The Relationship between Globalization, Energy Consumption, and Economic Growth in Selected South Asian Countries

The Relationship between Globalization, Energy Consumption, and Economic Growth in Selected South Asian Countries | 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 The Relationship between Globalization, Energy Consumption, and Economic Growth in Selected South Asian Countries Apu Chandra Das This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4637577/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzes the relationship between globalization, energy consumption, and economic growth among selected South Asian countries. This study also finds causal association between energy growth and nexus of CO 2 emissions, and employed the premises of the EKC framework. The study used annual time series analysis, starting from 1972 to 2017. The data set has been collected from the world development indicator (WDI). The result of a fully modified ordinary least square (FMOLS) method describes a significantly worsen the quality environment in the south Asian region. The individual country as Bangladesh shows a positively significant impact on the CO 2 emissions and destroying the level of environment regarding non-renewable energy and globalization index. However, negative and positive growth level (GDP) and square of GDP confirm the EKC hypothesis in this region. This study has identified the causality between GDP growth and carbon emission and found bidirectional causality between economic growth and energy use. Globalization energy consumption South Asian countries Figures Figure 1 Introduction Several economies try to increase the level of competition and economic growth; thus, globalization is playing an important role and can produce sustainable economic growth in developing countries. Recently, various countries have been connected to each especially culturally, socially, economically, and politically, due to globalization and advanced information systems. Globalization permits advanced technology and its transfer through FDI from advanced nations to less technological advancement countries. Therefore, globalization’s role in determining economic growth is crucial, and It enhances human economic activities by owing to technological innovations and investment activities (foreign direct investment) of the country. Conversely, the worldwide demand for goods and services has required increased energy demand; it concluded economic growth is mainly driven by energy. Thus, energy consumption of non-renewable is highly associated with the growth rate, worsening the environment’s quality. The leading cause of deforestation, depletion of natural resources, climate change, and global warming is carbon dioxide emissions and globalization. The economy’s growth (GDP) is highly connected with energy use and takes as a measure for the “oxygen” for the whole world’s countries. Non-renewable energy is a prerequisite for the achievement of economic growth. Various developing countries and their inhabitants (especially South Asian countries) are living below the poverty line. To lift millions of inhabitants out of poverty, many South Asian countries are trying to achieve economic growth through industrialization, globalization, and trade liberalization. Additionally, Climate change and global warming represent preeminent global issues. Sea levels are rising, snow and ice are melting in polar zones, and due to global warming, the average temperature of the earth is increasing. Notably, more significant government efforts can be reduced poverty and environmental degradation, and in these economies, the issue is to achieve sustainable economic growth. Unfortunately, land degradation, agricultural development, industrial development, infrastructure development, and other practices have deteriorated environmental quality. The leading cause of climate change and the rise in earth temperature is carbon dioxide emissions. GHGs represent a combination of gases, but CO 2 makes up approximately 75 percent of these gases in the atmosphere. These gases move from one place to another continuously, stay within the biosphere, and not disappear/dissipate for thousands of years. The Joint Research Center of the European Union (EU) reported that the world’s ninety percent CO 2 emissions are fossil fuel incineration. in recent decades, primarily responsible for environmental degradation is the most developed economies; however, the ratio of CO 2 emissions has also increased in developing countries. This converts ocean water into carbonic acid, and marine life and coral reefs are being damaged due to high carbon dioxide rates in the atmosphere. Global warming and environmental degradation have lately been a major challenge for the nations of the world. Increasing CO 2 emissions and other greenhouse gas (GHG) emissions have produced extensive environmental effects. These effects have created unexpected changes in weather conditions, increased earth temperatures, and presented more significant dangers to ecosystems. The answers to some questions can be obtained from the EKC model, such as whether an economy can achieve economic growth without worsening the ecological system and whether the environmental quality is deteriorating by the rapid economic growth. Prior empirical and theoretical studies have discussed the energy-growth-emission nexus in well-intentioned works; however, more research is required on this topic for further policy recommendations due to inconclusive findings. Additionally, the determinants of CO 2 emissions in terms of fossil fuel energy sources and economic growth have been discussed in several empirical works explored the growth and environmental degradation nexus and also discussed other pollutants. Similarly, nitrogen and sulphur dioxide have been examined; others have also observed sulphur dioxide. Different studies assessed different countries and different periods and used different methodologies, which leads to inconsistent findings; thus, the quality of the environment can be strengthened by the effective use of energy and sustainable development policies of growth. More effective policies are required, especially in Asian countries where emissions remain high. This study addresses the energy-growth carbon emission nexus, thus based on sequential past empirical research using some control variables under the EKC hypothesis. Energy demand is essential for growth, but the supply side is limited (the supply of conventional oil and gas are predicted to decline). Nevertheless, geopolitical, economic, environmental and technological challenges are confronted by the energy sector. Thus, energy is vital and increases environmental degradation in economic growth. The next century will face many energy challenges. Among these, energy demand and environmental degradation will be the largest issues due to rapid economic growth and dependence on energy sources. CO 2 emissions and climate change are becoming more prevalent due to fuel combustion. Energy is essential for industrial and agricultural production, and this energy increases CO 2 , nitrous oxide and methane emissions. As a primary energy source worldwide, the fossil energy ratio is rising as fossil fuels produced 82 percent of the global’s energy in 2015. This ratio has remained roughly the same for the last 40years, as reported by the IEA. Renewable energy sources represent alternatives on-renewable sources and can be helpful in overcoming these issues. Environmental pollution and CO 2 emission are strongly affected by economic growth and development. The growth-environmental degradation nexus is not a focus of the early stages of economic growth, environmental issues are not sufficiently considered, and advanced technologies have-not been accessible to solve these issues. Consequently, as per capita income increases, the level of environmental degradation also increases. Numerous researchers have found the links between CO 2 pollution, economic development and energy sources as significant. Various explanatory variables are also incorporated in the analysis of the emission, growth and energy consumption nexus, such as trade openness, financial development, population, and other factors. This well-studied topic uses different time period frameworks for different countries, with different methodologies and different controlled variables in various empirical analyses. The findings of these studies conflict with each other and vary according to the country assessed. The tri-variant nexus between CO 2 emission, growth and energy has been significantly explored by various researchers. Even though environmental pollution caused by energy and growth are especially important in Asian countries, however few researchers have analyzed the topic as a group for the South Asia region. In the context of the South Asian countries, this subject is also not well documented. Thus, this research includes existing studies on countries in South Asia. As a sub-sample, there have been some important past research, including in Asia-Pacific economies. Finally, following the global agenda for reducing CO 2 emissions, this study investigated the GDP growth, CO 2 emission relationship to determine the nexus between CO 2 emissions and growth, as well as to provide suggestions for further policy making. The adoption of weak econometrics techniques, wrong statistical data, ignoring diagnostic testing or neglecting random walk trends and serial dependence in time series analysis can be observed in testing the EKC hypothesis. The results maybe spurious if incorrect statistical techniques are applied. To overcome these issues, this study uses an overview of the cross-country panel time series to test the interim and long-term associations under the EKC scheme between the study variables. As an alternative, this study also used renewable energy and technological innovation impacts on environmental quality to obtain the most robust results. Therefore, this study examines the globalization, growth-emission nexus with other selected variables due to its importance in policymaking and sustainable economic growth across the globe. The study tries to identify the causal growth-energy and environmental degradation nexus under the EKC framework’s premises for selected South Asian economies. The unique contribution of our study is tried to overcome a vacuum in the recent studies, under the framework of EKC and by adding some plausible variables. This paper used the most recent data with the latest econometrics techniques and a robust model to fill this vacuum in the empirical literature. The research study question is tightly focused on the policies of environmental sustainability. The main objective of this study is to identify the role of globalization economic growth, i.e., Does economic growth significantly increase/ decrease CO 2 emissions in the south Asian countries? and Do the selected Asian countries demonstrate an environmental Kuznets curve to demonstrate the growth-CO 2 emission hypothesis? Similarly, Can GDP growth be achieved without worsening environmental health in these countries? The literature review is discussed in second section. The model’s theoretical framework, a model, and the econometrics methodologies, including the data description, are described in the third section. The results are predicted in fourth section. Finally, the conclusion is discussed in the fifth section and gives suggestions for further researchers and policymakers. Literature review A detailed description of the literature review is given, which is based on theoretical, conceptual, and empirical analyses of the nexus between energy-growth-induced emissions. The relation between GDP growth and CO2 emissions alone cannot be presented in comprehensive detail; therefore, following the literature review by Nassani et al., this study incorporates various other variables and examines their impacts on CO2 emissions. The studies on Asian countries are discussed as the subsample, and the findings are not identical. However, except for Niu et al., all other studies have taken various countries to be a whole group. Furthermore, only a few studies used subsamples in the context of Asian countries. This deficiency in previous studies provides motivation for further analysis on this topic with some additional information and changes. With all these deficiencies, this study seeks answers to the research queries. Recently, the EKC hypothesis has been tested by Jaunky and Cowan et al. and confirmed the existence of EKC tests for cross-country panel data. Besides, Cicea et al. and Ibrahim and Law have assessed panel data under the EKC hypothesis and found mixed results. Similarly, the EKC hypothesis has been tested by Ajmi et al. for G7 nations and found that the results do not verify the authenticity of the EKC by using Granger’s causality test. The energy use, CO2 emissions, and economic growth nexus has also been examined by various researchers who have not reached the same conclusions. In the USA setting, Soytas et al. applied enhanced vector autoregression and observed that CO2 emissions are a big source of energy consumption caused by Granger. In addition, they have also explored the energy, CO2 emission, and energy growth nexus but have not found any causality among them for the USA; similarly, the results of Soytas and Sari and Ghosh for Turkey and India, respectively, do not indicate any causal relationship among energy, growth, and CO2 emissions. The plentiful empirical analyses on the growth-environment nexus in the innovative EKC premises have made significant contributions to empirical analysis (especially the studies of Shafik and Bandyopadhyay, Panayotou, and Grossman and Krueger). Various past empirical works have been performed on this topic to investigate the EKC premises using panel data approaches. Similarly, various empirical works have employed time series data for cross-country or single-country analysis. Thus, this study attempts to estimate the panel group data analysis under the framework of EKC and will thus also contribute to the existing literature. Through the use of these possible variables within the EKC system, the contribution of this analysis is special, which makes this research distinct from other studies and helps fill a literature void. Furthermore, this research includes the structure of energy use (non-renewable and renewable energy), technological innovation based on CO2 mitigation, the financial growth role, and trade openness under the umbrella of the EKC framework. Globalization with non-renewable sources has not been grouped together (in the framework of the EKC hypothesis) in the South Asian countries background; this research used the new evidence in the scientific literature using the most modern econometric methods and a robust model. The research study question is tightly focused on the policies of environmental sustainability with the help of globalization and renewable sources of energy. Numerous past empirical studies have extensively covered environmental sustainability. They have provided a sustainable framework and various policy implications for environmental protection and sustainable economic growth in the literature; the growth environmental pollution nexus can be mentioned in the scheme of an inverted U-shaped environmental Kuznets curve (EKC). The environmental quality-growth relation in three stages was defined by Grossman and Krueger. Since the 1990s, the nexus of environmental-growth was determined by this curve. Grossman and Krueger have used this curve to examine the North American Free Trade Agreement’s environment-growth link. Different analytical analyses initially discussed the EKC’s premise for investigating the connection between climate and economic growth and overlooked the role of other related variables. The three different stages of the EKC framework show that economic growth initially worsens the quality of the environment at a threshold level, but after a certain maximum point, the curve moves downward; thus, the CO2 emissions-economic growth nexus is negative. Panayotou first defined the indicators of the pollution-GDP growth nexus under the EKC scheme. The EKC framework is discussed by Stern et al., Ekins, and Gani and evaluated the effects of economic growth on environmental quality. Subsequently, Dinda and Stern discussed this EKC hypothesis in their empirical analysis. Since the EKC theory was presented in the 1990s, researchers, economists, and environmentalists have focused their attention on the environment-growth nexus under EKC’s framework in their theoretical and empirical analyses. The income inequalities and growth (inverted U shaped) nexus is widely discussed in the past literature and was initially developed by Kuznets in 1995. This idea was supported by various researchers in their analysis to identify the environment and its relationship with income in the EKC scheme (followed by Grossman and Krueger, Selden and Song, and Vincent). Meadows et al. highlighted environmental sustainability awareness for the first time in the 1970s, but in the 1990s, Grossman and Krueger, Panayotou, and Selden and Song described the environmental issues resulting from rapid economic growth and development. These studies have also tested and confirmed the EKC hypothesis. The income-environment relationship under the scheme of the EKC has also been examined by numerous empirical studies. These analyses have supported the EKC in their analyses by using different techniques. Based on the assessments of economic growth and GHG emissions under the premises of the EKC hypothesis, Apergis and Ozturk, Al-Mulali et al., and Jebli et al. have included various additional explanatory variables in their analysis. The empirical studies of Tugcu et al., Mensah, Acaravci and Ozturk, Apergis and Ozturk, Al-Mulali et al., and Jebli et al. have included various additional explanatory variables in assessments of the carbon emissions-growth nexus. These studies included energy efficiency, energy dependency, and economic structure as control variables with economic growth to observe the EKC hypothesis’s growth-environment relationship. They found that economic growth activities can significantly increase the level of GHG emissions. Lise has also tested this hypothesis for Turkey and India and has not found any CO2 emission-growth nexus. The empirical findings of Robalino-Lopez et al. do not support Ecuador and Venezuela’s EKC hypothesis. Various prior literature includes various indicators of CO2 emissions, e.g., growth, the structure of energy use (renewable versus non-renewable), financial development, technological innovation, liberalization of trade and urbanization, and under the EKC premises. However, many countries have recently supported the EKC hypothesis, which asserts the globalization, economic growth-CO2 emission nexus. Generally, the production of goods and services and its growth cannot be achieved without impacting environmental quality and without consuming energy sources. So, Shahbaz et al. have applied the methodology of a NARDL model for Japan to find the nexus between globalization, environment, and energy-growth between 1970 and 2014. According to their results, the environment’s quality is significantly worsening due to rapid growth and development processes as a result of globalization’s growth and expanded use of fossil fuels. Recently, Shahbaz et al. have used panel data analysis between 1970 and 2012 for China. Their research investigated the EKC hypothesis and the correlations between globalization and CO2 emissions were also examined. The findings suggest a substantial decline in CO2 emissions from globalization. Moreover, Shahbaz et al. examined the EKC hypothesis over 1970–2010 for Turkey, and they also verified the EKC hypothesis: increases in the rate of globalization significantly decrease CO2 emissions. Shahbaz et al. explored the intensity of energy, globalization, and carbon emissions nexus for 19 African countries throughout 1971–2012. Their research supports the existence of the EKC hypothesis for Algeria, Congo Republic, Zambia, Cameroon, Morocco, and Tunisia. Additionally, the study Shahbaz et al. for 25 developed countries examined the globalization-carbon emissions relationship during 1970–2014. The findings show that globalization is significantly increasing CO2 emissions. Recently, Haseeb et al. have shed light on the nexus between CO2 emissions and its essential components: growth, globalization, energy use, financial development, and urbanization for Russia, India, Brazil, South Africa, and China nations. They have tested the EKC hypothesis and confirmed the EKC for these countries. Their findings show that CO2 emissions significantly decrease with an increase in globalization. However, Shahbaz et al. in their study, used the test “Bayer and Hanck cointegration” and “vector