Impact of Inflation and Economic Growth on Environmental Sustainability of the Indian Economy

Impact of Inflation and Economic Growth on Environmental Sustainability of the Indian Economy | 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 Impact of Inflation and Economic Growth on Environmental Sustainability of the Indian Economy Laxmidhar Panda, Seba Mohanty, Suman Chakraborty, Jampala Maheshchandra Babu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8533787/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Exponential growth of economic activity and high inflation are the major factors to contribute the decoration of environmental sustainability. The main objective of this study is to examine the effect of economic growth and the inflation on the level of CO2 emission, and secondly, to explore the evidence of Environmental Kuznets Curve (EKC) hypothesis in Indian Scenario over the period from 1970 to 2022. We employed ARDL and Non-linear ARDL (NARDL), which allow to capture both the long as well as short-run interdependency, in addition to that the NARDL able to capture the asymmetric effect of GDP and Inflation on the CO2 emission, with different specification in long and short-run equation. We include some factors which controlling the level of emission such as Consumption of Natural Resources (TNR), Consumption of Natural Gas (NGasCon) and Electricity (ElectCon) to obtain accurate inference about the study. The empirical analysis found the positive association of both the economic development (GDP growth) and the Inflation towards the CO2 emission in long-run. Implies the higher inflation and the substantial economic activities leads to the deteriorate the quality of environment. The result of NARDL reviles, neither short- or long-run asymmetric from negative and positive shock of GDP and Inflation, though some extend significant and negative effect of negative inflation reported. Overall, the hypothesis of EKC i.e., inverted U-shape in the association between economic growth and the quality of environment is not satisfying. JEL Classification : G0, C12, O44 Non-linear ARDL Model Emerging Economy Environmental Kuznets Curve (EKC) CO2 Emission inflation Figures Figure 1 Figure 2 1 Introduction The escalating threat of climate change and environmental degradation has prompted a surge in economic research examining the relationship between macroeconomic factors and environmental sustainability. Because of the intricate relationships between economic growth, inflation, and ecological effects, economies around the world are attempting to strike a balance between their aspirations for expansion and the pressing need for environmental preservation (Stern, 2004 ; Lopez and Toman, 2006). A fundamental viewpoint in this field is provided by the Environmental Kuznets Curve (EKC) hypothesis, which postulates an inverse U-shaped link between income levels and environmental degradation. This is especially relevant for large and diverse economies like India, where structural and regional disparities may influence environmental outcomes (Shahbaz et al., 2013 ). Further, the relationship between inflation and environmental sustainability is both complex and interdependent. Inflation, often viewed as a sign of macroeconomic instability, can hinder efforts toward environmental protection by reducing a country's economic capacity and redirecting policy focus (Apergis & Payne, 2009 ). This economic instability also affects industrial decision-making; as noted by Zhang et al. (2017), firms are often discouraged from investing in capital-intensive green technologies when the returns are unpredictable. These challenges are particularly evident in developing countries like India, where limited financial resources and competing policy objectives make balancing economic growth and environmental sustainability even more difficult. For over a decade, it has remained the third-largest carbon emitter globally, raising significant concerns about the sustainability of its development path. These intertwined economic and environmental dynamics highlight the urgent need for a more balanced and sustainable development strategy in India. Understanding the interplay between environmental sustainability and economic growth has attracted considerable scholarly attention. However, notable gaps persist in the literature, particularly concerning developing economies. Despite the widespread application, the EKC yields mixed and often inconclusive results, varying significantly across countries, pollutants, and time periods. In contrast, the role of inflation in influencing environmental outcomes remains relatively understudied. Existing studies that consider inflation often do so within broader growth frameworks, without explicitly exploring the mechanisms through which it affects environmental sustainability—such as changes in investment behavior, fiscal capacity, or consumption patterns (Apergis & Payne, 2009 ; Zhang et al., 2017). Consequently, the relationship between inflation and environmental outcomes remains both theoretically underdeveloped and empirically weak. Additionally, the majority of current research uses linear or bivariate econometric frameworks, which might not adequately represent the intricate, dynamic, and sometimes nonlinear relationships between environmental sustainability, growth, and inflation. Therefore, this study uses the non-linear Auto Regressive Distributed Lag (ARDL) model to find the asymmetric effect of independent variable both in short and long-run prospect. Additionally, there is dearth of literature is available on the integrated empirical research on how inflation and economic growth jointly affect several aspects of environmental sustainability in India. A major research gap is lack of comprehensive analysis, particularly in view of India's lofty climate obligations and its need for macroeconomic stability. Considering this research gap, this research adds significantly to the body of knowledge on inflation, economic growth, and environmental sustainability, especially when considering a growing nation like India. First, by specifically analyzing inflation's influence on environmental outcomes—an area that is still lacking in both theoretical modeling and empirical research—it contributes to the theoretical conversation. Secondly, by using a nonlinear Autoregressive Distributed Lag (NARDL) model, it transcends the conventional linear and bivariate analytical frameworks. By capturing the intricate relationships between inflation, growth, and environmental sustainability, this enables a more sophisticated understanding of asymmetric effects over the short and long terms. Third, unlike other studies focused on Indian economy, this study provides a more thorough examination of the combined effects of inflation and economic growth on several aspects of environmental sustainability by combining them into a single empirical framework. Finally, given India's international climate pledges and development objectives, the findings have important policy ramifications and provide information that can guide the country's twin pursuit of environmental sustainability and financial stability. The empirical analysis reveals that there is a positive association of both the economic development (GDP growth) and the Inflation towards the CO2 emission in long-run. This Implies that higher inflation and substantial economic activities leads to detoriation of quality environment. Furthermore, in the short run, GDP does not exhibit a statistically significant effect on CO₂ emissions. However, rising inflation appears to be associated with an improvement in environmental quality, suggesting that inflationary conditions may indirectly contribute to lower emissions—possibly through reduced industrial activity or shifts in consumption patterns. The results from the NARDL model show no evidence of asymmetric effects of GDP and inflation on CO₂ emissions in either the short run or the long run, indicating that positive and negative shocks have a broadly similar impact. However, a statistically significant negative relationship is identified in the case of negative inflation, implying that during periods of declining inflation, CO₂ emissions tend to decrease. This may reflect reduced industrial production and lower energy consumption during times of economic slowdown. Rest of the paper is organized as follow, section 2 demonstrate the extensive review on inflation, economic growth and environmental sustainability. Data and methodology with section 3 while the detailed analysis and discussion are reported in section 4. Finally, section 5 conclude the outcomes, practical implication, limitation and further research in this field. 