Revisiting The Environmental Kuznets Curve Hypothesis with Globalization for OECD Countries: The Role of Convergence Clubs

Revisiting The Environmental Kuznets Curve Hypothesis with Globalization for OECD Countries: The Role of Convergence Clubs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Revisiting The Environmental Kuznets Curve Hypothesis with Globalization for OECD Countries: The Role of Convergence Clubs Volkan Bektaş, Neslihan Ursavaş This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1914497/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2023 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract This paper aims to investigate the role of globalization in ecological footprint for OECD countries during the 1981–2015 period with the Environmental Kuznets Curve (EKC) framework. To do so, unlike the existing literature, we follow a different path. Firstly, we test the environmental convergence (EC) hypothesis using the Phillips and Sul ( 2007 ) methodology. Then, we examine the impact of globalization and energy consumption on ecological footprint (EF), and test the existence of EKC hypothesis using the dynamic ordinary least squares mean group (DOLSMG) estimator. The convergence test results indicate that OECD countries do not converge to same steady-state levels with regard to EF levels. However, we identify two convergence clubs that converging to a different steady-state equilibrium. The results of DOLSMG reveal that the EKC hypothesis is valid for both convergence groups. Furthermore, the impact of energy consumption and globalization on EF is higher for Club 2 which mostly includes developing countries. Ecological Footprint Club Convergence Environmental Kuznets Curve Globalization Figures Figure 1 1. Introduction The anthropogenic pressure on the environment is one of the main problems of humanity. Although global efforts to prevent environmental degradation and using of renewable energy in this direction are gradually increasing, the use of fossil fuels still continues to be the primary energy source all over the world. The use of fossil fuels increases environmental degradation by causing an increase in greenhouse gas (GHG) emissions. Moreover, the disparities in the economic development levels and growth prospects of the countries cause the damage to the environment to differ between countries. The geographical distribution of GHG emissions does not have any effect on the total amount of GHG emissions in the atmosphere, but this distribution is an important topic of multinational discussions on climate change (Aldy, 2006). Since carbon dioxide (CO 2 ) emissions are an important component of GHG emissions, discussions on how to reduce these emissions are ongoing. The convergence of these kinds of pollution indicators is regarded as a key goal in global efforts to stop environmental degradation, such as the Kyoto protocol and the Paris Agreement. However, there are two issues that need to be addressed regarding these negotiations. Firstly, targeting convergence in environmental indicators is very critical in terms of equality and fairness (Aldy, 2006; Barassi et al., 2011; Payne et al., 2014). Secondly, as a result of these negotiations, no success has been achieved in preventing environmental degradation yet. On the other hand, in order to limit global warming to 1.5°C, no more than 28 gigatons of CO 2 equivalent should be released into the atmosphere annually until 2030 (UNEP, 2021). Considering the recent emissions trend [1] , it is clear that our time for reducing CO 2 emissions is quite limited. Multinational negotiations on environmental degradation have also begun to attract the attention of researchers. In this direction, whether countries converge from the stand point of environmental degradation has become popular among researchers, and this subject analyzed in the EC framework (Apaydin et al., 2021). The theoretical basis of EC is derived from the EKC hypothesis introduced by Brock and Taylor (2004). As shown in the EKC literature, since an increase in efforts to stop environmental degradation and the use of cleaner technologies environmental degradation raises up to a particular income level and then decreases. According to this model, as countries get richer, it is expected that the growth rate of emission rates will decrease, and emission rates will converge (Acar and Lindmark 2017). Although there are different methods for measuring convergence (beta, sigma, and stochastic), the club convergence method is mostly used in recent studies (see Panopoulou and Pantelidis, 2009; Herrerias, 2012; Ulucak and Apergis, 2018; Haider and Akram, 2019; Solarin et al., 2019; Payne and Apregis, 2020; Erdogan and Okumus, 2021). The club convergence methodology introduced by Phillips and Sul (2007) enables us to test overall convergence and determine possible convergence clubs (Phillips and Sul, 2007, 2009). In the literature, several indicators are employed a proxy for environmental degradation. The two most used indicators in the literature are EF and CO 2 emissions. While early studies mostly used CO 2 emissions, recent studies have mainly focused on the EF proposed by Wachernagel and Rees (1996). EF is a more comprehensive indicator than CO 2 as a proxy for environmental degradation as it comprises six components: carbon, grazing land, cropland, forest land, the footprints of fishing ground, and built-up land. One of the factors which is widely considered as an important factor for environmental degradation is globalization. Globalization can generally be defined as the economic, political and social integration of the countries. The impact of globalization on the environment takes place in different directions with different factors. First of all, increasing international trade with globalization leads to a rise in CO 2 emissions due to transportation. In addition, the increase in international trade and investments causes an increase in industrial activities which stimulates the economic growth specially in developing countries. But these increasing activities also enhance the environmental degradation due to the increasing consumption of electricity which is still mostly produced from fossil fuels. With globalization, deforestation has also accelerated, which indirectly but significantly affects environmental degradation (Huwart and Loïc, 2013). On the other hand, it has increased environmental awareness worldwide, and in this direction, international negotiations for the prevention of environmental degradation have gained momentum. There are many studies investigating the impacts of globalization on the environment in recent years. Some of these studies employed trade openness as an indicator of globalization (Halicioglu, 2009; Jayanthakumaran et al., 2012; Shahbaz et al., 2012; Farhani et al., 2014; Lau et al., 2014; Shahbaz et al., 2015; Haq et al., 2016; Acaravci and Akalin, 2017; Chen et al., 2019; Pata, 2019; Dauda et al., 2021; Pata and Caglar, 2021; Wang and Zhang, 2021; Wang and Wang, 2021)). However, trade openness covers only the international trade dimension of globalization. Since globalization has other dimensions such as capital flows, information flows, political and social aspects, globalization indices are used in recent studies (Shahbaz et al., 2017; Haseeb et al., 2018; Ahmed et al., 2019; Khan and Ullah, 2019; Phong Le, 2019; Zaidi et al., 2019; Ansari et al., 2020; Godil et al., 2020; Saud et al., 2020; Suki et al., 2020; Hussain et al., 2021; Jun et al., 2021). Within this context, we examine the impact of globalization on EF and the existence of EKC hypothesis for OECD countries over the period 1981-2015 using the dynamic ordinary least squares mean group (DOLSMG) estimator. However, unlike the existing literature, we follow a different path by considering the EC hypothesis. In the current literature, most of the studies analyze the EKC hypothesis for a single country or a group of countries. However, OECD countries are not homogeneous in terms of EF per capita. For instance, the EF per capita in the United States is three-time of that in Mexico. So, testing the EKC hypothesis for OECD countries in the same basket may cause misleading results. Thus, we first test the EC hypothesis using the non-linear dynamic factor model introduced by Phillips and Sul (2007). Subsequent to identifying EF convergence clubs, we test the EKC hypothesis and the impact of globalization and energy consumption on EF for each convergence club (that is, a more homogeneous group of countries in terms of EF per capita). Within this context, the present research contributes to the literature by being the first study to test the EKC hypothesis for OECD countries within the framework of the EC hypothesis. The rest of study organized as follows: Section two summarizes the related empirical literature. Section three illustrates the empirical analysis and results. Finally, section four concludes the paper. [1] For instance, total CO 2 emissions were 33 gigatons in 2021 (IEA, 2021). 2. Literature Review 2.1. Environmental Convergence Hypothesis The EC hypothesis has been examined by using various indicators of environmental degradation. Some studies investigate the convergence in CO 2 emissions among global, states, country groups, or sector level. For instance, Nguyen Van ( 2005 ) tests the convergence in CO 2 emissions among 100 countries for the 1966–1996 period using the non-parametric method and finds that there is no convergence. Aldy ( 2006 ) investigates the convergence in CO 2 emissions across 88 countries over 1960–2000 and the results support the absence of convergence. Ezcurra ( 2007 ) tests the distribution of CO 2 emissions among 87 countries over 1960–1999 following non-parametric method. The findings show that cross-country disparities decrease over time. Westerlund and Basher ( 2008 ) examine the convergence in CO 2 emissions across 16 developed and 12 developing countries over 1870–2002 employing unit root tests methods. They find a stochastic convergence for the full panel. Different from these studies, Panopoulou and Pantelidis ( 2009 ) follow the club convergence method to test the convergence in CO 2 emissions across 128 countries for the 1960–2003 period. The results reveal that there are two convergence clubs. Similarly, Herrerias (2013) investigates whether CO 2 emissions among 162 countries convergences over 1980–2009 using the Phillips and Sul ( 2007 , 2009 ) method and pairwise test approach. The results of the pairwise test show that there is no convergence across countries. However, according to the findings of club convergence analysis, there are multiple convergence clubs. Li et al. ( 2020 ) test the convergence of global CO 2 emissions across 129 countries for the 1995–2015 period using sigma, beta, stochastic, and club convergence methods. The findings reveal that global CO 2 emissions are convergent and the speed of convergence of consumption is slower than the production side. Burnett ( 2016 ) tests the convergence in CO 2 emissions among 48 US states for the period of 1960–2010 using club convergence and conditional beta convergence methods. The findings reveal that there is one convergence club for 26 states. Apergis et al. ( 2017 ) examine the convergence intensity of