The effects of globalization,energy consumption and economic growth on carbon dioxide emissions in South Asian countries

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 CO₂ 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 CO₂ 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.

Keywords

globalization energy consumption South Asian countries JEL Classifications: E13 D50

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.¹

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.² 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.^3–6 GHGs represent a combination of gases, but CO₂ 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 CO₂ emissions are fossil fuel incineration.⁷ in recent decades, primarily responsible for environmental degradation is the most developed economies; however, the ratio of CO₂ emissions has also increased in developing countries.⁸ 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.⁹

Global warming and environmental degradation have lately been a major challenge for the nations of the world. Increasing CO₂ 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 CO₂ emissions in terms of fossil fuel energy sources and economic growth have been discussed in several empirical works.^6,10–22 Halkos^23,24 explored the growth and environmental degradation nexus and also discussed other pollutants. Similarly, nitrogen and sulphur dioxide have been examined;^18,20,25 others have also observed sulphur dioxide.²⁶ 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).²⁷ 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. CO₂ emissions and climate change are becoming more prevalent due to fuel combustion.²⁸ Energy is essential for industrial and agricultural production, and this energy increases CO₂, 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 40 years, as reported by the IEA.²⁸ Renewable energy sources represent alternatives on-renewable sources and can be helpful in overcoming these issues.²⁹

Environmental pollution and CO₂ 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.³⁰ Consequently, as per capita income increases, the level of environmental degradation also increases. Numerous researchers have found the links between CO₂ 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 CO₂ emission, growth and energy has been significantly explored by various researchers.^31–47

Even though environmental pollution caused by energy and growth are especially important in Asian countries, however few researchers have analysed 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.^{18,22,44,48–56}

Finally, following the global agenda for reducing CO₂ emissions, this study investigated the GDP growth, CO₂ emission relationship to determine the nexus between CO₂ 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 CO₂ emissions in the south Asian countries? and Do the selected Asian countries demonstrate an environmental Kuznets curve to demonstrate the growth-CO₂ 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.,⁵⁶ this study incorporates various other variables and examined their impacts on CO₂ emissions. The studies on Asian countries are discussed as the subsample, and the findings are not identical.^{18,22,44,51–56} However, except for Niu et al.⁴³ 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 have been tested by Jaunky⁵¹ and Cowan et al.⁵⁷ and confirmed the existence of EKC tests for cross-country panel data. Besides, Cicea et al.⁵⁸ and Ibrahim and Law⁵⁹ have assessed panel data under the EKC hypothesis and found mixed results. Similarly, the EKC hypothesis have been tested by Ajmi et al.⁶⁰ 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.³⁴ applied enhanced vector auto regression 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 Sari⁶¹ and Ghosh⁶² 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, but their contribution to empirical analysis is undeniable (especially the studies of Shaﬁk and Bandyopadhyay;⁶³ Panayotou;⁶⁴ and Grossman and Kruege⁶⁵) Various past empirical works have been performed on this topic to investigate the EKC premises using panel data approaches.^67–70 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.⁷⁰ Since the 1990s, the nexus of environmental-growth was used to determine by this curve. Grossman and Krueger⁷⁰ 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 the other related variables.^72–76

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, CO₂ emissions-economic growth nexus is negative. Panayotou⁶⁴ first defined the indicators of pollution- GDP growth nexus under the EKC scheme. The EKC framework is discussed by Stern et al.,⁷⁶ Ekins,⁷⁷ and Gani⁷⁸ and evaluated the effects of economic growth on environmental quality. Subsequently, Dinda³⁰ and Stern²⁹ 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.⁷⁹ The income inequalities and growth (inverted U shaped) nexus is widely discussed in the past literature and was initially developed by Kuznets in 1995.⁸⁰ 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,⁷⁰ Selden and Song,²⁶ and Vincent⁸¹) Meadows et al.⁸² highlighted environmental sustainability awareness for the first time in the 1970s, but in the 1990s, Grossman and Krueger,⁷⁰ Panayotou,⁶⁴ and Selden and Song²⁶ 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.^84–88 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,¹⁸ Al-Mulali et al.⁵ and Jebli et al.⁹² have included various additional explanatory variables in their analysis.