error correction model” (VECM) to examine the effects of globalization on CO2 emissions for India from 1970 to 2012. Environmental quality was found to be positively associated with globalization. Nonetheless, to the best of our knowledge, many researchers have given less attention to globalization, energy sources, and CO2 emissions for the countries of South Asia. Thus, this study is based on past research using the EKC hypothesis with a few additional variables to fill the gap with a chronological empirical analysis. This analysis utilizes these theoretical aspects and assesses economic growth, energy use, environmental pollution, globalization, and other variables under the EKC method scheme. This analysis used the most recent data (1972–2015) with the latest econometric techniques, and to fill the previous literature gap, a robust model is used in the empirical literature. Thus, various econometric techniques such as heterogeneous co-integrated panels (with cross-sectional dependence tests), panel unit root tests, the panel co-integration test (the Kao and Fisher), the Fully Modified OLS (FMOLS) test, the test of Granger causality, and “the Innovative Accounting Approach Methodology This paper examined the fuel consumption and growth-led CO 2 emission concerning the EKC hypothesis. Using data from 1972–2017 for selected South Asian countries such as Bangladesh, India, Nepal, Pakistan, and Sri Lanka, this paper is used World Development Indicator (WDI) 1 data to implement the panel time series analysis. Theoretical framework and hypothesis This study aims to identify the effects of globalization, energy growth, and technological change in South Asian countries and the extent to which the sustainable environmental agenda influences this causal relationship. It is observed that due to technological advancements and modern usage, the consumption of energy increases globally. This analysis comprises and tests the following two hypotheses: Hypothesis 1 There is an inverted U-shaped Environmental Kuznets curve (EKC) association between CO2 emissions and GDP growth for the selected South Asian countries. Hypothesis 2 It is expected that globalization can be harmful to the country’s economic growth, which could be a sustainable pollution haven hypothesis across the countries. The environmental degradation-growth nexus can be described in the EKC hypothesis (with an inverted U-shape). Grossman and Krueger followed up on the work of Kuznets and described the environmental quality-growth nexus in three stages. The authors discussed environmental degradation issues due to natural resource depletion. Environmental quality has been significantly reduced by countries attempting to achieve the highest economic growth. Table 1. Summary of data description and descriptive statistics. Data description CO 2 Carbon emissions (in per capita of metric tons) (WDI, 2018) GDP GDP per capita (US $ with the base year of 2010) (WDI, 2018) NRENW Non-renewable energy consumption % of total final energy consume. (WDI, 2018) GLOB The KOF index of globalization (KOF, 2019) Descriptive statistics Description LnCO 2 LnGDP Ln GDP 2 LnNREW LnGLOB Mean 9.757 6.141 12.282 3.793 3.444 Median 9.784 6.055 12.110 3.844 3.447 Maximum 11.200 6.826 13.653 4.300 3.955 Minimum 8.163 5.761 11.522 3.030 2.897 Sd. Dev. 0.891 0.299 0.598 0.392 0.331 Skew-ness –0.096 0.796 0.796 –0.456 –0.043 Kurtosis 1.887 2.515 2.515 1.925 1.811 Jarque-Bera 2.390 5.203 5.203 3.727 2.661 Probability 0.302 0.074 0.074 0.155 0.264 Sum 439.097 276.366 552.732 170.729 155.003 Sum Sq. Dev. 34.938 3.944 15.777 6.779 4.841 The author discussed environmental degradation issues due to natural resource depletion. Environmental quality has been significantly reduced by countries attempting to achieve the highest economic growth in this first stage. Beyond this initial stage, the economies’ main goal is to attain sustainable economic growth and welfare with technological innovation (clean environmental-based technologies) and to develop environmental policies to mitigate CO2 emissions. Thus, economies, after reaching the highest level of income per capita, wish to move from poor environmental conditions to a clean environment for sustainable economic growth. The analysis of EKC hypotheses regarding incomes, pollution, and other essential variables in a GDP square function has been used by various policymakers and researchers in the area of environmental economics. To analyze the growth-environmental pollution nexus, this study applied EKC’s theoretical framework in Eq. (1) (Grossman and Krueger). The theoretical framework of the EKC is used in the following econometric model: This study has included a few additional explanatory variables in assessments of the GHG emissions-economic growth nexus under the premises of the EKC hypothesis. Where CO2it represents the carbon emission (per capita) level (environmental pollution), Yit represents GDP (per capita) income (economic growth), and other influential macroeconomic variables are indicated by Xit. To make the model consistent and efficient with a meaningful interpretation, the natural log is used for Eq. (1): The influence of non-renewable energy sources, GDP growth, and globalization on CO 2 emissions in the selected South Asian countries through 1972–2017 are mentioned in equation (3) and can be written as follows: Before testing the co-integration method, it is necessary to identify the statistical properties of the model regarding stationary. In the model, it is essential to assess the unit root’s presence due to dependent and independent variables with its long-run association. Thus, following the co-integration test, the order of integration may be the same for all the employed variables. Thus, various unit root tests have been designed in this study. For this purpose, the prerequisite in time series econometrics analysis is unit root test. This study used various unit root tests to control the problem of non-stationary data in the time series data. The regression results will be biased or may calculate a spurious regression if time series variables are not stationary. Maddala and Wu suggested that multiple unit root tests might be employed to control the problem of individual regression inaccuracies across thecross-sections.Thisstudyfindsnoevidenceregardingthepresenceofunitrootinthepanel data series after applying the cross-section independence test. The two essential subgroups of unit root analysis are divided into line with cross-sectional independence. In this study, we examine the homogenous and heterogeneous cases of panel unit root tests, assessing the appropriateness and implications of each for robust statistical inferences. Homogeneous (Common Unit Root Process) Case Levin–Lin–Chu (LLC) Test The LLC test is widely used for testing unit roots in panel data. This test assumes homogeneous cross-sections, meaning that the dynamics of the panel are the same across all entities. The LLC test is an extension of the Augmented Dickey-Fuller (ADF) test, incorporating homogeneity in autoregressive coefficients. Studies such as Bildirici et al. ( 2012 ) have suggested that the LLC test offers superior non-stationary testing compared to other common panel unit root tests. Methodology: • Assumption Identical autoregressive coefficients across cross-sections. • Test Procedure : Extends the ADF test by incorporating a common unit root process. • Advantage : Robust against non-stationary data in homogeneous panels. Heterogeneous Case Tests for Heterogeneity Homogeneity is often a restrictive assumption in panel data analysis because it disregards the dynamic properties that may vary across different series. To address this, alternative tests allow for heterogeneity among cross-sections: Fisher-ADF and Fisher-PP Tests : These tests combine p-values from individual ADF or Phillips-Perron (PP) tests, accommodating heterogeneity across panels. Im, Pesaran, and Shin (IPS) Test : Designed by Im et al., this test allows for heterogeneity in the autoregressive coefficients. Methodology: • Assumption • Different dynamic properties across cross-sections. Test Procedure : Uses individual unit root tests combined to form a panel statistic. Advantage : Accounts for heterogeneity, reducing the risk of spurious results. Cross-Section Dependence (CSD) To address cross-section dependence, four significant tests are employed: 1. Breusch and Pagan LM Test 2. Pesaran's CD Test 3. Baltagi et al. LM Test 4. Pesaran Scaled LM Test Addressing Endogeneity and Serial Correlation Pedroni's Non-Parametric Approach To overcome issues of endogeneity and serial correlation, Pedroni's (1999) non-parametric approach is used. This approach helps in mitigating biases in coefficient estimates in panel data regression. The study utilizes Fully Modified Ordinary Least Squares (FMOLS) to obtain long-run parameter estimates. Methodology: Endogeneity and Serial Correlation : Addressed using Pedroni's non-parametric approach. Estimation Method : FMOLS for robust long-run parameter estimation. Granger Causality To identify causal relationships between dependent and explanatory variables, the study employs the Granger causality test. This involves examining whether past values of one variable can predict future values of another. Methodology: Granger Causality Test : Determines causal correlations with lagged values of variables. Panel Dumitrescu and Hurlin Test : Used to assess causality in a panel data context. Innovation Accounting Approach (IAA) The empirical analysis includes two methods under the Innovation Accounting Approach: Variance Decomposition Method (VDM) : This method quantifies the proportion of the forecast error variance of each variable that is attributed to shocks to other variables. Impulse Response Function (IRF) : This method traces the effect of a one-time shock to one of the innovations on current and future values of the variables in the system. Methodology: Variance Decomposition Method (VDM) : Assesses the contribution of each shock to the variance of forecast errors. Impulse Response Function (IRF) : Analyzes the dynamic effects of shocks on the variables over time. Empirical Analysis and Policy Implications The sequential steps in this empirical analysis ensure robust statistical inferences. The findings from the CSD tests (Table 3) and panel unit root tests (Table 4 ) provide a solid foundation for understanding the dynamic relationships and causal linkages among the variables studied. These insights are valuable for policymakers, enabling them to make informed decisions based on robust empirical evidence. Data description This paper examines the relationship between energy use, economic growth, and CO2 emissions under the Environmental Kuznets Curve (EKC) hypothesis in South Asian countries (Bangladesh, India, Pakistan, Nepal, and Sri Lanka) from 1972 to 2017. CO2 emissions are used as a proxy for environmental degradation, GDP per capita represents economic growth, and non-renewable energy use is measured per capita and as a percentage of total energy consumption. The globalization index, incorporating social, economic, and political dimensions, assesses globalization's impact on environmental degradation. The study highlights that economic growth initially leads to increased environmental degradation due to neglect of environmental issues and lack of advanced technologies. The complex relationship between CO2 emissions, growth, and energy consumption is influenced by factors like trade openness, financial development, and population, with findings varying across countries. The study uses robust statistical methods to offer insights for policymakers on balancing economic growth with environmental sustainability. Empirical Results and Discussion The descriptive statistics are essential for explaining the crucial features of the statistical data. The statistical results of descriptive statistics of the explanatory variables are given in Table 1. The statistical findings of Cross-Sectional Dependence (CSD) are reported in Table 2 . To find CSD’s presence between the panel data, we have used four tests: Pearson LM Normal, Pearson CD Normal, Breusch-Pagan Chi-square, and Friedman Chi-square. The findings of CSD show that in a panel data analysis, the cross-sectional dependency found between the data and significance of p-values are rejected the null hypothesis. The acceptance of the alternative hypotheses verified the cross-section reliance among these South Asian countries. Table 3 reports the unit root result by using the tests of Im et al.,Breitung, and Hadri,respectively. The cross-section dependence test can be used to detect the Table 2. The results of the residual cross-section dependence test. Test Statistic Prob. Null hypotheses Result Breusch-Pagan Chi- square 7.245 1 No cross section dependence ( CSD ) in residuals Reject Pearson LM Normal 1.039 1.08 No cross section dependence ( CSD ) in residuals Reject Pearson CD Normal 0.281 0.99 No cross section dependence ( CSD ) in residuals Reject Friedman Chi-square 23.895 0.78 No cross section dependence ( CSD ) in residuals Reject Table 3. Panel unit root test analysis. Variables t-values p-values t-values p-values t-values p-values lnCO2 it –1.078 0.4130 –1.875 0.0060*** –2.606 0.0040*** lnGDP it –19.949 0.01200*** –2.889 0.0050*** –3.037 0.0000*** ln(GDP it ) 2 –35.587 0.0000*** –2.909 0.0030*** –3.406 0.0030*** lnNRENW it 16.742 0.0010*** –4.293 0.0020*** –2.979 0.0010*** lnGLOB it –0.868 2.8010 –4.038 0.0020*** –4.62 0.0000*** Note: ***, ** signifies a 1 and 5% level of significance. heterogeneity in the panel model. Thus to control the heterogeneity across the panel model, this study used an alternative IPS test designed by Im et al. 1 Table 3 reports the results of the Hadri, Breitung,and Im et al. 109 tests, as all variable found stationary at the level in line with Hadri 107 and Im et al. while some variables are not stationary at the level in line with Breitung test. Also, except for the Breitung test, all the variables are found stationary at the level in line with Im et al.and Hadri tests. Different co-integration tests, i.e., Pedroni and Kao panel co-integration tests and FMOLS, are used in this study. The results of panel v-statistic, panel rho-statistic, panel Phillips–panel ADF-statistic and Perron (PP) (within dimension method) statistic is reported in Table 4 . These cointegrated tests are based on “Engle and Granger, where different methods, namely group ADF-test, group PP-statistic and group rho statistic, are also used in this analysis. All the variables are co-integrated according to the findings, and there is a long term association among the variables. According to the results of the Kao t-statistic, the long-term association was found among all these variables. The long-run nexus between CO 2 emissions, GDP growth, non-renewable energy, and globalization index in the selected South Asian countries. The studies of Zeshan and Ahmed, Apergis and Ozturk, and Ahmed et al. are supported the results of this empirical analysis. Table 4 The statistical results of the Pedroni and Kao co-integration. within-dimension Statistic Prob. Panel v-Statistic –0.5301 0.7020 Panel rho-Statistic –2.8286 0.0023*** Panel PP-Statistic –4.2235 0*** Panel ADF-Statistic Between the dimension 0.0413 0.5165 Statistic Prob. Group rho-Statistic –1.0664 0.1431 Group PP-Statistic –5.5722 0.000*** Group ADF-Statistic Kao (1999) panel cointegration test –2.1206 0.017*** ADF t-statistics p-value 22.07927 0.0000 Note: SIC is used to select the lag length criteria. Where *** and ** signify 1 and 5% levels of significance, respectively. This study investigated the relationship between economic growth and CO2 emissions in South Asian countries under the Environmental Kuznets Curve (EKC) hypothesis, finding strong support for the inverted U-shaped EKC. The results show that economic growth activities significantly increase greenhouse gas (GHG) emissions, aligning with previous empirical studies such as those by Zambrano-Monserrate et al., Awad and Abugamos, Keho, Nassani et al., and others. However, He and Richard's study on Canada did not support the EKC hypothesis. The EKC's existence was confirmed for China and France by Ang and Iwata et al. Additionally, studies by Copel and Taylor, Halicioglu, and Jalil and Mahmud have linked economic growth and trade to environmental impacts, showing that trade significantly increases CO2 emissions in China, Turkey, and Malaysia. Shahbaz et al. and Uddin et al.'s analyses for Indonesia and Sri Lanka, respectively, indicate that economic growth driven by energy consumption significantly increases CO2 emissions. The study used Kao and Pedroni co-integration and FMOLS tests to explore the nexus between CO2 emissions, energy use, globalization, and economic growth. The findings demonstrate that GDP growth, non-renewable energy, and globalization significantly contribute to environmental degradation in South Asia, affirming the EKC hypothesis in both short and long-term contexts. Table 5. The statistical findings of FMOLS technique (country-specific long-run elasticities) Country name Variables Coefficient t-statistics Prob Bangladesh lnGDP it 4.63 2.01 0.05 ln(GDP it ) 2 –0.30 –1.73 0.09 lnNRENW it 0.89 7.03 0.00 India lnGLOB it 0.88 4.52 0.00 lnGDP it 1.62 13.01 0.00 ln(GDP it ) 2 –0.06 –18.38 0.00 lnNRENW it 1.53 15.80 0.00 Nepal lnGLOBit 0.87 5.13 0.00 lnGDP it 15.81 237.88 0.00 ln(GDP it ) 2 –1.08 –19.58 0.00 lnNRENW it 0.63 4.43 0.00 Pakistan lnGLOB it 0.39 2.38 0.02 lnGDP it 13.56 5.39 0.00 ln(GDP it ) 2 –0.92 –4.83 0.00 lnNRENW it 0.47 2.37 0.02 Sri Lanka lnGLOB it 1.11 7.27 0.00 lnGDP it 2.50 2.10 0.04 ln(GDP it ) 2 –0.12 –1.54 0.13 lnNRENW it 1.08 11.23 0.00 lnGLOB it 0.08 The turning point of EKC 6455.579 per capita US $ Where ¼a 2 is naturallogGDP it and a 3 natural log(GDP it ) 2 0.33 0.75 Table 6. The statistical findings of FMOLS technique: Full Panel. Variables Coefficient t-statistics Prob LnGDP it 3.86 4.28 0.00 ln(GDP it ) 2 –0.22 –3.61 0.00 LnNRENW it 0.84 8.07 0.00 LnGLOB it 0.55 0.01 0.01 South Asian regions. Tables 5 and 6 have reported the results of full FMOLS and country specific, respectively. The full panel FMOLS findings in Table 6 indicate that GDP growth, non-renewable energy consumption, and globalization significantly increase environmental degradation in South Asian regions. Specifically, a unit change in non-renewable energy leads to a 0.84 unit increase in CO2 emissions, consistent with studies by Liu and Dietz, Soytas and Sari, Tao et al., Shahbaz et al., Saboori and Sulaiman, Ahmed et al., and Nasreen et al. These economies are predominantly reliant on emissions-intensive energy consumption, predicting increased future environmental degradation. Akbostanci et al., Jalil and Mahmud, Narayan and Narayan, Jaunky, and others have discussed the nexus of energy pollution and economic growth under the EKC framework, with findings supporting the hypothesis. GDP and GDP^2 have positive and negative coefficients, respectively, reinforcing the EKC hypothesis. Country-specific FMOLS results show that in Bangladesh, globalization and non-renewable energy significantly increase GHG emissions, supporting the EKC hypothesis with GDP and GDP^2 values. In India, energy use, globalization, and GDP growth significantly increase CO2 emissions, confirming the EKC hypothesis. In Nepal and Pakistan, GDP growth significantly raises CO2 emissions, with evidence supporting the EKC hypothesis. For Sri Lanka, GDP growth and energy consumption are the main contributors to CO2 emissions, while globalization has a smaller impact. Table 7 Panel causality Dumitrescu-Hurlin test (full panel). S.NO Hypothesis W-stat Z. Stat Prob Result Conclusion 1 LCO 2 ¥ LGDP 2.560 1.560 0.249 NO LGDP ¥ LCO 2 4.576 3.576 0.040 YES Unidirectional causality 2 LCO 2 ¥ LGDP 2 1.906 0.907 0.497 NO LGDP 2 ¥ LCO 2 4.650 3.650 0.030 YES Unidirectional causality 3 LCO 2 ¥ LNRENW 1.098 0.098 0.359 NO LNRENW ¥ CO 2 6.143 5.143 0.000 YES Unidirectional causality 4 LCO 2 ¥ LGLB 1.574 0.574 0.380 NO LGLB ¥ LCO 2 2.816 1.816 0.080 YES Unidirectional causality 5 6 LGDP ¥ GDP 2 LGDP 2 ¥ LGDP LGDP ¥ LRENW 0.208 0.262 4.951 0.739 0.613 3.951 0.477 0.466 0.041 NO NO YES Neutrality LNRENW ¥ LGDP 11.823 10.824 0.000 YES Bidirectional causality 7 LGDP ¥ LGLB 3.753 2.753 0.005 YES LGLB ¥ GDP 1.689 0.689 0.560 NO Unidirectional causality 8 LGDP 2 ¥ LNRENW 2.199 1.199 0.230 NO LNRENW ¥ LGDP 2 11.767 10.767 0.000 YES Unidirectional causality 9 LGDP 2 ¥ LGLB LGLB ¥ LGDP 2 0.775 12.697 0.224 10.697 0.822 0.000 NO YES Unidirectional causality 10 LNRENW ¥ LGLB 1.912 0.912 0.456 NO LGLB ¥ LNRENW 6.383 5.383 0.000 YES Unidirectional causality Causality is running from CO 2 to GDP, GDP 2 to CO 2 , GDP to Globalization, GDP to Globalization, non-renewable to Globalization, and non-renewable to GDP. The findings of the Variance Decomposition Method (VDM) for selected South Asian