CO 2 emissions across 50 US states for 1997–2013 the period using cross-sectional tests methods. The results support the existence of sigma and beta convergence while there is no stochastic convergence across states. Tiwari et al. ( 2021 ) conclude that there are four clubs across US states over 1976 − 201. Solarin ( 2014 ) tests the convergence in CO 2 emissions across 39 African countries for the period of 1960–2010 using univariate unit root test method. The findings confirm the existence stochastic convergence for 31 countries. Robalino-Lopez et al. ( 2016 ) test the convergence in CO 2 emissions for 10 S. American countries for the 1980–2010 period. According to the results, there are two convergence clubs for CO 2 emissions and energy intensity. Tiwari and Mishra ( 2017 ) reveal that there are absolute beta convergence and sigma convergence for 18 Asian countries over 1970–2010. Karakaya et al. ( 2019 ) conclude that there is a stochastic convergence in CO 2 emissions across 16 industrialized OECD countries for the 1990–2013 period. Some studies in the literature focus on the convergence for sector level such as Moutinho et al. ( 2014 ), Brännlund et al. ( 2015 ), and Apergis and Payne ( 2017 ). Moutinho et al. ( 2014 ) examine the convergence in CO 2 emissions intensity across 16 Portuguese energy and industry sectors over 1996–2009 using univariate and unit root test methods. The findings indicate that there are sigma convergence, gamma convergence and stochastic convergence among sectors. Brännlund et al. ( 2015 ) conclude that there is a conditional beta convergence in CO 2 emissions intensity for 14 Swedish manufacturing sectors over 1990–2008. Similarly, Apergis and Payne ( 2017 ) test the convergence in per capita CO 2 emissions among 50 US states by sector level and by fossil fuel source using panel unit root tests methods. The findings support the existence absolute beta convergence, sigma convergence, and stochastic convergence. The other group of studies focus on convergence in EF as environmental degradation indicator. For instance, Ulucak and Apergis ( 2018 ) investigate the club convergence in EF for EU countries for the period of 1961–2013, employing the Phillips and Sul ( 2007 ) method. The results confirm that there are convergence clubs. Similarly, Bilgili and Ulucak ( 2018 ) study the convergence in EF in G20 countries for the period of 1961–2014. According to the findings, the club clustering algorithms support the presence of convergence clubs. Using the RALS-LM and LM unit root tests methods, Solarin ( 2019 ) examine whether the carbon footprint, CO 2 emissions, and EF converge among OECD countries for the period of 1961–2013. The findings show that there is a conditional convergence in these indicators across countries. Ulucak et al. ( 2020 ) investigate the convergence in EF and its sub-components across 23 Africa countries for the 1961–2014 period. The findings indicate that there are convergences for carbon, cropland, grazing land, fishing ground footprints. Erdogan and Okumus ( 2021 ) analyze the convergence process for EF over 1961–2016 using club convergence method. The results show that there are two convergence clubs for middle and low-income countries and four convergence clubs for high-income countries. Employing club convergence method, Apaydin et al. ( 2021 ) test the convergence in EF among 130 countries for the period of 1980–2016, and conclude multiple convergence clubs across these countries. 2.2. Impact of globalization on environmental degradation Over the last three decades, the role of globalization in environmental degradation has been investigated by many researchers applying various econometric techniques and samples. Basically, we can classify the related literature into two categories: (i) The first group studies use CO 2 emissions, and (ii) the second group of studies use EF as a proxy for environmental degradation. 2.2.1. Impact of globalization on CO 2 Some studies in the literature use trade openness as an indicator of globalization to test the connection between globalization and carbon emissions. For instance, Lau et al. ( 2014 ) find a positive relationship between FDI, trade openness and carbon emissions for Malaysia over 1970–2008 using ARDL bound testing method. The results verify the existence of EKC hypothesis. Shahbaz et al. ( 2015 ) find a positive relationship between CO 2 and trade openness for Portugal over 1971–2018, and the findings confirm the EKC hypothesis. Haq et al. ( 2016 ) reveal that an increase in trade openness decreases carbon emissions for Morocco over 1971–2011 and the results corroborate the existence of the EKC hypothesis. Using the bootstrap ARDL approach, Pata ( 2019 ) shows that a rise in trade openness stimulates CO 2 emissions in Turkey over 1969–2017, and the results support the validity of the EKC hypothesis. Dauda et al. ( 2021 ) show that a rise in trade openness decreases the CO 2 emissions in African countries over 1990–2016 performing fixed-effect model and generalized method of moments panel estimation models. Pata and Caglar ( 2021 ) find that trade openness increases CO 2 emission in China over 1980–2016. The results reveal that the EKC hypothesis is not valid for both EF and CO 2 emissions. The second group of studies uses KOF globalization developed by Dreher ( 2006 ), which is more comprehensive rather than trade openness, as a proxy for globalization. Shahbaz et al. ( 2017 ) investigate the impact of globalization and sub-indices of globalization on CO 2 over 1970–2012 in China. The results show that globalization is negatively related to CO2 emissions for China and the EKC hypothesis is valid. Haseeb et al. ( 2018 ) examine the link between globalization and CO 2 emission for BRICS economies and find that the link between CO 2 emissions and globalization is not statistically significant; However, the findings confirm that the existence of EKC hypothesis. Zaidi et al. ( 2019 ) study the relationship between globalization, financial development, and carbon emissions in APEC countries and analyze the EKC hypothesis. The findings reveal that the relationship between globalization and carbon emissions is negative and EKC hypothesis is valid for APEC countries. Using Westerlund co-integration, Phong Le (2019) tests the impact of globalization on CO 2 emissions for ASEAN-5 countries and the EKC hypothesis. The findings reveal that the existence of the EKC hypothesis and globalization increases CO 2 emissions. Using ARDL methodology, Khan and Ullah ( 2019 ) analyze the link between globalization sub-indices and CO 2 emissions in Pakistan over 1975–2014. They conclude that an increase in social, political and economic globalization increases the CO 2 emissions and the EKC hypothesis is valid in Pakistan. Using KOF globalization index, Jun et al. ( 2021 ) analyze the impact of globalization on CO 2 emission for South Asian economies over 1985–2018. The results confirm the EKC hypothesis and indicate that globalization positively affects CO 2 emission. 2.2.2. Impact of globalization on ecological footprint Over the past years, EF is widely used, which is a more comprehensive indicator rather than carbon emissions, as an indicator for environmental deterioration. Similar to the studies analyzing the link between carbon emissions and globalization, the first group of studies uses trade openness as an indicator for globalization. Al-mulali et al. ( 2015 ) find that trade openness increases EF for 93 countries. Furthermore, the findings reveal that the EKC hypothesis is valid in the high and upper-middle-income countries, whereas it is not valid for middle and low-income countries. Al-mulali et al. ( 2016 ) show that trade openness increases the EF in 58 developed and developing countries over 1980–2019. Besides, the findings reveal that EKC hypothesis is not valid. Ozturk et al. ( 2016 ) study the link between energy consumption, urbanization, tourism, trade openness, and EF for 144 countries over 1988–2008. They find negative relationship between EF and other variables, and the EKC hypothesis is only valid for the upper-middle-income and high-income countries. Destek and Sinha ( 2020 ) detect negative relationship between EF and trade openness across 24 OECD countries over 1980–2014. The findings do not support the EKC hypothesis. The other group of studies uses the KOF globalization index as a proxy for globalization. Using Bayer and Hanck co-integration test and the ARDL bound methods, Ahmed et al. ( 2019 ) test the impact of globalization on the EF for Malaysia over 1971–2014. The findings indicate that globalization significantly increases ecological carbon footprint. Ansari et al. ( 2020 ) conclude that globalization impacts on the EF positively in Gulf Cooperation Council (GCC) countries over 1991–2017 applying the dynamic ordinary least square (DOLS), and the fully modified ordinary least square (FMOLS) models. Their findings do not support the validity of the EKC hypothesis. Saud et al. ( 2020 ) analyze the link between financial development, globalization, and EF for selected one-belt-one-road initiative countries. The results indicate that there is a positive relationship between EF and financial development and there is a negative relationship between EF and globalization. Suki et al. ( 2020 ) show that social and political globalization reduce the EF level, economic globalization and overall globalization increase the environmental degradation level in the long term, for Malaysia over 1970–2018, and the results corroborate the existence of the EKC hypothesis. Godil et al. ( 2020 ) find a positive relationship between globalization and EF, and authors identify the existence of the EKC hypothesis for Turkey over 1986–2018. Hussain et al. ( 2021 ) identify a significant and nonlinear relationship between globalization, natural resources and ecological EF and support the validity of the EKC hypothesis for Thailand over 1970–2018. 3. Empirical Analysis 3.1. Data and Model To examine the EKC hypothesis and the impact of energy consumption and globalization on EF in the EC framework, we use annual balanced panel data for the period 1981-2015 for 26 OECD countries [2] . The definitions, measure units, and the data source of the variables [3] are presented in Table I. We use EF per capita as a proxy of ecological degradation (denoted as EFC), GDP per capita as a proxy of economic growth (denoted as GDP), energy consumption per capita (denoted as ENR) and the KOF Globalization index (denoted as KOF). EF per capita and the KOF Globalization index data are collected from the Global Footprint Network webpage and the KOF Swiss Economic Institute webpage (Dreher, 2006 and Gygli et al., 2019), respectively. GDP per capita and energy consumption per capita data are obtained from the World Bank Development Indicators (WDI). Table 1 Definitions of Variables Variable Definition Measure Units Source EF Ecological footprint per capita Global hectares Global Footprint Network GDP GDP per capita 2010 US Dollars World Bank ENR Energy consumption Kg of oil equivalent per capita World Bank KOF Globalization index Unitless Swiss Economic Institute In order to analyze the EKC hypothesis and the impact of globalization and energy consumption on EF, we use the following log linear quadratic function; $${lnefc}_{it}={\propto }_{0}+{{\propto }_{1}lngdp}_{it}+{\propto }_{2}{lngdp}_{it}^{2}+{\propto }_{3}{lnenr}_{it}+{\propto }_{4}{lnkof}_{it}+{u}_{it}$$ 1 Where i = 1,2, 3,…,N refers the cross-sectional units and t is the time dimension in panel estimation. The \({\propto }_{i }\) indicates the long run elasticity of corresponding variables. 