The empirical studies of Tugcu et al.,⁸⁸ Mensah,⁸⁹ Acaravci and Ozturk,⁹⁰ Apergis and Ozturk,¹⁸ Al-Mulali et al.,⁹¹ and Jebli et al.⁹² have included various additional explanatory variables in assessments of carbon emissions-growth nexus. These studies included energy eﬃciency, energy dependency, and economic structure as the 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. Lise⁹³ have also tested this hypothesis for Turkey and India and have not found any CO₂ emission- growth nexus. The empirical findings of Robalino-Lopez et al.⁸⁷ do not support Ecuador and Venezuela's EKC hypothesis.

Various prior literature includes various indicators of CO₂ emission, 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.⁹⁴ have applied the methodology of a NARDL model for Japan to find the nexus between globalization, environment, and energy-growth between 1970 and 2014. Accordingly, to their results, the environment's quality is significantly worsening, a rapid growth and development processes as a result of globalisation's growth and expanded use of fossil fuels. Recently, Shahbaz et al.¹ have used panel data analysis between 1970 and 2012 for China. Their research investigated EKC hypothesis and the correlations between globalization and CO₂ emissions were also examined. The findings suggest a substantial decline in CO₂ emissions from globalisation.

Moreover, Shahbaz et al.⁹⁵ 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.⁹⁶ explored the intensity of energy, globalization, and carbon emissions nexus for the 19 African countries throughout 1971–2012. Their research supports the existence of the EKC hypothesis for the Algeria, Congo Republic, Zambia, Cameroon, Morocco, and Tunisia. Additionally, the study Shahbaz et al.⁹⁷ for the 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.⁹⁸ 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 African, 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.⁹⁹ 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 econometrics techniques, and to fill the previous literature gap, a robust model is used in the empirical literature. Thus various econometrics 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” (VDM and IRF) are used in our analysis. Additionally, the results for selected South Asian economies from 1972 to 2017 showed the long-run association between CO₂ emissions, growth, energy, and globalization under EKC's framework.

Methodology

This paper examined the fuel consumption and growth-led CO₂ 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)¹⁰⁰ data to implement the panel time series analysis.

Theoretical framework and hypothesis

This study tries 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. The empirical studies of Apergis and Ozturk,¹⁸ Al-Mulali et al.,⁹¹ and Jebli et al.⁹² have included various explanatory variables in GDP assessments and emissions of GHG under the scheme EKC hypothesis. Furthermore, this analysis comprises and tests on below two hypotheses.

Hypothesis1: 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-shaped). Grossman and Krueger⁷⁰ followed up on the work of Kuznets and described the environmental quality-growth nexus in three stages. 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 of the economy 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.⁶⁵ 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.

Table 1.

Summary of data description and descriptive statistics.

Data description
Variables	Description				Source
CO₂	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	LnCO₂	LnGDP	Ln GDP²	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

To analyze the growth-environmental pollution nexus, this study applied EKC's theoretical framework in equation (1) (Grossman and Krueger⁷⁰) The theoretical framework of the EKC framework is used in the following econometric model:

c o_{2 i t} = α_{1} + α_{2} Y_{i t} + α_{3} {(Y_{i t})}^{2} + α_{4} X_{i t} + μ_{i t}

(1)

This study has included a few additional explanatory variables in assessments of GHG emissions- economic growth nexus for premises of the EKC hypothesis. Where CO_2it shows the carbon emission (per capita) level (environmental pollution), Y_it shows GDP (per capita) income (economic growth), and other influential macroeconomic variables are indicated by X_it. To make the model consistent and efficient with a meaningful interpretation, we have thus; the natural log is used for the equation (1).

Inc o_{2 i t} = α_{0} + α_{1} I nGD P_{i t} + α_{2} {(InGD P_{i t})}^{2} + α_{3} I nGD P_{i t + e_{i t}}

(2)

The influence of non-renewable energy sources, GDP growth, and globalization on CO₂ emissions in the selected South Asian countries through 1972–2017 are mentioned in equation (3) and can be written as follows:

Inco 2 i t = α_{1} + α_{2} I nGD P_{i t} + α_{3} (InGD P_{2 i t}) + α_{4} I nNREWit + α_{5} I nGLO B_{i t} + e_{i t}

(3)

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.^102,103 For this purpose, the prerequisite in time series econometrics analysis is unit root test.¹⁰⁴

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 Wu¹⁰⁴ suggested that multiple unit root tests might be employed to control the problem of individual regression inaccuracies across the cross-sections. This study finds no evidence regarding the presence of unit root in the panel 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.