2 Review of Literature The nexus between inflation, economic growth, and environmental sustainability has gained prominence in academic and policy discussions, particularly in light of rising climate concerns and macroeconomic volatility. While economic growth has long been associated with environmental degradation, the role of inflation is relatively underexplored. This literature review aims to critically examine existing research on the individual and joint impacts of inflation and economic growth on environmental sustainability. It reviews theoretical frameworks, empirical evidence, methodological approaches, and key findings across global and Indian contexts. The review also identifies emerging trends and research gaps, offering a foundation for future inquiry into how macroeconomic policy can align with environmental priorities in a sustainable development framework. Inflation and Economic growth : For many years, one of macroeconomics' key concerns has been the connection between inflation and economic growth. This survey of the literature examines the relationships between inflation and economic growth in various economies by combining theoretical underpinnings with empirical data. According to early classical economists like David Ricardo and Adam Smith, inflation is primarily a monetary phenomenon with little long-term impact on real variables like output. Milton Friedman ( 1970 ) popularised the Quantity Theory of Money, which holds that inflation causes market efficiency to be distorted and growth to be impeded. Neoclassical models propose that inflation largely impacts nominal variables and assume full employment. Because it creates uncertainty, deters investment and saving, and reduces the purchasing power of money, inflation is harmful in this context. Post-Keynesians contend that inflation need not necessarily be detrimental to growth, particularly in economies with high unemployment and spare capacity, and that it can result from structural rigidities and power dynamics (such as wage-price spirals) (Palley, 2003). The available literature revels the interaction between inflation and economic growth in four different way such as inflation has a positive impact on economic growth ; Rapach, 2003 ; Benhabib and Spiegel, 2009 ; Qabaja & Tenekeci, 2024 )), inflation has a negative impact on economic growth ( Barro, 1995 ; Azam and Khan, 2020; Khan et al.,2022), inflation has no effect on economic growth (N H Tien,2021), and inflation influences economic growth within a certain threshold( Aydin et al., 2016). Many empirical studies provide evidence that inflation and growth are negatively correlated. India presents a contradictory picture: Strong growth was accompanied by modest inflation throughout the 2003–2011 high-growth period. Persistent inflation of food and fuel after 2012 weakened investment and consumption. According to research by Mohanty et al. (2014) and the RBI (2021), price stability has aided investment and growth recently, but inflation has a negative impact on low-income households and distorts investment. According to recent studies conducted in India, inflation above 6% starts to have a detrimental impact on economic activity by lowering consumer purchasing power and investment sentiment (Patra & Kapur, 2022). A long-term negative correlation between inflation and GDP growth in India was discovered by Chakraborty et al. (2023), indicating that inflation beyond 6% has a detrimental effect on employment and capital formation. Using a Vector Auto Regression (VAR) model, the RBI Working Paper (2023) demonstrated how inflation shocks quickly reduce household consumption and private investment, which slows GDP growth. Economic Growth and Environmental Sustainability In order to determine whether and how economic growth and environmental sustainability may coexist, this review summarizes key theoretical stances and empirical data from throughout the world, with an emphasis on India. According to the Environmental Kuznets Curve (EKC), there is an inverse U-shaped correlation between environmental deterioration and income. Economic growth causes more pollution at lower income levels because of industrialization and a lack of environmental consciousness. Societies want cleaner surroundings, and governments can invest in cleaner technology and impose stronger laws as income rises (Grossman & Krueger, 1991 ; Panayotou, 1993 ). Dinda (2004) emphasizes that EKC patterns vary by country and pollutant type, suggesting the need for context-specific analysis. Further the empirical review has been done. Chiu ( 2012 ) examined the EKC for 52 developing nations between 1972 and 2003 using the Panel Smooth Transition Regression (PSTR) model and discovered a substantial threshold impact between deforestation and GDP as well as an EKC link for deforestation. Similarly, using data from 1980 to 2008, Heidari, Turan Katircioglu, and Saeidpour (2015) investigated the relationship between economic growth, CO2 emissions, and energy consumption in five ASEAN (Association of South East Asian Nations) nations. Environmental degradation increased with economic growth in the first regime (GDP per capita below $ 4,686 USD), but the tendency was reversed in the second regime (GDP per capita over $ 4,686 USD). Shahbaz et al. ( 2015 ) support the early stage of the EKC hypothesis by finding a positive long-term link between GDP and CO₂ emissions in emerging economies. Energy use continues to be the main cause of environmental deterioration in developing nations, according to Apergis and Payne ( 2009 ). According to Rafiq & Salim ( 2014 ), India's industrial and transport sectors are mostly to blame for the country's rising CO2 emissions, which have coincided with its economic expansion. Although structural changes (the move to services) have reduced the intensity of emissions, Chakravorty & Dasgupta,2015; Al Mamun & Ehsanullah, 2025 ) contend that absolute emissions are still increasing. When Sinha & Bhattacharya ( 2016 ) test the EKC theory for India, they find mixed results: some pollutants (such vehicle emissions) increase steadily with GDP, while others follow the EKC trend. Inflation and Environmental Sustainability Recent research has examined the connection between carbon emissions and inflation. Using monthly data for Germany, France, Italy, and Spain, Ciccarelli et al. (2023) examine the asymmetric impacts of weather shocks on inflation in the euro region. The findings indicate that rising mean temperatures and temperature variability significantly raise inflation rates. Based on cross-national firm-level statistics, André et al. (2025) predict that businesses will likely see medium-term productivity gains after a relatively small energy price shock, particularly if they make energy efficiency investments. Duca-Radu et al. ( 2021 ) examine consumers' spending responses to anticipated inflation using a large multi-country survey and a novel pseudo panel dataset. They contend that improving financial literacy and inflation knowledge contributes to the positive spending response. Grolleau and Weber ( 2024 ) cast doubt on the empirical validity of aggregate inflation rates by offering empirical support for a weak but significant negative correlation between core inflation and CO2 emissions over a 50-year span. It also found that the headline inflation and carbon emissions do not closely correlate while the relationship between core inflation and emissions is considered too weak to be used to develop policy recommendations for achieving the net zero transition. There is a strong correlation between carbon emissions and inflation (and/or inflation variability), according to other studies that focus on a single economy, including the USA (Tahir et al. 2022), Pakistan (Khan 2019 ; Ullah et al. 2020 ), Malaysia (Musarat et al. 2021 ), Indonesia (Setyadharma et al. 2021 ), and China (Xu et al. 2023). According to Pata ( 2021 ), Turkey's CO₂ emissions are negatively impacted by inflation, most likely as a result of lower production and consumption during inflationary times. Sharma et al. ( 2022 ) discovered a nonlinear correlation between environmental quality and inflation in the Indian context. Reduced emissions were associated with moderate inflation, but green investments were disturbed by high inflation. According to Kumar, and Singh ( 2023 ), India's inflation causes short-term carbon reductions because of decreased industrial output, but it also jeopardises long-term clean energy investments. World Bank (2022): Warns that inflationary pressures have the potential to sabotage SDG achievement, especially when it comes to improving air and water quality in case of India. Sharma ( 2011 ) used time-series data from 1971 to 2007 and discovered that while inflation had a minor but noteworthy positive impact on environmental degradation, economic expansion and energy consumption raised carbon emissions in India. The literature shows that the relationship between inflation, economic growth, and environmental sustainability is intricate and dynamic. Inflation's impact is still less understood but is becoming more important, even though economic development is frequently associated with environmental degradation, particularly in emerging nations. Inflation may have a direct impact on environmental effects as well as interact with development dynamics, according to recent research. 3 Sample and Methodology 3.1 Data In this study, carbon dioxide (CO₂) emissions are used as the dependent variable, while inflation (INF) and economic growth (GDP) are treated as the main independent variables. To control for additional influences, we also include Natural Resource Rents (TNR), Total Electricity Consumption (ElectCon), and Natural Gas Consumption (NGasCon) as control variables. The choice of electricity and natural gas consumption reflects their dual role in the emissions landscape. On one hand, these energy sources are generally considered cleaner alternatives to coal and oil, potentially helping to reduce CO₂ emissions. On the other hand, their production and distribution processes can still contribute to environmental degradation, depending on the energy mix and efficiency of technologies used. This dual effect underscores the importance of examining both consumption and production dynamics when assessing their overall environmental impact. To examine the validity of the EKC hypothesis, this study incorporates both GDP and the squared term of GDP into the empirical model. According to the EKC framework, the hypothesis is supported if the coefficient of GDP is positive while the coefficient of GDP squared is negative, and both are statistically significant. This pattern indicates an inverted U-shaped relationship between economic growth and environmental degradation, suggesting that environmental impact initially worsens with economic growth but improves after reaching a certain income threshold. Annual data for the key variables are sourced from the World Bank database ( https://databank.worldbank.org/databases ). Information on natural gas and electricity consumption is collected from the Annual Energy Statistics Report published by the Ministry of Power, Government of India ( https://mospi.gov.in/sites/default/files/publication_reports/Energy_Statistics_2025/Energy%20Statistics%20India%202025_27032025.pdf ). The growth in energy consumption is measured as the year-on-year percentage change. Based on data availability, the study utilizes annual observations spanning the period from 1970 to 2022. Following data compilation, the next section outlines the econometric techniques employed to test the EKC hypothesis and assess the dynamic relationships among the selected variables The detail of the variables is reported in Appendix 1. The movement of selected variable are reported in Fig. 1 . 