3.2. Empirical Methods and Results In this study, to investigate convergence in EF, we employ the club convergence procedure [4] (i.e., “log regression test) developed by Phillips and Sul (2007). Using the Phillips and Sul (2007) methodology, it can be determined whether there is convergence or not. In case of convergence, sub-convergence groups can also be determined with this methodology. Convergence is tested with the following log- regression: $$\text{log}\left(\frac{{H}_{1}}{{H}_{t}}\right)-2logL\left(t\right)=\widehat{\alpha }+\widehat{b}logt+{\epsilon }_{t},$$2 where \({H}_{t}=\frac{1}{N}\sum _{i=1}^{N}{({h}_{it}-1)}^{2}\) denoting the computation of variance ratio \(\frac{{H}_{1}}{{H}_{t}}\) of cross-sections; and \({h}_{it}=\frac{{X}_{it}}{\frac{1}{N}{\sum }_{i=1}^{N}{X}_{it}}\) represents the relative transition parameter. If \({t}_{\widehat{b }}<-1.65\) , then the null hypothesis of convergence can be rejected at the 5% level of significance. The results in Table 2 indicate that the null hypothesis of full panel convergence in EF is rejected. That is there is no convergence for EF values of OECD countries in that period. Table 2 Log- T Test Results (26 Countries) Variable Coefficient Standard Error T-statistic EF − 0.2571 0.0136 -18.9086 Notes: The null hypothesis of convergence is rejected with the T-stats is smaller than − 1.65. Despite the rejection of null hypothesis for the full panel, there may be convergence clubs which converge to different equilibrium. In order to determine possible convergence clubs within the panel, club clustering procedure is applied. The results show that there are two convergence clubs. Each of these clubs converges to a different constant. The first and second clubs consist of 16 and 10 countries, respectively. Table 3 Final Club Classification Clubs Countries Coefficient T-statistic Club 1 [16] | Australia | Austria | Belgium | Canada | Denmark | Finland | France | Ireland | Israel | Korea Rep.| Netherlands | | N. Zealand | Norway | Sweden | Switzerland | | USA | 0.047 3.309 Club 2 [10] | Chile | Germany | Greece | Italy | Japan | Mexico | Portugal | | Spain | Turkey | UK | 0.321 11.027 Note: The null hypothesis of convergence is rejected with the T-stats is smaller than − 1.65. Figure 1 reports the relative transition paths of all clubs which are helpful to comprehend the long-run tendencies between clubs. First of all, we can observe that while Club 1 is above panel mean which entirely consists of developed countries, club 2 is below panel average which includes some developing countries. That is, Club 1 and Club2 converge higher and lower EF levels, respectively. Furthermore, we do not observe convergence tendencies between convergence clubs. Globalization increases socio-economic interactions between the countries. These interactions make it necessary to consider the cross-sectional dependency in panel data models. Besides, each country may have its own characteristics. Therefore, to determine whether or not the slope coefficients are homogenous is also a critical issue in panel data analysis. Ignoring the cross-sectional dependency and the slope homogeneity can cause serious problems, such as inference results, invalid test statistics, and loss of efficiency (Grossman and Kruger, 1995; Pesaran and Smith, 1995 : Coakley et al., 2002 ; Phillips and Sul, 2003 ; Chudik and Pesaran, 2013 ). Thus, we test the cross-section dependency and the slope homogeneity to determine the appropriate panel data methods. Table 4 Cross-Section Dependency Test Results Pesaran CD Test Variables CD Full Panel Lnefc 106.005***(0.00) Lngdp 106.638*** (0.00) Lnkof 106.627 ***(0.00) Lnenr 106.622*** (0.00) Club 1 Lnefc 64.490*** (0.00) Lngdp 64.797*** (0.00) Lnkof 64.799*** (0.00) Lnenr 64.790*** (0.00) Club 2 Lnefc 39.672*** (0.00) Lngdp 39.681*** (0.00) Lnkof 39.673*** (0.00) Lnenr 39.672*** (0.00) ***, ** and * indicate the significance levels at the 1%, 5% and 10% respectively. The value in the parentheses are P value. We employ Pesaran’s ( 2004 ) cross-sectional dependence test (CD-test) which is derived from arithmetic mean of pairwise correlation coefficients of OLS residuals from individual regressions. This test can be applied for spatial panels, for balanced and unbalanced panels, for dynamic heterogeneous panels with multiple breaks and unit roots (Pesaran, 2004 ). The results indicate that the null hypothesis of nonexistence of cross-section dependence is rejected for all variables in each model. So, there is a strong cross-section dependence between countries. After specifying the cross-section dependency, we employ Pesaran and Yamagata ( 2008 ) slope homogeneity test which is based on Swamy’s test. This test estimates restricted and unrestricted models and compares them. The standard errors for the individual cross section units are estimated using the weighted fixed effect in the restricted model, and the unit specific estimates of the ordinary least squares’ estimator in the unrestricted model. This test is dependent on the difference of these two models. If the test statistic large enough, this means inconsistency between the fixed effects and the unit specific estimates and for this reason the null hypothesis of slope homogeneity can be rejected (Pesaran and Yamagata, 2008 ; Bersvendsen and Ditzen, 2021 ). Test results indicate that homogeneity of slope coefficients is rejected for all models. Therefore, country-specific characteristics should be considered to avoid misleading inferences. Table 5 Test for Slope Homogeneity Full Panel Club 1 Club2 Delta p-value Delta p-value Delta p-value Δ 21.979 0.000 14.470 0.000 14.694 0.000 Δ adj 22.724 0.000 14.961 0.000 15.192 0.000 To specify the stationary level of the variables, we use Pesaran's (2007) unit root test which is based on augmented Dickey–Fuller (ADF) test. This test allows cross-section dependence in heterogeneous panels. The first-differences of the individual series and the cross-section averages of lagged levels are used for eliminating the cross section dependence of the series in this test (Pesaran, 2007 ). The unit root test results reveal that the null hypothesis of the non-stationarity cannot be rejected at levels for all variables in both with and without trend models. On the other hand, all the variables become stationary at their first difference. Table 6 Panel Unit Root Test With Trend Without Trend Level First Difference Level First Difference T-bar P-value T-bar P-value T-bar P-value T-bar P-value Full Panel Lnefc -2.223 0.752 -3.599*** 0.000 -1.756 0.53 -3.542*** 0.000 Lngdp -2.111 0.908 -2.787*** 0.005 -1.975 0.133 -2.575*** 0.000 Lnkof -2.314 0.559 -3.854*** 0.000 -1.896 0.247 -3.419*** 0.000 Lnenr -2.494 0.186 -3.573*** 0.000 -1.855 0.323 -3.509*** 0.000 Club 1 Lnefc -2.321 0.535 -3.838*** 0.000 -1.788 0.469 -3.819*** 0.000 Lngdp -1.818 0.991 -2.623 * 0.099 -1.906 0.282 -2.355*** 0.009 Lnkof -2.286 0.597 -3.741*** 0.000 -2.042 0.123 -3.290*** 0.000 Lnenr -2.445 0.316 -3.574*** 0.000 -1.945 0.228 -3.503*** 0.000 Club 2 Lnefc -1.959 0.915 -3.207*** 0.001 --1.540 0.791 -3.209 *** 0.000 Lngdp -2.017 0.877 -2.978 ** 0.011 -1.554 0.777 -2.650*** 0.002 Lnkof -2.002 0.888 -4.047*** 0.000 -1.711 0.592 -3.567*** 0.000 Lnenr -1.984 0.899 -3.144 *** 0.002 -0.871 0.999 -2.940 *** 0.000 ***, ** and * Indicate the significance levels at the 1%, 5% and 10% respectively. After specifying the stationary level of variables, we use error correction based Gengenbach, Urbain and Westerlund ( 2016 ) and residual-based Pedroni (2014) co-integration tests which consider cross-sectional dependency and panel heterogeneity. The null hypothesis of no co-integration is rejected for both tests. That is, all variables are cointegrated for all clubs. Table 7 Panel Cointegration Tests Results Pedroni Cointegration Full Club 1 Club 2 Panel Group Panel Group Panel Group V 1.586 - 1.559 - 0.908 - Rho -3.667*** -2.399** -4.215*** -2.989*** -1.93** 1.345* T -8.061*** -8.74*** -8.038*** -8.719*** -4.345*** 4.818*** adf -6.368*** -6.766*** -6.824*** -7.224*** -3.948*** 4.9*** GUW Cointegration Test Full Panel Club 1 Club2 Coeff -1.025 -1.069 -1.112 T-bar -4.187 -4.501 -5.071 p-value <=0.01 <=0.01 <=0.01 ***, ** and * Indicate the significance levels at the 1%, 5% and 10% respectively. Ultimately, we estimated the long-run coefficients through the dynamic ordinary least squares mean group (DOLSMG) method. This method considers the cross-sectional dependency and the panel heterogeneity. The coefficients are estimated by transforming variables by taking the difference from the cross-sectional averages. As can be seen from the Table 8, all the estimated coefficients are statistically significant in our model. The findings indicate that GDP per capita has positive and GDP per capita square has negative effects on EF per capita for all clubs and full panel. These results show that the EKC hypothesis is valid for both clubs and full panel. These results are similar to those of Leal and Marques (2019), Zafar et al. (2019), and Chen et al. (2020) who find that EKC is valid for OECD countries. However, our result differs from Destek and Sinha (2020), Erdogan and Okumus (2021), and Ng et al. (2022). Table 8 DOLSMG Results FULL PANEL CLUB 1 CLUB 2 DOLSMG DOLSMG DOLSMG lngdp 3.411*** (0.000) 2.112** (0.000) 2.031*** (0.000) lngdp2 -0.135** -0.109*** -0.102*** (0.000) (0.033) (0.000) lnkof 0.392*** (0.000) 0.121** 0.826*** (0.000) (0.018) lnenr 1.12*** 0.297*** 0.683*** (0.000) (0.000) (0.000) ***, ** and * Indicate the significance levels at the 1%, 5% and 10% respectively. The value in the parentheses are P value. The results also indicate that globalization has a positive impact on EF. This result is consistent with that of Leal and Marques (2019), but inconsistent with those of Zafar et al. (2019), Yang et al. (2021), Chen et al. (2020), and Erdogan and Okumus (2021). According to the results, a 1% increase in KOF index approximately increases EFC by 0.4%, 0.1%, and 0.8% for full panel, Club 1, and Club 2, respectively. That is, the impact of globalization on EFC is higher for Club 2. On the other hand, a 1% increase in energy consumption approximately increases EFC 1.1%, 0.3%, 0.7% for full panel, Club 1, and Club 2, respectively. These results can be interpreted by the fact that Club 1 countries implement more environmentally friendly energy policies. When the countries in the clubs are considered, it is striking that developing countries such as Chile, Turkey, Mexico, and Portugal are in the 2nd club. As these countries are in relatively earlier stages of development, the income per capita level is relatively low. While these countries primarily focus on economic growth and employment by taking advantage of the opportunities such as international investment and trade that increase with globalization, the prevention of environmental degradation remains in the background. Therefore, the coefficients of globalization and energy consumption are higher as expected. On the other hand, as per capita income increases, environmental awareness can increase, and cleaner policies can be followed in these countries. [2] Due to the availability of data, the scope of the analysis is limited to the countries of Australia, Austria, Belgium, Canada, Chile, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, Korea Rep., Mexico, Netherlands, N. Zealand, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, UK, and USA. [3] See the Annex for the descriptive statistics of the variables. [4] For more detailed information about this methodology, see Phillips Sul (2007, 2009). 