(a) Homogenous (common unit root process) case

The panel Levin–Lin–Chu (LLC)¹⁰⁵ is the more common test compared with the other two tests developed by Breitung¹⁰⁶ and Hadri.¹⁰⁷ Identical or homogenous cross-sections are the assumption of this group. The extension of the Augmented Dickey-Fuller (ADF) approach is the LLC test; the assumption of homogeneity (in cross-sectional independence) is incorporated in the autoregressive coefficients under the test of ADF. Bildirici et al.¹⁰⁸ proposed that this non-stationary test is comparatively superior to common panel unit root tests.

(b) Heterogeneous case

Homogeneity in panel data analysis is a very restrictive assumption, and the dynamic properties of the same variable for all series are difficult to calculate; following the assumption of homogeneity can guide to spurious findings. Thus, based on Maddala and Wu,¹⁰⁴ an additional alternative (two) tests are used by many researchers, namely the approaches of Fisher-ADF and Fisher-PP (Philips Perron). In order to permit heterogeneity across the panel, this study uses another alternative test, namely the Im et al.¹⁰⁹ has designed the test of IPS. This study identifies the problem of cross-section dependence; four significant CD tests for robustness are employed. The study applied the (Breusch and Pagan,¹¹⁰ Pesaran,¹¹¹ Baltagi et al.¹¹²) (divided by LM test) then finally the Pesaran¹¹³ test of CD. The findings of the CSD test are presented in Table 3. Besides, the findings of the panel unit root were reported in Table 4.

This study used the non-parametric approach designed by Pedroni¹¹⁵ in the model to overcome the endogeneity and serial correlation problem. The severe issues of autocorrelation and endogeneity (which can generate nuisance problems and bias the results of coefficient estimates from panel data regression) may have arisen in the panel least square regression; therefore, this study used the FMOLS approach to identify the long-run parameter estimates. Granger causality is used to identify the causal correlation between the dependent variable and the explanatory variables with its lagged values. This study used the panel Dumitrescu and Hurlin¹¹⁴ causality test. Based on the Innovation Accounting Approach (IAA), the next step is consists of two methods, including the “Variance Decomposition Method” (VDM) and the “Impulse Response Function” (IRF). This empirical analysis accounts for these sequential steps to provide robust statistical inferences, and these findings will offer appropriate suggestions to policymakers in a given set of economies.

Data description

This paper examined the energy use and growth that led to CO₂ emission nexus under the EKC hypothesis in selected South Asian countries such as Bangladesh, India, Pakistan, Nepal, and Sir Lanka through 1972 to 2017. This paper used CO₂ emission as a proxy of environmental degradation measured in metric tons per capita. The GDP per capita (based on constant 2010 US$) is used as the proxy of economic growth. Besides, energy per capita is used to measure non-renewable, while the EKC hypothesis is the measure of the square term of GDP. Similarly, the energy consumption (i.e., non-renewable) proxy is used to measure the percentage of total final energy consumption. Next, to measure globalization's impact on environmental degradation, the globalization index is an important explanatory variable. Furthermore, social globalization index amalgamation, economic and political globalization index are employed for the globalization index. This paper follows the preceding subsequent values to fill the data gaps for the model's mentioned variables.

Environmental pollution and CO₂ 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.³⁰ Consequently, as per capita income increases, the level of environmental degradation also increases. The nexus of CO₂ emissions, economic growth and energy sources has been considered significant by numerous researchers. 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 CO₂ emission, growth and energy has been significantly explored by various researchers.^31–47

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 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 reports the unit root result by using the tests of Im et al.,¹⁰⁹ Breitung,¹⁰⁶ and Hadri,¹⁰⁷ respectively. The cross-section dependence test can be used to detect the 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.¹⁰⁹ Table 3 reports the results of the Hadri,¹⁰⁷ Breitung,¹⁰⁶ and Im et al.¹⁰⁹ tests, as all variable found stationary at the level in line with Hadri¹⁰⁷ and Im et al.¹⁰⁹ while some variables are not stationary at the level in line with Breitung¹⁰⁶ test. Also, except for the Breitung¹⁰⁶ test, all the variables are found stationary at the level in line with Im et al.¹¹⁰ and Hadri¹⁰⁸ tests.