3.2 Methodology To examine the validity of the EKC hypothesis, this study incorporates both GDP and the squared term of GDP into the empirical model. According to the EKC framework, the hypothesis is supported if the coefficient of GDP is positive while the coefficient of GDP squared is negative, and both are statistically significant. This pattern indicates an inverted U-shaped relationship between economic growth and environmental degradation, suggesting that environmental impact initially worsens with economic growth but improves after reaching a certain income threshold. Annual data for the key variables are sourced from the World Bank database. Information on natural gas and electricity consumption is collected from the Annual Energy Statistics Report published by the Ministry of Power, Government of India. The growth in energy consumption is measured as the year-on-year percentage change. Based on data availability, the study utilizes annual observations spanning the period from 1970 to 2022. Following data compilation, the next section outlines the econometric techniques employed to test the EKC hypothesis and assess the dynamic relationships among the selected variables. The base model in our analysis is as follows: $$\:C{O}_{2}=f(Inflation,\:GDP,\:TNR,\:\text{E}\text{l}\text{e}\text{c}\text{t}\text{C}\text{o}\text{n},\text{N}\text{G}\text{a}\text{s}\text{C}\text{o}\text{n})$$ It can be rewrite as follow in long-run prospective: $$\:C{O}_{2t}={C}_{0}+{\beta\:}_{1}Inflatio{n}_{t}+{\beta\:}_{2}GDP+{\beta\:}_{3}C{V}_{t}+{u}_{t}$$ 1 Where the \(\:C{V}_{t}\) includes the list of control variable such as \(\:\:GD{P}_{t}^{2},\:TN{R}_{t},\:{\text{E}\text{l}\text{e}\text{c}\text{t}\text{C}\text{o}\text{n}}_{\text{t}}\:\text{a}\text{n}\text{d}\:{\text{N}\text{G}\text{a}\text{s}\text{C}\text{o}\text{n}}_{\text{t}}\) . The significant value of the long-run coefficient, i.e., \(\:{\beta\:}_{i}\) reported the long-run effect towards the CO2 emission. Following the Pesaron et al. (2001), the short-run specification could be: $$\:{\Delta\:}C{O}_{2t}=c0+{\sum\:}_{i=0}^{p}{\varphi\:}_{i}{\Delta\:}C{O}_{2t-i}+{\sum\:}_{i=0}^{p}{\theta\:}_{i}{\Delta\:}IN{F}_{t-i}+{\sum\:}_{i=0}^{p}{\gamma\:}_{i}{\Delta\:}GD{P}_{t-i}+{\sum\:}_{i=0}^{p}{\pi\:}_{i}{\Delta\:}C{V}_{t-i}+{\beta\:}_{1}C{O}_{2,t-1}+{\beta\:}_{2}IN{F}_{t-1}+{\beta\:}_{3}GD{P}_{t-1}+{\beta\:}_{4}C{V}_{t-1}+{e}_{t}$$ 2 In Eq. 2 , \(\:{\beta\:}_{i}\) are the long-run coefficient while \(\:{\varphi\:}_{i},\:{\theta\:}_{i},\:{\gamma\:}_{i}\:and\:{\pi\:}_{i}\) are the short-run coefficients. After determination of respective coefficient value, Wald test being employed to check the evidence of long-run effect. Suppose to check the long-run effect of \(\:IN{F}_{t}\) : the null hypothesis will be \(\:{\beta\:}_{2}/{\beta\:}_{1}=0\) , No long-run effect of \(\:IN{F}_{t}\) . Each long-run coefficient normalize to \(\:{\beta\:}_{1}\) (Ullah et al. 2020 ). The hypothesis of no short-run effect is just tested by using \(\:{\varphi\:}_{i},\:{\theta\:}_{i},\:{\gamma\:}_{i}\:and\:{\pi\:}_{i}=0\) . Here this model allowed the model having different integration properties as well. After the identification of long and short-run association, it is important whether the positive/negative change in independent variable symmetrically affecting the CO2 level of not. Thus the Eq. 2 could be modified as follow for the Non-linear ARDL specification: $$\:{\Delta\:}C{O}_{2t}=c0+{\sum\:}_{i=0}^{p}{\varphi\:}_{i}{\Delta\:}C{O}_{2t-i}+{\sum\:}_{i=0}^{p}{\theta\:}_{i}^{+}{\Delta\:}IN{F}_{t-i}^{+}+{\sum\:}_{i=0}^{p}{\theta\:}_{i}^{-}{\Delta\:}IN{F}_{t-i}^{-}+{\sum\:}_{i=0}^{p}{\gamma\:}_{i}^{+}{\Delta\:}GD{P}_{t-i}^{+}+{\sum\:}_{i=0}^{p}{\gamma\:}_{i}^{-}{\Delta\:}GD{P}_{t-i}^{-}+{\sum\:}_{i=0}^{p}{\pi\:}_{i}^{+}{\Delta\:}C{V}_{t-i}^{+}+{\sum\:}_{i=0}^{p}{\pi\:}_{i}^{-}{\Delta\:}C{V}_{t-i}^{-}+{\beta\:}_{1}C{O}_{2,t-1}+{\beta\:}_{2}^{+}IN{F}_{t-1}^{+}+{\beta\:}_{2}^{-}IN{F}_{t-1}^{-}+{\beta\:}_{3}^{+}GD{P}_{t-1}^{+}+{\beta\:}_{3}^{-}GD{P}_{t-1}^{-}+{\beta\:}_{4}^{+}C{V}_{t-1}^{+}+{\beta\:}_{4}^{-}C{V}_{t-1}^{-}+{e}_{t}$$ 3 In Eq. 3 , all the independent variable is decomposing into positive and negative shock. Suppose, considered only one variable \(\:IN{F}_{t}\) , the specification could be as follow: $$\:IN{F}_{t}^{+}={\sum\:}_{q=1}^{t}{\Delta\:}IN{F}_{t}^{+}={\sum\:}_{q=1}^{t}\text{m}\text{a}\text{x}({\Delta\:}IN{F}_{t},\:0)\:IN{F}_{t}^{-}={\sum\:}_{q=1}^{t}{\Delta\:}IN{F}_{t}^{-}={\sum\:}_{q=1}^{t}\text{m}\text{i}\text{n}({\Delta\:}IN{F}_{t},\:0)$$ 4 Like the Eq. 4 , the it could be decomposing all the selected independent variable into positive and negative specification. Again the Wald-test is employed to examine the asymmetric effect assuming there is no asymmetric effect in long- as well as short-run. The specification for the short-run asymmetric test is (for the variable \(\:IN{F}_{t})\) : \(\:\left[{\sum\:}_{i=0}^{p}{\theta\:}_{i}^{+}={\sum\:}_{i=0}^{p}{\theta\:}_{i}^{+}\right]\) while for the long-run specification \(\:[{\beta\:}_{2}^{-}/{\beta\:}_{1}={\beta\:}_{2}^{+}/{\beta\:}_{1}]\) (No asymmetric long-run effect). 4 Result Discussion 4.1 Descriptive Statistic Before applying any statistical or econometric tools to analyze the time-series data, it is essential to understand the basic characteristics of the variables through descriptive statistics. The average inflation rate over the sample period is 7.25%, with values ranging from a low of -1.65% to a high of 17.83% observed in 1973. A closer examination of the inflation series reveals that inflation remained relatively low during the periods 1999–2003 and 2014–2019. However, in recent years—particularly from 2020 to 2022—inflation has shown a noticeable upward trend, indicating renewed macroeconomic pressure in the post-pandemic period. The average annual GDP growth rate over the study period was 5.36%, with a peak of 9.69% and a trough of -5.78%, the latter occurring in 2020 due to the economic disruption caused by the COVID-19 pandemic. Descriptive statistics indicate that natural gas consumption exhibits the highest level of volatility, as reflected in its substantial standard deviation, as well as elevated skewness and kurtosis values. Among the variables analyzed, all except TNR and GDP are positively skewed, suggesting a consistent pattern of positive growth across the sample period. Normality tests show that approximately half of the variables satisfy the assumption of normal distribution. However, the presence of non-normality in key variables—particularly the dependent variable, CO₂ emissions—supports the need for non-linear modeling techniques, as linear models may not adequately capture the complex relationships among the variables The descriptive statistic of the present study demonstrated in Table 1 . Table 1 Result of Descriptive Statistic Min Max Q1 Median Mean Q3 SD Skewness Kurtosis JB Test CO2 -0.97 0.64 -0.68 -0.16 -0.18 -0.68 0.53 0.05 -1.28 3.65 Inflation -1.65 17.83 3.97 7.19 7.25 3.97 3.80 0.53 0.71 3.55 GDP -5.78 9.69 3.87 5.95 5.36 3.87 3.17 -1.58 3.21 44.83*** TNR -0.33 1.96 0.75 1.03 0.96 0.75 0.44 -0.83 1.18 9.12*** ElectCon -1.43 16.13 5.32 6.98 6.99 5.32 3.58 0.12 0.14 0.17 NGasCon -22.67 87.12 0.86 6.10 10.59 0.86 16.94 1.99 6.61 131.46*** GDP2 0.31 93.89 16.40 35.37 38.56 16.40 24.50 0.34 -0.68 2.04 This table reports the descriptive statistic such as Mean, Median, Min, Max, Q1, Q3, Standard deviation (SD), skewness, Kurtosis and the test of normality through Jarque-Bera test (JB) considering the annual data of selected variables. The null hypothesis of JB test is that the series follow the normal distribution while ***, ** and * respectively reported for 1%, 5% and 10% level of significance. Sources : Authors Calculation Table 2 Result of Pearson’s Correlation Coefficient CO2 Inflation GDP TNR ElectCon NGasCon GDP2 CO2 1 Inflation -0.33*** 1 GDP 0.29** -0.35*** 1 TNR 0.25* 0.07 0.23* 1 ElectCon -0.05 0.18 -0.08 0.31** 1 NGasCon -0.24* 0.1 -0.04 0.09 0.44*** 1 GDP2 0.38*** -0.32** 0.74*** 0.21 -0.11 -0.04 1 This table reports the result of Pearson’s correlation coefficient among the variables and test the null hypothesis that no correlation among the variables over the study period. ***, ** and * respectively reported for 1%, 5% and 10% level of significance. Sources: Authors Calculation After conducting the descriptive analysis, it is crucial to examine the correlation among the selected variables to identify potential multicollinearity issues and evaluate the suitability of each variable for inclusion in the model. Initially, GDP per capita was considered as a proxy for national economic growth. However, a very strong correlation was found between GDP per capita and its squared term, indicating a high risk of multicollinearity that could undermine the reliability of the regression estimates. To mitigate this issue, the annual GDP growth rate was chosen instead as a proxy for national growth. This alternative showed a more acceptable correlation of 0.38 between GDP growth and its squared term, suggesting a significantly lower risk of multicollinearity and better model stability for subsequent analysis. The result of cross variable correlation reported in Table 2 . It shows significant and inverse relation between CO2 and INF while GDP positively associated with CO2 emission. Indeed, the correlation of ElectCon and NGasCon show the negative relationship with CO2. The rolling correlation (with 5 observation window size) plot of CO2 with all selected variable are showing in Fig. 2 . All the figure (except GDP) found to show dynamic correlation with CO2 during time zone. Though initially the GDP negatively correlated with CO2 but after 1980 it reveled significant and positive correlation with the CO2 emission. If we are ignoring the effect of GDP (as positive correlation over the period), the asymmetric effect of other variables again supports the use of non-linear specification for our analysis. 