4. Conclusion And Discussion Global efforts to reduce the damage caused by humanity to the environment, especially GHG emissions, are increasing. One of the most important of these efforts is to target convergence in pollution indicators among countries. However, targeting convergence in pollution indicators is important in terms of equality and fairness, since the damage caused by countries to the environment differs significantly. Therefore, this heterogeneity between countries should be considered in econometric analyzes. Within this motivation, in this study, we analyze the role of globalization in EF and test the existence the EKC hypothesis for OECD countries over the period 1981–2015. To do so, we follow a different path from the existing literature. We first identify EF convergence clubs using the non-linear factor model introduced by Phillips and Sul ( 2007 ). Afterwards, we examine the impact of the globalization on the EF for all convergence clubs within the framework of EKC hypothesis. The club convergence test results show that there are two convergence clubs, each converging to a different state. While Club 1, which converges to a higher EF levels and includes 16 developed countries, the Club 2, which converges to a lower EF levels, includes 10 countries and some of them are developing countries. The results show that the EKC hypothesis holds for both convergence groups. However, the impact of globalization and energy use on the EF is higher for Club 2 which mostly includes developing countries. These results show that a different approach should be put forward in international negotiations on the environment. The EF levels are still higher in developed countries in OECD. On the other hand, the effects of globalization on the environment are more harmful for developing countries. Besides, relatively more polluted energy is used in these countries. Determining the same target for each country creates negativities in terms of applicability, fairness, and equality. Instead, a rapid transition period should be identified for developing countries that are at a relatively early stage of industrialization. In addition, financial and technical support should be provided to these countries to use cleaner energy in this process. However, in order to limit global warming to 1.5 degrees, countries should take measures in cooperation as soon as possible. Therefore, new commitments should be determined for all countries in order to reduce CO 2 emissions urgently, and all the nations must immediately start implementing policies in line with these new commitments. Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose. 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Introduction","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003cp\u003eThe anthropogenic pressure on the environment is one of the main problems of humanity. Although global efforts to prevent environmental degradation and using of renewable energy in this direction are gradually increasing, the use of fossil fuels still continues to be the primary energy source all over the world. The use of fossil fuels increases environmental degradation by causing an increase in greenhouse gas (GHG) emissions. Moreover, the disparities in the economic development levels and growth prospects of the countries cause the damage to the environment to differ between countries. The geographical distribution of GHG emissions does not have any effect on the total amount of GHG emissions in the atmosphere, but this distribution is an important topic of multinational discussions on climate change (Aldy, 2006). Since carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions are an important component of GHG emissions, discussions on how to reduce these emissions are ongoing. The convergence of these kinds of pollution indicators is regarded as a key goal in global efforts to stop environmental degradation, such as the Kyoto protocol and the Paris Agreement. However, there are two issues that need to be addressed regarding these negotiations. Firstly, targeting convergence in environmental indicators is very critical in terms of equality and fairness (Aldy, 2006; Barassi et al., 2011; Payne et al., 2014). Secondly, as a result of these negotiations, no success has been achieved in preventing environmental degradation yet. On the other hand, in order to limit global warming to 1.5\u0026deg;C, no more than 28 gigatons of CO\u003csub\u003e2\u003c/sub\u003e equivalent should be released into the atmosphere annually until 2030 (UNEP, 2021). Considering the recent emissions trend\u003csup\u003e[1]\u003c/sup\u003e, it is clear that our time for reducing CO\u003csub\u003e2\u003c/sub\u003e emissions is quite limited. \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMultinational negotiations on environmental degradation have also begun to attract the attention of researchers. In this direction, whether countries converge from the stand point of environmental degradation has become popular among researchers, and this subject analyzed in the EC framework (Apaydin et al., 2021). The theoretical basis of EC is derived from the EKC hypothesis introduced by Brock and Taylor (2004). As shown in the EKC literature, since an increase in efforts to stop environmental degradation and the use of cleaner technologies environmental degradation raises up to a particular income level and then decreases. According to this model, as countries get richer, it is expected that the growth rate of emission rates will decrease, and emission rates will converge (Acar and Lindmark 2017). Although there are different methods for measuring convergence (beta, sigma, and stochastic), the club convergence method is mostly used in recent studies (see Panopoulou and Pantelidis, 2009; Herrerias, 2012; Ulucak and Apergis, 2018; Haider and Akram, 2019; Solarin et al., 2019; Payne and Apregis, 2020; Erdogan and Okumus, 2021). The club convergence methodology introduced by Phillips and Sul (2007) enables us to test overall convergence and determine possible convergence clubs (Phillips and Sul, 2007, 2009). \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIn the literature, several indicators are employed a proxy for environmental degradation. The two most used indicators in the literature are EF and CO\u003csub\u003e2\u003c/sub\u003e emissions. While early studies mostly used CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions, recent studies have mainly focused on the EF proposed by\u0026nbsp;Wachernagel and Rees (1996). EF is a more comprehensive indicator than CO\u003csub\u003e2\u003c/sub\u003e as a proxy for environmental degradation as it comprises six components: carbon, grazing land, cropland, forest land, the footprints of fishing ground, and built-up land.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOne of the factors which is widely considered as an important factor for\u0026nbsp;environmental degradation\u0026nbsp;is globalization.\u0026nbsp;Globalization can generally be defined as the economic, political and social integration of the countries. The impact of globalization on the environment takes place in different directions with different factors. First of all, increasing international trade with globalization leads to a rise in CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions due to transportation. In addition, the increase in international trade and investments causes an increase in industrial activities which stimulates the economic growth specially in developing countries. But these increasing activities also enhance the environmental degradation due to the increasing consumption of electricity which is still mostly produced from fossil fuels. With globalization, deforestation has also accelerated, which indirectly but significantly affects environmental degradation (Huwart and Lo\u0026iuml;c, 2013). On the other hand, it has increased environmental awareness worldwide, and in this direction, international negotiations for the prevention of environmental degradation have gained momentum. There are many studies investigating the impacts of globalization on the environment in recent years. Some of these studies employed trade openness as an indicator of globalization (Halicioglu, 2009; Jayanthakumaran et al., 2012; Shahbaz et al., 2012; Farhani et al., 2014; Lau et al., 2014; Shahbaz et al., 2015; Haq et al., 2016; Acaravci and Akalin, 2017; Chen et al., 2019; Pata, 2019; Dauda et al., 2021; Pata and Caglar, 2021; Wang and Zhang, 2021; Wang and Wang, 2021)). However, trade openness covers only the international trade dimension of globalization. Since globalization has other dimensions such as capital flows, information flows, political and social aspects, globalization indices are used in recent studies (Shahbaz et al., 2017; Haseeb et al., 2018; Ahmed et al., 2019; Khan and Ullah, 2019; Phong Le, 2019; Zaidi et al., 2019; Ansari et al., 2020; Godil et al., 2020; Saud et al., 2020; Suki et al., 2020; Hussain et al., 2021; Jun et al., 2021).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWithin this context, we examine the impact of globalization on EF and the existence of EKC hypothesis for OECD countries over the period 1981-2015 using the dynamic ordinary least squares mean group (DOLSMG) estimator. However, unlike the existing literature, we follow a different path by considering the EC hypothesis. In the current literature, most of the studies analyze the EKC hypothesis for a single country or a group of countries. However, OECD countries are not homogeneous in terms of EF per capita. For instance, the EF per capita in the United States is three-time of that in Mexico. So, testing the EKC hypothesis for OECD countries in the same basket may cause misleading results. Thus, we first test the EC hypothesis using the non-linear dynamic factor model introduced by Phillips and Sul (2007). Subsequent to identifying EF convergence clubs, we test the EKC hypothesis and the impact of globalization and energy consumption on EF for each convergence club (that is, a more homogeneous group of countries in terms of EF per capita). Within this context, the present research contributes to the literature by being the first study to test the EKC hypothesis for OECD countries within the framework of the EC hypothesis. The rest of study organized as follows: Section two summarizes the related empirical literature. Section three illustrates the empirical analysis and results. Finally, section four concludes the paper.