countries are reported in Table 8 , highlighting the contribution of various exogenous variables and innovative shocks to changes in CO2 emissions between 1972 and 2015. The results indicate that innovative shocks account for a significant endogenous contribution of 49.73 percent to CO2 emissions. The dominant elements driving CO2 emissions in the region are sources of energy, GDP growth, and globalization. These findings align with the regression analysis results and are projected to remain relevant for the next ten years. The impulse response function (IRF) illustrated in Fig. 1 further supports these conclusions. The IRF shows how CO2 emissions respond to shocks in other variables, with the lower and upper bounds representing one standard deviation. The graphical analysis demonstrates that an increase in energy consumption leads to higher environmental degradation. Similarly, globalization and economic growth exhibit a positive relationship with CO2 emissions, following an increasing trend. Specifically, the response of CO2 emissions to energy use is initially positive and stabilizes beyond the 9th period. The growth rate and the square of GDP consistently contribute to CO2 emissions throughout the sample period. These insights are crucial for understanding the dynamic interactions among these variables and their impact on environmental degradation in South Asia. Discussion of analysis and EKC This study try to examine the relationship between energy, environment, growth, and other variables under the premises of the EKC framework to evaluate an inverted U-shaped Table 8 The results of the Variance Error Decomposition forecast model. PeriodSE CO 2it NRENW it GDP it 2 (GDP it ) GLOB it Variance Decomposition of CO 2 it 1 0.053463 100.0000 0.000000 0.000000 0.000000 0.000000 2 0.064543 79.09123 0.004104 0.015498 1.688469 19.20070 3 0.072142 66.04123 0.851420 0.246249 1.614706 31.24639 4 0.078430 62.30278 1.326685 0.703257 1.411935 34.25534 5 0.084180 59.37292 2.441953 0.824865 1.289188 36.07107 6 0.089467 56.56463 2.752867 0.941740 1.208907 38.53185 7 0.094385 54.16059 3.052759 0.971155 1.113945 40.70155 8 0.098988 52.41041 3.346803 0.992818 1.015428 42.23455 9 0.103322 50.97510 3.654140 0.995887 0.933091 43.44178 10 0.107395 49.73146 3.894635 0.986339 0.878239 44.509 Variance Decomposition of NRENW it 10.034535 31.87078 68.12922 0.000000 0.000000 0.000000 20.041244 36.66831 49.58632 0.969486 3.605675 9.170207 30.044430 33.72261 43.59082 0.907179 5.520787 16.25861 40.047971 34.41351 37.40430 2.211239 7.149582 18.82138 50.051180 33.83550 33.57368 2.392792 9.461705 20.73632 60.054482 33.04709 29.77826 2.822463 11.99424 22.35796 70.057849 31.94314 26.72074 3.050981 14.83178 23.45336 80.061297 30.88558 24.02483 3.319412 17.92518 23.84499 90.064986 29.65769 21.59964 3.583062 21.49490 23.66470 100.069006 28.19449 19.32307 3.846801 25.62172 23.01391 Variance Decomposition of GDP it 10.014070 10.00017 0.920363 99.07946 0.000000 0.000000 20.016145 20.07378 13.61939 75.39856 0.289688 3.618581 30.017505 20.76224 11.80742 65.29934 5.685593 10.44540 40.019871 30.90184 12.16973 51.21608 15.60387 15.10846 50.022793 30.72339 9.811252 39.18111 29.04921 16.23504 60.026456 40.20847 8.355201 29.65772 40.90296 15.87564 70.030838 40.48563 6.739829 22.57663 51.41638 14.78153 80.036092 30.75645 5.417601 17.34617 60.18060 13.29917 90.042379 30.04408 4.351367 13.57158 67.37784 11.65513 100.049879 30.41008 3.494791 10.85631 73.19366 10.04516 Variance Decomposition of (GDP) 2 10.164948 0.007600 0.983546 98.95070 0.058150 0.000000 20.190252 7.389810 13.64122 74.52314 0.768169 3.677658 30.208039 6.820125 11.58595 63.30471 7.825880 10.46333 40.239443 5.732097 11.69370 48.42815 19.37892 14.76713 50.278498 5.342239 9.214914 36.13943 33.80341 15.50001 60.327042 4.713989 7.710922 26.87897 45.83009 14.86603 70.384881 3.961818 6.136183 20.24527 56.02468 13.63205 80.453880 3.253186 4.887105 15.50049 64.22880 12.13042 90.536104 2.594748 3.904165 12.16592 70.78248 10.55268 100.633860 2.027605 3.129035 9.816157 75.97134 9.055861 ( continued ) Table 8 Continued. PeriodSE CO 2it NRENW it GDP it 2 (GDP it ) GLOB it Variance Decomposition of GLOB it 1 0.033245 2.431226 5.949910 3.884664 5.465288 82.26891 2 0.038717 4.797875 4.388962 7.918514 4.715521 78.17913 3 0.042687 16.83483 5.322983 6.853499 4.650501 66.33819 4 0.044803 18.97840 7.591718 6.221787 4.221725 62.98638 5 0.046351 19.91540 7.133334 5.941443 3.995294 63.01453 6 0.047843 20.66431 7.016624 5.615584 3.802581 62.90090 7 0.049216 21.64652 6.943423 5.377727 3.620832 62.41149 8 0.050529 22.43852 6.956506 5.174904 3.467411 61.96266 9 0.051752 23.02819 6.923553 4.997245 3.332943 61.71807 10 0.052921 23.55079 6.892059 4.838889 3.204856 61.51341 The relationship between energy use, economic growth, globalization, and CO2 emissions in selected South Asian countries was explored using various econometric techniques including panel unit root tests, Kao and Pedroni panel co-integration tests, Fully Modified OLS (FMOLS), and the Innovative Accounting Approach. The full FMOLS findings reveal that GDP growth, non-renewable energy consumption, and the globalization index significantly contribute to environmental degradation in South Asia, primarily through increased CO2 emissions. This study underscores the substantial role of fossil fuels in driving CO2 emissions across the region, offering crucial insights for future policy-making and governmental strategies, particularly in environmental management. Previous empirical studies by Velthuijsen and Worrell, Acaravci and Ozturk, and others have also examined economic growth and greenhouse gas (GHG) emissions under the Environmental Kuznets Curve (EKC) hypothesis, incorporating additional variables such as energy efficiency and economic structure. Their findings generally support the notion that economic growth intensifies GHG emissions, although exceptions exist, as seen in studies for Turkey and India by Lise, which found no significant relationship between growth and CO2 emissions. Conversely, studies like those of Grossman and Krueger and Shafik and Bandyopadhyay support an inverted U-shaped EKC, indicating that environmental pollution initially increases with economic growth but may decline after a certain income threshold is reached. Figure 1 . Panayotou64 initially delineated the indicators of the nexus between environmental pollution and GDP growth under the Environmental Kuznets Curve (EKC) hypothesis. Recent research underscores non-renewable energy as a primary driver of environmental degradation, particularly through fossil fuel combustion. Studies like those by Apergis and Payne, Jalil and Mahmud, Nasir and Rehman for Pakistan, Kanjilal and Ghosh, Shahbaz et al. for Tunisia, Seker et al. for Turkey, Javid and Sharif, Ahmad et al. for India, and Rafindadi for China and Japan, have extensively explored the relationship between economic growth, non-renewable energy consumption, and carbon emissions across various regions. This study employs the Innovation Accounting Approach (IAA), integrating the Variance Decomposition Method (VDM) and the Impulse Response Function (IRF). The IRF method predicts interactions among variables over time, illustrating how shocks to one variable affect others beyond specified periods. It quantifies the magnitude and direction of responses among study variables, crucially identifying how CO2 emissions respond to shocks in other explanatory factors. The IRF results reveal that growth, non-renewable energy consumption, and globalization significantly influence CO2 emissions when shocks are applied to the carbon emission variable. They underscore non-renewable energy as the dominant driver of CO2 emissions in the region, alongside globalization's impact. These findings emphasize the importance of including energy consumption sources, economic growth, CO2 emissions, and globalization in future frameworks for policy-making over the next decade. While Yihdego and Webb used the transfer function-noise model for IRF, this study's approach is grounded in IAA, presenting graphical representations for clear interpretation. The graphical analysis of IRF under IAA provides detailed insights into the dynamic relationships among variables, offering robust empirical support for policy formulation aimed at mitigating environmental impacts in South Asia and beyond. Conclusion This analysis employs theoretical frameworks such as the Environmental Kuznets Curve (EKC) to assess the long-term relationships between CO2 emissions, economic growth, energy use, and globalization in selected South Asian economies spanning from 1972 to 2017. Various econometric techniques including heterogeneous co-integrated panels, unit root tests (panel), Kao and Pedroni panel co-integration tests, Fully Modified OLS (FMOLS), Dumitrescu-Hurlin tests, and the Innovative Accounting Approach were utilized. The full panel FMOLS findings reveal that economic growth, non-renewable energy consumption, and globalization significantly contribute to environmental degradation in South Asia. Specifically, the study highlights that fossil fuel consumption is a major driver of CO2 emissions and greenhouse gas issues in the region. This underscores the environmental challenges faced by South Asian countries, where GDP growth, energy consumption, and globalization are identified as key determinants of environmental quality. Furthermore, within the EKC framework, the study confirms the existence of an inverted U-shaped relationship between economic growth and CO2 emissions in South Asia. While economic growth initially exacerbates environmental degradation, policies promoting sustainable development and clean energy could mitigate these effects. The study suggests that reliance on fossil fuels hampers sustainable development in the region, advocating for regional cooperation among South Asian countries under the South Asian Association of Regional Cooperation (SAARC) to address environmental issues collectively. Policy recommendations include initiatives to curb CO2 emissions through clean energy policies, enhanced energy efficiency, investment in renewable resources, and reducing energy intensity. Addressing these factors can improve environmental quality while supporting economic growth in South Asia amidst increasing globalization pressures. Overall, the study emphasizes the importance of integrated regional strategies and sustainable energy policies to mitigate environmental degradation and foster economic development in South Asia. Declarations Author Contribution Whole manuscript have been written by main author. 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Testing for stationarityin heterogeneouspanel data. Econ J 2000; 3: 148–161. Bildirici ME, Bakirtas T and Kayikci F. Economic growth and electricity consumption: auto regressive distributedlag analysis. J Energy South Afr 2012; 23:29–45. Im KS, Pesaran MH and Shin Y. Testing for unit roots in heterogeneous panels. J Econometr 2003; 115:53–74. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4637577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319143862,"identity":"5e3bbe64-2746-4400-a986-0d2c9bf1ad97","order_by":0,"name":"Apu Chandra Das","email":"data:image/png;base64,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","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Apu","middleName":"Chandra","lastName":"Das","suffix":""}],"badges":[],"createdAt":"2024-06-25 15:36:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4637577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4637577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60530459,"identity":"1e063203-edd1-4e91-a9bc-244e762bbee7","added_by":"auto","created_at":"2024-07-17 20:06:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1374791,"visible":true,"origin":"","legend":"\u003cp\u003eImpulse response function.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4637577/v1/93c9bdaa419950c0f1b055ea.png"},{"id":60742604,"identity":"c590cb0e-c749-4b9c-8d72-e5fcb57976d1","added_by":"auto","created_at":"2024-07-20 14:14:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2709835,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4637577/v1/3a695446-74e2-4943-be11-ea44df4e8431.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship between Globalization, Energy Consumption, and Economic Growth in Selected South Asian Countries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSeveral economies try to increase the level of competition and economic growth; thus, globalization is playing an important role and can produce sustainable economic growth in developing countries. Recently, various countries have been connected to each especially culturally, socially, economically, and politically, due to globalization and advanced information systems. Globalization permits advanced technology and its transfer through FDI from advanced nations to less technological advancement countries. Therefore, globalization\u0026rsquo;s role in determining economic growth is crucial, and It enhances human economic activities by owing to technological innovations and investment activities (foreign direct investment) of the country.\u003c/p\u003e \u003cp\u003eConversely, the worldwide demand for goods and services has required increased energy demand; it concluded economic growth is mainly driven by energy. Thus, energy consumption of non-renewable is highly associated with the growth rate, worsening the environment\u0026rsquo;s quality. The leading cause of deforestation, depletion of natural resources, climate change, and global warming is carbon dioxide emissions and globalization.\u003c/p\u003e \u003cp\u003eThe economy\u0026rsquo;s growth (GDP) is highly connected with energy use and takes as a measure for the \u0026ldquo;oxygen\u0026rdquo; for the whole world\u0026rsquo;s countries. Non-renewable energy is a prerequisite for the achievement of economic growth. Various developing countries and their inhabitants (especially South Asian countries) are living below the poverty line. To lift millions of inhabitants out of poverty, many South Asian countries are trying to achieve economic growth through industrialization, globalization, and trade liberalization. Additionally, Climate change and global warming represent preeminent global issues. Sea levels are rising, snow and ice are melting in polar zones, and due to global warming, the average temperature of the earth is increasing. Notably, more significant government efforts can be reduced poverty and environmental degradation, and in these economies, the issue is to achieve sustainable economic growth.\u003c/p\u003e \u003cp\u003eUnfortunately, land degradation, agricultural development, industrial development, infrastructure development, and other practices have deteriorated environmental quality. The leading cause of climate change and the rise in earth temperature is carbon dioxide emissions. GHGs represent a combination of gases, but CO\u003csub\u003e2\u003c/sub\u003e makes up approximately 75 percent of these gases in the atmosphere. These gases move from one place to another continuously, stay within the biosphere, and not disappear/dissipate for thousands of years. The Joint Research Center of the European Union (EU) reported that the world\u0026rsquo;s ninety percent CO\u003csub\u003e2\u003c/sub\u003e emissions are fossil fuel incineration. in recent decades, primarily responsible for environmental degradation is the most developed economies; however, the ratio of CO\u003csub\u003e2\u003c/sub\u003e emissions has also increased in developing countries. This converts ocean water into carbonic acid, and marine life and coral reefs are being damaged due to high carbon dioxide rates in the atmosphere.\u003c/p\u003e \u003cp\u003eGlobal warming and environmental degradation have lately been a major challenge for the nations of the world. Increasing CO\u003csub\u003e2\u003c/sub\u003e emissions and other greenhouse gas (GHG) emissions have produced extensive environmental effects. These effects have created unexpected changes in weather conditions, increased earth temperatures, and presented more significant dangers to ecosystems. The answers to some questions can be obtained from the EKC model, such as whether an economy can achieve economic growth without worsening the ecological system and whether the environmental quality is deteriorating by the rapid economic growth. Prior empirical and theoretical studies have discussed the energy-growth-emission nexus in well-intentioned works; however, more research is required on this topic for further policy recommendations due to inconclusive findings.\u003c/p\u003e \u003cp\u003eAdditionally, the determinants of CO\u003csub\u003e2\u003c/sub\u003e emissions in terms of fossil fuel energy sources and economic growth have been discussed in several empirical works explored the growth and environmental degradation nexus and also discussed other pollutants. Similarly, nitrogen and sulphur dioxide have been examined; others have also observed sulphur dioxide. Different studies assessed different countries and different periods and used different methodologies, which leads to inconsistent findings; thus, the quality of the environment can be strengthened by the effective use of energy and sustainable development policies of growth. More effective policies are required, especially in Asian countries where emissions remain high. This study addresses the energy-growth carbon emission nexus, thus based on sequential past empirical research using some control variables under the EKC hypothesis.\u003c/p\u003e \u003cp\u003eEnergy demand is essential for growth, but the supply side is limited (the supply of conventional oil and gas are predicted to decline). Nevertheless, geopolitical, economic, environmental and technological challenges are confronted by the energy sector. Thus, energy is vital and increases environmental degradation in economic growth. The next century will face many energy challenges. Among these, energy demand and environmental degradation will be the largest issues due to rapid economic growth and dependence on energy sources. CO\u003csub\u003e2\u003c/sub\u003e emissions and climate change are becoming more prevalent due to fuel combustion. Energy is essential for industrial and agricultural production, and this energy increases CO\u003csub\u003e2\u003c/sub\u003e, nitrous oxide and methane emissions. As a primary energy source worldwide, the fossil energy ratio is rising as fossil fuels produced 82 percent of the global\u0026rsquo;s energy in 2015. This ratio has remained roughly the same for the last 40years, as reported by the IEA. Renewable energy sources represent alternatives on-renewable sources and can be helpful in overcoming these issues.\u003c/p\u003e \u003cp\u003eEnvironmental pollution and CO\u003csub\u003e2\u003c/sub\u003e emission are strongly affected by economic growth and development. The growth-environmental degradation nexus is not a focus of the early stages of economic growth, environmental issues are not sufficiently considered, and advanced technologies have-not been accessible to solve these issues. Consequently, as per capita income increases, the level of environmental degradation also increases. Numerous researchers have found the links between CO\u003csub\u003e2\u003c/sub\u003e pollution, economic development and energy sources as significant. Various explanatory variables are also incorporated in the analysis of the emission, growth and energy consumption nexus, such as trade openness, financial development, population, and other factors. This well-studied topic uses different time period frameworks for different countries, with different methodologies and different controlled variables in various empirical analyses. The findings of these studies conflict with each other and vary according to the country assessed. The tri-variant nexus between CO\u003csub\u003e2\u003c/sub\u003e emission, growth and energy has been significantly explored by various researchers.\u003c/p\u003e \u003cp\u003eEven though environmental pollution caused by energy and growth are especially important in Asian countries, however few researchers have analyzed the topic as a group for the South Asia region. In the context of the South Asian countries, this subject is also not well documented. Thus, this research includes existing studies on countries in South Asia. As a sub-sample, there have been some important past research, including in Asia-Pacific economies.