27) LKOF 0.335** (2.55) LKOFe 0.344** (2.27) LKOFs –0.0990 (–0.86) LKOFp 0.0751 (0.51) Const –16.95*** (–22.96) –16.40*** (–17.05) –16.24*** (–15.07) –16.69*** (–21.34) N 220 220 220 220 R2 0.8738 0.9683 0.9677 0.8950 *P<0.10, **P<0.05, ***P<0.01. Source: Author’s calculation Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 201948 The limitation of this article entails inadequate data for all ASEAN countries. Future studies about this topic may have an advantage when the data for the whole ASEAN region is available. Besides, sources of energy consumption at disaggregated level as well as other proxies for environmental degradation will be the focuses of the author’s subsequent research. Also, expanding the study to global level is a worthy attempt in future studies. 6. ACKNOWLEDGMENTS The author gratefully expresses the author’s gratitude for the financial support from the University of Economics Ho Chi Minh city, Vietnam for this research. REFERENCES Ahmed, K. (2014), Environmental Kuznets curve for CO2 emission in Mongolia: An empirical analysis. Management of Environmental Quality: An International Journal, 25(4), 505-516. Ahmed, K., Long, W. (2012), Environmental Kuznets curve and Pakistan: An empirical analysis. Procedia Economics and Finance, 1, 4-13. Alam, M.J., Begum, I.A., Buysse, J., Huylenbroeck, G.V. (2012), Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis. Energy Policy, 45, 217-225. Al-Mulali, U., Ozturk, I., Lean, H.H. (2015), The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe. Natural Hazards, 79(1), 621-644. Al-Mulali, U., Solarin, S.A., Ozturk, I. (2016), Investigating the presence of the environmental Kuznets curve (EKC) hypothesis in Kenya: An autoregressive distributed lag (ARDL) approach. Natural Hazards, 80(3), 1729-1747. Ang, J.B. (2007), CO2 emissions, energy consumption, and output in France. Energy Policy, 35(10), 4772-4778. Apergis, N., Payne, J.E. (2009), CO2 emissions, energy usage, and output in Central America. Energy Policy, 37(8), 3282-3286. Arouri, M.E.H., Youssef, A.B., M’henni, H., Rault, C. (2012), Energy consumption, economic growth and CO2 emissions in Middle East and North African countries. Energy Policy, 45, 342-349. Baek, J. (2015), Environmental Kuznets curve for CO2 emissions: The case of Arctic countries. Energy Economics, 50, 13-17. Baek, J., Kim, H.S. (2013), Is economic growth good or bad for the environment? Empirical evidence from Korea. Energy Economics, 36, 744-749. Balaguer, J., Cantavella, M. (2016), Estimating the environmental Kuznets curve for Spain by considering fuel oil prices (1874-2011). Ecological Indicators, 60, 853-859. Begum, R.A., Sohag, K., Abdullah, S.M.S., Jaafar, M. (2015), CO2 emissions, energy consumption, economic and population growth in Malaysia. Renewable and Sustainable Energy Reviews, 41, 594-601. Bento, J.P.C., Moutinho, V. (2016), CO2 emissions, non-renewable and renewable electricity production, economic growth, and international trade in Italy. Renewable and Sustainable Energy Reviews, 55, 142-155. Boutabba, M.A. (2014), The impact of financial development, income, energy and trade on carbon emissions: Evidence from the Indian economy. Economic Modelling, 40, 33-41. Cho, C.H., Chu, Y.P., Yang, H.Y. (2014), An environment Kuznets curve for GHG emissions: A panel cointegration analysis. Energy Sources, Part B: Economics, Planning, and Policy, 9(2), 120-129. Cole, M.A. (2004), Trade, the pollution haven hypothesis and the environmental Kuznets curve: Examining the linkages. Ecological Economics, 48(1), 71-81. Cole, M.A., Rayner, A.J., Bates, J.M. (1997), The environmental Kuznets curve: An empirical analysis. Environment and Development Economics, 2(4), 401-416. Dar, J.A., Asif, M. (2017), Is financial development good for carbon mitigation in India? A regime shift-based cointegration analysis. Carbon Management, 8(5-6), 435-443. Dar, J.A., Asif, M. (2018), Does financial development improve environmental quality in Turkey? An application of endogenous structural breaks based cointegration approach. Management of Environmental Quality: An International Journal, 29(2), 368-384. Day, K.M., Grafton, R.Q. (2003), Growth and the environment in Canada: An empirical analysis. Canadian Journal of Agricultural Economics, 51(2), 197-216. Dinda, S. (2004), Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49(4), 431-455. Dogan, E., Seker, F. (2016), An