4.2 Test of Unit-root After conducting the correlation analysis, it is essential to test for stationarity, a key assumption in time-series econometrics. In this study, the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests were applied to examine the order of integration of the variables. Both tests were performed under three specifications: without drift, with drift, and with drift and trend. As presented in Table 3 , the ADF test results indicate that all variables, except ElectCon and CO₂ emissions, are stationary at level. However, when first differenced, all variables reject the null hypothesis of a unit root at the 1% significance level, confirming stationarity. The PP test results are largely consistent, with ElectCon found to be stationary at level (under the drift and trend specifications), while CO₂ emissions remain non-stationary in level form—consistent with the ADF findings. Overall, these results suggest that the dependent variable (CO₂) is integrated of order zero [I(0)], while the independent variables exhibit a mix of integration at level and at first difference [I(0) and I(1)]. Given this combination of integration orders, the Autoregressive Distributed Lag (ARDL) modeling approach is the most appropriate, as it can effectively handle variables with different levels of integration and is suitable for testing long-run cointegration relationships. Table 3 Results of Unit Root Test At Level At First Difference ADF PP ADF PP None Drift Drift & Trend None Drift Drift & Trend None Drift Drift & Trend None Drift Drift & Trend CO2 -1.40 -0.63 -2.24 -1.16 0.08 -13.60 -1.15 -2.82* -2.71 -24.49*** -45.71*** -45.82*** Inflation -1.54 -3.76*** -4.22*** -4.47 -32.81*** -35.12*** -6.73*** -6.73*** -6.69*** -54.88*** -54.95*** -55.13*** GDP -0.67 -4.01*** -4.69*** -10.19** -53.49*** -55.16*** -6.38*** -6.32*** -6.45*** -67.14*** -67.15*** -67.15*** TNR -0.55 -4.14*** -3.99** -1.04 -12.70* -12.31 -3.23*** -3.21** -3.095 -50.05*** -50.51*** -50.60*** ElectCon -0.89 -2.49 -2.48 -4.52 -37.10*** -37.45*** -5.08*** -5.02*** -5.07*** -60.46*** -60.46*** -60.39*** NGasCon -2.14** -3.44** -4.16*** -38.23*** -49.90*** -50.44*** -5.98*** -5.91*** -5.88*** -65.31*** -65.31*** -65.29*** GDP2 -0.49 -3.98*** -5.61*** -10.30** -47.42*** -50.91*** -6.52*** -6.53*** -6.52*** -65.98*** -65.92*** -65.87*** This table reports the result of test of stationarity such as ADF and PP respectively represent for the Augmented-Dickey Fuller test and Phillips-Perron test of unit root with the null hypothesis of series having unit-root. On the view point of robustness, we test the unit-root based on no-drift, with drift and drift and trend. The optimum lag order selected based on the BIC criteria. ***, ** and * respectively reported rejecting the null hypothesis at 1%, 5% and 10% significance level. Sources : Authors Calculation Result of ARDL After accessing the level of integration, we use the ARDL model to examine the long run effect of selected variable on CO2 emission. We fixed the maximum order of lag for the ARDL model is two, because of small sample and use Bayesian Information Criterion (BIC) to select the appropriate lag for the analysis as this criterion is most suitable in case of small sample (Li et al. 2024 ). The summary 1 result of ARDL model with five different cases are presented in Table 4 . The specification of different cases is given in Appendix 2. The result of long-run bound test show the long-run cointegration in all the cases (except case 1) in both the 5% and 10% level of significance but not significant at 1% level, which indicate further examining the long-run effect with different specification. The positive long-run effects of GDP (0.0355) and electricity consumption (0.0167) on CO₂ emissions suggest that economic growth contributes to increased emissions through channels such as infrastructure expansion, industrial development, and rising trade activities. These findings highlight the environmental cost associated with sustained economic growth, particularly in rapidly developing economies like India. On the other hand, the positive long-run effect of electricity consumption on CO2 emission indicate major part of the production of electricity depends on the non-rentable sources. If the production of electricity mostly from renewable sources, it could show negative effect of ElectCon towards CO2 emission. From the EKC hypothesis prospective, though the square-GDP found to be negative at case 4th and 5th but not significant. The result reviles the rejection of EKC hypothesis in case of Indian economy. Table 4 Result of ARDL UECM Case-1 Case-2 Case-3 Case-4 Case-5 Long-run Cointegration: Bound Test 10% critical value No Long-run Long-run No Long-run Long-run Long-run 5% critical value No Long-run Long-run No Long-run Long-run Long-run 1% critical value No Long-run No Long-run No Long-run No Long-run No Long-run Long Run Effect: Coefficient Inflation_1 -0.304 -1.62 -1.62 0.0124 0.0124 GDP_1 -0.175 -2.47 -2.47 0.0355** 0.0355** GDP2_1 0.0339 0.0634 0.0634 -0.00239 -0.00239 TNR_1 -2.76 -8.46 -8.46 -0.0304 -0.0304 ElectCon_1 0.179 0.51 0.51 0.0167*** 0.0167*** NGasCon_1 0.0253 -0.00728 -0.00728 -0.000787 -0.000787 Residual Diagnostics BG_SC_lm_test 9.46* 7.59 7.59 8.98* 8.98* LM_ARCH_test 3.43 5.87 5.87 3.93 3.93 normality_test 0.615 1.31 1.31 0.743 0.743 RESET_test 2.57 1.68 1.68 0.23 0.23 This table demonstrate the results of ARDL model following the eq-2. Due to the small numbers of observations, we set the maximum lag for the all the independent variable is two and here only results of long-run coefficient part is reported. ***, ** and * respectively reported for 1%, 5% and 10% level of significance. Sources : Authors Calculation The next step, we are altering the independent variable in the system equation to verify the accuracy of the result of ARDL model. The study fixing the maximum lag to tow and detect the best fit model specification with altering the independent variable which is reported in Table 5 and the summary result in Table 6 . In Table 5 , the coefficient of INF and GDP found to be positive and significant in all the cases in the long-run part (i.e., GDP_1 and INF_1). Case 3 & 5 detected as the best fit model at different specification. The coefficient of square-GDP not significant at long-run while negative but insignificant at short-run, again verify the rejection of EKC hypothesis i.e., initially the level of CO2 emission increases with the economic growth but as the economic growth exceeding a threshold level the, the CO2 emission will negatively have affected by economic growth. The summery result of the ARDL model with variable alteration reported the similar result as previous i.e., evidence of long-run effect at 10% and 5% level of significance and ensure the positive and significance long-run effect of GDP and INF implies the both the economic growth and the high inflation leads high level of CO2 emission over the sample period (in Table 6 ). Surprisingly, the result showing significant only when we only considered GDP and INF as the independent variable. Thus, here after we are eliminating all other variable and considered only GDP and the INF for in case of using non-linear ARDL model. Table 5 Result of Auto ARDL-UECM ARDL_ECM Details Best Fit Case 3 Case 3 Case 5 Case 5 IV ALL Except: GDP2 GDP,GDP2, Inflation Inflation & GDP Coefficient Estimate Estimate Estimate Estimate (Intercept) -0.0613*** -0.0707*** -0.386*** -0.397*** CO2_1 0.000551 0.00639 -0.251*** -0.275*** Inflation_1 0.00513*** 0.00679*** 0.00971*** 0.00867*** GDP_1 0.00562* 0.00835*** 0.00826*** 0.00754*** GDP2_1 0.000276 0.000362 TNR_1 0.0337*** 0.0299*** ElectCon_1 -0.00338** -0.00371** NGasCon_1 0.000278 0.000336 D.CO2_1 -0.207 D.Inflation 0.00376*** 0.00446*** 0.00354*** 0.00374*** D.GDP 0.00472** 0.00564*** 0.00538*** 0.00502*** D.Inflation_1 -0.00198 -0.00505*** -0.00447*** D.Inflation_2 -0.00249* -0.00205* D.GDP_1 -0.00119 D.GDP2 2.54E-05 -2.97E-05 D.GDP2_1 -0.000262 D.GDP2_2 -0.00012 Trend 0.00912*** 0.0101*** F-Stat 3.891*** 4.173*** 3.984*** 6.524*** R-Square 0.499 0.517 0.59 0.56 Adj.R-Square 0.371 0.393 0.442 0.474 This table demonstrate the results of ARDL model following the eq-2. Due to the small numbers of observations, we set the maximum lag for the all the independent variable is two and check the appropriate lag based on BIC criteria while the lag for the dependent variable considered one. Here we detect the best fir model by altering the independent variable with different specification such as all variable, all variable except GDO^2, Only GDP, GDP^2 and Inflation, only Inflation and GDP. ***, ** and * respectively reported for 1%, 5% and 10% level of significance Sources : Authors Calculation Table 6 Summary of Auto ARDL-UECM ARDL_ECM Details Best Fit Case 3 Case 3 Case 5 Case 5 IV ALL Except: GDP2 GDP,GDP2, Inflation Inflation & GDP Long-run Cointegration: Bound Test 10% critical value Long-run Long-run Long-run Long-run 5% critical value Long-run Long-run Long-run Long-run 1% critical value No Long-run No Long-run Long-run Long-run # Long Run Effect: Coefficient Inflation_1 -9.32 -1.06 0.0386*** 0.0316*** GDP_1 -10.2 -1.31 0.0328** 0.0275*** GDP2_1 -0.501 -4.68 0.00144 TNR_1 -61.2 0.58 ElectCon_1 6.14 -0.0526 NGasCon_1 -0.505 Residual Diagnostics BG_SC_lm_test 7.96** 8.18** 2.47 1.3 LM_ARCH_test 0.86 0.3 2.38 4.45 normality_test 0.67 1.35 0.03 0.17 RESET_test 0.94 0.58 0.43 0.05 This table reports the cointegration and residual diagnostics part of Table 5 . For other details follow the notes on Table 5 . ***, ** and * respectively reported for 1%, 5% and 10% level of significance Sources : Authors Calculation 4.3 Result of Non-linear ARDL Model In this step, we are considering the CO2 as the dependent variable while GDP and the INF as the independent variable. In both the long and short-run system equation, the positive and negative decomposed factor of all the independent variables have included to detect the asymmetric effect on CO2. The result of ARDL indicate that there is a positive effect of INF and the GDP to emission level, means the CO2 will move in the same direction with the levels of INF and GDP. Now the question is does the effect of the change in the emission level is same when the independent variable moves in up and downward? In other words, does a one-unit