\u003c/p\u003e \u003cp\u003eFinally, following the global agenda for reducing CO\u003csub\u003e2\u003c/sub\u003e emissions, this study investigated the GDP growth, CO\u003csub\u003e2\u003c/sub\u003e emission relationship to determine the nexus between CO\u003csub\u003e2\u003c/sub\u003e emissions and growth, as well as to provide suggestions for further policy making. The adoption of weak econometrics techniques, wrong statistical data, ignoring diagnostic testing or neglecting random walk trends and serial dependence in time series analysis can be observed in testing the EKC hypothesis. The results maybe spurious if incorrect statistical techniques are applied. To overcome these issues, this study uses an overview of the cross-country panel time series to test the interim and long-term associations under the EKC scheme between the study variables. As an alternative, this study also used renewable energy and technological innovation impacts on environmental quality to obtain the most robust results.\u003c/p\u003e \u003cp\u003eTherefore, this study examines the globalization, growth-emission nexus with other selected variables due to its importance in policymaking and sustainable economic growth across the globe. The study tries to identify the causal growth-energy and environmental degradation nexus under the EKC framework\u0026rsquo;s premises for selected South Asian economies. The unique contribution of our study is tried to overcome a vacuum in the recent studies, under the framework of EKC and by adding some plausible variables. This paper used the most recent data with the latest econometrics techniques and a robust model to fill this vacuum in the empirical literature. The research study question is tightly focused on the policies of environmental sustainability. The main objective of this study is to identify the role of globalization economic growth, i.e., Does economic growth significantly increase/ decrease CO\u003csub\u003e2\u003c/sub\u003e emissions in the south Asian countries? and Do the selected Asian countries demonstrate an environmental Kuznets curve to demonstrate the growth-CO\u003csub\u003e2\u003c/sub\u003e emission hypothesis? Similarly, Can GDP growth be achieved without worsening environmental health in these countries? The literature review is discussed in second section. The model\u0026rsquo;s theoretical framework, a model, and the econometrics methodologies, including the data description, are described in the third section. The results are predicted in fourth section. Finally, the conclusion is discussed in the fifth section and gives suggestions for further researchers and policymakers.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eA detailed description of the literature review is given, which is based on theoretical, conceptual, and empirical analyses of the nexus between energy-growth-induced emissions. The relation between GDP growth and CO2 emissions alone cannot be presented in comprehensive detail; therefore, following the literature review by Nassani et al., this study incorporates various other variables and examines their impacts on CO2 emissions. The studies on Asian countries are discussed as the subsample, and the findings are not identical. However, except for Niu et al., all other studies have taken various countries to be a whole group. Furthermore, only a few studies used subsamples in the context of Asian countries. This deficiency in previous studies provides motivation for further analysis on this topic with some additional information and changes. With all these deficiencies, this study seeks answers to the research queries.\u003c/p\u003e \u003cp\u003eRecently, the EKC hypothesis has been tested by Jaunky and Cowan et al. and confirmed the existence of EKC tests for cross-country panel data. Besides, Cicea et al. and Ibrahim and Law have assessed panel data under the EKC hypothesis and found mixed results. Similarly, the EKC hypothesis has been tested by Ajmi et al. for G7 nations and found that the results do not verify the authenticity of the EKC by using Granger\u0026rsquo;s causality test. The energy use, CO2 emissions, and economic growth nexus has also been examined by various researchers who have not reached the same conclusions.\u003c/p\u003e \u003cp\u003eIn the USA setting, Soytas et al. applied enhanced vector autoregression and observed that CO2 emissions are a big source of energy consumption caused by Granger. In addition, they have also explored the energy, CO2 emission, and energy growth nexus but have not found any causality among them for the USA; similarly, the results of Soytas and Sari and Ghosh for Turkey and India, respectively, do not indicate any causal relationship among energy, growth, and CO2 emissions.\u003c/p\u003e \u003cp\u003eThe plentiful empirical analyses on the growth-environment nexus in the innovative EKC premises have made significant contributions to empirical analysis (especially the studies of Shafik and Bandyopadhyay, Panayotou, and Grossman and Krueger). Various past empirical works have been performed on this topic to investigate the EKC premises using panel data approaches. Similarly, various empirical works have employed time series data for cross-country or single-country analysis. Thus, this study attempts to estimate the panel group data analysis under the framework of EKC and will thus also contribute to the existing literature.\u003c/p\u003e \u003cp\u003eThrough the use of these possible variables within the EKC system, the contribution of this analysis is special, which makes this research distinct from other studies and helps fill a literature void. Furthermore, this research includes the structure of energy use (non-renewable and renewable energy), technological innovation based on CO2 mitigation, the financial growth role, and trade openness under the umbrella of the EKC framework. Globalization with non-renewable sources has not been grouped together (in the framework of the EKC hypothesis) in the South Asian countries background; this research used the new evidence in the scientific literature using the most modern econometric methods and a robust model. The research study question is tightly focused on the policies of environmental sustainability with the help of globalization and renewable sources of energy.\u003c/p\u003e \u003cp\u003eNumerous past empirical studies have extensively covered environmental sustainability. They have provided a sustainable framework and various policy implications for environmental protection and sustainable economic growth in the literature; the growth environmental pollution nexus can be mentioned in the scheme of an inverted U-shaped environmental Kuznets curve (EKC). The environmental quality-growth relation in three stages was defined by Grossman and Krueger. Since the 1990s, the nexus of environmental-growth was determined by this curve. Grossman and Krueger have used this curve to examine the North American Free Trade Agreement\u0026rsquo;s environment-growth link. Different analytical analyses initially discussed the EKC\u0026rsquo;s premise for investigating the connection between climate and economic growth and overlooked the role of other related variables.\u003c/p\u003e \u003cp\u003eThe three different stages of the EKC framework show that economic growth initially worsens the quality of the environment at a threshold level, but after a certain maximum point, the curve moves downward; thus, the CO2 emissions-economic growth nexus is negative. Panayotou first defined the indicators of the pollution-GDP growth nexus under the EKC scheme. The EKC framework is discussed by Stern et al., Ekins, and Gani and evaluated the effects of economic growth on environmental quality. Subsequently, Dinda and Stern discussed this EKC hypothesis in their empirical analysis.\u003c/p\u003e \u003cp\u003eSince the EKC theory was presented in the 1990s, researchers, economists, and environmentalists have focused their attention on the environment-growth nexus under EKC\u0026rsquo;s framework in their theoretical and empirical analyses. The income inequalities and growth (inverted U shaped) nexus is widely discussed in the past literature and was initially developed by Kuznets in 1995. This idea was supported by various researchers in their analysis to identify the environment and its relationship with income in the EKC scheme (followed by Grossman and Krueger, Selden and Song, and Vincent). Meadows et al. highlighted environmental sustainability awareness for the first time in the 1970s, but in the 1990s, Grossman and Krueger, Panayotou, and Selden and Song described the environmental issues resulting from rapid economic growth and development. These studies have also tested and confirmed the EKC hypothesis. The income-environment relationship under the scheme of the EKC has also been examined by numerous empirical studies. These analyses have supported the EKC in their analyses by using different techniques. Based on the assessments of economic growth and GHG emissions under the premises of the EKC hypothesis, Apergis and Ozturk, Al-Mulali et al., and Jebli et al. have included various additional explanatory variables in their analysis.\u003c/p\u003e \u003cp\u003eThe empirical studies of Tugcu et al., Mensah, Acaravci and Ozturk, Apergis and Ozturk, Al-Mulali et al., and Jebli et al. have included various additional explanatory variables in assessments of the carbon emissions-growth nexus. These studies included energy efficiency, energy dependency, and economic structure as control variables with economic growth to observe the EKC hypothesis\u0026rsquo;s growth-environment relationship. They found that economic growth activities can significantly increase the level of GHG emissions. Lise has also tested this hypothesis for Turkey and India and has not found any CO2 emission-growth nexus. The empirical findings of Robalino-Lopez et al. do not support Ecuador and Venezuela\u0026rsquo;s EKC hypothesis.\u003c/p\u003e \u003cp\u003eVarious prior literature includes various indicators of CO2 emissions, e.g., growth, the structure of energy use (renewable versus non-renewable), financial development, technological innovation, liberalization of trade and urbanization, and under the EKC premises. However, many countries have recently supported the EKC hypothesis, which asserts the globalization, economic growth-CO2 emission nexus. Generally, the production of goods and services and its growth cannot be achieved without impacting environmental quality and without consuming energy sources. So, Shahbaz et al. have applied the methodology of a NARDL model for Japan to find the nexus between globalization, environment, and energy-growth between 1970 and 2014. According to their results, the environment\u0026rsquo;s quality is significantly worsening due to rapid growth and development processes as a result of globalization\u0026rsquo;s growth and expanded use of fossil fuels. Recently, Shahbaz et al. have used panel data analysis between 1970 and 2012 for China. Their research investigated the EKC hypothesis and the correlations between globalization and CO2 emissions were also examined. The findings suggest a substantial decline in CO2 emissions from globalization.\u003c/p\u003e \u003cp\u003eMoreover, Shahbaz et al. examined the EKC hypothesis over 1970\u0026ndash;2010 for Turkey, and they also verified the EKC hypothesis: increases in the rate of globalization significantly decrease CO2 emissions. Shahbaz et al. explored the intensity of energy, globalization, and carbon emissions nexus for 19 African countries throughout 1971\u0026ndash;2012. Their research supports the existence of the EKC hypothesis for Algeria, Congo Republic, Zambia, Cameroon, Morocco, and Tunisia. Additionally, the study Shahbaz et al. for 25 developed countries examined the globalization-carbon emissions relationship during 1970\u0026ndash;2014. The findings show that globalization is significantly increasing CO2 emissions.\u003c/p\u003e \u003cp\u003eRecently, Haseeb et al. have shed light on the nexus between CO2 emissions and its essential components: growth, globalization, energy use, financial development, and urbanization for Russia, India, Brazil, South Africa, and China nations. They have tested the EKC hypothesis and confirmed the EKC for these countries. Their findings show that CO2 emissions significantly decrease with an increase in globalization. However, Shahbaz et al. in their study, used the test \u0026ldquo;Bayer and Hanck cointegration\u0026rdquo; and \u0026ldquo;vector error correction model\u0026rdquo; (VECM) to examine the effects of globalization on CO2 emissions for India from 1970 to 2012. Environmental quality was found to be positively associated with globalization. Nonetheless, to the best of our knowledge, many researchers have given less attention to globalization, energy sources, and CO2 emissions for the countries of South Asia. Thus, this study is based on past research using the EKC hypothesis with a few additional variables to fill the gap with a chronological empirical analysis.\u003c/p\u003e \u003cp\u003eThis analysis utilizes these theoretical aspects and assesses economic growth, energy use, environmental pollution, globalization, and other variables under the EKC method scheme. This analysis used the most recent data (1972\u0026ndash;2015) with the latest econometric techniques, and to fill the previous literature gap, a robust model is used in the empirical literature. Thus, various econometric techniques such as heterogeneous co-integrated panels (with cross-sectional dependence tests), panel unit root tests, the panel co-integration test (the Kao and Fisher), the Fully Modified OLS (FMOLS) test, the test of Granger causality, and \u0026ldquo;the Innovative Accounting Approach\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis paper examined the fuel consumption and growth-led CO\u003csub\u003e2\u003c/sub\u003e emission concerning the EKC hypothesis. Using data from 1972\u0026ndash;2017 for selected South Asian countries such as Bangladesh, India, Nepal, Pakistan, and Sri Lanka, this paper is used World Development Indicator (WDI)\u003csup\u003e1\u003c/sup\u003e data to implement the panel time series analysis.\u003c/p\u003e\n\u003cp\u003eTheoretical framework and hypothesis\u003c/p\u003e\n\u003cp\u003eThis study aims to identify the effects of globalization, energy growth, and technological change in South Asian countries and the extent to which the sustainable environmental agenda influences this causal relationship. It is observed that due to technological advancements and modern usage, the consumption of energy increases globally.\u003c/p\u003e\n\u003cp\u003eThis analysis comprises and tests the following two hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is an inverted U-shaped Environmental Kuznets curve (EKC) association between CO2 emissions and GDP growth for the selected South Asian countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is expected that globalization can be harmful to the country\u0026rsquo;s economic growth, which could be a sustainable pollution haven hypothesis across the countries.\u003c/p\u003e\n\u003cp\u003eThe environmental degradation-growth nexus can be described in the EKC hypothesis (with an inverted U-shape). Grossman and Krueger followed up on the work of Kuznets and described the environmental quality-growth nexus in three stages. The authors discussed environmental degradation issues due to natural resource depletion. Environmental quality has been significantly reduced by countries attempting to achieve the highest economic growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1. Summary of data description and descriptive statistics.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eData description\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCarbon emissions (in per capita of metric tons) (WDI, 2018)\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\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita (US\u003cspan\u003e$\u003c/span\u003e with the base year of 2010) (WDI, 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRENW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-renewable energy consumption % of total final energy consume. (WDI, 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe KOF index of globalization (KOF, 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive statistics\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLn GDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnNREW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnGLOB\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\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSd. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkew-ness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJarque-Bera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e439.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e276.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e552.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum Sq. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe author discussed environmental degradation issues due to natural resource depletion. Environmental quality has been significantly reduced by countries attempting to achieve the highest economic growth in this first stage. Beyond this initial stage, the economies\u0026rsquo; main goal is to attain sustainable economic growth and welfare with technological innovation (clean environmental-based technologies) and to develop environmental policies to mitigate CO2 emissions. Thus, economies, after reaching the highest level of income per capita, wish to move from poor environmental conditions to a clean environment for sustainable economic growth. The analysis of EKC hypotheses regarding incomes, pollution, and other essential variables in a GDP square function has been used by various policymakers and researchers in the area of environmental economics.\u003c/p\u003e\n \u003cp\u003eTo analyze the growth-environmental pollution nexus, this study applied EKC\u0026rsquo;s theoretical framework in Eq.\u0026nbsp;(1) (Grossman and Krueger). The theoretical framework of the EKC is used in the following econometric model:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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height=\"48\" width=\"521\"\u003e\u003c/p\u003e\n \u003cp\u003eThis study has included a few additional explanatory variables in assessments of the GHG emissions-economic growth nexus under the premises of the EKC hypothesis. Where CO2it represents the carbon emission (per capita) level (environmental pollution), Yit represents GDP (per capita) income (economic growth), and other influential macroeconomic variables are indicated by Xit. To make the model consistent and efficient with a meaningful interpretation, the natural log is used for Eq.