Table 3.

Panel unit root test analysis.

			Im-Pesaran-Shin (IPS) test		Hadri test
Variables	t-values	p-values	t-values	p-values	t-values	p-values
lnCO_{2 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.

Different co-integration tests, i.e., Pedroni^115–116 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,¹¹⁷ 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 CO₂ emissions, GDP growth, non-renewable energy, and globalization index in the selected South Asian countries. The studies of Zeshan and Ahmed,⁵² Apergis and Ozturk,¹⁸ and Ahmed et al.^118–119 are supported the results of this empirical analysis.

Table 4.

The statistical results of the Pedroni and Kao co-integration.

1,04,106Pedroni (1,99,92,004) residual co-integration test
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	0.0413	0.5165
Between the dimension
	Statistic	Prob.
Group rho-Statistic	–1.0664	0.1431
Group PP-Statistic	–5.5722	0.000***
Group ADF-Statistic	–2.1206	0.017***
Kao (1999) panel cointegration test
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 growth driven emissions for the South Asian countries under the scheme of the EKC hypothesis. This study's results fully support the inverted EKC hypothesis, and the findings of the study show that growth activities significantly increase GHG emissions. The findings of various previous empirical studies have provided consistent results for the framework of the EKC hypothesis (Zambrano-Monserrate et al.,¹²⁰ Awad and Abugamos,¹²¹ Keho,⁵³ Nassani et al.,⁵⁶ and Rahman¹²² and Shahbaz et al.¹ The EKC test for Canada was examined by He and Richard¹²³ from 1948 to 2004; their findings could not support more for the EKC hypothesis regarding environmental pollution-growth nexus. Ang³² and Iwata et al.¹²⁴ have tested this hypothesis and confirmed the EKC's existence for China and France. Various prior studies of Copel and Taylor,¹²⁵ Halicioglu,¹²⁶ and Jalil and Mahmud¹²⁷ have also used economic growth and environment with trade to identify the EKC hypothesis. The empirical results of Jalil and Mahmud¹²⁷ and Ang¹²⁸ show that trade significantly increases CO₂ emissions for China, Turkey, and Malaysia. The empirical analyses of Shahbaz et al.¹²⁹ and Uddin et al.¹³⁰ for Indonesia and Sri Lanka, respectively, indicate that economic growth is significantly increasing the level of CO₂ emissions by energy consumption. The CO₂ emissions-growth relationship fully supported the EKC (inverted U-shaped) in all of the studies mentioned above. An inverted U-shaped EKC curve was found in the context of short and long-term analyses. Consequently, it is concluded that the discussions on energy and growth have driven CO₂ emission nexus supporting the positive connection between environmental quality and the use of energy for South Asian countries.

The nexus between CO₂ emissions and its three essential components: energy use, globalization, and economic growth, are used in this study. Thus Kao, Pedroni co-integration, and FMOLS tests were used to identify the associations among these variables. The full FMOLS findings indicate that these variables significantly increase environmental degradation in South Asian regions. Moreover, these findings showed that GDP growth, non-renewable energy, and globalization index significantly influence the CO₂ emissions in the South Asian regions. Tables 5 and 6 have reported the results of full FMOLS and country speciﬁc, respectively.

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
	lnGLOB_it	0.88	4.52	0.00
India
	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
	lnGLOBit	0.87	5.13	0.00
Nepal
	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
	lnGLOB_it	0.39	2.38	0.02
Pakistan
	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
	lnGLOB_it	1.11	7.27	0.00
Sri Lanka
	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	0.33	0.75
The turning point of EKC 6455.579 per capita US$Where =α₂ is naturallogGDP_it and α₃ natural log(GDP_it)²

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

The full panel of FMOLS findings in Table 6 indicates that these variables significantly increase South Asian regions' environmental degradation. Furthermore, these economies' empirical results suggest that fossil fuel is substantially increasing the CO₂ emissions in this region. Thus, full FMOLS results show that if there is a unit change in non-renewable, it will lead to a 0.84 unit change in CO₂ emissions holding all other variables being constant. The findings of Liu and Dietz,¹³ Soytas and Sari,¹³¹ Tao et al.,¹⁴ Shahbaz et al.,⁸⁴ Saboori and Sulaiman,^132,133 Ahmed et al.,^118,119 and Nasreen et al.²² are supported the results of this study. Furthermore, all these five economies are predominantly involved in emissions-intensive energy consumption, and increased future demand and environmental degradation are anticipated for these economies.