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e\u003csup\u003e[1]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eFor instance, total\u0026nbsp;CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions were 33 gigatons in 2021 (IEA, 2021).\u003c/p\u003e\n \u003c/div\u003e "},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Environmental Convergence Hypothesis\u003c/h2\u003e \u003cp\u003eThe EC hypothesis has been examined by using various indicators of environmental degradation. Some studies investigate the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions among global, states, country groups, or sector level. For instance, Nguyen Van (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) tests the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions among 100 countries for the 1966\u0026ndash;1996 period using the non-parametric method and finds that there is no convergence. Aldy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) investigates the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions across 88 countries over 1960\u0026ndash;2000 and the results support the absence of convergence. Ezcurra (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) tests the distribution of CO\u003csub\u003e2\u003c/sub\u003e emissions among 87 countries over 1960\u0026ndash;1999 following non-parametric method. The findings show that cross-country disparities decrease over time. Westerlund and Basher (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) examine the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions across 16 developed and 12 developing countries over 1870\u0026ndash;2002 employing unit root tests methods. They find a stochastic convergence for the full panel. Different from these studies, Panopoulou and Pantelidis (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) follow the club convergence method to test the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions across 128 countries for the 1960\u0026ndash;2003 period. The results reveal that there are two convergence clubs. Similarly, Herrerias (2013) investigates whether CO\u003csub\u003e2\u003c/sub\u003e emissions among 162 countries convergences over 1980\u0026ndash;2009 using the Phillips and Sul (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) method and pairwise test approach. The results of the pairwise test show that there is no convergence across countries. However, according to the findings of club convergence analysis, there are multiple convergence clubs. Li et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) test the convergence of global CO\u003csub\u003e2\u003c/sub\u003e emissions across 129 countries for the 1995\u0026ndash;2015 period using sigma, beta, stochastic, and club convergence methods. The findings reveal that global CO\u003csub\u003e2\u003c/sub\u003e emissions are convergent and the speed of convergence of consumption is slower than the production side. Burnett (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) tests the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions among 48 US states for the period of 1960\u0026ndash;2010 using club convergence and conditional beta convergence methods. The findings reveal that there is one convergence club for 26 states. Apergis et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examine the convergence intensity of CO\u003csub\u003e2\u003c/sub\u003e emissions across 50 US states for 1997\u0026ndash;2013 the period using cross-sectional tests methods. The results support the existence of sigma and beta convergence while there is no stochastic convergence across states. Tiwari et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conclude that there are four clubs across US states over 1976\u0026thinsp;\u0026minus;\u0026thinsp;201. Solarin (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) tests the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions across 39 African countries for the period of 1960\u0026ndash;2010 using univariate unit root test method. The findings confirm the existence stochastic convergence for 31 countries. Robalino-Lopez et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) test the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions for 10 S. American countries for the 1980\u0026ndash;2010 period. According to the results, there are two convergence clubs for CO\u003csub\u003e2\u003c/sub\u003e emissions and energy intensity. Tiwari and Mishra (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reveal that there are absolute beta convergence and sigma convergence for 18 Asian countries over 1970\u0026ndash;2010. Karakaya et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conclude that there is a stochastic convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions across 16 industrialized OECD countries for the 1990\u0026ndash;2013 period.\u003c/p\u003e \u003cp\u003eSome studies in the literature focus on the convergence for sector level such as Moutinho et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Br\u0026auml;nnlund et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Apergis and Payne (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moutinho et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) examine the convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions intensity across 16 Portuguese energy and industry sectors over 1996\u0026ndash;2009 using univariate and unit root test methods. The findings indicate that there are sigma convergence, gamma convergence and stochastic convergence among sectors. Br\u0026auml;nnlund et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) conclude that there is a conditional beta convergence in CO\u003csub\u003e2\u003c/sub\u003e emissions intensity for 14 Swedish manufacturing sectors over 1990\u0026ndash;2008. Similarly, Apergis and Payne (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) test the convergence in per capita CO\u003csub\u003e2\u003c/sub\u003e emissions among 50 US states by sector level and by fossil fuel source using panel unit root tests methods. The findings support the existence absolute beta convergence, sigma convergence, and stochastic convergence.\u003c/p\u003e \u003cp\u003eThe other group of studies focus on convergence in EF as environmental degradation indicator. For instance, Ulucak and Apergis (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) investigate the club convergence in EF for EU countries for the period of 1961\u0026ndash;2013, employing the Phillips and Sul (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) method. The results confirm that there are convergence clubs. Similarly, Bilgili and Ulucak (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) study the convergence in EF in G20 countries for the period of 1961\u0026ndash;2014. According to the findings, the club clustering algorithms support the presence of convergence clubs. Using the RALS-LM and LM unit root tests methods, Solarin (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examine whether the carbon footprint, CO\u003csub\u003e2\u003c/sub\u003e emissions, and EF converge among OECD countries for the period of 1961\u0026ndash;2013. The findings show that there is a conditional convergence in these indicators across countries. Ulucak et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) investigate the convergence in EF and its sub-components across 23 Africa countries for the 1961\u0026ndash;2014 period. The findings indicate that there are convergences for carbon, cropland, grazing land, fishing ground footprints. Erdogan and Okumus (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyze the convergence process for EF over 1961\u0026ndash;2016 using club convergence method. The results show that there are two convergence clubs for middle and low-income countries and four convergence clubs for high-income countries. Employing club convergence method, Apaydin et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) test the convergence in EF among 130 countries for the period of 1980\u0026ndash;2016, and conclude multiple convergence clubs across these countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Impact of globalization on environmental degradation\u003c/h2\u003e \u003cp\u003eOver the last three decades, the role of globalization in environmental degradation has been investigated by many researchers applying various econometric techniques and samples. Basically, we can classify the related literature into two categories: (i) The first group studies use CO\u003csub\u003e2\u003c/sub\u003e emissions, and (ii) the second group of studies use EF as a proxy for environmental degradation.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Impact of globalization on CO\u003csub\u003e2\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eSome studies in the literature use trade openness as an indicator of globalization to test the connection between globalization and carbon emissions. For instance, Lau et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) find a positive relationship between FDI, trade openness and carbon emissions for Malaysia over 1970\u0026ndash;2008 using ARDL bound testing method. The results verify the existence of EKC hypothesis. Shahbaz et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) find a positive relationship between CO\u003csub\u003e2\u003c/sub\u003e and trade openness for Portugal over 1971\u0026ndash;2018, and the findings confirm the EKC hypothesis. Haq et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reveal that an increase in trade openness decreases carbon emissions for Morocco over 1971\u0026ndash;2011 and the results corroborate the existence of the EKC hypothesis. Using the bootstrap ARDL approach, Pata (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) shows that a rise in trade openness stimulates CO\u003csub\u003e2\u003c/sub\u003e emissions in Turkey over 1969\u0026ndash;2017, and the results support the validity of the EKC hypothesis. Dauda et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that a rise in trade openness decreases the CO\u003csub\u003e2\u003c/sub\u003e emissions in African countries over 1990\u0026ndash;2016 performing fixed-effect model and generalized method of moments panel estimation models. Pata and Caglar (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that trade openness increases CO\u003csub\u003e2\u003c/sub\u003e emission in China over 1980\u0026ndash;2016. The results reveal that the EKC hypothesis is not valid for both EF and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eThe second group of studies uses KOF globalization developed by Dreher (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), which is more comprehensive rather than trade openness, as a proxy for globalization. Shahbaz et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigate the impact of globalization and sub-indices of globalization on CO\u003csub\u003e2\u003c/sub\u003e over 1970\u0026ndash;2012 in China. The results show that globalization is negatively related to CO2 emissions for China and the EKC hypothesis is valid. Haseeb et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examine the link between globalization and CO\u003csub\u003e2\u003c/sub\u003e emission for BRICS economies and find that the link between CO\u003csub\u003e2\u003c/sub\u003e emissions and globalization is not statistically significant; However, the findings confirm that the existence of EKC hypothesis.