investigation on the determinants of carbon emissions for OECD countries: Empirical evidence from panel models robust to heterogeneity and cross-sectional dependence. Environmental Science and Pollution Research, 23(14), 14646- 14655. Dogan, E., Turkekul, B. (2016), CO2 emissions, real output, energy consumption, trade, urbanization and financial development: Testing the EKC hypothesis for the USA. Environmental Science and Pollution Research, 23(2), 1203-1213. Dreher, A. (2006), Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 38(10), 1091-1110. Dreher, A., Gaston, N., Martens, P. (2008), Measuring Globalisation- Gauging its Consequences. New York: Springer. ISBN 978-0-387- 74067-6. Du, L., Wei, C., Cai, S. (2012), Economic development and carbon dioxide emissions in China: Provincial panel data analysis. China Economic Review, 23(2), 371-384. Ertugrul, H.M., Cetin, M., Seker, F., Dogan, E. (2016), The impact of trade openness on global carbon dioxide emissions: Evidence from the top ten emitters among developing countries. Ecological Indicators, 67, 543-555. Farhani, S., Ozturk, I. (2015), Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), 15663-15676. Farhani, S., Shahbaz, M., Sbia, R., Chaibi, A. (2014), What does MENA region initially need: Grow output or mitigate CO2 emissions? Economic Modelling, 38, 270-281. Fodha, M., Zaghdoud, O. (2010), Economic growth and pollutant emissions in Tunisia: An empirical analysis of the environmental Kuznets curve. Energy Policy, 38(2), 1150-1156. Ghosh, S. (2010), Examining carbon emissions-economic growth nexus for India: A multivariate cointegration approach. Energy Policy, 38(6), 3008-3014. Giovanis, E. (2013), Environmental Kuznets curve: Evidence from the British household panel survey. Economic Modelling, 30, 602-611. Gokmenoglu, K., Taspinar, N. (2016), The relationship between CO2 emissions, energy consumption, economic growth and FDI: The case of Turkey. The Journal of International Trade and Economic Development, 25(5), 706-723. Grossman, G., Krueger, A. (1991), Environmental Impacts of a North American Free Trade Agreement, National Bureau of Economics Research Working Paper, No. 3194. Cambridge: NBER. Halkos, G.E. (2003), Environmental Kuznets curve for sulfur: Evidence using GMM estimation and random coeffcient panel data models. Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 2019 49 Environment and Development Economics, 8(4), 581-601. Haseeb, A., Xia, E., Danish, B.M.A., Abbas, K. (2018), Financial development, globalization, and CO2 emission in the presence of EKC: Evidence from BRICS countries. Environmental Science and Pollution Research, 25(31), 31283-31296. Hausman, J.A. (1978), Specification tests in econometrics. Econometrica, 46(6), 1251-1271. Islam, F., Shahbaz, M., Ahmed, A.U., Alam, M. (2013), Financial development and energy consumption nexus in Malaysia: A multivariate time series analysis. Economic Modeling, 30, 435-441. Jalil, A., Mahmud, S.F. (2009), Environment Kuznets curve for CO2 emissions: A cointegration analysis for China. Energy Policy, 37(12), 5167-5172. Jaunky, V.C. (2011), The CO2 emissions-income nexus: Evidence from rich countries. Energy Policy, 39(3), 1228-1240. Javid, M., Sharif, F. (2016), Environmental Kuznets curve and financial development in Pakistan. Renewable and Sustainable Energy Reviews, 54, 406-414. Jayanthakumaran, K., Verma, R., Liu, Y. (2012), CO2 emissions, energy consumption, trade and income: A comparative analysis of China and India. Energy Policy, 42, 450-460. Kasman, A., Duman, Y.S. (2015), CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Economic Modelling, 44, 97-103. Lacheheb, M., Rahim, A.S.A., Sirag, A. (2015), Economic growth and carbon dioxide emissions: Investigating the environmental Kuznets curve hypothesis in Algeria. International Journal of Energy Economics and Policy, 5(4), 1125-1132. Lindmark, M. (2002), An EKC-pattern in historical perspective: Carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870-1997. Ecological Economics, 42(1-2), 333-347. Maki, A. (2011), Introduction to Estimating Economic Models. New York, USA: Routledge, ISBN 978-0-415-58986-4. Mallick, L., Tandi, S.M. (2015), Energy consumption, economic growth, and CO2 emissions in SAARC countries: Does environmental Kuznets curve exist? The Empirical Econometrics and Quantitative Economics Letters, 4, 