\u0026nbsp;(1):\u003c/p\u003e\n \u003cp\u003e\u003cimg 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Eefk4OLlU35YQtzdSvhuVUKdKrojz8vDzQpRtY3uXi1TSu4bCmyeUJf6KrMf+98LjH3XQW5Jg8WJj0LiCupGqYCuLsxi8X11Sc2wBsatjkKoVjSEipkf76ove58YBD1rEZBZPzfflxXqelpbSUFR0/RqcEn3NLXcyVq/8zK8bBAhycsFU3TdfXWZaL6hIdRwxitUQuT5XN8LEjzs3DzCR66mUzWzGqGiexOu6m5jg/By73ROG6EpbV3B2Z7k0zJKnilfzTDWlXq1SJsn1GV4T2Fy1IN7/imfOkiW1rnWtJiI6GfZvatZXumkoBCqI3WFujw31lFb8vbpQytjy4iP7fMzHW8CXE4eUlRUPmxxM7pqkAozAIy+cj0uBmEXED7oV7qpwZhKqGZ5jFeoBIkHU10VRHm4OPgVTtypJQ8LqxLqUku4lbogG4Sff9+tHLBnnq76P/dxA4RK50NFj9anhHscl+Te8huX4Y3QuLAAV5fr93y9HYw1xNi2iGrfIpMJoUKI1mz3M/qWd182DM8BAH6iMvastCC3sOqdjC7KtH2sPNHe6JhDwJuG3jH4WN+XjDBzBWEBcV2fV5Ubmw7+XAS/Km0yP+DSXvbf/vm//7FVWCe0cJAwilc+UuHhkDjqXkXj7Pkq4UZebJJQcYOfgg32cP7rH//3t38JngutwAIAeuSLx2G+P0ROvqyjMXStIQuVUM3fU1aoy4i20jfeJioCbCzbdmt4P08PunnFzeOOj7udzm5uDm4Jh+gv5O1B2Pqw09s5OTnZJRziGXn21iia/NV6CBXAwvMenBJm4+Tj5D3uX7Gy1KIl1IdltPOH8VQndQEIB6egwu3XjWRRyeV95+dmCXUgL/DYbo7f/vGPf24RuRxSSFjezFVdU+DlFj8W+2VdZq7KoHPSslafVnEy0RAq1YcKby165menyL/ld3ZpW++Q2PA7V6/cenDX/ZKeNM+/2ZVtY3rJg+1Y+TPjfRAuCJf46fCJzfApsD5CBYbfemvs5ubi4JPWvl5OYZEVQiX4UH1pfaiI1jRrle2crGwiys7F4xsWa3my6abWvjN33/2onkpsO5sl1PmOp1eOCG75n3/+n3/yy5ikthJ4pv2ZnTgXu5JlwrqmVAAAEAkVwgnyocIaC2K8LWR5/v07ROFqQERU0O2r124/8HY10ZLg/jdE69qrMTKlAX0FV3R3c7Fz7ZAwz9ncCnt9hAr0JJ2REeVl59ghf+JZHWkKSCZUOh8qAK9+biInxMnKvlvbvWpzUn1np14l+YYIdYehazplyY9DLS5M92f6HBf+AyJ/wuXJ+7pxog7Qw4Xux/dzse6/W0R2YEzV+Z+Xkz8f3kce1QBgMNpCnW/L7wdMnqzMLlBdWU7au9Udkmm9IRMV4Ue2Q/j3Hgv/uOmDGavUm/IaO1b8MiYsODgsJGQ9f6EhjwtapyiTAEo2lMBEUeARGXmTqw9iwn1NDykY30qB4WdeXVYUFJZ2SyYbkiixNx3YFKFiZipv60qqnLwWFh11x/yQpKZHK3KxOcllLyufkU8GbJ2DD5VQL73vJg/rWDQSiZqseWwkISwkfMDyQUp198qSCtmYCN3LzqN07l7DylCIn39xWZKPi5OTR9ErtQ7kxiSjgV0a/pzyNDIkJCIiMioyMioyIiwk+HFSXieM1MvXRajAQtVLZylWDj4uLmnrl/Mk9iStUPkFJZyeNc8Si58fbUryMZMW4ODi3X3WK2VgU7NwsuyMPjdHqBNF17Rl1c9dC4uOuGWiqXjCowqG631xeSfPdsProG7CqMiVd1Mv7A7t17vXt4pmSYTKJ2r1hEqoqIX52Yn26IuSHByiepe80r90r0A3XZ9ipcDNInY2rXWljy7Vp7orQNh5ubjloClkeMGyYOe6Kt7FhoWGh0UQ9RgZERoSEfW8qJ109oRCqDuN11ihAriaWANpYW52zt3yZ5IpS0ciofKwc0oehqaRtrWh+itSXE8pCLFuFZY6Efqhf41+ChaU5mlh+PEFaQ2bqO5VQKOJuv7g5ggVXf/8qrqMhp1n6OOAm0bKSjZhlXhUrYe+pJCIdkwTaPPOWqJQCNXpNYV6UAvzM8P1gcbi7Oy7jKD3s2r6FomWh/GyJ+elIex7LfIHSPMmWEn8qf0QLk6eXYrXaxlbOtCjLZ+TokLDQsNXtBwZHhoaEZNW2kPa701LqKttWwMAYLn0muo+AQ4OXvFDflmEdQgAACuEys3OKW3omttB7P54ZHdxotMxaQEWlh0Hz8eVkVlmLQh+3nebJ1SijAvVifZ7/sV2xD1jnDqrH0tyPyH6B49lYj9RR7jWN7eV+EQd33TSqmOqOT868FFqxTBxoJt863VqJ6esXzV5gF5BAD+X6azEycKrZRNOtncQXLXL81MjI+PzoO35wFRbcerbUhjRmbsB/HCj+dF+Xh4enh631/HncdvD713jqkfVAGDw2VUb99giOBGNyS+J7nej8pMeHd0rKKnnRec/wmCWlpbAddiA3E0++rp+JZTp+rpSDma4nrKJbp8l6AE73xZpZR1dVn5PV5B7/9mXoH0dhNywi4uLaCzVvkcpgSGhrnw7V2ArJ7Z9t1ZUJbU1LMGSDHk49uk5f1wxv2HhkafE+Lg5OfmUvd80Mhy18DgUYry3+JnnkX1CgvyC27eJn/NObxuhHvMnE6okNLqIvF6iyEcJIGtTr8uxsvNyc+29GIcglUQkVB4OAYFdxyyuOFw6a6SnoyEvtVdsj5qhTVha5QSSsmzFjtXlpOXUrLnVhlLWmoHJUg+Ty+ElG/OhNj+5auYW1z5BakkP3XzSc5Nu6OzbvudobD1l+QAAODRifHgUzpDR2gJOyh+4EEeZBNNJyZBQCXEw8x88NFhZ9tpFFVGNQ4i2qMvSf7Cqh1WvXBU/X/7cYT9hvsKt4JjOsHgAj1lATPZUvLTTkhQREBDk3a562v194xACSfLSUQn19FqECjQ+Py4rws0OEZMxTGgmV4JIqLycPHsP6l62c7h0xkjvkJqCpPjefQrnXYIKmscoTWthqP5dRl7XFLVNkrNg9ImCZ7qo7D9xt/ZH2hExrdn3LS396qloMiqa7h286qELNIA0WVhuy4nzuR/x+omXmjBEyT6VQo3ES0twS4gpBP1WBXJ2jAiV8N3SRPoVxT9YDlx/WU0l54nq+2f3sUB0nrSTljb9eWH6O1m5uHh2qd4kHWQmZ0z+xC3NTY90fA40VREVFBTm5durYR7/oRU2S97RSyVU87w1CBWouqUuIcjBwSWm6v2GNG9aIVQeTl5JFUNrO6jZ6eN6Gqry+3bvk1Qxc40s7pigzplmez+kZ1T1g07fkSX8eZ8bJdQM8PR9vjIBKv4b+3HffJrzzuNvbhvv3Aoxe9pHGI6xQ6/d9QS2KofUjdCNztR9Mf35LjpiXNvPZ311rn604NYBdlYhZYvMJmLJ6In8EKiOvOQe0R0S8oa+qQ0rEysAQBQEXpLbczqZ1vrPcNcTPbZ4HIZw3916/2Ew2DWybc/0CUhogVEGZdj718F2agdEBcWdk2lODGCny5/7XL3qccftumdwatdap5PxqAXExOgobGyM5m8CPvrRVUfTI6N3cpz2/RhsdAw+PbcKS8+mutkndFFnNXPFAVBrk70QIYMbiUM0usEuwL6kPjitbBxew+gkHw2h5lBWqCuozubbyIrt2Kv7lGaXwBI8zZiPbY+uQ8HKpBM/+/SiOB83FyeH1JWECnBzAutmuub+OTkeFvZtO0+ngZfP6yPU2bJYm73EFaqSM2XEp/pQ732cQS3OTo2PDQ2NTc0uLC2D79fBzr92UJDTv/JxmEYqPLX/0rylBPHoxWkaNRFVMzGNaHl75eR534zK0Un4OEiPY+OTc1R9ULIhBNofewfkNpOvnAGA7qwkH2u1ncJCmg4plFnnfHu2x3ltafHdu0TFtU3vFfeBqRNTdfuIlLz169WYhEqoT8G7gDGzBbfUWVilrj6jWliB2Y4Ya3mW35WDKgeJKCBrXl+TZSPOV0xi1nahlvsZiXKzQ4RkXeJAu4TWSajI0rAj+wW52Ln3KJpmUmxVFB/qmTulQ1jU/PT4yNDw6OQ8cgkN2l+33JR2W01UO7h8gNItCT2YpsGDgEdP53toiuu6lm3Y/UrMBr+MmBgHtwHY1NTQ50T3CyYeHzpnpifAHXZsHIGkUD9IkKGSN0Fhid0U5SF68h876SrIbtum/biR0gSAhb6SIIeTSpJ794nLXfJJ6f+6R5EJVdcpGUToyPE0F4U/WA96pTdSDUWTdf4mB1hZNR+3kRxAI0WPjfayc3HyiB6wLqQSL0jUlYeZD57SfFycLCKnbyeDlo3rJNTZD1BFcX4ODoF9h4Pyx1fyJJt8OVUtgmrHsMi5adjw0AhsakXLtELAK56dPbjr0uMmqhl0LTXTJv2R4Q0SqlsmWF8kQjW8l0tz2m/8jafxzq0cZvFEQp1ti7KUZ2XViFh9G8VESfwpSTauXeZ5EzRTb2I1ET3Pj/OxskmcTCgl6Kgnw8vU6vazN5nJUXdN1PYIbFPx/0xS/OJw4+cvLQjqPsTF5sbWyZk1m8CPRJKQV19u8vPsz7RT26YXN5UFf9+h5dVIZn4AQNc9geqdD+oitOLRFE8LS+9UGLVF08m0WJ8WYKZ3+KjeUYOjlD99w6OasmJicup6htSXRw2O6ukdPmrt9ayD4aptqfyhZ9I4zTiCh6WeEuERlNCPKqERGbM4UJvtBzXYzX3Qv4qRm4ZCqAcscrppJ8oAMJtvIycmskc3nrKMIMyD04z52fbo2BeQZhToknu6gpw8PGwCx73S1tqkhWwJtVQXZOUQkbQpBp+pWBehLo2m3dTmZefkhYjaJLaRB1MqodIdm6HDHQBwM301n790URo8CjVaXbX28RJ0b9b903pHjtEqRV/fSFddevfeg2paBvr6NEo8ekxP3/RawooZh7703tyIZ9ndNP4PXOOLcweFuLZpx5JM5wCw0BDiaO0a8CQj7VXQ9XNyAtzSF8I6ETSUv1juprNfCfqGMibTlUIhVGvGhHrgypMyav9ZIdQtSoEkQgX6C8P1d7Bzc3GLyDtWzJEBpiuD+NgWbbqbD8IlpuKVSrUtE5ZW5GMzO9dcobYmXlcSgXBB+BWMH1EsvgCFUM/e+8q8AhJiebK3orxmdI7MW8vjPR3tI6v5SDEzH72P7NG6UkwzAQZlt/bDcnO4tfGxo3q0ijY8dvSwkozkXmmtIwaG+pReTOiw+vomEZ9hNJ2SkvtcZf6rpIxGGnVOFYSa7fwXi6LjqxWzBSHqbNvL0MDY13kVJe9DHI+ICMh7JJHdj5ScKITqnMKIUOU902hWnkRCZWPRjCYT6nLbWxv17Zwc3MKi6mF1qzUlQmELNUFqgtwQjr2XA8HO1vURKq42Sk9SmJuNc4/a5Swy+GRChahZhoOOzVBqRwmgYLUlFR0TlHEW1dnWNjYOqjElLmZpbgK2YkakvPsxgR9EqD65cOpMezzd03jXVo4Lsd2EhjLbFmmlwL5F3DVrVa/EaNFjo32snALHXw1S++9K/eaH35mKsnLKnE+uRgAAPDv2Res8qflNlUca7BNSdn7LkIyw/bnXbjxqopuz/xjQVssFVfkuLbeU5loA7OBLt2MikN2OyZ2UjoHry7ikuN86geRPheX76xzUjaqaWDXTOfhAd0dHJ+2/np6O7Cta6jeSGnu7ad93dnR0DcEQVFXQZIrtT34Y10A77PUmO0ryC+vYxZH8FTSRh3Ifae+Ue/Blc4SqE0/d9w6gJtLOCrDtPmyX10cqYKo6Qmc7Fw+EfcehK5/76TVOlQLZGmapQSDU/bbF4JsdSITKLgl9vKrJd3Go+JraNi52tm2yNrnUZrB+QqUKQgzhmtM87X2L6N7SPaIX4P1ddLrqH6x67XD8zJ1XhR19vV0gdXX1w+hO2JLym/3yLjGndIyqSGxD4k3lbVzK0GSKCXrmy9ukovbFFY1ix9LvGO3g145rpiFhdNVtPR7Hi9cAABpvSURBVCl566TVRkEaQgV790kr1AMutIQ61xlro8C6RdGfvNTDj33xOinBw8YlICDtngNak9DB0vbYbDcfhHt1Qt21BqHi+58664lCOHgFJB1f0kxoqIQKPodKV/ZXj1MNr33uhlWsJi9m+oOnprjujdJNrlDR08O9ICV3dvX1t+XEXD939sbb2sE+UIft6OoanGHopcKMlb5PzC6nmbBONYdc3M8lpBtdTjX1LQ33dsNIq5ClwRIXxX1n/XPppwrfItTbtIQ6Ve9vIs3GohHZTB6RsP1PnY+IQDh4+USM79LfWUaL7mJNsDqJUHNp5AYACqGKr2HyxZSHXN4vyMnNtd3INYNSBTKhfnUOlbZgRmE8rNz71v3iBpruQBNtvKUw4n4c2CxD8/V3BDdCqNw7jt/MBI9/pBWqwV1aQp1Iv3N6FwuHyeNOAvXhh197GApvYVVyTJ2iDhBEkXHY0bKCJhSAbkwxVxKEcOy9+VW3nOt/fYqPff/J2xUE/c5NTNOyZ4uPvvRBq5crq1rc3Eh9VdMEwX6Cm2zPv39RTUzOODwhs7h6ze3l2J7X3lfsrK3trG3W8WdtY+XytHSMlpZowEcUZacXVZJ5AwDg1YnnJdkhci5VK/u1iFGbnzrJbj8YWEtucu1pZgqiOt5F4DUYTa6Mg8339HX9SlcbKhmkwTTFB6cNUufCCzWeenv5haW8ciitl5pqMMdfS0x+LULdysojfTm3h1yLlaSkFSodoaafFWLbrW2bSxm0l0ZeOqnws0G4ucStYivBXEmVAVhqDbfWEGLjEJGCbphQcYtFQefEOTk4uPZaR5eQ52BrHZuhKZgURE31N9S2TKAJB2fLXvoYSO1UNffPKyzvHGU87f06B9KbqbLbJpbhZRvwoY59yk4tqqTaDSaq752V5GSRD6gkD3MAsDA9u4Si9qihbD+tnarBdSRbGbHo9sBT8lLnYlZzK1EI1eYZtdESEq4QKst+ekK1VWDdcvBhKcV2ulz77LqCEISbk0f25MPW1Rtje+ylFUL1fkNpBIRyKCvUXWdW9aEOfgo7LinAxQ6ROvWwjXb0WT+hLk211dYPTi0CAHa4Mvn6cVlpTfPwpI+VbWOg6/NWtIWayLiicuDkD/ahtuXct7J8uAEfKnKoLCshv5ky0mB6cu+rQP4laRrWwaC/EpU2WOVtetLvPViVNLt8da+kgExXJJOv3O20eqrqpusDLkqzs6iHN1Jb2mRJuIGEADcH9y5Fs7RuikgreFH/R9YFawhxQyD7Lj/KA40LVEK9tJoPFdWVBdUU52OH7JA3S+umDvIbIFQ8cqippnmY0Njn+kpCbI/sljp6//GbosrOr/22Yw3Z/rfCO6iyg0LLsKa81MTE11l1/TSeTFCUVR82SqhvaZs0ADAkVHj6nTO7WdjPRXUQh29sY5KHqiALt4jGQ9K+FJI0y4MFAQ9eEbx3y60hl9WFWDmUHdJAKgeA0TzXfbx7rEIKKZY3mqo0eOspWca3AQBmtCbF7bymrNa1UqIXdm6o9JG1pqjU2ScZhXXtoEvSaZITg/jp+pyUxITnic8T1vH3PDHhTfUAzZlKUHbI8uyMD1/aSYvRhcFEFw0BbnG1464lE4RqodEoHB6edcdYnF/3xTAZyLEPzod3ixlHbeyWtI3v8sX2vgxKbCcTKrr6sa20INcenfOPkok9EL04t4imXBYwmPPwewiV5mQ2gIJnnhNi26Vlm0MzIUS0v3XQFONh4xA+YPy0mkocIDhR7RE2mkKsHCJS9iVg1kXUEm9KYpe0Z7xCxfe8u3tYlIeDY5uObUTrDMU6sH5CXWzNibx8WPmYmW/1PACgJps+BumLi2lah1fUNA7AGTVGkOjgh43v8p0pe59U8GWcJPhi9fMrMtw88krHHxRP4An7kJYXF0lLU0pJ/Zl3dfXsCkE2nsmXUM0DR3x6VhkDqYT6vJ+SDyFAJdRScjMFgPnOODtF1i3yD0r6qfktdMTZH97BBeEVkLAKodnBBMoO6IqzEOeDcO9U9U6jJ9Q7xJuSdp2JYHgOdb63wPXoPl42NhEFi5RmquOQkP26CBU/2/3B31ZfVsk8pYHgkIY35Nw7fWCPps2T7Orm/kkGm+4WBmMuyKpbR3dT5yrgymzmaeO7fNGjFbkv8si7hTAzDfcNdvGL7NK1j+8m7ifAIOdAP4+ERzZlh92PzGNwlpy0QhXWvZIKGl1XIdRAUxn2raphDbTeIURRsLkkPxc31/ajjnHd1DYBwgLZEHaISKiWj/JBU05sT7wz8aYk8Uv5X5MbAOCnW6NttUQgHHwi6veyQIy9TkJFTba+8LbQPKDuSUy+BKuOuWGwc49+yMu82tbBRdoBgCjyWMP7AI8IGnMHtSJTNUn+j54XlpW+DnQ+pn06rKCHSu/UWKuG1kWo+C9hWnsFiVcPpoKQAuYqn9ns+o1V3zuXMJEn/YMl3z4ptpX9VGgLafieKPc6JcPLwr5dQsfj6af+qcXF2bH67EjoqYvRlaROMloQeHQvL98O7QiaeTmwPBxnKidv5FHO6ITkcm3MudPun1cKxo+ne50UlzT9MLqi7bmiUPN9CtDKn3vT+UB+UkxqAQxN2ERbk3hFjotN7lJIRqin56uqhVlYeVZCQVNHyo2jokLGmTDyBt3pEjd9ye0694eoznQykGt8bpxQgaUKf/f4XuLOO1h5/DlZIcHdRhH5WYHX/RtQQP+7hFflHZSeMvB+dUJFTWS7axPu8pUwfdcNni3P5FjKiIqIa8fQ3DC3NJ5ykp9N7JBVFqj94serX9lo7eVjg+yQORGa14WkkDml1guNQZdU+f5g3S7rUAYm1JlSX0U+Tg56ky/h9pjlmd7sIDsVUX4+gT0nXGJb6XfLILLvmezjYuffJuX9kawCSonUAB49O/TUXOrAUbeKlRa/XGYvL2Fw5yM1yvpDGydUoD8/9HFqC8Hmix2rSbwoxc0tZZGSHnb1VtLA/GxreVZyRhW4n09k+thdCc6jXglFFO9LwOkDMhYMZ6MAALS9clcRIdzlaxEDnqyj53LdVFi2Sjo/KaHOHWbbH1vJsWyRvvsJdAwHN9UYZaO9kxfCKyRt9fDtIKMRsznqohg3K6eoik8GiLkxA+W39EU5Wdl3nQ4fpzHR4HE4DGq+PT/ykpo4Pyef9FGXjEY4eS5Iwh2PqrqmuJ2bjUPmlNcX2mU5vV7myp44S4npxFcTjbzoyVwPbanjd6qpCzBQAvRk8y1tifP334MHOlCcjT9snFCB+eq8xOevKwnmt2X4e5/jwmzbjT3CovwDn+U1IOYmPkSFFZEnGHN9Fc/vWantEpY55lrQ/ZXgtTH6B7YRzqE6vAR1V+REiqPcH1tlb6XRuEYnax6e38+2VfFRDdizi4blB5hJCfFwc+046hBRN8ag78xUBqjwQ9jY91oFF4CYG9XxGKolxMaxbY856GIHPA6HXoI1vPM4rSjCzSUmczIkpxPcqgE0oggqJcDFyqFoFtQEkp5eCSMFIUfFxe2T24lEtFT7yuWA1IWcVtBSmZIG1pwb6BlJ9cdTvkCU37W6nlQziMQC6KHSO8fFBTVu9sKX8YuDFR+/gHdGUtKAAmsSKuH6MfjoSGeq+4mdnFtZfmffo27zpmFofGIajcEBOORER1mQmQzb//wmesQtr64fgcSgkbMjtekuR/aw//a7+PF7lT1jc0SegH95funQPkEONggbBy8nFx8Xj8hORaen1VRTA7BY88xNe98O8YNnI3MbhoaHB3ta3gXZHjvh/Lad2qmpsqP7k7yuheR2kncaYMsjrWVlTfPJhPqJQKj2P5lQgamqQJfLdlc83G2M9vNxS2hD05rmgbFMcxXZg4pqx83923Dw5KtHRLeBCNX1mMR2rXv9fzahAuiyIIsz1jd93Ky0JLYLix3yyuzCImGJDmoH9sso6Dpkkc4XEjAmEiq9DxWPmpsYH+koS7FR4GX5Yysr2x7bqILu4fGpWSThlM3cZNPL6/KC7GxcO80DP45Mz6FRyCnYUFn05V1bt7AJK92ML4YtLNPy5mJ/aZC90cE9O7YL7Tpy+e6borqu3r7+/v7+3s6GknTfy3r7RUUPKOi5BOeS12qY+anx4d6Gp/YqPKysbFv5lU86Rr7O+lRaUVlanJf5Kszb+dQh+QMSUlon7WOyG2dpZqZ4HBoxMTZYl+ait4/z33+ws/Fo2MY19g+PT83R37S20shwqAI3TZljN8mEWgqVkzDw/lmECky993exsL7q7WpzeC+fgLiO75tmNACLuaAqJaukdexSYg2oU0zWpnp5Pir/6vL12cK7ypIaoTQDJqFyeMzcJGysrzzIUlPoj99ZWSCyp/0q+4bGJxGEn3tBzvQ35V1R5d3y/yBqlgElXZOLGOzS7Hhr4ZOLMpxbfoMcvp7YPDBNPhJBBAsNKwhzPiq/b4eAsIy2WdDrTy2dPf39/QP9fV2NpS8f2GrsFRXfJ3/SPrCkjzQQYxdnYKM9RU9vqgr8wbaVlVfS0Dc8MedjSUVFeVFe5rPQe/antWT2SChqGN0Ie9dLvpV+RTPoRQRsuL8m+YYcDwfHH79DRDV9XlUMjsFmqEeeViKS/u96/0BDUje+mnj7LoFQtaSMvGtWIdTZ7tenZVXvpINW0qDsNvOwCUIFFjoKvG1NHG96XTmntp1bWPWsVxkcmCsOMlSSUVFWOnk1hcIv6IXJvsaiJ56msvw8KnYxPSt7vfE45Cx8bLQ7w/vcHu6tLL+z7VQ0T6odhE1MLWNwOOR0d1WG7UHOf/+TS8c5uqp3ahGDRc7CGrPDTkpw/P5vnmOeaZ3DM6AxCb/UkhlwUUt2t5DAbll9t7C06taV/trX016XG3/n1EFxsV37tY2vpVaTTgfgMQuTsJHmnIjTMnysf7By88rZ+MWk5xSWV1SUFuYmxwe7WZ5UltwjfVDb0j2meojGMwMAy/PTY0O95c/s90PYOP7Ywil+LCizZmhsHAFqeVRlLDe+NjkoZf+aQqjOBEJtWyFUPAaJgMPGxmGwcRhschLW/OmV91W/iknE1DjhzTgMNjFFOFg3luV9+MgZn4Cw6IiomAh/10sGKurQ/hHEQneqm5VXManNoBZXkQEAgDUJFYscqCtIjHx40xlqbwt1hEKhdk6e98NfZldMz6Pxsx0ZUX437G0doPb2to5ej+JLumam2j/G+HtdhZJe+kS9aSFfVjLTmh9608bEyNBA3/CC1fXY3Jav2j9uqDzF19H87GkTh2sed9xd7wQlNcGp5iUqeLi5xozokKQSmqNl2PIIKxkZ07wRmhWq4k8nVACYrM/wsjxteMzY1jW4kERRC19irp87ZxNbOAIAs5m3T4gLHkshET8AjH900d0jdiJ8Y9b6TaxQAQA72/bslsXJY4Ymlm7PC7uINyVjRysT7U+e9nhaMk21MRAIVVOMnlBxE625aS/CvK8RG4O9I9TOwcUr8vmbz/UjAIDsLk8PcHN2sLOzt4U6X/GIfV8Hh/WXZMR5X7F3gNo72Ni5uN5LLu376nrmpd4v76LuudleMDYyPHHR3MrR0cnRxuKC8Wlzm+sBsRlNY7TX/Mx1lOc+D75zxQ7q6ODg7ODoBLWDrji/bWzt7Z3db/uGxSZ9qhv4usHjlqdqs54G+7o72RIap6Md1MHh6r2g+OzSNsabuHDIfDdNGX030r6Q5VLCCvXnESqAhzfEe1id1DcwsXJ7/qFthT/hFTFWp897RIN+Bg012fgyLPRdNfWMDbWnzH52VN1Pd9kpHrPQ+iklLtj3ur2tvR0BCnt7F5+guLfFDZNLAKq/PCH4tpMN1BFqB7W/9jA8tRm+0FeWGuZ708nGjjAIQG+EJHwcopkLE4vDzQ/Vvol+cN3G1NjA4PT5S1A7RxeorcW5MyYXbdwfPM6tAbE9su9L8pMIn2v2UDt7ZwdHZ3t7qI0taRODrcN119sPgx6/KagdYbRna6a35l1CsOcVB3s7qCOhadm63PSNfpL8ZZUNbl3Z9zUkdGOrKISqLWV0ZxVCxfWmOasqm7/9yhFJhXQzoc0QKgAsdxU+cTlnaGh07sq957Urnit0f7IP9PzFG+/Av8pCEAoNe+ulLyxlltNOZBEcaqTh44uowFsu9iujt72do8e90BfvyuCzy4s9xXEBt0hadrgR+DizfXqhq+hVkLerI1HL9vZuka9Lvt6Ej0L0fXwV7ulkdd7o2IlTF2xsoc6O9tYXz509Z37lVsDrD420G2Uwc10FL2P9b1+1t4E6OTg6Ozg42NqQOyzU5epNn4ehiRlFXTDQ7HAFYHhHefrTRx7OUEITJWr5qsfD2GdvaobpFrEkfaAak0zkD0CT2sgrVFpCxcCacqMe3Pd/6B/w0D8wwP/ONdsz+mduBQY/Ir7xv+8X/uRtHwqojbRUO3uvc3x+CYlcXEAikahlNP0xyemqxMSiKdqFAW2LWJNQaSP+mDAePTc5Dp9FkdeVDHLFY5dmYAMDIzMoxisHAL80UJ4S8yJ/CLTXAlsRaSUrY5pL8k3OF4WZ71P82SZfUnVwqPmvfoMeRbZo1cdAZYSVwynXGHdnWCjvUL/59e8aMcCG+grf4Hn40L0iBg2RGodhCIddRhJ+d5LmHw4DoOkUMpDtpylKT6g0Sf6EIA4zPzU20Nvd1dXTPwSbW+Vw3p9Q8CpZ4pD5N7Wkj94oWzGsLZc5HNwsoU6WuJ02DS7axK/N4JDzizTzHIKoeEpLIgqORQ28j49OL/vq105J1VouumugvMqvzaxS8+99jZqfHh3oIyiybwiOWGLgp/zeEjaWvuv9fQ1J3dhKIqFiCCZf6eOrECp26qWNmv7VlFW8+hsrlyY2puXtXTOze5SdiDRffSOIRy0uouggXF5a5Sj8QH7QYfUz6WvbRr9R4Hq/xqHmxkcGeru6unoGxibnMKsxzHrz+954qKbXJvIH7F61EvsLqi7JRUqaYvLFL8/D+7u6urq6u7u6e/u6v2Q99XC8+6FvuL+b8Kars6tvcBwNADXRNrLyFz5Q/HEAACAXAMz8SFPFp8KKUTSwNFrhd1b9rHdqdTvjo44/mVC/FzUAwE405yQkfRhAkFwqaPREP+GWLFxFlLWszEUyoc4VhZntU7KvWvWn+75fkk3mgGl/eU5uv30S6aTVVHH4UTn1h8XUrfDryxdeGBdTPETHg+tLuo5Y/wFCXYdUPzUKDvnhprb00etkQi11OChh6F24GRlQ/TlJaV/6KCa6zeTBOA1uvOjl03eVA5R2MNnbOw3+ZZ6l3kyrw0eDShk7kxhn+9/1lmjy1QERqtHdGkbbMlDtiefUTz1d/61+6wUKP95WlPbm4yjjxdV6c/lmvJ7sYEvo3dpVrNnfTP63jrDclHxBfr/dq5VTi0t1Sc5S0hdySSZf+prBWvIfeUZ87UOdLPDXFhc95VdE3k0yW57zEQEfacm8e+aYS+E0AMzX3T+laub/oZd6rhmU+d+LUHGjFU9sjU1c7z2KiggPCwkNC/R1s3NOasMAALLwkZmkuHFm/4pPfqk8FnpATCfuy9BUf01dL/19ESAMfvbD4ie/89qX40YIy8SpbF+Lsy5xDO43+ZZUWDQatM78VvwNfd+VcVd1m4TXp3665dGGMvl7R0bPvXVSkDjsVLiy4QVd76qyW8UsomdqtK2lEw6+kuhbNSX8/Bb1XrBvxV7v96jh9LvWF6yuB4RERoSGhoWGBtyGQh/kgB2OAIBHfomxPweN3cCpnfVK8LeIh2lO91TcoR5WQrxCAzdT6GcoJmOe0TQ51lDdQbyGk1yNmYwbBib3i+iN2eSvv+cTj8NhMQwO6XxPnoQ9671fUpPS6oYIlnH8bHdyyN3H2StOxO/M+O+XfK7qmfGBneZPGoimQExzmoe8iHJAXtfMcF019XYWUr1W3eWLanlkoigsJHn+enBqZnrsHRfXsCw4CkB0PjE/DC0gHGqdeG6r7/xi1Wui/06Eiht4f9VIQ1FOQU1FRU2Z8KeqpGxoFzeKw8BqUlxNjijKaNo9TO6YISxe55rfuhjIS8npOAXljf95zLO5hrc8lPHA2S0gMTXW78atkC9/qWU0Hj3RkvfQ+ZyGjKyRY+D76j9tFbw56H5Sqtnm9zE2ugeVNI57xBXDCbevoL+EX1aSlNI8df1NzY/9udTNVWmx4on7MUUZJUUVdSVCX1BXVlZRNPD/NEyzE4uUM2aqKfyKhW8KzZUjmyvz75cKj+gseGh/UvmA8kW3qCriCnG0KtFCVVJW1dDzZTXt2aOB9/fPW/jXgg5Z/dUrPPAh8rzKPhkVI2fPByER8XnVIEf1X136HycfEt6c6GmlIy91xOxOVgNhhY7sLfI8o3RA+pCVT9rXByjGGrIfrnIOdXmwxM/iiOw+CSkpxbNXohqIl9QjehOtjtgTCXXsqfVRp4Tur3vZSm3+ToS6UfxxizMT0wv/adv+KlLjsdP9LY0dQ5QL0VaJx3z9l0EAj1ucnpihHYP/MqJ9UxBEe+796zdflv+a0yN6eFBzM9OztIrEjdelujt6Zq9s56GP/ld+xs3DR3o6Ovpha979+FeuwZ8mG2GX8/Qc2AFCKmy8+WO4bxzoGkxaMfC4WdjQCJz8gxoAMDP4ykbP4QPBaTP2xOqI86tBLADg6FzbxBz+mwmVFiJmmInAr40AHlafk5D2mXrX6a8NB6j2y2Of017l1o9SXNGgb5kP/3UILCEmulq71+u3wS+2ZN3SOnAsrnYeAJDvXPV1L/vnljVPMDiLu/axmf86HJkVYiLwKyOAXkLjcKsZq35hYDBoNGaVn2j6hVFhVp2EAG5xqL4wPe19XR/BH4DoKkqMTfhY00d7no+CFXOFSoGCGWAiwESAiQATASYCm0eASaibx46ZkokAEwEmAkwEmAhQEGASKgUKZoCJABMBJgJMBJgIbB4BJqFuHjtmSiYCTASYCDARYCJAQYBJqBQomAEmAkwEmAgwEWAisHkEmIS6eeyYKZkIMBFgIsBEgIkABQEmoVKgYAaYCDARYCLARICJwOYR+P+1XAqSoRXCPQAAAABJRU5ErkJggg==\" height=\"42\" width=\"624\"\u003e\u003c/p\u003e\n \u003cp\u003eThe influence of non-renewable energy sources, GDP growth, and globalization on CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions in the selected South Asian countries through 1972\u0026ndash;2017 are mentioned in equation (3) and can be written as follows:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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D07yH7igmlFcXd/atwjez9gEdsx4g6mWbuWOOAFmaX6B3vdXlxcW1mr3dZlROQGckRMsTvS1dQ9jAQCLHGuoKK1u6lmiqhAAALAzdUZXRVjZj9jHMNrJGMWPN9b0g4yU2+EEmKl64+siLGzCNp+q17QaWfZwvscxTlZ+sbu5xNKRPzPeykROoB0C4gSThY9PHeJgEXZM3YJ/UAX86BtREaaiGzD+o2i09wsTPaV52bkFpZ1Dmy0zaHHpDz/gBGq+FE5AwMxVpISavbhzRu5xSnnLVz+Ta2dOiAodlnloXtJHt3OujNT42Vp4BX9KiY+0Ur0jf1sztYGmwrBN6WGWFo7B4e997AzuyMlrO38c2pyudmZ7nNp7MqKRKHy8Od/XWvvaxQvm4SS4MQt1GR/1b5/m5T2qaeMe/j6mun+GXqc/+QlTGefx8OrlhxqurXPEkdqUDLt/Vye3b0erb4YyrbbEqqnDtssJ8At5YQ53r8g8s3hLyhNdGGb35LlJ2dC6JTlDJhROICarTeUEuLHW4gA73ZvnpbV8c7qKPqnflj0hcljslJxHXB1V1kzg8wvcLKxyhp/oY5pR7Ea/qJxAewM7AQAAkzWhZ3dzcu05bBdLXcFQOMHZkKohmsDeFNhxPmYO/suf637sCEZLteFDTYyTsfu2OcFCf6i16tXLV63ekHyRVqfeWygpW7yfoQ32DfNA1egoyPlm07o3NdJ6ToBF1mZ+slR7eOnstQ+lvVnB1grnJUUFhS/8/iq7nTJ2cMNZd8X3cXIIu2buZJ1E4QQidvE11OwZvnPgj3azsQsev5MzTJ5kKJzgmuF7+rIK2Wx2S5ztV447Vp+JnpI/8enPdJa5adI2C57QthA35P3q5fY5weJwFVzn0ZUr9/y+NhLV9HS9g9oTc/80xNa5UTjBfm2/PLJyR8/0pYTCX/5+Wf6BfV1ftY/+43MSR4WFxZ+avRsE8VwAAHJ9tXV8CgBgMdtfW5iLlYtP3C6ygjbfbsgJ7G4S7QQmkXSzzWh+0NkDbGy7Jf1z17YshROcflzQB8KdMBge+GlsBTwT4Wu/2B3j3CUiq1U1Tok5UhwgvY+Xh4Nb5jlskEqrGqPtVK0jEeSkOFRlSrjpy3uycg9SO4kYLU/3JLxxuHqU78Cp255+7z4k5k0tgPLdpKEw4w1m2nrb5wTTTemGSjflFZ7FVBMXtIjeXP17d+BJzZuIpwbTOEE2BSXEYH3ka5sHMmeULMNa6vIsn92QOnbk6FFpPd80Wqnbv3pI8jLtO3U/q50KAVXeFt/b4QR9hW8u8DFzi1yOLKfbZsAyF4byHopws/MK2X9pp4ZPv34ixcmyWy+inhpCaI62P8qza7fY/cyuzddY1Ng//EZUhKnqBW6TE7TmfnRydA8JeWuldu/ipd99k2q3qdK3ywmwq/Od5ak6cqJchyR1LbwSsyrHpydy3hkKsXEowjLIynN1utFa8YZF+PcVUk8bK/twlvv/Hrtp0018jWtKdLmpoJHfg8ABAAGLbv4KP7WPT8Hg3Qg59lo8cJkejy8owsdJQ3BhvOOD7RM+dp5XQeXEiAQCHo+vfavKzSMVUtaHw+HxhI0UA35lpKejrbXtB5/W9uHphY3SE3MaLnhzReLYuXPnxQ6Lmn1oxK926Z/bL/7QZXgRPGjXln7r3zviBK1fXc+JHr14/swRQSmvnN7Vscpnp/jOq76d+dFwJtsJQJwAPdnX+snlxUE29gtPLMMi4poGp/qrEpRO7T98UaNqjNxm+UqSApwsvOpBpeursDI/3tHa0tHZ1dPT09PV2drSMTZLXrT9gBMsj1e+PMnDxr7viX0iZeySOQGXpF8BZW9orrfE7HcJdibeh9ZRU8RGJywjEUvbWMesLycAADvgBDhUoqOy+LGTl6RPiEnezhrAzZX7iXHx64eWbg3wUt3nWzIKKb3rusF6TrCC6KwrsLgrzsMtpG7v9yW5eHR64nuE5VEennuWn+dJAnqTnI7xsXHvlY1pW6+DsDPDvW0tbV3dJOA7Wls7+5Fkh8AfcYLODBcxdjaegycDcsmahMIJLr8KnCAPWvRcVqCBKOeuQ1JKqU2bE2v8KnIe9SPDCTDXGXdd6k5GB32RsGHrUAN3wAkI6FEftcviEmcunjxy7JrJ4PJq9QcTIT4Rr+yhTUYuNZN1nGBpZqQi9c3Vw5wHxW7D3oRkVnTPjLQEaF3l2SPmk8EwbRM5gXcuURBuNtZB6RAn626Bc55JTeQm34gT9JI5gUHY96XFhZnxodqM0PuSB/kOHDfyTVvvOEHkBIKsfEdknPw/pyQnpSQlp6YkBlq9lH/iSmkdaiWwQ/nKZw6wcUl4pLaSwhajbF5q6xhfO8bDffhabA3JnEyY9tNS8sumbS2tDNZm6MiL7BG4kEii4kStiR13unnooLxp7yyWqDWp8rf43hEnwExXGdyUOiF5TvqosLyq3wwem2Z9g0fgemo7bfdqk6zWcgICcrw3L9zqKA/HaYXnsIAvle3D4z0VFr8f5eK/ktJNMnsRFuMd7u3+jeX4Df36deIJq0sjve2tbR3EMdPT3dHW2tU3Rra+bIMT4Evf6wkysxyS/D2jkzxO1hYbO9tpILOXmW2vvlMi9d3U68enudh4H1mGVVRUVZWXxvhZyxw7JH72TsBX2qILWF1CoZZoxJKadHvf2+cE07VfNNWtq8kWo8VuhzviPILyX6pJay3sCgJBo1UbZPwjTuCXBSo+/qvdXb4Dp9/mUtz68Ij2V6c5xJ74kefH0qCXxy5qtVI3KnGI/nBHHVPYl0ksgJ4sf3lCSCWoGFQE1EddWXauY14ZG2y04GZqDe7dDf1O35scLQqTFtitGVRGk1BH5ATSYaCFJu0V5QEz8hlmqaerZ6ivv/mfnp6OcURu+zrVTpKxPBRoruX5pXR2YaElM8zaOST7i/2RA8dfp3dRhxO2r/rb28h0BHVnYW0ZNvqNb49X13CnL5A3ikMJQ/e5a6gEZjQhFxFVX9ws/VKygrQP7T39oZaivkfqMj2sTQyNbSLSatbYnCmc4IpOPc3gCADjNV9kD7JdN/5MNdAQvtnf2SNyJZF0WgnbFCt3jI+TlV8vvHJ9oVAj7VmffZSvnT0hLn7q3C230JTWQfKSkMgJ+Jl/k93EToCZ6dS7tJuNZc9tgzBKuUdzH546yM0tomxg5+vt4WxlePvC8aMSl0w8vvSRT+Mv9fsZP3eg+2qtL85WIfVxLiae6RuPZsZ0iOavepom2c0ji/OjX7wsg6KzAjUuCl/SrJmgsOrB6jQPW3MrR5/iNgaTQH+Gt9yFJ+UgbCmCaZzALhFEzLG5AaoHOIVg6VRXCWyL3rnDp+7b9ZAklL/VO8zDzHPoZmrXOvUGrPY3lkW46l85ffyEuPiV+7qfsyrHyc5NP+IE/eX+ZzjY2fcdc00gr2aInICfle34lceunn7uLg56yrckDh+5/tg4tWaQ2qVx3SWJzmYGRmYOCUVt5OE/35ym/VQlvQukDBhhJP9aHil/InU+uJI2LW0UiR426qulEt1MNVHRwzd4Gv3+VvWVa03/LGq8zc/Y5Ethgfn1wycewCcYidtsS5ZfYByDkzeNE/jn08f4woDDXdH9J56VU5n1Yvlbsb2HtAJywaWhcwIAIKAG3+rd2MPCwn/selQ50YdpE05wgoed79pTfQcLgwdXpIX2C97VdE4v2dgvjMgJhNj4hKRemTq/9vTw8vDwhLsZPbl69pEzbZONigUq0uAGLxOrrP77RQAgjOVpPtQu6e9yfSjJybznpXcWDgBWu5JfKJk0UHeJSAmXEuwfCgqeT6QtZfGTRE5w1Wxw21ZOwlSzuY5+zQgj0NRiMX6vlkY46dmH9kyhpntKXcyt0nO+3hc7dN/mC30KWhjLiArw8nzt7eWbXktX7wCREwhysou654B8DOeLFUX3Sj50pDmaNyZaCbPxu2SSfMgws8Fa59l2sZ++bdbMUGtiofDL801l6W4690+LS5w4fvqJgXtOeRt5BbMNToDN83vJz8widOZB3hoHYmqF8fN95tcOMTHv0beIpIZNeilK8XJwS19/4ejsaqH99PzRA1zsfFdfOFT00Uz3mGRXFV2vtD+2olyofq+m/2aKmt+m34TZUIPfH5m97+zqaG9r6+zqjrO/z/ELy0PXNAIAjBWFqaiYVG3ulbAlJ/h1jzoDJ8B9tbuzV/RKcj3FDIJHDJjI8By+5050SiWMO14/LP3Sh6pLiQUm4DDkbfe2aKN9nCeDSvrA1RjKhR9hZZE1jFqraQiLqV765oG54E214YJQKYHdmoHrOEElVcmCRVOe8UsoxPwPP3OIJfTG08dMa2FAcByltoSlwggr+VMSZ+7bkvYhicuHkcYCh+fnhWRN1/gfgcuyipoe7O8fAP11Zwc9eWJR0scQODA4voSlay2yBGTNF/j7EkooYSzS1+Gu6N7z6kFkszaqv8j2laaNo5OuoqzQQXGHWMoKhpyWzgmo8z8AAGPV0VcEOBTdC2glLHRX4j18KbaS2Efw7YnXxPg5WXle+BTSIjA+ID4Y3eL99ZeTSq7D9BnvB5xgZaJG5RQPG9teRZtYSllIdgJOHimvtLLO1ubqivLaxrah8Vl6M2ARxYmRmdRNBMzM5AgCRZ20GEsE4JDTYwOMYKYFmmhave8EYd7f1z8yMbvOBwNdmhAck0thpSvjHW+N7h0VOawb+J2kAglzrTk+Ht4hb33Urp44fsOwbpTeVRtjHC7LqDeB5xByuaicQMkuiR4bWM0JUBPgEQ+vpxk5eyxlj0jctiAvqmvCjIV3s3DzXYxqYmAetKoSxotfXhBg/nW/ztt8WiDwI07QleUqwc7OfeCEbxZZ/5LsBMwsVzR9Gjo7muuqKipqu/qG5+mbtPjx6iQzHT0HBzu1W2cEjsi8ySHanVcn22I+RndQl7roibGRRVpF6MXBTLZonDkJywOpdepLAm5pfHCgH9QiA/1l9k8V/TM6wEOjv39gBgWCjZIcneHnlkYddSttKebaSof3iHmC5w8AICwMeDyVFpUx6QcPIyon0PEvoAcj+xzuHT18wYRmFsA2fDy195Cqdzq9U5P3Dsh2AlIxcPPtTorSPMwswueU09tRAKLLeK2PYa/tjeNcLPte+Wf0tRSZ3JJgZ95zzyJy3YRFqRVt76AI5G9MWGrxcQ8fW3O5BACM5niI8bLvO/awdA7XFm2nbPphFQDqw3QPsDMLyZr0Y/GVISbqrqmMU/dCHJETnEuk7WXjKJxgYJMdF+wycnSwH9xMPdUZWs9VUko7GZppcARJ92+iVIeAGvgQEFA3RoG5qyrG4Pp5ETG5WNBJmd6SSP2nzzTU1dQ1LdNbQF2dygk8wG06X6x4ZN8VnXdU5ga0ZbqJs/PaJJM6M3b+vd5lDiZWsataNSBJlNKQvobK3p3jYeY+eCqkjE6CtsEJCBVRxsLMzAclb6d3MiJKlY6dodkJkqlhE56PJDlZeDTfVhAAAga9ODXU+Nb4zn5OziPSj2JryPuh+Oa8mPj8VgxJl+FwqKHBTSfnFcTkGrXWnOzxWMW5mmEQ9Q8OT64wWnwIo6Wql4/9ruHyLigwyD8gKCDQ3xNmZ2UZmFBFPJ86UBkV9XWYuk22ODI4zjj9bc0J+NT9skEDlMQJjsiS15TEKYTICXiF7sFRRMepBnUJ/rMqvmBOQAULSLG6xs59OqSM4dwgojfx6h7Wo4qedK8hYgJsa/oba/inSUZtO1xI4gQBIE7wjmQn2IoT0PL/Yw/4toqUtHzawQfC95BX+1kPOaXSNAlxF6Pik5HUFbMtOMFI+Sd9VRV1VTUN8p+ahrridcmTF5+qqWvSAlVevjL2bV17vQWhPj4yu4fGC3Gpdvd275MMLqSYGLoKkop7SQoHM/VWS05awZa21qP5GIpd0QWfeiRzgkcwkkWUhEqBhxKP0MWYCpJtGVWnIyPKzcp2ViVwE0WGTnN5uo9pl7x+GNUeRGyyre0E040fLvIR/XbEPiQAAB3aSURBVAns49f4E5wJrQatFTZtJXS2v0dU9QYzDSnFYnGkp+aLFxR4VdW1NNUfXbtw9qKCmpomDWHV5yr2AUmToN5MTIueyY77WE/VYsDSkPM9kb3Hn1RMk+kHdrinZ46k+1Z6kuVPy8fV0toCqP1kIyOr1UZnMdTS0ziB/XpOIBZaQyNoPZZXRCR+N+8gUc7posDTB3i4OQ+aRW/iX7nQZHJTnI1Z1CmJtlsJ/JATFHkr72FlFzh+L3eYrKkpewdXDSM2tu/jUJW56U1kN5+VYefb4pe03lGZALWChIVEN9ekbjoU1BfEpbP2hZOOG1n+MIhWX2NNVRVVWjO9UleWPXnixkMVLTXq0FBVVVXV+1K2zsyA6Xz/LoWyTU7coy5VOrr3+H3HfgbFgS6P89VSvCXzu9UgffIHgM05gdB5wy5qTEz9R8m9h1RefwMb28B2AnIdF4bKTYgnC9hOKBgW1dZZaqgwnjsgcgKiP0EU8SwioiPtseR+dk4hw7f5YLE0uCicYO25g9X2hhbUuuUBHtWie/4gB7egTXhGgL7y6yzi+go9nHtfdDc334mAbyVeWi/CyaOYlgGwEL9DTjDfXuCor0prJk01DQ3lh+ckpR4+VtVSVye3nbrKS3U924K2tRvkyP7muLg0WuhUQ4L8fuarBlG02Ri/PPrZx7eBFoNeTuL0QbYTrOcEl7XeTFFHWSuRE/DYJJF7yGq2n5oAK/OeYzfj69Zb14jSp1uTbh5k23vkSjLIGL0NTgAMlYTL7mfhFLoY8X3jOXthKO8RyZ/AMY4mesKDxAkYzh0stBpfO8rJzHJa0WOK2tlo9W7NfuP5YQOLLDlCV26I9suXtIlDU01D5b68pKTsC3UNmlpTV3mpaxXcx+B9Aiy2pN0+JWD6mTZz0TJc94AeD7d2LppnmGt3zAn4iJyAYgDGIwdMZHcL3XEjDldcu570/kOyBh105yViCfD4FQyGkGp7g4NF2OUbeTOMUjLUUNbt/WzndD6AuflIbYorLKSLsZQAAIwUhkoL7NEIKKFVqy74R5yAgEXNTo2Njv7gb2wSsfEGz2pTYUxRE1WJrwy73BUVkTfvAbmwAgBQ9cVEWm4rToDHoheQCPoHiZyu/PDihWPzPJIheGGZke0RHcdz4pI6JiljijBdp3NF9Nxj2CBVwSwv0FcTzVGW8rfNekBLaaqdQK+BWgOaneCRWw4NxkLPJzyCF76Uk2nsajbsCR8bK7/ojbRecLPQoqPTXJT3MTHJ64WCXPDInGCXrHbw+rOIAIDLginxsbAcvqBRR+sbG507oOUBAAAGjeyozC1tmQYAdFWcu4yIhKZbcGF1ByP2lBRY9BIYSRQKURRho+ccO4lEgcMX1129hUdMZMXH91GJwmxjtOwBvqdeueuXBssDueZ6zq1zVOUEAM1xTpdlVBvoAdTi0ziBQzJVMABQ7ATHQqppjdFjLXdETMG0nWxWXO6yuH6Ug4X97GOPSVAjUoUCAKrR5IY4G6uoI/gqArKdgFnELmEjH8PlLotrouwsnHcsYqirAionAJ9FpOcBAHgseoXu4FPu+1RWPWAGi1+ZHy7Lzu1B4AECMj/M6szhU8b+UaWN/Wu6CGaqWf3MKfeCjdgbAb+8Zhgg2mCqT8NLJxgaCbmwuu4gAWGi9GNSLc2boSvZ6dhBUaf4FnAzjVXEB35Kzwgxu3LTfAisfMmcgHW/TkAhHVeSnUDovEEnNSamMVJy78GXnqlg68d6TgAAwHx7urK0AAcb74XrT24rqhT10BoUAAAKJ6CdRez45n1+PzuPwIW3G9lONuEE1PbA43BgNykCOtn5Ph8774mTMteVTRrJIw2HDNO5xM3Bd17mrqKmfRfRWQv8WSDtHZxLaKVWHTfprEDcO9jMTgDgMYug9kAikTNdpYYar/JbpkDBCCRqcR1pAcZ6KlLTq6iI4gv8XhzgPxdVT3dSmSwNuSJx8s5zw+iCjjUFpXKCox45IFsyyU5w+VXQFHXOasuCSXDwWCVQbMOzdXG3ju5mYzug4Ze9ViAJhumWxJuH2PYekds+J8Ah56dQKzhEq9VtcRYWQdPwUmqNwMACw/keYhysB48/KqJ6fQLAuPtDSeK5A/BZRABItLjOy84uKKZUiiLgMCsDDWVFNUQXu97ijw/OiN/W9sgoqEPSOjcoExx2BYWgf5BI5HB+4PNXr7uRDBMHanHtgZmV9ow7x/lvm8eBOzNpLiYQ8FjURE9RTtHoCgCgJxJgaidFLrp8TKrupC2MNv0fSKT7CX7lU/fPAek1op2A74hsAogTmF3ZLXjbleQntRSqLsXOKQZPo21eEa3rjblxtUOY3iSLAyyst2wSqY1LrPpsU7gUr5BtAt0ldbazwNcruG2W3goE7BIKRdQ8I4VhRE7gv4YTnAmv2nzvANMXZKSq9EjxqdLjzf+UlB48fZ1Yt1GXWm0ujClppwDQneZylIfjtm0KeRagzd9Vn3/ACUCtTHnEtcWra8A3LzctBSoz8WvfNFnp4MvDjQW5+TW8Kb2fQCsBMTquKMBQ14vi7ElOT+QEzCxiVzbgBA9BnKDIS5lX8PynMso1Dytj5ZoXBNlZuO+YftzoOmT0N7dn/EzMV/XDaAY9qp2AyAnWX4UyUflB/jAvF99JeArNggkAP+AEuPasNzdOHtEPbwAAwmRL/tPTJ23jSibmtnsbY12cs4lHOriz0TAFP+DmJ7ITkilTCB4ZrnOZl/tUIGm3GCBQ9DEBuzzSWRNk/sorqRXcSUZyA+UvPiqibRTS5JI4AQszr5JDCqgA5L2DYyFVtCmk10b+iNhNkzaqhO4MD6l9nNx7xWEpHfQBQBO70GR6U5yN9egGdgIiJ6BdkkNLsJznq3mQneXwOZXiYVqmP+IEtNTEB2yqi7plRMUqbjbJTe2kuHwK8RYo3GB53J3j0t65DdMI+pYDOd3iUJGitExE7TaPjYz4aKlEN4EUDEPu9B+EseLPaU1kqoBDttvcEdsnrpDWSkd3ZaI+6HVwD2q1MsxI5qYFg9shlRPoBq7jBOfonADbFHl638EX7l/BanRDTkDURZWfbkvs52Jh3nfmWSltr5tY3rWcAACWi94ZinAyH5J6nNZKbWlqzbbkBMuF0TEN01QuR0oyUvpeTpCTjWXvU3gKdV0AdGZ4Se9lZ2M7qOUPtumS81hIcCDuHcSv4QTXzAc32TugFo3+jZ9qMtfRr6afuKO/WvM00lOWnkW93nS0+MnxvYdkDLpIV5uTB9N0Z+XnYE89petHhcRNggsY7s2k2AmOeuZuyQmy4RIcPBbxNHsz6qvriwNsTPwnHmb3bGDZnG5LVhBg3ysql0w/0g5saSfAln+NKWgmKsOOFJgED/Ox29bd6zctcbNv1S+ws+3XeVMMGqrj8AckTgA+iwgsh2pe5GFjEz6n3YYlTDZnvDgj+tAhfgkAlmerjRTOv3ROHJ+a36Y32kLVezX9oE32SUCtMd9geFX04PH7GaAD47iphq859cjpvlCjO6cuPCufBQACpjnF98oJ+U8NvbNIOrHYzE4w4fdChuvXPer+DGumVPs7e4Rlaf4EwOKw6eXdAgou5NvrutLdJbiZBCUfROYTryME0LPfY31d/L4iiP44tToXDuw59qBwhDaSCbnwJ9K3LVrnKIuN2c4ibxePnIbBuZmpqamp6empkc6ayLf+xaSKjReHSwvs1ggknTsgVb8xRI2TXcK/iNg/EJMTOOz6JRtAwGPQ6BX01p/VjUgaMQt8W1liWiFRPnq4TOO8sOQdFTun4A4EDtFSXkE64g8AhMrPxlvvHYDaivK47XMHuOr4qLweItFGdWUqSvCdvmsZ6BfYigSmO0tqu+jubbiZJg8bx7IBsEID2jPJnEAXfCvMZG2M7EH2R7A8WqmKvZ7wHDr3iWInIDqBDJVFPTh1kItXRMsjad26H58Je8b3yy9X9EJBewf4LPfn/Ey/yeqGMlqkMc054Y+kBXbvO279vpB+/J94ujTvwcn9HFzSwVXrDMXkkmH7HJVkdN8Rl7+4mQ6dC9Je3zfx9qHVBPSw3XMHKzNZsZGNJCt41ze4BP9hVX1TeEQxBlipKSyZWCb2VfzKTHl84JOLoodPP/4GOhSAaYm/LXMttg3ME8glWPpsLEfmBKB3uLwAtUM8x0LpB4L6reWOHL1hTN47IKbEInPfGIjt5jgocTMkp426sqPWaqXV9OYxpl3CjomgvQN0u8UtCTbmIw7JFF1Mib00luJrcHIf15GzyvFVlGOIpFfoJHvFvbuY5PTDGVuKmgvoGzNe4WgBaySxvMWG2N8vXksgXaS33F/6VOpCxEae5DMtUdfOKRb10igISNwGj9s+d7DSGhGSRjKXYfIDXwnzCas7e0fGlq9glutKyqbRK/kfA1JriaqyPMzo8u+2DNZezISvijQTC79uYBG9CIuDjvdEBc8Z0qyrhOao03sPPIN/BVs+cn1evfKkW9ToyQFce7r3+QPsPJJPGDlBv90NCeLeAdg3Fj3x3vgmLxPb6btmDbQFL0nWZOkHmYNMeyWV8ntpWpGSSW/uW0PnyLk1c8VCu80dce795yJK6VXETFRpXxbkFrr8pXq9/Xw50fGRoOC5JOqWHYCfdbl1kO+8bjcRzaXxiTmwJQJUQfrj9s8dzPc3xsalE7USdirK5OaBo/I2Fs6xdWPAYnd+aRd9CYObzwoyPCF2LYlGh4lap0lbSpCT/YgH2KkPWaJ4ZK+M1tsZ6sTbkQMXZ+c2j6OrAhyy10fr+j5WVsnbJt+71rKu+c7UmweYeQ/LgjkBMNNqJHeAiUfUJQHEFEg1nqiJMzaFtZFVG3Y21vHpAa79Wn45NAZGioVrinMQ2817VdOvHwluuAnY/VOczLx64XSCPlX/+caRPWys+1ReZ5Oi4mMMr92ziSHp7mFnJTm9wA1OeNHRZ3za9rmDpWz4sz2sHBcUrco6ieeYsDMdbx2sPhYQfYMms1/Lyj0lX381VflFQepWNuPu4HpOgEVOj/dWxDw6tX/X//3t5EP7uu7hOdQKHo+ZHiq3/F18F4ugeXD2xPziysJce+H7y3t/4xK6GV83uIjGEbAzMY7PhLlYuXkPHpc4ISlxXE7RomacMsoGvn+4f0r47D3T7LqeocH+qtS3KsraaS3kVsRO1Cc/Pn1o9+79x46Iih4WFj0sfFRYRHDfPrmXPuM4YHFmLMVLg/+3XVfUfTvGKffnjhb5HediFZN78drbJ+hz3hLZbYMRxJ/8tTBQCTPTh8GcnlyWEDl5J6l5rjPR+aa8/G1F0/IR8pT8l3ICYKkj3czAzBNud09KWExGtbBruvKj+dXL1x+r2dRMUwcKYaEo+l1cEe0oBLC6OD820BFseJXzX7/wCJwPSm8Zm5zDEIDl+YmMIL2Dv/771H2HxuHpldXlycE650endv26T+t18jjo3qfZjjxzJVmRQwKXbr/6kFba3ds/NDTY29mS8s7+uuRRidPXPOKqyO26gpweaCswUZBg/Z9fRC48Do7JLK+sqiwtTPwYZKH26OLp0wrKRimVfXS+hkdPjw6WR1of283M9AvXQ7uo3sExJGn2ZWgsTK/Tk8t6b4nnvEm71Gc8i0ELCIaoG/zYLicAcE0Z78yMbWE2OmeFD8qpvh5cmAjTf3T92g1Vx2gktUcRDW7DVToXDz90y6RP88tNBreuuCXTlixEl1rU3GRfQ47a2X2//ovpxE3D4s6hGeL/Z8PMDDfDlCWZfuXR9M0Zm11cXZzrL4+8fpiTXVDuQ14bkrYdiEfXJfvfO3vssJC4spFnblVL38Dg0EB/Z32Rr8njkyIil+7oZ7aS1gkEzPzkaMM3fxkhzl2/8N7QsE/JKa6uqirJS3/v4/Tyjpy01EUN6zfNY/TpeRkxNdCSq3/tGMv//Itf8kladdfEDHKdnZ4CJmF1+ltoQEYD5Sz0YkPc7UvX47uIam2p77uy1MX3revPTAKdiVbyj+z76Xlu0DSgoG1zAgD3PdTeyNbNxUj52H4hZaf4qYlup+dXr9645xBW3Fv+wdjCr6S2sbGhKtz26RlZtYyGTuINXwQsYma8ozT+oQT3v/+H5YKyU3Xf8PwCmrC60F0Wd/sIK+cBmQ9l/YjlVdTMSF6gLj/TLyfvW1X3EWnwCmJ6oK3Y9NZxcTmDko7+2QWw6ifXAFv50eT4OcUiyjWWeNTMePf3iGsiu5n+teuimndr/yitV+NmWmzunmD9henkTe3EkpbJ+WXC6uLYYNdnp2cHmP7FsvuYvltYevq39G/fMjIy0r8mvHHWOSkgbhdbv5YXAviC16rnb5kyXnu4nGT36Pwd6741ZcSvTPaWW94RZ2YXtIusnKZ4bq7GW13jZN33UM/R/7Xf1yr65ApqF4bH7XMCwspktJeFtRPM/MUN4UNHLd5XLg7mqChcVbjxwC+TtulOEo4d83wuZ/KRRGTx6Jmx4eY0L6m97Ey/sN2xiOofGketYFaX5tq+uktw/br/vGpu0+jiCnpuYuCj1W3O//ntql5I/ySC1nXxS6NJ3kbnRQVEJGTMPaPqOroHBocG+3rqC+NNFC+LCokpGXh3kjdg8Zj5qdGqZO+zfEy/MfPIK5sHh4aGvgsOeRcS+u6tu6XmaUHBF26ge02ws5lvzC6dklKx9C9p7BwYHOzrbon1Mb184uRzm/dDdDMZfmF2vKcsSkF0D/OvbFdUnDOLKmqrSr/42906LSIgKPHS5v0Q5XJoTKzxzXvWZE4w5KwopwvaDWfAfaMf2+YEAHam0faBNC8r2/5Douekz0idlFa1jxwnUcyJHO8rcs9KSAuhycrPCtK3shitu+s5wVJnVVFMWICHm5sHHA5z8/wYm97YO4nBoKpTI33cYXA3N0//sO8tQ5MDjfEhfjA3YkhQZPrgLIlLYeaK4t+aa6u9fKHh4B3ZPMZg+5rrLn/jbG5oZOHh5RMU8rlplPZ2qTbtg5uTMxxGlAZ3g7nDYO4wOAzmkVpFNOMM1uWF+rrDXd3cPX0TClvJvYGAnskMcdFSUbfzjen9qUtZN4KfEobr/h5rqa1hZO2V10zSj8iuIGsTv/gq6pmIP8YJYrZ9jyGuKT3USFPTzDGgspfonLM0VudjYRiSQbMGrrYVJiXkgv3fCXPDbbmJUb5wN3c43N0N5v8uKq+kEYknTLR8f+/nDnODubt7RaVVTc+PlSZH+XjA4W5u7q+DC5tGGHQQDtVUkOTnaqP/6pWhkZmdrZ21mbG5pePbqK/dU3TePNlVnRgZ7AVz83T38ITDYa5uMFILevsERnxKrGwbpk+iZEgxM1XZySG+nnA3mAcMBvfw+5Kc1zVB8ZmgtwSmz/nxZZ03xCuqsDNt2hfP7owTRDsawbd3t/HKVEaom7a6lktgXD9pO3aiJt7cyCG3fY0PHT7X9+UD42jQOnI1yfbxU4d4Oha4lb7GkviIIHdXNw84DA7zCv/yta59HCCgarPjfWGucDeYl8/b3IbBuaHm+PdBHjA3uKtbwPuE7mkG+/nqbF9WTKizhaH2Kx0zcyt7G2tzIyM7Z++4rCr6P4LAo1pKsiOCvN3d4J4eHh4wGMyVKA0O8wgIColJye2ZWDMzE8bbKxI+vPEkjmt3uCssKCy6uK6bwX5LQx+/XPPtS2o5/d8fEDnBxWvxpLOIS/3flaU35ASYWPMHau4Mbno0kRs9EO8x/LS9ewxxyN6PMLNXr/T9P+eT0MI2pQebW3o2TRMGMv3uXZe7IX/t5rWrF06ICh4UvvjIpLwXBeCXOmoKv4T6w11hxOaAe3+MS2/tn8HP96dGvXEnYgULDE1oH5/vrckK9fNyd3ODuXl9+VaJJACTHVUJH954uLrC3NyDP8Q1bPiv83CIgvTUrjGycQ7TV1fwOSTAnaQ24TCvj/E54F4921Ua8hoOc3Hx9A8ra53Azw/lp3zyc4e5w9094XC4q4ubiyvMlfznBndxgQdEtW90Q/5id96nNPrFhWRUZxvSIlIb1yK8Ol3y9bMP3A3uCvMJiqim3iyHHKjyt9ZT1zAM+1ZLd0dam5j+e0f3GC4M1vjZ6L3StYhIqyaNi5XCcGdzWOQw8epb8GclzcMIHkcsMwEzW5ef/iHIm6wQ3D384r4W9EwiESNNMW993d3c4K7uYbHZw5MzTcVfAz1gxNnB3SeltANNXROR5c70VEe/e21lqKOjbWBlZWtvbWFqZOrmHZxXB1qQrCJbK7IjArxgbnAPEuyuzs70PycnZ7j/9861PpATrcXBHg7mJmbEfyZqZmxp75FSsubWAmx/fUF0WNBrD4/XXl5e7nA4aVp0h3sEhUYV14OtQJg4k5t3rb9Q7ASK8jvjBOWh27/HED3dERvgoq3yXFVdz+dDBk3HTOb4XpF7+p20siBxgt9/yAnALfdHnwlY9OqaTkAXhVleWFihrxvpL/7QEx7H2DX+kJCfSVQTbSItZzG1aXU3kL3akaSt670Nf4IN0jIGEfoqM+PSyhCreBwWg5qZGBhad7CZMcEf+YVbXUDOz88jFhmPrPwRUdtPQxhyVZbVfUe8shQ33ap1XtqLeM8/Abe95q5LcLfyyQDN39vPmCEmAU/rXeg0P3OP6FowbZqqCn/40KhhvcmWQcYf/4FFLyHm5+YRqM0H0x8XvnlKXEdRYmJ+08IqAYfFLIz09s9j0a3Jd2WuJ5AuaFrqK35y+vwH4hlKAo5uEQaA+Uqdew9jGtbRu01zGg00eBXbzLDhtWnczV9gV1DTU1PTU9MzM+NJ7irnrpu2zMyvrneB21zCP+oN1c+FodB4ej9lCN/0B22RvWkM6gvsRJONsWk16OQx9c0Ovwn4VTR1JYXqfeMGK+r64f7VDrMgRV9ZRM3PzyMX/uz/u45dQczP//iS5h8UmZBoduuubSyp0w87PZLVJd3Ch91ed0XWfNQ2CVmzWNk6QzwGvebit9mCADn5Z6Uk18+pys83pW7lUoYyRbettxNsnQX0lgGBpZnBSNs7B0XuZjYPb5/nENCz3d1jP02LVhqSfW+dk77z6NlL5SfPFB/eeaia3LiW5DIU9x/zAz/Vnv7svPBNg4/jqFXC4pDzvRPy6s4pOWVDM2uWvxtXaXF6ZHBsu1fsbiyCGLqYH+5m4RiYX1pVV5YTFZk4gARTAgDAzr23VHdPYNzL31zcP+ANZrEkwkr27CVFxWfPlZWfP7qr8NCscmSuKc5FUkjU9WvnMhbATjcZyovdM/JJy6ucQNKsJNiaCJtXLvHbah4KENixnq5pum/Tz8NDyPVVlbpuS/d0/3mRkITVxcH+/g3ujNgpMst9/kYqRi6h2Tl5iZFhiQUtP60Ad1qC/+/x8cjxDsffj0oqObZOLgIA8oPejbMKutGpWa0jNJP5VoXELU119665pmur+OvfYVdmC3y1REXPh5YMYwjAUneW4hkxddfwzKJ6JHXJB3GC9bjtIGRxaqA0+2t8XEpF68D6PfEdCPojUVdair/FRMfExcTExsTEfolOzakEnZX7IxL/NmnwE53V3xLjvmaWkP6NFn64PvuNT0BGVS+Dkf0vLy6683uSp5Pzu6iUspqWGdr0B8oX0Z5uquvUCPK3BL38Bz5iFmpzk0Cd6su3761LKwttZbmJsXH5VZ1LREWO7i5PDfB5W9BMd0ZfGi611jMv7v/ZRf/PQUYY66jILmzcqKF+TjCU+ucRwC+2FsR7ucJDIhMqWijXZfy81H+UBDxitCMnOS4xNbd7nLh7MN9bEe7vF51Z92fbNDZFBbs8XVuQnhCXWNI4QHSXwi/U50T7+7+v6qG7B0GcYFP4oBcQAttAgNCVE+rgFT2M/M/SlW2U7D8WZRXR/9HdPqoQdJfXfyxvKCMIAQiBPxUBiBP8qXBCwv43IoBpzP9W3r7xVUP/G/CYbC5M/96+E4+a/w2oQHWEEPhHIgBxgn9ks0GF/rshQGC8Q+rvVry/tDz47Xus/aXlgIRDCEAI/DQCECf4aQghARACEAIQAhACEAL/FQhAnOC/ohmhSkAIQAhACEAIQAj8NAIQJ/hpCCEBEAIQAhACEAIQAv8VCECc4L+iGaFKQAhACEAIQAhACPw0AhAn+GkIIQEQAhACEAIQAhAC/xUIQJzgv6IZoUpACEAIQAhACEAI/DQCECf4aQghARACEAIQAhACEAL/FQhAnOC/ohmhSkAIQAhACEAIQAj8NAL/D1LZPePG37M2AAAAAElFTkSuQmCC\" width=\"640\" height=\"41\"\u003e\u003c/p\u003e\n \u003cp\u003eBefore testing the co-integration method, it is necessary to identify the statistical properties of the model regarding stationary. In the model, it is essential to assess the unit root\u0026rsquo;s presence due to dependent and independent variables with its long-run association. Thus, following the co-integration test, the order of integration may be the same for all the employed variables. Thus, various unit root tests have been designed in this study. For this purpose, the prerequisite in time series econometrics analysis is unit root test.\u003c/p\u003e\n \u003cp\u003eThis study used various unit root tests to control the problem of non-stationary data in the time series data. The regression results will be biased or may calculate a spurious regression if time series variables are not stationary. Maddala and Wu suggested that multiple unit root tests might be employed to control the problem of individual regression inaccuracies across thecross-sections.Thisstudyfindsnoevidenceregardingthepresenceofunitrootinthepanel data series after applying the cross-section independence test. The two essential subgroups of unit root analysis are divided into line with cross-sectional independence.\u003c/p\u003e\n \u003cp\u003eIn this study, we examine the homogenous and heterogeneous cases of panel unit root tests, assessing the appropriateness and implications of each for robust statistical inferences.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eHomogeneous (Common Unit Root Process) Case\u003c/h2\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003eLevin\u0026ndash;Lin\u0026ndash;Chu (LLC) Test\u003c/h2\u003e\n \u003cp\u003eThe LLC test is widely used for testing unit roots in panel data. This test assumes homogeneous cross-sections, meaning that the dynamics of the panel are the same across all entities. The LLC test is an extension of the Augmented Dickey-Fuller (ADF) test, incorporating homogeneity in autoregressive coefficients. Studies such as Bildirici et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) have suggested that the LLC test offers superior non-stationary testing compared to other common panel unit root tests.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eMethodology:\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Assumption\u003c/strong\u003e Identical autoregressive coefficients across cross-sections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u0026bull;\u0026nbsp;\u003c/strong\u003eTest Procedure\u003c/strong\u003e: Extends the ADF test by incorporating a common unit root process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u0026bull;\u0026nbsp;\u003c/strong\u003eAdvantage\u003c/strong\u003e: Robust against non-stationary data in homogeneous panels.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneous Case\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eTests for Heterogeneity\u003c/h2\u003e \u003cp\u003eHomogeneity is often a restrictive assumption in panel data analysis because it disregards the dynamic properties that may vary across different series. To address this, alternative tests allow for heterogeneity among cross-sections:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFisher-ADF and Fisher-PP Tests\u003c/b\u003e: These tests combine p-values from individual ADF or Phillips-Perron (PP) tests, accommodating heterogeneity across panels.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIm, Pesaran, and Shin (IPS) Test\u003c/b\u003e: Designed by Im et al., this test allows for heterogeneity in the autoregressive coefficients.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003e\u0026bull; Assumption\u003c/strong\u003e \u003cp\u003e\u0026bull; Different dynamic properties across cross-sections.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTest Procedure\u003c/b\u003e: Uses individual unit root tests combined to form a panel statistic.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAdvantage\u003c/b\u003e: Accounts for heterogeneity, reducing the risk of spurious results.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCross-Section Dependence (CSD)\u003c/h2\u003e \u003cp\u003eTo address cross-section dependence, four significant tests are employed:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e1. Breusch and Pagan LM Test\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2. Pesaran's CD Test\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e3. Baltagi et al. LM Test\u003c/h2\u003e \u003cp\u003e \u003cb\u003e4. Pesaran Scaled LM Test\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAddressing Endogeneity and Serial Correlation\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003ePedroni's Non-Parametric Approach\u003c/h2\u003e \u003cp\u003eTo overcome issues of endogeneity and serial correlation, Pedroni's (1999) non-parametric approach is used. This approach helps in mitigating biases in coefficient estimates in panel data regression. The study utilizes Fully Modified Ordinary Least Squares (FMOLS) to obtain long-run parameter estimates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEndogeneity and Serial Correlation\u003c/b\u003e: Addressed using Pedroni's non-parametric approach.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEstimation Method\u003c/b\u003e: FMOLS for robust long-run parameter estimation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGranger Causality\u003c/h2\u003e \u003cp\u003eTo identify causal relationships between dependent and explanatory variables, the study employs the Granger causality test. This involves examining whether past values of one variable can predict future values of another.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGranger Causality Test\u003c/b\u003e: Determines causal correlations with lagged values of variables.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePanel Dumitrescu and Hurlin Test\u003c/b\u003e: Used to assess causality in a panel data context.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eInnovation Accounting Approach (IAA)\u003c/h2\u003e \u003cp\u003eThe empirical analysis includes two methods under the Innovation Accounting Approach:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVariance Decomposition Method (VDM)\u003c/b\u003e: This method quantifies the proportion of the forecast error variance of each variable that is attributed to shocks to other variables.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImpulse Response Function (IRF)\u003c/b\u003e: This method traces the effect of a one-time shock to one of the innovations on current and future values of the variables in the system.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVariance Decomposition Method (VDM)\u003c/b\u003e: Assesses the contribution of each shock to the variance of forecast errors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImpulse Response Function (IRF)\u003c/b\u003e: Analyzes the dynamic effects of shocks on the variables over time.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eEmpirical Analysis and Policy Implications\u003c/h2\u003e \u003cp\u003eThe sequential steps in this empirical analysis ensure robust statistical inferences. The findings from the CSD tests (Table\u0026nbsp;3) and panel unit root tests (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e) provide a solid foundation for understanding the dynamic relationships and causal linkages among the variables studied. These insights are valuable for policymakers, enabling them to make informed decisions based on robust empirical evidence.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003eData description\u003c/h2\u003e \u003cp\u003eThis paper examines the relationship between energy use, economic growth, and CO2 emissions under the Environmental Kuznets Curve (EKC) hypothesis in South Asian countries (Bangladesh, India, Pakistan, Nepal, and Sri Lanka) from 1972 to 2017. CO2 emissions are used as a proxy for environmental degradation, GDP per capita represents economic growth, and non-renewable energy use is measured per capita and as a percentage of total energy consumption. The globalization index, incorporating social, economic, and political dimensions, assesses globalization's impact on environmental degradation. The study highlights that economic growth initially leads to increased environmental degradation due to neglect of environmental issues and lack of advanced technologies. The complex relationship between CO2 emissions, growth, and energy consumption is influenced by factors like trade openness, financial development, and population, with findings varying across countries. The study uses robust statistical methods to offer insights for policymakers on balancing economic growth with environmental sustainability.\u003c/p\u003e \u003c/div\u003e "},{"header":"Empirical Results and Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003cp\u003eThe descriptive statistics are essential for explaining the crucial features of the statistical data. The statistical results of descriptive statistics of the explanatory variables are given in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe statistical findings of Cross-Sectional Dependence (CSD) are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. To find CSD\u0026rsquo;s presence between the panel data, we have used four tests: Pearson LM Normal, Pearson CD Normal, Breusch-Pagan Chi-square, and Friedman Chi-square. The findings of CSD show that in a panel data analysis, the cross-sectional dependency found between the data and significance of p-values are rejected the null hypothesis. The acceptance of the alternative hypotheses verified the cross-section reliance among these South Asian countries.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;3 reports the unit root result by using the tests of Im et al.,Breitung, and Hadri,respectively. The cross-section dependence test can be used to detect the\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 2. The results of the residual cross-section dependence test.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"522\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9080459770115%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.268199233716476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull hypotheses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.938697318007663%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9080459770115%\" valign=\"top\"\u003e\n \u003cp\u003eBreusch-Pagan Chi- square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\" valign=\"top\"\u003e\n \u003cp\u003e7.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.268199233716476%\" valign=\"top\"\u003e\n \u003cp\u003eNo cross section dependence ( CSD ) in residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.938697318007663%\" valign=\"top\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9080459770115%\" valign=\"top\"\u003e\n \u003cp\u003ePearson LM Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\" valign=\"top\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.268199233716476%\" valign=\"top\"\u003e\n \u003cp\u003eNo cross section dependence ( CSD ) in residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.938697318007663%\" valign=\"top\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9080459770115%\" valign=\"top\"\u003e\n \u003cp\u003ePearson CD Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\" valign=\"top\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.268199233716476%\" valign=\"top\"\u003e\n \u003cp\u003eNo cross section dependence ( CSD ) in residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.938697318007663%\" valign=\"top\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9080459770115%\" valign=\"top\"\u003e\n \u003cp\u003eFriedman Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.091954022988507%\" valign=\"top\"\u003e\n \u003cp\u003e23.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.268199233716476%\" valign=\"top\"\u003e\n \u003cp\u003eNo cross section dependence ( CSD ) in residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.938697318007663%\" valign=\"top\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\n\u003cp\u003eTable 3. Panel unit root test analysis.\u003c/p\u003e \n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnCO2 it\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0060***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;2.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0040***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;19.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01200***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;2.