\u003c/p\u003e \u003cp\u003eZaidi et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) study the relationship between globalization, financial development, and carbon emissions in APEC countries and analyze the EKC hypothesis. The findings reveal that the relationship between globalization and carbon emissions is negative and EKC hypothesis is valid for APEC countries. Using Westerlund co-integration, Phong Le (2019) tests the impact of globalization on CO\u003csub\u003e2\u003c/sub\u003e emissions for ASEAN-5 countries and the EKC hypothesis. The findings reveal that the existence of the EKC hypothesis and globalization increases CO\u003csub\u003e2\u003c/sub\u003e emissions. Using ARDL methodology, Khan and Ullah (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analyze the link between globalization sub-indices and CO\u003csub\u003e2\u003c/sub\u003e emissions in Pakistan over 1975\u0026ndash;2014. They conclude that an increase in social, political and economic globalization increases the CO\u003csub\u003e2\u003c/sub\u003e emissions and the EKC hypothesis is valid in Pakistan. Using KOF globalization index, Jun et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyze the impact of globalization on CO\u003csub\u003e2\u003c/sub\u003e emission for South Asian economies over 1985\u0026ndash;2018. The results confirm the EKC hypothesis and indicate that globalization positively affects CO\u003csub\u003e2\u003c/sub\u003e emission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Impact of globalization on ecological footprint\u003c/h2\u003e \u003cp\u003eOver the past years, EF is widely used, which is a more comprehensive indicator rather than carbon emissions, as an indicator for environmental deterioration. Similar to the studies analyzing the link between carbon emissions and globalization, the first group of studies uses trade openness as an indicator for globalization. Al-mulali et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) find that trade openness increases EF for 93 countries. Furthermore, the findings reveal that the EKC hypothesis is valid in the high and upper-middle-income countries, whereas it is not valid for middle and low-income countries. Al-mulali et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) show that trade openness increases the EF in 58 developed and developing countries over 1980\u0026ndash;2019. Besides, the findings reveal that EKC hypothesis is not valid. Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) study the link between energy consumption, urbanization, tourism, trade openness, and EF for 144 countries over 1988\u0026ndash;2008. They find negative relationship between EF and other variables, and the EKC hypothesis is only valid for the upper-middle-income and high-income countries. Destek and Sinha (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) detect negative relationship between EF and trade openness across 24 OECD countries over 1980\u0026ndash;2014. The findings do not support the EKC hypothesis.\u003c/p\u003e \u003cp\u003eThe other group of studies uses the KOF globalization index as a proxy for globalization. Using Bayer and Hanck co-integration test and the ARDL bound methods, Ahmed et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) test the impact of globalization on the EF for Malaysia over 1971\u0026ndash;2014. The findings indicate that globalization significantly increases ecological carbon footprint. Ansari et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conclude that globalization impacts on the EF positively in Gulf Cooperation Council (GCC) countries over 1991\u0026ndash;2017 applying the dynamic ordinary least square (DOLS), and the fully modified ordinary least square (FMOLS) models. Their findings do not support the validity of the EKC hypothesis. Saud et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) analyze the link between financial development, globalization, and EF for selected one-belt-one-road initiative countries. The results indicate that there is a positive relationship between EF and financial development and there is a negative relationship between EF and globalization.\u003c/p\u003e \u003cp\u003eSuki et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) show that social and political globalization reduce the EF level, economic globalization and overall globalization increase the environmental degradation level in the long term, for Malaysia over 1970\u0026ndash;2018, and the results corroborate the existence of the EKC hypothesis. Godil et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find a positive relationship between globalization and EF, and authors identify the existence of the EKC hypothesis for Turkey over 1986\u0026ndash;2018. Hussain et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identify a significant and nonlinear relationship between globalization, natural resources and ecological EF and support the validity of the EKC hypothesis for Thailand over 1970\u0026ndash;2018.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Empirical Analysis","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.1. Data and Model\u003c/h2\u003e\n \u003cp\u003eTo examine the EKC hypothesis and the impact of energy consumption and globalization on EF in the EC framework, we use annual balanced panel data for the period 1981-2015 for 26 OECD countries\u003csup\u003e[2]\u003c/sup\u003e. The definitions, measure units, and the data source of the variables\u003csup\u003e[3]\u003c/sup\u003e are presented in Table I. We use EF per capita as a proxy of ecological degradation (denoted as EFC), GDP per capita as a proxy of economic growth (denoted as GDP), energy consumption per capita (denoted as ENR) and the KOF Globalization index (denoted as KOF). EF per capita and the KOF Globalization index data are collected from the Global Footprint Network webpage and the KOF Swiss Economic Institute webpage (Dreher, 2006 and Gygli et al., 2019), respectively. GDP per capita and energy consumption per capita data are obtained from the World Bank Development Indicators (WDI).\u003c/p\u003e\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDefinitions of Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasure Units\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEcological footprint per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal hectares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal Footprint Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010 US Dollars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKg of oil equivalent per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKOF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobalization index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnitless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwiss Economic Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eIn order to analyze the EKC hypothesis and the impact of globalization and energy consumption on EF, we use the following log linear quadratic function;\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${lnefc}_{it}={\\propto }_{0}+{{\\propto }_{1}lngdp}_{it}+{\\propto }_{2}{lngdp}_{it}^{2}+{\\propto }_{3}{lnenr}_{it}+{\\propto }_{4}{lnkof}_{it}+{u}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere i\u0026thinsp;=\u0026thinsp;1,2, 3,\u0026hellip;,N refers the cross-sectional units and t is the time dimension in panel estimation. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\propto }_{i }\\)\u003c/span\u003e\u003c/span\u003eindicates the long run elasticity of corresponding variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\u003ch2\u003e3.2. Empirical Methods and Results\u003c/h2\u003e\n\u003cp\u003eIn this study, to investigate convergence in EF, we employ the club convergence procedure\u003csup\u003e[4]\u003c/sup\u003e (i.e., \u0026ldquo;log \u0026nbsp; regression test) developed by Phillips and Sul (2007). Using the Phillips and Sul (2007) methodology, it can be determined whether there is convergence or not. In case of convergence, sub-convergence groups can also be determined with this methodology. Convergence is tested with the following log- \u0026nbsp; regression:\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\text{log}\\left(\\frac{{H}_{1}}{{H}_{t}}\\right)-2logL\\left(t\\right)=\\widehat{\\alpha }+\\widehat{b}logt+{\\epsilon }_{t},$$2\u003c/div\u003e\n \u003c/div\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{t}=\\frac{1}{N}\\sum _{i=1}^{N}{({h}_{it}-1)}^{2}\\)\u003c/span\u003e\u003c/span\u003e denoting the computation of variance ratio \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{H}_{1}}{{H}_{t}}\\)\u003c/span\u003e\u003c/span\u003e of cross-sections; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{it}=\\frac{{X}_{it}}{\\frac{1}{N}{\\sum }_{i=1}^{N}{X}_{it}}\\)\u003c/span\u003e\u003c/span\u003e represents the relative transition parameter. If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{\\widehat{b }}\u0026lt;-1.65\\)\u003c/span\u003e\u003c/span\u003e, then the null hypothesis of convergence can be rejected at the 5% level of significance.\u003c/p\u003e\n \u003cp\u003eThe results in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eindicate that the null hypothesis of full panel convergence in EF is rejected. That is there is no convergence for EF values of OECD countries in that period.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLog-\u003cem\u003eT\u003c/em\u003e Test Results (26 Countries)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT-statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;0.2571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.9086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNotes: The null hypothesis of convergence is rejected with the T-stats is smaller than \u0026minus;\u0026thinsp;1.65.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eDespite the rejection of null hypothesis for the full panel, there may be convergence clubs which converge to different equilibrium. In order to determine possible convergence clubs within the panel, club clustering procedure is applied. The results show that there are two convergence clubs. Each of these clubs converges to a different constant. The first and second clubs consist of 16 and 10 countries, respectively.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFinal Club Classification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClubs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT-statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClub 1 [16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e| Australia | Austria | Belgium | Canada | Denmark | Finland | France | Ireland | Israel | Korea Rep.| Netherlands |\u003c/p\u003e\n \u003cp\u003e| N. Zealand | Norway | Sweden | Switzerland |\u003c/p\u003e\n \u003cp\u003e| USA |\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClub 2 [10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e| Chile | Germany | Greece | Italy | Japan | Mexico | Portugal |\u003c/p\u003e\n \u003cp\u003e| Spain | Turkey | UK |\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: The null hypothesis of convergence is rejected with the T-stats is smaller than \u0026minus;\u0026thinsp;1.65.