increase in inflation (INF) have the same magnitude of impact on CO₂ emissions as a one-unit decrease in inflation, but in the opposite direction? The asymmetric effect indicates the different magnitude in two different situations. Similar to the ARDL analysis, this session examines the long- and short-run effect in two step. First, run the NARDL with lag two for each case (i.e., case 1 to 5) considering both the variable are decomposing variable. Second, assume the maximum lag for NARDL is two and run the model altering one variable as the control and other is decompose variable. Table 7 and Table 8 demonstrate the NARDL result and summary respectively considering both INF and GDP decomposed into negative and positive factor. If the Wald test result of difference between the positive and negative coefficient found to be, implies the evidence of asymmetric effect. Table 7 Result of Auto NARDL UECM (Both Inflation and GDP are Decompose Variable) Coefficient Case 1 Case 2 Case 3 Case 4 Case 5 (Intercept) -0.11* -0.11* -0.393*** -0.393*** CO2_1 -0.0143 -0.0771* -0.0771* -0.336*** -0.336*** Inflation_pos_1 0.0048 0.0053* 0.0053* 0.00809*** 0.00809*** Inflation_neg_1 0.00704** 0.00663** 0.00663** 0.00907*** 0.00907*** GDP_pos_1 0.00646 0.00593 0.00593 0.00668 0.00668 GDP_neg_1 0.00431 0.00246 0.00246 0.00586 0.00586 D.CO2_1 -0.25 -0.269 -0.269 -0.134 -0.134 D.Inflation_pos_1 -0.00339 -0.00105 -0.00105 -0.00435 -0.00435 D.Inflation_pos_2 -0.00418 -0.00151 -0.00151 -0.00348 -0.00348 D.Inflation_neg_1 -0.002 -0.00402 -0.00402 -0.00487* -0.00487* D.Inflation_neg_2 -0.000484 -0.00131 -0.00131 -0.0034 -0.0034 D.GDP_pos_1 0.00213 0.000619 0.000619 -0.00146 -0.00146 D.GDP_pos_2 -0.00182 -0.00171 -0.00171 -0.00375 -0.00375 D.GDP_neg_1 -0.00478 -0.00197 -0.00197 -0.00288 -0.00288 D.GDP_neg_2 0.000972 0.0019 0.0019 0.000107 0.000107 Trend 0.012** 0.012** F-Stat 4.407*** 1.317 1.317 1.748* 1.748* R-Square 0.631 0.345 0.345 0.435 0.435 Adj.R-Square 0.488 0.083 0.083 0.186 0.186 This table reports the results of NARDL model with the eq-3 and decompose the inflation and GDP based on eq-4. Based on eq-3 the inflation (GDP) is divided in to two part i.e., Inflation_pos and Inflation_neg (GDP_pos and GDP_neg) respectively. The result of this table demonstrates whether the increasing Inflation (GDP) and decreasing Inflation (GDP) have same impact in CO2 or not. The lag order selected for this model is two and the symbol ***, ** and * respectively reported for 1%, 5% and 10% level of significance Sources : Authors Calculation Table 8 Summary Result of Auto NARDL UECM (Both Inflation and GDP are decompose Variable) Case 1 Case 2 Case 3 Case 4 Case 5 Long-run Cointegration: Bound Test 10% critical value Long-run Long-run No Long-run No Long-run No Long-run 5% critical value Long-run Long-run No Long-run No Long-run No Long-run 1% critical value Long-run No Long-run No Long-run No Long-run No Long-run Long Run Effect: Coefficient Inflation_pos_1 0.337 0.0687 0.0687 0.0241** 0.0241** GDP_pos_1 0.453 0.077 0.077 0.0199 0.0199 Inflation_neg_1 0.494 0.0861 0.0861 0.027** 0.027** GDP_neg_1 0.302 0.032 0.032 0.0175 0.0175 Asymmetric Effect: Coefficient SR Asym. effect from: Inflation 0.637 0.133 0.133 0.00376 0.00376 SR Asym. effect from: GDP 0.383 0.021 0.021 0.132 0.132 LR Asym. effect from: Inflation 0.625 0.229 0.229 0.139 0.139 LR Asym. effect from: GDP 0.686 1.74 1.74 0.0903 0.0903 Residual Diagnostics BG_SC_lm_test 0.772 1.26 1.26 0.731 0.731 LM_ARCH_test 1.08 1.99 1.99 3.6 3.6 normality_test 58.7*** 49.7*** 49.7*** 140*** 140*** RESET_test 0.0166 5.25** 5.25** 0.926 0.926 This table reports the summary results of Table 7 i.e., long-run effect, short and long-run asymmetric effect. For other details follow the notes of Table 7 . The symbol ***, ** and * respectively reported for 1%, 5% and 10% level of significance Sources : Authors Calculation From Table 7 , long-run prospective, first, the result of long-run bound test reject the evidence of long-run cointegration from the overall decomposing factor to emission level. Second, the coefficient of INF_neg found to be positive and significant in all the cases, similarly the INF_pos also show the similar result as INF_neg except case 1. It conforms the overall change in INF to CO2 is positive. Third, the positive and negative factor of GDP are not significant at all though the value is positive throughout the result table. It revels the similar types of effect from the positive and negative INF to CO2 level. Fortunately, in short-run, the INF_neg reported significant and negative value in case 4 and 5. Overall, it can be concluding that whether there is a upward or downward movement of INF, in long-run inflation leads CO2 emission while there is a certain possibility that the negative shock of inflation could control the emission level. Again the Table 8 confirm the positive significant effect from INF_pos and INF_neg while no long-run effect of positive and negative GDP factor reported. Non-of the variable reporting either short- or long-run asymmetric effect forwards emission level indicate the sizable effect form the independent variable are quite same even they are moves up or moves down. In step two, we are fixing one variable as decomposing (DV) and other as the control variable (CV); the results are presented in Table 9 . First, where DV is INF and the CV is GDP, the long-run factor of GDP (0.0039) is positive and significance while the negative and positive long-run factor of INF (0.00692 & 0.0061) also found to be positive. The result reviles, with the positive long-run effect of GDP, both the INF decompose factor positively affecting emission level. As the GDP growth rate increase leads to money flow to the economy, which shift the price level upward shifting. In the other hand, with the high inflation, the energy consumption of the society moves towards the conventional energy resources and leads emission level. The result also revels the long-run effect both from INF decompose factor and the GDP. Second, where DV is GDP and CV is INF, long-run inflation factor found to be positive and significant while non-of the decomposing factor of GDP are significant. Further, in short-run inflation reported negative effect towards the emission level. Table 9 Result of Auto NARDL UECM (With Decompose and Control Variable) Model Details Residual Diagnostics Best Fit Case 5 Case 5 BG_SC_lm_test 3.46 3.32 Decompose Variable (DV) Inflation GDP LM_ARCH_test 0.844 1.72 Control Variable (CV) GDP Inflation normality_test 108*** 93.7*** RESET_test 0.358 0.791 Coefficient Estimate Estimate (Intercept) -0.336*** -0.335*** Long-run Cointegration: Bound Test CO2_1 -0.253*** -0.279*** 10% critical value Long-run No Long-run DV_pos_1 0.00692*** 0.00433 5% critical value No Long-run No Long-run DV_neg_1 0.0061*** 0.00375 1% critical value No Long-run No Long-run CV_1 0.0039** 0.00556*** D.DV_pos_1 -0.145 0.000108 Long Run Effect: Coefficient D.DV_pos_2 -0.00299 -0.002 DV_pos_1 0.0273** 0.0155 D.DV_neg_1 -0.00405** -0.00249 DV_neg_1 0.0241** 0.0134 D.DV_neg_2 0.0025 CV_1 0.0154* 0.0199** D.CV_1 -0.00269* Trend 0.00852** 0.00924** Asymmetric Effect : F-Stat 3.006*** 2.276** SR Asym. effect from: DV 0.183 0.156 R-Square 0.37 0.369 LR Asym. effect from: DV 0.49 0.284 Adj.R-Square 0.247 0.207 This table reports the results of NARDL model while considering Inflation as decompose variable (DV) and GDP as the Control Variable (CV) and alternatively. This specification would able to examine the asymmetric effect of one with controlling another. It observe the best fit model is Case 5 in both the specification. The symbol ***, ** and * respectively reported for 1%, 5% and 10% level of significance. Sources : Authors Calculation 5 Conclusion and policy Implication India has ranked as the third-highest carbon-emitting country for over a decade, following the United States and China. While carbon emissions from the U.S. have gradually declined over the years, China surpassed the U.S. in total emissions in 2005. Similarly, India overtook Japan in 2006 and has continued to show a steady upward trend in emissions since then. A comparison between developed nations such as the U.S. and Japan and emerging economies like India and China reveals a contrasting pattern: while the former have begun to reduce their CO₂ emissions, the latter are experiencing significant increases alongside rapid economic growth. Moreover, inflation plays a critical role in shaping a country's energy consumption patterns. High inflation can influence both the affordability and preference for specific energy sources, while also affecting industrialization patterns—where low-cost labor and inflation-driven input adjustments may attract global manufacturing. In addition, the consumption of natural resources, electricity, and natural gas directly impacts CO₂ emission levels. Against this backdrop, the present study aims to examine the dynamic effects of GDP growth and inflation on CO₂ emissions in India, covering the period from 1970 to 2022—an era during which the country has emerged as both a rapidly growing economy and a significant contributor to global carbon emissions. This study applies both the Autoregressive Distributed Lag (ARDL) and Non-linear ARDL (NARDL) models to explore the short-run and long-run dynamics of CO₂ emissions in relation to economic growth and inflation in India. The empirical findings yield several important insights. First, the results do not confirm the validity of the EKC hypothesis, which posits an inverted U-shaped relationship between economic growth and environmental degradation (Grossman & Krueger, 1995; Dinda, 2004). This suggests that India has not yet reached the critical income threshold required for economic growth to begin reducing environmental impact. Similar outcomes have been observed in other developing countries where industrialization and urban expansion continue to drive emissions (Shahbaz et al., 2017). Second, both inflation and economic growth are found to have a significant and positive impact on CO₂ emissions, indicating a deterioration in environmental quality. This aligns with the findings of Apergis and Payne ( 2009 ) and Zhang et al. (2017), who argue that macroeconomic instability and growth-oriented policies in developing economies often come at the cost of environmental sustainability. Third, the NARDL results indicate that the effects of inflation and GDP on CO₂ emissions are largely symmetric—meaning that increases and decreases in these variables have a proportionate impact on emissions. This contrasts with studies that report asymmetric effects in other contexts (e.g., Balcilar et al., 2020), highlighting the uniqueness of India's macroeconomic-environmental relationship. Finally, the results suggest that controlling inflation in the short run may offer a modest yet effective policy tool for improving environmental quality. 