Akbostanci et al.,¹³⁴ Jalil and Mahmud,¹²⁷ Narayan and Narayan,⁵⁰ Jaunky⁵¹ discussed the energy pollution and economic growth (under EKC) nexus besides, Soytas et al.,³⁴ Ang,^32,128 Apergis and Payne,³⁹ Sadorsky,^135,136 Apergis et al.,¹³⁷ Hatzigeorgiou et al.,¹³⁸ Hamit-Haggar,¹³⁹ and similarly Ozcan¹⁴⁰ and Le and Quah⁵⁴ also discussed the nexus of energy sources, growth and environmental quality. The positive (+) and negative (–) values of GDP and ${GDP}^{2}$ respectively also support the EKC hypothesis in this region. Furthermore, a unit increase in GDP and globalization index increases the emissions level by 3.86 and 0.55, respectively. Similarly, a unit increase GDP square significantly decreases the level of CO₂ emission by 0.22. These finds are endorsed by the findings of Ahmed et al.¹¹⁹

Additionally, the FMOLS results for the country speciﬁc shows that in Bangladesh, globalization index and non-renewable energy have a significantly positive impact on the GHGs and destroy the environment's level. Similarly, this study's findings support EKC's evidence because the GDP and GDP square values are positive and negative, respectively. Thus, the results of FMOLS show that if there is one unit change in non-renewable and globalization, it will lead to a total of 0.89 and 0.88, respectively, in the unit change in CO₂ emissions if all other variables are constant. The findings are supported by the results of Shahbaz et al.,¹ Shahbaz et al.⁹⁴ Furthermore, results show that the level of CO₂ emission significantly increases by.463 if there is a 1% increase in the level of growth, and a 1% increase in GDP square substantially decreases the level of CO₂ emission by.030 if there is no change in other variables. For Bangladesh, the GDP growth is the most significant contributing variable in the destruction of the environment. Whereas according to the results of FMOLS, India indicates the use of energy, globalization, and GDP growth rate significantly increases the level of CO₂ emissions.

The values of GDP and ${GDP}^{2}$ indicate both (positive and negative) to confirm the evidence of the EKC hypothesis. If there is a 1% increase in GDP, significantly increases the level of CO₂ emission by .162, and a 1% increase in GDP square substantially decreases the level of CO₂ emission by 0.06. Thus, FMOLS results show that if there is one unit change in non-renewable and globalization, it will lead to 1.53 and 0.87, respectively unit change in CO₂ emissions if holding all other variables constant. In Nepal and Pakistan, the level of CO₂ emission significantly increases due to the GDP growth rate. The evidence of the EKC hypothesis was found in both these countries. For Sri Lanka, the CO₂ emission is also enhanced by the GDP growth and energy consumption significantly and found the most significant contributors of CO₂ emission in this country. Globalization is the smallest contributor to environmental degradation in the homeland of Sri Lanka. Thus results show that if there is one unit change in non-renewable and globalization, it will lead to .108 and .008, respectively unit change in CO₂ emissions. Moreover, a 1% rise in GDP significantly worsens the environment quality (CO₂ emission) by .250, and a 1% rise in the level of GDP square substantially decreases the level of CO₂ emission by 0.12.

This study gives us vital information about future policies and suggestions to government agencies to estimate FMOLS. Thus, this study employed the Dumitrescu-Hurlin test¹¹⁴ to determine the causal relationship between energy, GDP, GDP square, and globalization. Table 7 reported the statistical results of the Dumitrescu-Hurlin test (Granger causality test). The bidirectional causality is moving from energy use to GDP. The uni-directional causality is running from CO₂ to GDP, GDP² to CO₂, GDP to Globalization, GDP² to Globalization, non-renewable to Globalization, and non-renewable to GDP².

Table 7.

Panel causality Dumitrescu-Hurlin test (full panel).