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reports the relative transition paths of all clubs which are helpful to comprehend the long-run tendencies between clubs. First of all, we can observe that while Club 1 is above panel mean which entirely consists of developed countries, club 2 is below panel average which includes some developing countries. That is, Club 1 and Club2 converge higher and lower EF levels, respectively. Furthermore, we do not observe convergence tendencies between convergence clubs.\u003c/p\u003e\n \u003cp\u003eGlobalization increases socio-economic interactions between the countries. These interactions make it necessary to consider the cross-sectional dependency in panel data models. Besides, each country may have its own characteristics. Therefore, to determine whether or not the slope coefficients are homogenous is also a critical issue in panel data analysis. Ignoring the cross-sectional dependency and the slope homogeneity can cause serious problems, such as inference results, invalid test statistics, and loss of efficiency (Grossman and Kruger, 1995; Pesaran and Smith, \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e: Coakley et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Phillips and Sul, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Chudik and Pesaran, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thus, we test the cross-section dependency and the slope homogeneity to determine the appropriate panel data methods.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCross-Section Dependency Test Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePesaran CD Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eFull Panel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnefc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.005***(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLngdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.638*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnkof\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.627 ***(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnenr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.622*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eClub 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnefc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.490*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLngdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.797*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnkof\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.799*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnenr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.790*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eClub 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnefc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.672*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLngdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.681*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnkof\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.673*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnenr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.672*** (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e***, ** and * indicate the significance levels at the 1%, 5% and 10% respectively. The value in the parentheses are \u003cem\u003eP\u003c/em\u003e value.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eWe employ Pesaran\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) cross-sectional dependence test (CD-test) which is derived from arithmetic mean of pairwise correlation coefficients of OLS residuals from individual regressions. This test can be applied for spatial panels, for balanced and unbalanced panels, for dynamic heterogeneous panels with multiple breaks and unit roots (Pesaran, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The results indicate that the null hypothesis of nonexistence of cross-section dependence is rejected for all variables in each model. So, there is a strong cross-section dependence between countries.\u003c/p\u003e\n \u003cp\u003eAfter specifying the cross-section dependency, we employ Pesaran and Yamagata (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) slope homogeneity test which is based on Swamy\u0026rsquo;s test. This test estimates restricted and unrestricted models and compares them. The standard errors for the individual cross section units are estimated using the weighted fixed effect in the restricted model, and the unit specific estimates of the ordinary least squares\u0026rsquo; estimator in the unrestricted model. This test is dependent on the difference of these two models. If the test statistic large enough, this means inconsistency between the fixed effects and the unit specific estimates and for this reason the null hypothesis of slope homogeneity can be rejected (Pesaran and Yamagata, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bersvendsen and Ditzen, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Test results indicate that homogeneity of slope coefficients is rejected for all models. Therefore, country-specific characteristics should be considered to avoid misleading inferences.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest for Slope Homogeneity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFull Panel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eClub 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eClub2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDelta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTo specify the stationary level of the variables, we use Pesaran\u0026apos;s (2007) unit root test which is based on augmented Dickey\u0026ndash;Fuller (ADF) test. This test allows cross-section dependence in heterogeneous panels. The first-differences of the individual series and the cross-section averages of lagged levels are used for eliminating the cross section dependence of the series in this test (Pesaran, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The unit root test results reveal that the null hypothesis of the non-stationarity cannot be rejected at levels for all variables in both with and without trend models. On the other hand, all the variables become stationary at their first difference.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePanel Unit Root Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWith Trend\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWithout Trend\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFirst Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eFull Panel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnefc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.599***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.542***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLngdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.787***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.575***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnkof\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.854***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.419***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnenr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.573***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.509***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eClub 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnefc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.838***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.819***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLngdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.623 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.355***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnkof\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.741***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.290***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnenr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.574***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.503***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eClub 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnefc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.207***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.209 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLngdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.978 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.650***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnkof\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.047***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.567***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnenr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.144 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.940 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e***, ** and * Indicate the significance levels at the 1%, 5% and 10% respectively.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAfter specifying the stationary level of variables, we use error correction based Gengenbach, Urbain and Westerlund (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and residual-based Pedroni (2014) co-integration tests which consider cross-sectional dependency and panel heterogeneity. The null hypothesis of no co-integration is rejected for both tests. That is, all variables are cointegrated for all clubs.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab7\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePanel Cointegration Tests Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ePedroni Cointegration\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eClub 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eClub 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.667***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.399**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.215***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.989***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.93**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.345*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.061***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.74***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.038***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.719***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.345***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.818***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.368***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.766***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.824***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.224***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.948***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eGUW Cointegration Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull Panel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClub 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClub2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoeff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-bar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e***, ** and * Indicate the significance levels at the 1%, 5% and 10% respectively.