5.1 Policy implication Economic sustainability is vital for maintaining steady growth, yet environmental sustainability is equally indispensable for ensuring long-term ecological balance and human well-being. The results of this study provide important insights for researchers and policymakers seeking to understand the complex interplay between macroeconomic variables and environmental outcomes within a country-specific context. While directly restricting economic growth is neither practical nor desirable, it is essential for policymakers to implement regulatory frameworks that manage inflation effectively. Controlling inflation can indirectly contribute to lower carbon emissions by influencing production costs, consumption patterns, and the choice of energy sources. In the long run, such measures may encourage a transition from conventional, carbon-intensive energy sources to cleaner, renewable alternatives. As the study finds limited short-run effects, policy efforts must focus on long-term strategies that integrate economic and environmental goals. Developing such forward-looking, sustainable policies is especially critical for emerging economies like India, where rapid growth must be balanced with environmental responsibility. 5.2 Limitation and Future Scope The present study focuses exclusively on India, aligning with its objective to analyze the country-specific relationship between macroeconomic factors and environmental sustainability. While this national focus provides valuable insights, it also limits the generalizability of the findings. Future research could broaden the scope by including other major CO₂-emitting economies, which would offer a more comparative perspective and contribute to global policy discussions on emission reduction. Additionally, this study does not account for specific environmental policies or regulatory interventions. Future studies could examine the effectiveness of policy measures—such as carbon pricing, renewable energy mandates, or environmental regulations—in shaping emission outcomes in India. Moreover, incorporating additional macroeconomic variables, such as foreign direct investment, trade openness, technological innovation, or energy pricing, could provide a more holistic understanding of the determinants of CO₂ emissions and help in designing more targeted and effective sustainability strategies. Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and materials: The datasets analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: There is no funding to report. Clinical trial number: Not Applicable. Authors' contributions: SM, SC are responsible for conducting the literature review and drafting the initial manuscript. LP and SM carried out the data analysis. SC, SM and JMB contributed to the manuscript by reviewing and revising it to enhance its quality and coherence. All authors reviewed the manuscript. Acknowledgements: No acknowledgements are to be reported. References Apergis, N., & Payne, J. E. (2009). Energy consumption and economic growth: evidence from the Commonwealth of Independent States. Energy economics, 31(5), 641-647 Azam, M., & Khan, S. (2022). Threshold effects in the relationship between inflation and economic growth: Further empirical evidence from the developed and developing world. International Journal of Finance & Economics, 27(4), 4224-4243. Barro, R. J. (1995). Inflation and economic growth Benhabib, J., & Spiegel, M. M. (2009). Moderate inflation and the deflation–depression link. Journal of Money, Credit and Banking, 41(4), 787-798. 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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-8533787","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582126774,"identity":"f282e142-8395-45b5-9cce-8dee19d7c2a0","order_by":0,"name":"Laxmidhar Panda","email":"","orcid":"","institution":"XIM University","correspondingAuthor":false,"prefix":"","firstName":"Laxmidhar","middleName":"","lastName":"Panda","suffix":""},{"id":582126775,"identity":"3f6e60c9-1843-4d1e-8c63-fb22725e6726","order_by":1,"name":"Seba Mohanty","email":"","orcid":"","institution":"KIIT Deemed to be University","correspondingAuthor":false,"prefix":"","firstName":"Seba","middleName":"","lastName":"Mohanty","suffix":""},{"id":582126776,"identity":"4178feba-5645-499f-afc8-689231483ef1","order_by":2,"name":"Suman Chakraborty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYPCCAwwSDMwHgAwJGVK0sCWAtPCQooXHAMQirEV+Rnbi54KaO/KS/Wc+v7pRY8HDwH746AZ8Wgxu5G6WnnHsmeFsidxt1jnHgA7jSUu7gVeLRO4Gad6Gw4zzJHi3GeewAbVI8Jjh1SI/I3fzb6AW+3n8Z54Z5/wjQgvDjdxtIFsSZzPkMD/ObSNCi8GZt9useY4dTp45I82MObdPgoeNkF/k23M33+apOWw74/zhx59zvtXJ8bMfPobfYUiATQJMEqscBJg/kKJ6FIyCUTAKRg4AAJPYSMfXsQOhAAAAAElFTkSuQmCC","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":true,"prefix":"","firstName":"Suman","middleName":"","lastName":"Chakraborty","suffix":""},{"id":582126777,"identity":"ec650210-80da-46d3-9f9a-ff25d9b5aebe","order_by":3,"name":"Jampala Maheshchandra Babu","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Jampala","middleName":"Maheshchandra","lastName":"Babu","suffix":""}],"badges":[],"createdAt":"2026-01-06 17:08:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8533787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8533787/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101489053,"identity":"a380e656-b885-4cb1-b4f7-b0a927668bd6","added_by":"auto","created_at":"2026-01-30 09:42:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":372172,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic movement of selected variables over the study period\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8533787/v1/c267a9761bd8d22f7209aa08.jpeg"},{"id":101489025,"identity":"74699277-55d3-41ae-a4f8-b2742db2c3b3","added_by":"auto","created_at":"2026-01-30 09:42:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37058,"visible":true,"origin":"","legend":"\u003cp\u003eRolling Correlation Plot (Considering 5 observation window period)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8533787/v1/59aa6ed9ed58e4ecc4fff35d.png"},{"id":101752196,"identity":"69545c91-2769-46b2-801e-51a5bca66d63","added_by":"auto","created_at":"2026-02-03 10:25:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2063825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8533787/v1/81b72389-0050-4c9f-bced-d2914deb9e9a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Inflation and Economic Growth on Environmental Sustainability of the Indian Economy","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe escalating threat of climate change and environmental degradation has prompted a surge in economic research examining the relationship between macroeconomic factors and environmental sustainability. Because of the intricate relationships between economic growth, inflation, and ecological effects, economies around the world are attempting to strike a balance between their aspirations for expansion and the pressing need for environmental preservation (Stern, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lopez and Toman, 2006). A fundamental viewpoint in this field is provided by the Environmental Kuznets Curve (EKC) hypothesis, which postulates an inverse U-shaped link between income levels and environmental degradation. This is especially relevant for large and diverse economies like India, where structural and regional disparities may influence environmental outcomes (Shahbaz et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Further, the relationship between inflation and environmental sustainability is both complex and interdependent. Inflation, often viewed as a sign of macroeconomic instability, can hinder efforts toward environmental protection by reducing a country's economic capacity and redirecting policy focus (Apergis \u0026amp; Payne, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This economic instability also affects industrial decision-making; as noted by Zhang et al. (2017), firms are often discouraged from investing in capital-intensive green technologies when the returns are unpredictable. These challenges are particularly evident in developing countries like India, where limited financial resources and competing policy objectives make balancing economic growth and environmental sustainability even more difficult. For over a decade, it has remained the third-largest carbon emitter globally, raising significant concerns about the sustainability of its development path. These intertwined economic and environmental dynamics highlight the urgent need for a more balanced and sustainable development strategy in India.\u003c/p\u003e \u003cp\u003eUnderstanding the interplay between environmental sustainability and economic growth has attracted considerable scholarly attention. However, notable gaps persist in the literature, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eparticularly concerning developing economies.