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;3.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;35.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;2.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0030***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;3.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0030***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0010***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;4.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0020***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;2.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0010***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;4.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0020***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ***, ** signifies a 1 and 5% level of significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eheterogeneity in the panel model. Thus to control the heterogeneity across the panel model, this study used an alternative IPS test designed by Im et al.\u003csup\u003e1\u003c/sup\u003eTable 3 reports the results of the Hadri, Breitung,and Im et al.\u003csup\u003e109\u003c/sup\u003e tests, as all variable found stationary at the level in line with Hadri\u003csup\u003e107\u003c/sup\u003e and Im et al. while some variables are not stationary at the level in line with Breitung test. Also, except for the Breitung test, all the variables are found stationary at the level in line with Im et al.and Hadri tests.\u003c/p\u003e \u003cp\u003eDifferent co-integration tests, i.e., Pedroni and Kao panel co-integration tests and FMOLS, are used in this study. The results of panel v-statistic, panel rho-statistic, panel Phillips\u0026ndash;panel ADF-statistic and Perron (PP) (within dimension method) statistic is reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These cointegrated tests are based on \u0026ldquo;Engle and Granger, where different methods, namely group ADF-test, group PP-statistic and group rho statistic, are also used in this analysis. All the variables are co-integrated according to the findings, and there is a long term association among the variables. According to the results of the Kao t-statistic, the long-term association was found among all these variables. The long-run nexus between CO\u003csub\u003e2\u003c/sub\u003e emissions, GDP growth, non-renewable energy, and globalization index in the selected South Asian countries. The studies of Zeshan and Ahmed, Apergis and Ozturk, and Ahmed et al. are supported the results of this empirical analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe statistical results of the Pedroni and Kao co-integration.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewithin-dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel v-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.5301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel rho-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;2.8286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0023***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel PP-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;4.2235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel ADF-Statistic\u003c/p\u003e \u003cp\u003eBetween the dimension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup rho-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;1.0664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup PP-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;5.5722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup ADF-Statistic\u003c/p\u003e \u003cp\u003eKao (1999) panel cointegration test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;2.1206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.07927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: SIC is used to select the lag length criteria. Where *** and ** signify 1 and 5% levels of significance, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study investigated the relationship between economic growth and CO2 emissions in South Asian countries under the Environmental Kuznets Curve (EKC) hypothesis, finding strong support for the inverted U-shaped EKC. The results show that economic growth activities significantly increase greenhouse gas (GHG) emissions, aligning with previous empirical studies such as those by Zambrano-Monserrate et al., Awad and Abugamos, Keho, Nassani et al., and others. However, He and Richard's study on Canada did not support the EKC hypothesis. The EKC's existence was confirmed for China and France by Ang and Iwata et al. Additionally, studies by Copel and Taylor, Halicioglu, and Jalil and Mahmud have linked economic growth and trade to environmental impacts, showing that trade significantly increases CO2 emissions in China, Turkey, and Malaysia. Shahbaz et al. and Uddin et al.'s analyses for Indonesia and Sri Lanka, respectively, indicate that economic growth driven by energy consumption significantly increases CO2 emissions. The study used Kao and Pedroni co-integration and FMOLS tests to explore the nexus between CO2 emissions, energy use, globalization, and economic growth. The findings demonstrate that GDP growth, non-renewable energy, and globalization significantly contribute to environmental degradation in South Asia, affirming the EKC hypothesis in both short and long-term contexts.\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable 5. The statistical findings of FMOLS technique (country-specific long-run elasticities)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProb\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eln(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eln(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;18.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNepal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGLOBit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e237.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eln(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;19.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e13.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eln(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eln(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elnNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003elnGLOB\u003csub\u003eit\u003c/sub\u003e 0.08\u003c/p\u003e \u003cp\u003eThe turning point of EKC 6455.579 per capita US\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026frac14;a\u003csub\u003e2\u003c/sub\u003e is naturallogGDP\u003csub\u003eit\u003c/sub\u003e and a\u003csub\u003e3\u003c/sub\u003e natural log(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e\u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003eTable 6. The statistical findings of FMOLS technique: Full Panel.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"446\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.18385650224215%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.16591928251121%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.14798206278027%\" valign=\"top\"\u003e\n \u003cp\u003et-statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.502242152466367%\" valign=\"top\"\u003e\n \u003cp\u003eProb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.18385650224215%\" valign=\"bottom\"\u003e\n \u003cp\u003eLnGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.16591928251121%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.14798206278027%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.502242152466367%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.18385650224215%\" valign=\"top\"\u003e\n \u003cp\u003eln(GDP\u003csub\u003eit\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.16591928251121%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.14798206278027%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.502242152466367%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.18385650224215%\" valign=\"top\"\u003e\n \u003cp\u003eLnNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.16591928251121%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.14798206278027%\" valign=\"top\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.502242152466367%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.18385650224215%\" valign=\"top\"\u003e\n \u003cp\u003eLnGLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.16591928251121%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.14798206278027%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.502242152466367%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSouth Asian regions. Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e and 6 have reported the results of full FMOLS and country specific, respectively.\u003c/p\u003e \u003cp\u003eThe full panel FMOLS findings in Table\u0026nbsp;6 indicate that GDP growth, non-renewable energy consumption, and globalization significantly increase environmental degradation in South Asian regions. Specifically, a unit change in non-renewable energy leads to a 0.84 unit increase in CO2 emissions, consistent with studies by Liu and Dietz, Soytas and Sari, Tao et al., Shahbaz et al., Saboori and Sulaiman, Ahmed et al., and Nasreen et al. These economies are predominantly reliant on emissions-intensive energy consumption, predicting increased future environmental degradation. Akbostanci et al., Jalil and Mahmud, Narayan and Narayan, Jaunky, and others have discussed the nexus of energy pollution and economic growth under the EKC framework, with findings supporting the hypothesis. GDP and GDP^2 have positive and negative coefficients, respectively, reinforcing the EKC hypothesis.\u003c/p\u003e \u003cp\u003eCountry-specific FMOLS results show that in Bangladesh, globalization and non-renewable energy significantly increase GHG emissions, supporting the EKC hypothesis with GDP and GDP^2 values. In India, energy use, globalization, and GDP growth significantly increase CO2 emissions, confirming the EKC hypothesis. In Nepal and Pakistan, GDP growth significantly raises CO2 emissions, with evidence supporting the EKC hypothesis. For Sri Lanka, GDP growth and energy consumption are the main contributors to CO2 emissions, while globalization has a smaller impact.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePanel causality Dumitrescu-Hurlin test (full panel).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.NO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW-stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ. Stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCO\u003csub\u003e2\u003c/sub\u003e \u0026yen; LGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDP \u0026yen; LCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCO\u003csub\u003e2\u003c/sub\u003e \u0026yen; LGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDP\u003csup\u003e2\u003c/sup\u003e \u0026yen; LCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCO\u003csub\u003e2\u003c/sub\u003e \u0026yen; LNRENW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLNRENW \u0026yen; CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCO\u003csub\u003e2\u003c/sub\u003e \u0026yen; LGLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGLB \u0026yen; LCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDP \u0026yen; GDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLGDP\u003csup\u003e2\u003c/sup\u003e \u0026yen; LGDP\u003c/p\u003e \u003cp\u003eLGDP \u0026yen; LRENW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003cp\u003e0.262\u003c/p\u003e \u003cp\u003e4.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.739 0.613\u003c/p\u003e \u003cp\u003e3.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003cp\u003e0.466\u003c/p\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003cp\u003eNO\u003c/p\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNeutrality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLNRENW \u0026yen; LGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDP \u0026yen; LGLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGLB \u0026yen; GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDP\u003csup\u003e2\u003c/sup\u003e \u0026yen; LNRENW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLNRENW \u0026yen; LGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDP\u003csup\u003e2\u003c/sup\u003e \u0026yen; LGLB\u003c/p\u003e \u003cp\u003eLGLB \u0026yen; LGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003cp\u003e12.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.224 10.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLNRENW \u0026yen; LGLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGLB \u0026yen; LNRENW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnidirectional causality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCausality is running from CO\u003csub\u003e2\u003c/sub\u003e to GDP, GDP\u003csup\u003e2\u003c/sup\u003e to CO\u003csub\u003e2\u003c/sub\u003e, GDP to Globalization, GDP to Globalization, non-renewable to Globalization, and non-renewable to GDP.\u003c/p\u003e \u003cp\u003eThe findings of the Variance Decomposition Method (VDM) for selected South Asian countries are reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e8\u003c/span\u003e, highlighting the contribution of various exogenous variables and innovative shocks to changes in CO2 emissions between 1972 and 2015. The results indicate that innovative shocks account for a significant endogenous contribution of 49.73 percent to CO2 emissions. The dominant elements driving CO2 emissions in the region are sources of energy, GDP growth, and globalization. These findings align with the regression analysis results and are projected to remain relevant for the next ten years.\u003c/p\u003e \u003cp\u003eThe impulse response function (IRF) illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e further supports these conclusions. The IRF shows how CO2 emissions respond to shocks in other variables, with the lower and upper bounds representing one standard deviation. The graphical analysis demonstrates that an increase in energy consumption leads to higher environmental degradation. Similarly, globalization and economic growth exhibit a positive relationship with CO2 emissions, following an increasing trend. Specifically, the response of CO2 emissions to energy use is initially positive and stabilizes beyond the 9th period. The growth rate and the square of GDP consistently contribute to CO2 emissions throughout the sample period. These insights are crucial for understanding the dynamic interactions among these variables and their impact on environmental degradation in South Asia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eDiscussion of analysis and EKC\u003c/h2\u003e \u003cp\u003eThis study try to examine the relationship between energy, environment, growth, and other variables under the premises of the EKC framework to evaluate an inverted U-shaped\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the Variance Error Decomposition forecast model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePeriodSE CO\u003csub\u003e2it\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (GDP\u003csub\u003eit\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVariance Decomposition of CO\u003csub\u003e2 it\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 0.053463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e100.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 0.064543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e79.09123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.688469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.20070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 0.072142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e66.04123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.851420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.614706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.24639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 0.078430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e62.30278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.326685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.703257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.411935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.25534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 0.084180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e59.37292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.441953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.824865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.289188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.07107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 0.089467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e56.56463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.752867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.941740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.208907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.53185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 0.094385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e54.16059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.052759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.971155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.113945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.70155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 0.098988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e52.41041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.346803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.992818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.015428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.23455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 0.103322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e50.97510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.654140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.995887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.933091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.44178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 0.107395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e49.73146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.894635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.878239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVariance Decomposition of NRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e10.034535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e31.87078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.12922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e20.041244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e36.66831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.58632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.969486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.605675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.170207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e30.044430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e33.72261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.59082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.520787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.25861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e40.047971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e34.41351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.40430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.211239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.149582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.82138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e50.051180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e33.83550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.57368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.392792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.461705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.73632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e60.054482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e33.04709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.77826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.822463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.99424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.35796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e70.057849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e31.94314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.72074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.050981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.83178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.45336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e80.061297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e30.88558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.02483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.319412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.92518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.84499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e90.064986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e29.65769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.59964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.583062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.49490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.66470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e100.069006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e28.19449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.32307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.846801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.62172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.01391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVariance Decomposition of GDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e10.014070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10.00017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.920363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.07946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e20.016145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20.07378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.61939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.39856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.289688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.618581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e30.017505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20.76224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.80742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.29934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.685593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.44540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e40.019871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e30.90184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.16973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.21608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.60387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.10846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e50.022793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e30.72339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.811252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.18111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.04921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.23504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e60.026456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40.20847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.355201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.65772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.90296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.87564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e70.030838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40.48563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.739829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.57663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.41638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.78153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e80.036092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e30.75645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.417601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.34617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.18060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.29917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e90.042379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e30.04408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.351367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.57158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.37784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.65513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e100.049879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e30.41008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.494791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.85631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.19366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.04516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVariance Decomposition of (GDP)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e10.164948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.95070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.058150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e20.190252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.389810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.64122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.52314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.768169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.677658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e30.208039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.820125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.58595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.30471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.825880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.46333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e40.239443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.732097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.69370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.42815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.37892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.76713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e50.278498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.342239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.214914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.13943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.80341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.50001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e60.327042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.713989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.710922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.87897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.83009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.86603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e70.384881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.961818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.136183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.24527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.02468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.63205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e80.453880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.253186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.887105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.50049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.22880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.13042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e90.536104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.594748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.904165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.16592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.78248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.55268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e100.633860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.027605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.129035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.816157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.97134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.055861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e( continued )\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinued.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePeriodSE CO\u003csub\u003e2it\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRENW\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (GDP\u003csub\u003eit\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariance Decomposition of GLOB\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 0.033245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.431226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.949910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.884664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.465288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.26891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 0.038717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.797875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.388962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.918514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.715521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.17913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 0.042687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.83483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.322983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.853499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.650501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.33819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 0.044803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.97840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.591718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.221787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.221725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.98638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 0.046351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.91540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.133334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.941443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.995294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.01453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 0.047843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.66431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.016624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.615584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.802581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.90090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 0.049216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.64652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.943423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.377727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.620832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.41149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 0.050529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.43852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.956506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.174904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.467411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.96266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 0.051752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.02819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.923553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.997245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.332943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.71807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 0.052921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.55079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.892059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.838889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.204856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.51341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationship between energy use, economic growth, globalization, and CO2 emissions in selected South Asian countries was explored using various econometric techniques including panel unit root tests, Kao and Pedroni panel co-integration tests, Fully Modified OLS (FMOLS), and the Innovative Accounting Approach. The full FMOLS findings reveal that GDP growth, non-renewable energy consumption, and the globalization index significantly contribute to environmental degradation in South Asia, primarily through increased CO2 emissions. This study underscores the substantial role of fossil fuels in driving CO2 emissions across the region, offering crucial insights for future policy-making and governmental strategies, particularly in environmental management. Previous empirical studies by Velthuijsen and Worrell, Acaravci and Ozturk, and others have also examined economic growth and greenhouse gas (GHG) emissions under the Environmental Kuznets Curve (EKC) hypothesis, incorporating additional variables such as energy efficiency and economic structure. Their findings generally support the notion that economic growth intensifies GHG emissions, although exceptions exist, as seen in studies for Turkey and India by Lise, which found no significant relationship between growth and CO2 emissions. Conversely, studies like those of Grossman and Krueger and Shafik and Bandyopadhyay support an inverted U-shaped EKC, indicating that environmental pollution initially increases with economic growth but may decline after a certain income threshold is reached.\u003c/p\u003e\u003cp\u003eFigure\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003ePanayotou64 initially delineated the indicators of the nexus between environmental pollution and GDP growth under the Environmental Kuznets Curve (EKC) hypothesis. Recent research underscores non-renewable energy as a primary driver of environmental degradation, particularly through fossil fuel combustion. Studies like those by Apergis and Payne, Jalil and Mahmud, Nasir and Rehman for Pakistan, Kanjilal and Ghosh, Shahbaz et al. for Tunisia, Seker et al. for Turkey, Javid and Sharif, Ahmad et al. for India, and Rafindadi for China and Japan, have extensively explored the relationship between economic growth, non-renewable energy consumption, and carbon emissions across various regions.\u003c/p\u003e \u003cp\u003eThis study employs the Innovation Accounting Approach (IAA), integrating the Variance Decomposition Method (VDM) and the Impulse Response Function (IRF). The IRF method predicts interactions among variables over time, illustrating how shocks to one variable affect others beyond specified periods. It quantifies the magnitude and direction of responses among study variables, crucially identifying how CO2 emissions respond to shocks in other explanatory factors. The IRF results reveal that growth, non-renewable energy consumption, and globalization significantly influence CO2 emissions when shocks are applied to the carbon emission variable. They underscore non-renewable energy as the dominant driver of CO2 emissions in the region, alongside globalization's impact. These findings emphasize the importance of including energy consumption sources, economic growth, CO2 emissions, and globalization in future frameworks for policy-making over the next decade.\u003c/p\u003e \u003cp\u003eWhile Yihdego and Webb used the transfer function-noise model for IRF, this study's approach is grounded in IAA, presenting graphical representations for clear interpretation. The graphical analysis of IRF under IAA provides detailed insights into the dynamic relationships among variables, offering robust empirical support for policy formulation aimed at mitigating environmental impacts in South Asia and beyond.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis analysis employs theoretical frameworks such as the Environmental Kuznets Curve (EKC) to assess the long-term relationships between CO2 emissions, economic growth, energy use, and globalization in selected South Asian economies spanning from 1972 to 2017. Various econometric techniques including heterogeneous co-integrated panels, unit root tests (panel), Kao and Pedroni panel co-integration tests, Fully Modified OLS (FMOLS), Dumitrescu-Hurlin tests, and the Innovative Accounting Approach were utilized.\u003c/p\u003e \u003cp\u003eThe full panel FMOLS findings reveal that economic growth, non-renewable energy consumption, and globalization significantly contribute to environmental degradation in South Asia. Specifically, the study highlights that fossil fuel consumption is a major driver of CO2 emissions and greenhouse gas issues in the region. This underscores the environmental challenges faced by South Asian countries, where GDP growth, energy consumption, and globalization are identified as key determinants of environmental quality.\u003c/p\u003e \u003cp\u003eFurthermore, within the EKC framework, the study confirms the existence of an inverted U-shaped relationship between economic growth and CO2 emissions in South Asia. While economic growth initially exacerbates environmental degradation, policies promoting sustainable development and clean energy could mitigate these effects. The study suggests that reliance on fossil fuels hampers sustainable development in the region, advocating for regional cooperation among South Asian countries under the South Asian Association of Regional Cooperation (SAARC) to address environmental issues collectively.\u003c/p\u003e \u003cp\u003ePolicy recommendations include initiatives to curb CO2 emissions through clean energy policies, enhanced energy efficiency, investment in renewable resources, and reducing energy intensity. Addressing these factors can improve environmental quality while supporting economic growth in South Asia amidst increasing globalization pressures. Overall, the study emphasizes the importance of integrated regional strategies and sustainable energy policies to mitigate environmental degradation and foster economic development in South Asia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWhole manuscript have been written by main author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHettigeH,ManiMandWheelerD.Industrialpollutionineconomicdevelopment:theenvironmentalKuznetscurve revisited. J Dev Econ 2000; 62: 445\u0026ndash;476.\u003c/li\u003e\n\u003cli\u003eCanasA,FerraoPandConceicaoP.AnewenvironmentalKuznetscurve?Relationshipbetween direct material input and income per capita: evidence from industrialised countries. Ecol Econ 2003; 46:217\u0026ndash;229.\u003c/li\u003e\n\u003cli\u003eKahuthuA.Economicgrowthandenvironmentaldegradationinaglobalcontext. 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Economic growth and electricity consumption: auto regressive distributedlag analysis. J Energy South Afr 2012; 23:29\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eIm KS, Pesaran MH and Shin Y. Testing for unit roots in heterogeneous panels. J Econometr 2003; 115:53\u0026ndash;74.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Globalization, energy consumption, South Asian countries","lastPublishedDoi":"10.21203/rs.3.rs-4637577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4637577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study analyzes the relationship between globalization, energy consumption, and economic growth among selected South Asian countries. This study also finds causal association between energy growth and nexus of CO\u003csub\u003e2\u003c/sub\u003e emissions, and employed the premises of the EKC framework. The study used annual time series analysis, starting from 1972 to 2017. The data set has been collected from the world development indicator (WDI). The result of a fully modified ordinary least square (FMOLS) method describes a significantly worsen the quality environment in the south Asian region. The individual country as Bangladesh shows a positively significant impact on the CO\u003csub\u003e2\u003c/sub\u003e emissions and destroying the level of environment regarding non-renewable energy and globalization index. However, negative and positive growth level (GDP) and square of GDP confirm the EKC hypothesis in this region. This study has identified the causality between GDP growth and carbon emission and found bidirectional causality between economic growth and energy use.\u003c/p\u003e","manuscriptTitle":"The Relationship between Globalization, Energy Consumption, and Economic Growth in Selected South Asian Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 20:06:01","doi":"10.21203/rs.3.rs-4637577/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c21ba893-bae0-4ce6-b4ed-a0c394c490d2","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-20T14:06:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-17 20:06:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4637577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4637577","identity":"rs-4637577","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}