S.NO	Hypothesis	W-stat	Z. Stat	Prob	Result	Conclusion
1	LCO₂ ¥ LGDP	2.560	1.560	0.249	NO
	LGDP ¥ LCO₂	4.576	3.576	0.040	YES	Unidirectional causality
2	LCO₂ ¥ LGDP²	1.906	0.907	0.497	NO
	LGDP² ¥ LCO₂	4.650	3.650	0.030	YES	Unidirectional causality
3	LCO₂ ¥ LNRENW	1.098	0.098	0.359	NO
	LNRENW ¥ CO₂	6.143	5.143	0.000	YES	Unidirectional causality
4	LCO₂ ¥ LGLB	1.574	0.574	0.380	NO
	LGLB ¥ LCO₂	2.816	1.816	0.080	YES	Unidirectional causality
5	LGDP ¥ GDP²	0.208	−0.739	0.477	NO
	LGDP² ¥ LGDP	0.262	−0.613	0.466	NO	Neutrality
6	LGDP ¥ LRENW	4.951	3.951	0.041	YES
	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	LGDP² ¥ LNRENW	2.199	1.199	0.230	NO
	LNRENW ¥ LGDP²	11.767	10.767	0.000	YES	Unidirectional causality
9	LGDP² ¥ LGLB	0.775	−0.224	0.822	NO
	LGLB ¥ LGDP²	12.697	10.697	0.000	YES	Unidirectional causality
10	LNRENW ¥ LGLB	1.912	0.912	0.456	NO
	LGLB ¥ LNRENW	6.383	5.383	0.000	YES	Unidirectional causality

The findings of VDM are reported in Table 8 in the context of selected South Asian countries. The change in a variable due to its contribution through various exogenous variables and innovative shock can be accounted for by this method. Furthermore, regarding CO₂ emission between 1972 and 2015, the significant endogenous contribution of CO₂ is 49.73 percent due to innovative shock. These results reveal that in the Asian region, the (GDPit) sources of energy and globalization were dominant elements for CO₂ emission. The findings of VDM are consistent with the regression analysis findings, and for the next ten years, all these variables are included in the proposed framework. The graphical representation of the “impulse response function (IRF)” illustrated in Figure 1 explains that when the shock is given to one variable, then the other factor's response. The lower and upper bounds values can show the one standard deviation’s values. The graphical analysis of the impulse reaction function represents the variable and their response, respectively. The reaction of CO₂ emission to energy consumption is positive, which shows that energy increases the level of environmental degradation. Globalization, growth, and CO₂ emission are positively related to each other in an increasing trend. The response of CO₂ emission to energy use is positive initially, reaching a steady state beyond the 9th period of the sample. The role of growth rate and the GDP square toward emission of CO₂ are positively reaching a constant state throughout the sample.

Table 8.

The results of the Variance Error Decomposition forecast model.

Period	SE	CO_2it	NRENW_it	GDP_it	2 (GDP_it)	GLOB_it
Variance Decomposition of CO_2it
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
1	0.034535	31.87078	68.12922	0.000000	0.000000	0.000000
2	0.041244	36.66831	49.58632	0.969486	3.605675	9.170207
3	0.044430	33.72261	43.59082	0.907179	5.520787	16.25861
4	0.047971	34.41351	37.40430	2.211239	7.149582	18.82138
5	0.051180	33.83550	33.57368	2.392792	9.461705	20.73632
6	0.054482	33.04709	29.77826	2.822463	11.99424	22.35796
7	0.057849	31.94314	26.72074	3.050981	14.83178	23.45336
8	0.061297	30.88558	24.02483	3.319412	17.92518	23.84499
9	0.064986	29.65769	21.59964	3.583062	21.49490	23.66470
10	0.069006	28.19449	19.32307	3.846801	25.62172	23.01391
Variance Decomposition of GDP_it
1	0.014070	10.00017	0.920363	99.07946	0.000000	0.000000
2	0.016145	20.07378	13.61939	75.39856	0.289688	3.618581
3	0.017505	20.76224	11.80742	65.29934	5.685593	10.44540
4	0.019871	30.90184	12.16973	51.21608	15.60387	15.10846
5	0.022793	30.72339	9.811252	39.18111	29.04921	16.23504
6	0.026456	40.20847	8.355201	29.65772	40.90296	15.87564
7	0.030838	40.48563	6.739829	22.57663	51.41638	14.78153
8	0.036092	30.75645	5.417601	17.34617	60.18060	13.29917
9	0.042379	30.04408	4.351367	13.57158	67.37784	11.65513
10	0.049879	30.41008	3.494791	10.85631	73.19366	10.04516
Variance Decomposition of (GDP)²
1	0.164948	0.007600	0.983546	98.95070	0.058150	0.000000
2	0.190252	7.389810	13.64122	74.52314	0.768169	3.677658
3	0.208039	6.820125	11.58595	63.30471	7.825880	10.46333
4	0.239443	5.732097	11.69370	48.42815	19.37892	14.76713
5	0.278498	5.342239	9.214914	36.13943	33.80341	15.50001
6	0.327042	4.713989	7.710922	26.87897	45.83009	14.86603
7	0.384881	3.961818	6.136183	20.24527	56.02468	13.63205
8	0.453880	3.253186	4.887105	15.50049	64.22880	12.13042
9	0.536104	2.594748	3.904165	12.16592	70.78248	10.55268
10	0.633860	2.027605	3.129035	9.816157	75.97134	9.055861
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