57-69. María, P.P., Jesús, J.D. (2016), Economic growth and energy consumption: The energy-environmental Kuznets curve for Latin America and the Caribbean. Renewable and Sustainable Energy Reviews, 60, 1343-1350. Mundlak, Y. (1978), On the pooling of time series and cross section data. Econometrica, 46(1), 69-85. Nasir, M., Rehman, F.U. (2011), Environmental Kuznets curve for carbon emissions in Pakistan: An empirical investigation. Energy Policy, 39(3), 1857-1864. Neve, M., Hamaide, B. (2017), Environmental Kuznets curve with adjusted net savings as a trade-off between environment and development. Australian Economic Papers, 56(1), 39-58. Nye, J.S., Donahue, N.D. (2000), Governance in a Globalizing World. Washington, DC, USA: Brookings Institution Press, ISBN 978-0- 815-76407-6. Omri, A. (2013), CO2 emissions, energy consumption and economic growth nexus in MENA countries: Evidence from simultaneous equations models. Energy Economics, 40, 657-664. Omri, A., Daly, S., Rault, C., Chaibi, A. (2015), Financial development, environmental quality, trade and economic growth: What causes what in MENA countries. Energy Economics, 48, 242-252. Ozatac, N., Gokmenoglu, K.K., Taspinar, N. (2017), Testing the EKC hypothesis by considering trade openness, urbanization, and financial development: The case of Turkey. Environmental Science and Pollution Research, 24(20), 16690-16701. Ozcan, B. (2013), The nexus between carbon emissions, energy consumption and economic growth in Middle East countries: A panel data analysis. Energy Policy, 62, 1138-1147. Ozturk, I., Acaravci, A. (2013), The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey. Energy Economics, 36, 262-267. Pal, D., Mitra, S.K. (2017), The environmental Kuznets curve for carbon dioxide in India and China: Growth and pollution at crossroad. Journal of Policy Modeling, 39(2), 371-385. Pao, H.T., Tsai, C.M. (2010), CO2 emissions, energy consumption and economic growth in BRIC countries. Energy Policy, 38(12), 7850- 7860. Pao, H.T., Tsai, C.M. (2011), Multivariate granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russia Federation, India, and China) countries. Energy, 36(1), 685-693. Pao, H.T., Yu, H.C., Yang, Y.H. (2011), Modeling the CO2 emissions, energy use, and economic growth in Russia. Energy, 36(8), 5094- 5100. Pata, U.K. (2018), The effect of urbanization and industrialization on carbon emissions in Turkey: Evidence from ARDL bounds testing procedure. Environmental Science and Pollution Research, 25(8), 7740-7747. Phong, L.H., Van, D.T.B., Bao, H.H.G. (2018), The role of globalization on carbon dioxide emission in Vietnam incorporating industrialization, urbanization, gross domestic product per capita and energy use. International Journal of Energy Economics and Policy, 8(6), 275-283. Rehman, M.U., Rashid, M. (2017), Energy consumption to environmental degradation, the growth appetite in SAARC nations. Renewable Energy, 111, 284-294. Roca, J., Padilla, E., Farre, M., Galletto, C. (2001), Economic growth and atmospheric pollution in Spain: Discussing the environmental Kuznets curve hypothesis. Ecological Economics, 39(1), 85-99. Saboori, B., Sulaiman, J., Mohd, S. (2012), Economic growth and CO2 emissions in Malaysia: Acointegration analysis of the environmental Kuznets Curve. Energy Policy, 51, 184-191. Saidi, K., Mbarek, M.B. (2017), The impact of income, trade, urbanization, and financial development on CO2 emissions in 19 emerging economies. Environmental Science and Pollution Research, 24(4), 12748-12757. Salahuddin, M., Alam, K., Ozturk, I., Sohag, K. (2017), The effects of electricity consumption, economic growth, financial development and foreign direct investment on CO2 emissions in Kuwait. Renewable and Sustainable Energy Reviews, 81, 2002-2010. Shahbaz, M., Dube, S., Ozturk, I., Jalil, A. (2015a), Testing the environmental Kuznets curve hypothesis in Portugal. International Journal of Energy Economics and Policy, 5(2), 475-481. Shahbaz, M., Jam, F.A., Bibi, S., Loganathan, N. (2016a), Multivariate granger causality between CO2 emissions, energy intensity and economic growth in Portugal: Evidence from cointegration and causality analysis. Technological and Economic Development of Economy, 22(1), 47-74. Shahbaz, M., Khraief, N., Uddin, G.S., Ozturk, I. (2014), Environmental Kuznets curve in an open economy: A bounds testing and causality analysis for Tunisia. Renewable