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eUltimately, we estimated the long-run coefficients through the dynamic ordinary least squares mean group (DOLSMG) method. This method considers the cross-sectional dependency and the panel heterogeneity. The coefficients are estimated by transforming variables by taking the difference from the cross-sectional averages. As can be seen from the Table 8, all the estimated coefficients are statistically significant in our model. The findings indicate that GDP per capita has positive and GDP per capita square has negative effects on EF per capita for all clubs and full panel. These results show that the EKC hypothesis is valid for both clubs and full panel. These results are similar to those of Leal and Marques (2019), Zafar et al. (2019), and Chen et al. (2020) who find that EKC is valid for OECD countries. However, our result differs from Destek and Sinha (2020), Erdogan and Okumus (2021), and Ng et al. (2022).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab8\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDOLSMG Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFULL PANEL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCLUB 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCLUB 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOLSMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOLSMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOLSMG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elngdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.411***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.112**\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.031***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003elngdp2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.135**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.102***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003elnkof\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.392***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.826***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003elnenr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.297***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.683***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e***, ** and * Indicate the significance levels at the 1%, 5% and 10% respectively. The value in the parentheses are \u003cem\u003eP\u003c/em\u003e value.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eThe results also indicate that globalization has a positive impact on EF. This result is consistent with that of Leal and Marques (2019), but inconsistent with those of Zafar et al. (2019), Yang et al. (2021), Chen et al. (2020), and Erdogan and Okumus (2021). According to the results, a 1% increase in KOF index approximately increases EFC by 0.4%, 0.1%, and 0.8% for full panel, Club 1, and Club 2, respectively. That is, the impact of globalization on EFC is higher for Club 2. On the other hand, a 1% increase in energy consumption approximately increases EFC 1.1%, 0.3%, 0.7% for full panel, Club 1, and Club 2, respectively. These results can be interpreted by the fact that Club 1 countries implement more environmentally friendly energy policies. When the countries in the clubs are considered, it is striking that developing countries such as Chile, Turkey, Mexico, and Portugal are in the 2nd club. As these countries are in relatively earlier stages of development, the income per capita level is relatively low. While these countries primarily focus on economic growth and employment by taking advantage of the opportunities such as international investment and trade that increase with globalization, the prevention of environmental degradation remains in the background. Therefore, the coefficients of globalization and energy consumption are higher as expected. On the other hand, as per capita income increases, environmental awareness can increase, and cleaner policies can be followed in these countries.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e\u003csup\u003e[2]\u003c/sup\u003e Due to the availability of data, the scope of the analysis is limited to the countries of Australia, Austria, Belgium, Canada, Chile, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, Korea Rep., Mexico, Netherlands, N. Zealand, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, UK, and USA.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"ftn2\"\u003e\n \u003cp\u003e\u003csup\u003e[3]\u003c/sup\u003e See the Annex for the descriptive statistics of the variables.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e[4]\u003c/sup\u003e For more detailed information about this methodology, see Phillips Sul (2007, 2009).\u003c/p\u003e\n \u003c/div\u003e "},{"header":"4. Conclusion And Discussion","content":"\u003cp\u003eGlobal efforts to reduce the damage caused by humanity to the environment, especially GHG emissions, are increasing. One of the most important of these efforts is to target convergence in pollution indicators among countries. However, targeting convergence in pollution indicators is important in terms of equality and fairness, since the damage caused by countries to the environment differs significantly. Therefore, this heterogeneity between countries should be considered in econometric analyzes.\u003c/p\u003e \u003cp\u003eWithin this motivation, in this study, we analyze the role of globalization in EF and test the existence the EKC hypothesis for OECD countries over the period 1981\u0026ndash;2015. To do so, we follow a different path from the existing literature. We first identify EF convergence clubs using the non-linear factor model introduced by Phillips and Sul (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Afterwards, we examine the impact of the globalization on the EF for all convergence clubs within the framework of EKC hypothesis. The club convergence test results show that there are two convergence clubs, each converging to a different state. While Club 1, which converges to a higher EF levels and includes 16 developed countries, the Club 2, which converges to a lower EF levels, includes 10 countries and some of them are developing countries. The results show that the EKC hypothesis holds for both convergence groups. However, the impact of globalization and energy use on the EF is higher for Club 2 which mostly includes developing countries.\u003c/p\u003e \u003cp\u003eThese results show that a different approach should be put forward in international negotiations on the environment. The EF levels are still higher in developed countries in OECD. On the other hand, the effects of globalization on the environment are more harmful for developing countries. Besides, relatively more polluted energy is used in these countries. Determining the same target for each country creates negativities in terms of applicability, fairness, and equality. Instead, a rapid transition period should be identified for developing countries that are at a relatively early stage of industrialization. In addition, financial and technical support should be provided to these countries to use cleaner energy in this process. However, in order to limit global warming to 1.5 degrees, countries should take measures in cooperation as soon as possible. Therefore, new commitments should be determined for all countries in order to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions urgently, and all the nations must immediately start implementing policies in line with these new commitments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVolkan Bektaş\u003c/strong\u003e Conceptualization, Data curation, Methodology, Software, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNeslihan Ursavaş\u003c/strong\u003e Methodology, Software, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003c/div\u003e"},{"header":"References","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003col\u003e\n \u003cli\u003eAcar, S., Lindmark, M., (2017) Convergence of CO2 emissions and economic growth in the OECD countries: Did the type of fuel matter?, Energy Sources, Part B: Economics, Planning, and Policy, 12:7, 618-627, DOI: 10.1080/15567249.2016.1249807\u003c/li\u003e\n \u003cli\u003eAcaravci, A., Akalin, G., (2017) Environment\u0026ndash;economic growth nexus: a comparative analysis of developed and developing countries. 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Journal of Cleaner Production, 228, 533-543.\u003c/li\u003e\n \u003c/ol\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ecological Footprint, Club Convergence, Environmental Kuznets Curve, Globalization","lastPublishedDoi":"10.21203/rs.3.rs-1914497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1914497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper aims to investigate the role of globalization in ecological footprint for OECD countries during the 1981\u0026ndash;2015 period with the Environmental Kuznets Curve (EKC) framework. To do so, unlike the existing literature, we follow a different path. Firstly, we test the environmental convergence (EC) hypothesis using the Phillips and Sul (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) methodology. Then, we examine the impact of globalization and energy consumption on ecological footprint (EF), and test the existence of EKC hypothesis using the dynamic ordinary least squares mean group (DOLSMG) estimator. The convergence test results indicate that OECD countries do not converge to same steady-state levels with regard to EF levels. However, we identify two convergence clubs that converging to a different steady-state equilibrium. The results of DOLSMG reveal that the EKC hypothesis is valid for both convergence groups. Furthermore, the impact of energy consumption and globalization on EF is higher for Club 2 which mostly includes developing countries.\u003c/p\u003e","manuscriptTitle":"Revisiting The Environmental Kuznets Curve Hypothesis with Globalization for OECD Countries: The Role of Convergence Clubs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-26 18:28:06","doi":"10.21203/rs.3.rs-1914497/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2022-10-10T21:30:44+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-08-24T19:35:43+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-24T17:22:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2022-08-18T16:44:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-10T05:24:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2022-07-31T09:19:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e0652d58-f808-4f67-9a72-39e77a66a0c5","owner":[],"postedDate":"August 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-20T14:56:58+00:00","versionOfRecord":{"articleIdentity":"rs-1914497","link":"https://doi.org/10.1007/s11356-023-25577-6","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2023-02-03 18:40:49","publishedOnDateReadable":"February 3rd, 2023"},"versionCreatedAt":"2022-08-26 18:28:06","video":"","vorDoi":"10.1007/s11356-023-25577-6","vorDoiUrl":"https://doi.org/10.1007/s11356-023-25577-6","workflowStages":[]},"version":"v1","identity":"rs-1914497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1914497","identity":"rs-1914497","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}