Figure 1.

Impulse response function.

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 relationship between energy, growth and globalization with CO₂ emissions in a panel of selected South Asia countries. Thus Kao, Pedroni co-integration and FMOLS tests were used to identify the associations among these variables. The full FMOLS findings indicate that these variables significantly increase environmental degradation in South Asian regions. Moreover, these findings showed that GDP growth, non- renewable energy and globalization index significantly influence the CO₂ emissions in the South Asian regions. The full panel of FMOLS findings that these variables significantly increase environmental degradation in South Asian regions. Furthermore, the empirical results for these economies suggest that fossil fuel is substantially increasing the CO₂ emissions in this region. This study gives us vital information about future policies and suggestions to government agencies with the estimation of FMOLS. Various econometrics techniques in this study such as heterogeneous co-integrated panels also included unit root tests (panel), the Kao and Pedroni panel co-integration test, the test of Fully Modified OLS (FMOLS), and the Innovative Accounting Approach. This study also employed Dumitrescu-Hurlin test¹¹⁴ to find out the causal relationship between energy, GDP, GDP square and globalization.

The empirical studies of Velthuijsen and Worrell,¹⁴¹ Shi,¹⁴² Acaravci and Ozturk,⁹⁰ Saleem et al.,¹⁴³ Tugcu et al.,⁸⁸ Mensah,⁸⁹ Apergis and Ozturk,¹⁸ Al-Mulali et al,⁹¹ Muhammad et al.,¹⁴⁴ and Jebli et al.⁹² have included various additional explanatory variables in assessments of economic growth and GHG emissions under the premises of the EKC hypothesis. These studies included energy eﬃciency, energy dependency and economic structure as the control variables with economic growth to observe the growth-environment relationship under the EKC hypothesis. Their results show that economic growth activities significantly increase GHG emissions. Lise⁹³ have also tested this hypothesis for Turkey and India and have not found any relationship between growth and CO₂ emission. The results of Robalino-Lopez et al.,⁸⁷ using time series techniques,do not support the EKC hypothesis for Venezuela and Ecuador.

The link between growth and environmental pollution has been well defined by Grossman and Krueger⁷⁰ in their analysis indicating that as per capita income increases SO₂ also increases, but it decreases with increases in the per capita income after a certain point. Subsequently, Shafik and Bandyopadhyay¹⁴⁵ examined the GDP growth driven environmental pollution nexus and confirmed the presence of the inverted U-shaped EKC. Panayotou⁶⁴ first defined the indicators of the environmental pollution and GDP growth nexus in the presence of the EKC. In recent years, various researchers have found that non-renewable energy is the main source of environmental degradation. Environmental pollution can be increased through combustion of fossil fuels. Economic growth and non-renewable energy driven carbon emissions are well studied in the existing literature, e.g., Apergis and Payne³⁸ and Jalil and Mahmud¹²⁷ for Central America and China, respectively; Nasir and Rehman¹⁴⁶ for Pakistan; Kanjilal and Ghosh¹⁴⁷ and Shahbaz et al.¹⁴⁸ for Tunisia; Seker et al.¹⁴⁹ for Turkey; Javid and Sharif¹⁵⁰ and Ahmad et al.¹⁵¹ for India and Rafindadi¹⁵² for China and Japan.