and Sustainable Energy Reviews, 34, 325-336. Shahbaz, M., Mallick, H., Mahalik, M.K., Loganathan, N. (2015b), Does globalization impede environmental quality in India? Ecological Indicators, 52, 379-393. Shahbaz, M., Nasreen, S., Ahmed, K., Hammoudeh, S. (2017a), Trade openness-carbon emissions nexus: The importance of turning points of trade openness for country panels. Energy Economics, Phong: Globalization, Financial Development, and Environmental Degradation in the Presence of EKC: Evidence from Asean-5 Countries International Journal of Energy Economics and Policy | Vol 9 • Issue 2 • 201950 61, 221-232. Shahbaz, M., Ozturk, I., Afza, T., Ali, A. (2013a), Revisiting the environmental Kuznets curve in a global economy. Renewable and Sustainable Energy Reviews, 25, 494-502. Shahbaz, M., Shahzad, S.J.H., Ahmad, N., Alam, S. (2016b), Financial development and environmental quality: The way forward. Energy Policy, 98, 353-364. Shahbaz, M., Shahzad, S.J.H., Kumar, M. (2017b), Is Globalization Detrimental to CO2 Emissions in Japan? New Threshold Analysis. MPRA Paper 82413. Germany: University Library of Munich. Shahbaz, M., Tiwari, A.K., Nasir, M. (2013b), The effects of financial development, economic growth, coal consumption and trade openness on CO2 emissions in South Africa. Energy Policy, 61, 1452-1459. Shandra, J.M., Leckband, C., London, B. (2009), Ecologically unequal exchange and deforestation: A cross-national analysis of forestry export flows. Organization and Environment, 22(3), 293-310. Sinha, A., Sen, S. (2016), Atmospheric consequences of trade and human development: A case of BRIC countries. Atmospheric Pollution Research, 7(6), 980-989. Solarin, S.A., Al-Mulali, U., Musah, I., Ozturk, I. (2017), Investigating the pollution haven hypothesis in Ghana: An empirical investigation. Energy, 124, 706-719. Stern, D.I. (2004), The rise and fall of the environmental Kuznets curve. World Development, 32(8), 1419-1439. Stock, J.H., Watson, M.W. (2014), Introduction to Econometrics. Essex, UK: Pearson, ISBN 0133592693. Tamazian, A., Chousa, J.P., Vadlamannati, K.C. (2009), Does higher economic and financial development lead to environmental degradation: Evidence from BRIC countries. Energy Policy, 37(1), 246-253. Tang, C.F., Tan, B.W. (2015), The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam. Energy, 79, 447-454. Torras, M., Boyce, J.K. (1998), Income, inequality, and pollution: A reassessment of the environmental Kuznets curve. Ecological Economics, 25(2), 147-160. Twerefou, D.K., Danso-Mensah, K., Bokpin, G.A. (2017), The environmental effects of economic growth and globalization in Sub-Saharan Africa: A panel general method of moments approach. Research in International Business and Finance, 42, 939-949. Wang, Y., Kang, L., Wu, X., Xiao, Y. (2013), Estimating the environmental Kuznets curve for ecological footprint at the global level: A spatial econometric approach. Ecological Indicators, 34, 15-21. Xing, T., Jiang, Q., Ma, X. (2017), To facilitate or curb? The role of financial development in China’s carbon emissions reduction process: A novel approach. International Journal of Environmental Research and Public Health, 14(10), 1222. Yavuz, N.C. (2014), CO2 emission, energy consumption, and economic growth for Turkey: Evidence from a cointegration test with a structural break. Energy Sources, Part B: Economics, Planning, and Policy, 9(3), 229-235. Zaman, K., Shahbaz, M., Loganathan, N., Raza, S.A. (2016), Tourism development, energy consumption and environmental Kuznets curve: Trivariate analysis in the panel of developed and developing countries. Tourism Management, 54, 275-283. Zambrano-Monserrate, M.A., García-Albán, F.F., Henk-Vera, K.A. (2016), Bounds testing approach to analyze the existence of an environmental Kuznets curve in Ecuador. International Journal of Energy Economics and Policy, 6(2), 159-166. Zhang, S., Liu, X., Bae, J. (2017), Does trade openness affect CO2 emissions: Evidence from ten newly industrialized countries? Environmental Science and Pollution Research, 24(21), 17616- 17625. Zhang, X.P., Cheng, X.M. (2009), Energy consumption, carbon emissions, and economic growth in China. Ecological Economics, 68(10), 2706-2712. Zoundi, Z. (2017), CO2 emissions, renewable energy and the environmental Kuznets curve, a panel cointegration approach. Renewable and Sustainable Energy Reviews, 72, 1067-1075.