\u003c/span\u003e Despite the widespread application, the EKC yields mixed and often inconclusive results, varying significantly across countries, pollutants, and time periods. In contrast, the role of inflation in influencing environmental outcomes remains relatively understudied. Existing studies that consider inflation often do so within broader growth frameworks, without explicitly exploring the mechanisms through which it affects environmental sustainability\u0026mdash;such as changes in investment behavior, fiscal capacity, or consumption patterns (Apergis \u0026amp; Payne, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhang et al., 2017). Consequently, the relationship between inflation and environmental outcomes remains both theoretically underdeveloped and empirically weak. Additionally, the majority of current research uses linear or bivariate econometric frameworks, which might not adequately represent the intricate, dynamic, and sometimes nonlinear relationships between environmental sustainability, growth, and inflation. Therefore, this study uses the non-linear Auto Regressive Distributed Lag (ARDL) model to find the asymmetric effect of independent variable both in short and long-run prospect. Additionally, there is dearth of literature is available on the integrated empirical research on how inflation and economic growth jointly affect several aspects of environmental sustainability in India. A major research gap is lack of comprehensive analysis, particularly in view of India's lofty climate obligations and its need for macroeconomic stability.\u003c/p\u003e \u003cp\u003eConsidering this research gap, this research adds significantly to the body of knowledge on inflation, economic growth, and environmental sustainability, especially when considering a growing nation like India. First, by specifically analyzing inflation's influence on environmental outcomes\u0026mdash;an area that is still lacking in both theoretical modeling and empirical research\u0026mdash;it contributes to the theoretical conversation. Secondly, by using a nonlinear Autoregressive Distributed Lag (NARDL) model, it transcends the conventional linear and bivariate analytical frameworks. By capturing the intricate relationships between inflation, growth, and environmental sustainability, this enables a more sophisticated understanding of asymmetric effects over the short and long terms. Third, unlike other studies focused on Indian economy, this study provides a more thorough examination of the combined effects of inflation and economic growth on several aspects of environmental sustainability by combining them into a single empirical framework. Finally, given India's international climate pledges and development objectives, the findings have important policy ramifications and provide information that can guide the country's twin pursuit of environmental sustainability and financial stability.\u003c/p\u003e \u003cp\u003eThe empirical analysis reveals that there is a positive association of both the economic development (GDP growth) and the Inflation towards the CO2 emission in long-run. This Implies that higher inflation and substantial economic activities leads to detoriation of quality environment. Furthermore, in the short run, GDP does not exhibit a statistically significant effect on CO₂ emissions. However, rising inflation appears to be associated with an improvement in environmental quality, suggesting that inflationary conditions may indirectly contribute to lower emissions\u0026mdash;possibly through reduced industrial activity or shifts in consumption patterns. The results from the NARDL model show no evidence of asymmetric effects of GDP and inflation on CO₂ emissions in either the short run or the long run, indicating that positive and negative shocks have a broadly similar impact. However, a statistically significant negative relationship is identified in the case of negative inflation, implying that during periods of declining inflation, CO₂ emissions tend to decrease. This may reflect reduced industrial production and lower energy consumption during times of economic slowdown.\u003c/p\u003e \u003cp\u003eRest of the paper is organized as follow, section 2 demonstrate the extensive review on inflation, economic growth and environmental sustainability. Data and methodology with section 3 while the detailed analysis and discussion are reported in section 4. Finally, section 5 conclude the outcomes, practical implication, limitation and further research in this field.\u003c/p\u003e"},{"header":"2 Review of Literature","content":"\u003cp\u003eThe nexus between inflation, economic growth, and environmental sustainability has gained prominence in academic and policy discussions, particularly in light of rising climate concerns and macroeconomic volatility. While economic growth has long been associated with environmental degradation, the role of inflation is relatively underexplored. This literature review aims to critically examine existing research on the individual and joint impacts of inflation and economic growth on environmental sustainability. It reviews theoretical frameworks, empirical evidence, methodological approaches, and key findings across global and Indian contexts. The review also identifies emerging trends and research gaps, offering a foundation for future inquiry into how macroeconomic policy can align with environmental priorities in a sustainable development framework.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInflation and Economic growth\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eFor many years, one of macroeconomics' key concerns has been the connection between inflation and economic growth. This survey of the literature examines the relationships between inflation and economic growth in various economies by combining theoretical underpinnings with empirical data. According to early classical economists like David Ricardo and Adam Smith, inflation is primarily a monetary phenomenon with little long-term impact on real variables like output. Milton Friedman (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) popularised the Quantity Theory of Money, which holds that inflation causes market efficiency to be distorted and growth to be impeded. Neoclassical models propose that inflation largely impacts nominal variables and assume full employment. Because it creates uncertainty, deters investment and saving, and reduces the purchasing power of money, inflation is harmful in this context. Post-Keynesians contend that inflation need not necessarily be detrimental to growth, particularly in economies with high unemployment and spare capacity, and that it can result from structural rigidities and power dynamics (such as wage-price spirals) (Palley, 2003).\u003c/p\u003e \u003cp\u003eThe available literature revels the interaction between inflation and economic growth in four different way such as inflation has a positive impact on economic growth ; Rapach, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Benhabib and Spiegel, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Qabaja \u0026amp; Tenekeci, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)), inflation has a negative impact on economic growth ( Barro, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Azam and Khan, 2020; Khan et al.,2022), inflation has no effect on economic growth (N H Tien,2021), and inflation influences economic growth within a certain threshold( Aydin et al., 2016). Many empirical studies provide evidence that inflation and growth are negatively correlated. India presents a contradictory picture: Strong growth was accompanied by modest inflation throughout the 2003\u0026ndash;2011 high-growth period. Persistent inflation of food and fuel after 2012 weakened investment and consumption. According to research by Mohanty et al. (2014) and the RBI (2021), price stability has aided investment and growth recently, but inflation has a negative impact on low-income households and distorts investment. According to recent studies conducted in India, inflation above 6% starts to have a detrimental impact on economic activity by lowering consumer purchasing power and investment sentiment (Patra \u0026amp; Kapur, 2022). A long-term negative correlation between inflation and GDP growth in India was discovered by Chakraborty et al. (2023), indicating that inflation beyond 6% has a detrimental effect on employment and capital formation. Using a Vector Auto Regression (VAR) model, the RBI Working Paper (2023) demonstrated how inflation shocks quickly reduce household consumption and private investment, which slows GDP growth.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEconomic Growth and Environmental Sustainability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn order to determine whether and how economic growth and environmental sustainability may coexist, this review summarizes key theoretical stances and empirical data from throughout the world, with an emphasis on India. According to the Environmental Kuznets Curve (EKC), there is an inverse U-shaped correlation between environmental deterioration and income. Economic growth causes more pollution at lower income levels because of industrialization and a lack of environmental consciousness. Societies want cleaner surroundings, and governments can invest in cleaner technology and impose stronger laws as income rises (Grossman \u0026amp; Krueger, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Panayotou, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Dinda (2004) emphasizes that EKC patterns vary by country and pollutant type, suggesting the need for context-specific analysis.\u003c/p\u003e \u003cp\u003eFurther the empirical review has been done. Chiu (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) examined the EKC for 52 developing nations between 1972 and 2003 using the Panel Smooth Transition Regression (PSTR) model and discovered a substantial threshold impact between deforestation and GDP as well as an EKC link for deforestation. Similarly, using data from 1980 to 2008, Heidari, Turan Katircioglu, and Saeidpour (2015) investigated the relationship between economic growth, CO2 emissions, and energy consumption in five ASEAN (Association of South East Asian Nations) nations. Environmental degradation increased with economic growth in the first regime (GDP per capita below \u003cspan\u003e$\u003c/span\u003e4,686 USD), but the tendency was reversed in the second regime (GDP per capita over \u003cspan\u003e$\u003c/span\u003e4,686 USD).