The study based on the Innovation Accounting Approach (IAA), which consists of two methods including the “Variance Decomposition Method” (VDM) and the “Impulse Response Function” (IRF). The response of carbon emission to impulses of time series variables can be modeled by a Impulse Response Function” (IRF) model. The “Impulse Response Function” (IRF) method is used to predict the interactions among all above-mentioned variables over a period of time. In other words, the impulse response function is used to determine the associations among the study variables. If shocks are given to a specific variable, then the IRF technique shows the magnitude of the correlation between the selected variables beyond the specified time period, which identifies the response of one variable when a shock is given to another variable. The results illustrate the response of CO₂ emission when one SD shock is given to the other explanatory variables. The overall statistical results from IRF indicate that growth, non-renewable energy consumption and globalization vary if shock is given to the carbon emission variable. They also show that non-renewable energy use is the dominant resource in this region for GDP growth and found also that globalisation spur CO₂ emission in this region. The overall findings of the IRF showed that the sources of energy consumption, economic growth, CO₂ emission and globalizations should be included in the projected framework for the next 10 years. We used Innovation Accounting Approach (IAA), and Impulse Response Function is the part of IAA approach. Whereas, Yihdego and Webb¹⁵³ used transfer function-noise (TFN) model for IRF, our study based on IRF based on IAA with graphical representation. Graphical illustration of our study based on IRF is revised and interpretation of the results according to IAA approach in detail.

Conclusion

This analysis utilizes these theoretical aspects and assesses economic growth, energy use, and globalization and affluence within the environmental Kuznets curve analysis framework. The long-run association between CO₂ emissions, real GDP growth, the square of GDP growth, energy sources, and globalization in selected South Asian economies from 1972 to 2017. Various econometrics techniques in this study, such as heterogeneous co-integrated panels, also included unit root tests (panel), the Kao and Pedroni panel co-integration test, the test of Fully Modified OLS (FMOLS), the Dumitrescu-Hurlin test, and the Innovative Accounting Approach. The full panel of FMOLS findings shows that these variables significantly increase environmental degradation in South Asian regions. Furthermore, the empirical results for these economies indicate that fossil fuel and energy use are substantially increasing the CO₂ emissions and resulting in GHG issues in this region. These South Asian countries are facing severe environmental degradation challenges. Moreover, these findings showed that GDP growth, non-renewable energy, and globalization index significantly influence the environment's quality in the south Asian region. The full FMOLS findings indicate that these variables significantly increase environmental degradation in the South Asian region. The country-specific FMOLS test findings are also consistent with the full FMOLS results because in the south Asian countries, the key determinants of CO₂ emission are GDP growth, energy consumption, and globalization.

Moreover, to detect the growth-environment association, the EKC frame was used. Moreover, this study examines the globalization, growth-energy relationship, which sheds light on the emission relationship in the South Asian economies background. A variety of studies have noted that economic growth and CO₂ emissions are highly associated with each other according to the EKC framework but several studies do not support the growth-induced CO₂ emissions hypothesis. This study has fully supported the existence of the EKC hypothesis in this selected South Asian region. The conflict between globalization and environmental degradation can revisit by sustainable development policies. Additionally, massive dependence on fossil fuel energy consumption is not environmentally friendly to this South Asian region's sustainable development. However, these countries under the “South Asian Association of Regional Cooperation” (SAARC) block can overcome environmental degradation through global cooperation. Policies for environment friendly energy use can significantly control the emissions and increase GDP growth. In contrast, the expansion of globalization increases energy use.

The study recommends policy implications in terms of vital initiatives to control CO₂ emissions and regional integration to control environmental degradation in this region. To improve environmental quality from an energy policy standpoint, policymakers should focus on clean energy policies. Improving energy efficiency, investing in renewable resources, boosting the utilization of cleaner energy sources, and decreasing energy intensity are the main options to mitigate carbon emission.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Malik Shahzad Shabbir

Muhammad Bilal Khan is a PhD candidate in School of Economics and Management at Wuhan University, P.R. China.

Hummera Saleem has PhD in Economics and working as a Lecturer at National University of Modern Languages, Islamabad, Pakistan.

Malik Shahzad Shabbir is a researcher and affiliated with University of Lahore, Pakistan.

Xie Huobao is working as a Professor in School of Economics and Management at Wuhan University, P.R. China.

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