\u003c/p\u003e \u003cp\u003eShahbaz et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) support the early stage of the EKC hypothesis by finding a positive long-term link between GDP and CO₂ emissions in emerging economies. Energy use continues to be the main cause of environmental deterioration in developing nations, according to Apergis and Payne (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to Rafiq \u0026amp; Salim (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), India's industrial and transport sectors are mostly to blame for the country's rising CO2 emissions, which have coincided with its economic expansion. Although structural changes (the move to services) have reduced the intensity of emissions, Chakravorty \u0026amp; Dasgupta,2015; Al Mamun \u0026amp; Ehsanullah, 2025 ) contend that absolute emissions are still increasing. When Sinha \u0026amp; Bhattacharya (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) test the EKC theory for India, they find mixed results: some pollutants (such vehicle emissions) increase steadily with GDP, while others follow the EKC trend.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInflation and Environmental Sustainability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRecent research has examined the connection between carbon emissions and inflation. Using monthly data for Germany, France, Italy, and Spain, Ciccarelli et al. (2023) examine the asymmetric impacts of weather shocks on inflation in the euro region. The findings indicate that rising mean temperatures and temperature variability significantly raise inflation rates. Based on cross-national firm-level statistics, Andr\u0026eacute; et al. (2025) predict that businesses will likely see medium-term productivity gains after a relatively small energy price shock, particularly if they make energy efficiency investments. Duca-Radu et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examine consumers' spending responses to anticipated inflation using a large multi-country survey and a novel pseudo panel dataset. They contend that improving financial literacy and inflation knowledge contributes to the positive spending response. Grolleau and Weber (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) cast doubt on the empirical validity of aggregate inflation rates by offering empirical support for a weak but significant negative correlation between core inflation and CO2 emissions over a 50-year span. It also found that the headline inflation and carbon emissions do not closely correlate while the relationship between core inflation and emissions is considered too weak to be used to develop policy recommendations for achieving the net zero transition. There is a strong correlation between carbon emissions and inflation (and/or inflation variability), according to other studies that focus on a single economy, including the USA (Tahir et al. 2022), Pakistan (Khan \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ullah et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Malaysia (Musarat et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Indonesia (Setyadharma et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and China (Xu et al. 2023).\u003c/p\u003e \u003cp\u003eAccording to Pata (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Turkey's CO₂ emissions are negatively impacted by inflation, most likely as a result of lower production and consumption during inflationary times. Sharma et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) discovered a nonlinear correlation between environmental quality and inflation in the Indian context. Reduced emissions were associated with moderate inflation, but green investments were disturbed by high inflation. According to Kumar, and Singh (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), India's inflation causes short-term carbon reductions because of decreased industrial output, but it also jeopardises long-term clean energy investments. World Bank (2022): Warns that inflationary pressures have the potential to sabotage SDG achievement, especially when it comes to improving air and water quality in case of India. Sharma (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) used time-series data from 1971 to 2007 and discovered that while inflation had a minor but noteworthy positive impact on environmental degradation, economic expansion and energy consumption raised carbon emissions in India.\u003c/p\u003e \u003cp\u003eThe literature shows that the relationship between inflation, economic growth, and environmental sustainability is intricate and dynamic. Inflation's impact is still less understood but is becoming more important, even though economic development is frequently associated with environmental degradation, particularly in emerging nations. Inflation may have a direct impact on environmental effects as well as interact with development dynamics, according to recent research.\u003c/p\u003e"},{"header":"3 Sample and Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cp\u003eIn this study, carbon dioxide (CO₂) emissions are used as the dependent variable, while inflation (INF) and economic growth (GDP) are treated as the main independent variables. To control for additional influences, we also include Natural Resource Rents (TNR), Total Electricity Consumption (ElectCon), and Natural Gas Consumption (NGasCon) as control variables. The choice of electricity and natural gas consumption reflects their dual role in the emissions landscape. On one hand, these energy sources are generally considered cleaner alternatives to coal and oil, potentially helping to reduce CO₂ emissions. On the other hand, their production and distribution processes can still contribute to environmental degradation, depending on the energy mix and efficiency of technologies used. This dual effect underscores the importance of examining both consumption and production dynamics when assessing their overall environmental impact. To examine the validity of the EKC hypothesis, this study incorporates both GDP and the squared term of GDP into the empirical model. According to the EKC framework, the hypothesis is supported if the coefficient of GDP is positive while the coefficient of GDP squared is negative, and both are statistically significant. This pattern indicates an inverted U-shaped relationship between economic growth and environmental degradation, suggesting that environmental impact initially worsens with economic growth but improves after reaching a certain income threshold. Annual data for the key variables are sourced from the World Bank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://databank.worldbank.org/databases\u003c/span\u003e\u003cspan address=\"https://databank.worldbank.org/databases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Information on natural gas and electricity consumption is collected from the \u003cem\u003eAnnual Energy Statistics Report\u003c/em\u003e published by the Ministry of Power, Government of India (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mospi.gov.in/sites/default/files/publication_reports/Energy_Statistics_2025/Energy%20Statistics%20India%202025_27032025.pdf\u003c/span\u003e\u003cspan address=\"https://mospi.gov.in/sites/default/files/publication_reports/Energy_Statistics_2025/Energy%20Statistics%20India%202025_27032025.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The growth in energy consumption is measured as the year-on-year percentage change. Based on data availability, the study utilizes annual observations spanning the period from 1970 to 2022. Following data compilation, the next section outlines the econometric techniques employed to test the EKC hypothesis and assess the dynamic relationships among the selected variables The detail of the variables is reported in Appendix 1. The movement of selected variable are reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Methodology\u003c/h2\u003e \u003cp\u003eTo examine the validity of the EKC hypothesis, this study incorporates both GDP and the squared term of GDP into the empirical model. According to the EKC framework, the hypothesis is supported if the coefficient of GDP is positive while the coefficient of GDP squared is negative, and both are statistically significant. This pattern indicates an inverted U-shaped relationship between economic growth and environmental degradation, suggesting that environmental impact initially worsens with economic growth but improves after reaching a certain income threshold. Annual data for the key variables are sourced from the World Bank database. Information on natural gas and electricity consumption is collected from the Annual Energy Statistics Report published by the Ministry of Power, Government of India. The growth in energy consumption is measured as the year-on-year percentage change. Based on data availability, the study utilizes annual observations spanning the period from 1970 to 2022. Following data compilation, the next section outlines the econometric techniques employed to test the EKC hypothesis and assess the dynamic relationships among the selected variables. The base model in our analysis is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:C{O}_{2}=f(Inflation,\\:GDP,\\:TNR,\\:\\text{E}\\text{l}\\text{e}\\text{c}\\text{t}\\text{C}\\text{o}\\text{n},\\text{N}\\text{G}\\text{a}\\text{s}\\text{C}\\text{o}\\text{n})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIt can be rewrite as follow in long-run prospective:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:C{O}_{2t}={C}_{0}+{\\beta\\:}_{1}Inflatio{n}_{t}+{\\beta\\:}_{2}GDP+{\\beta\\:}_{3}C{V}_{t}+{u}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C{V}_{t}\\)\u003c/span\u003e\u003c/span\u003e includes the list of control variable such as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:GD{P}_{t}^{2},\\:TN{R}_{t},\\:{\\text{E}\\text{l}\\text{e}\\text{c}\\text{t}\\text{C}\\text{o}\\text{n}}_{\\text{t}}\\:\\text{a}\\text{n}\\text{d}\\:{\\text{N}\\text{G}\\text{a}\\text{s}\\text{C}\\text{o}\\text{n}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e. The significant value of the long-run coefficient, i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e reported the long-run effect towards the CO2 emission. Following the Pesaron et al. (2001), the short-run specification could be:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\Delta\\:}C{O}_{2t}=c0+{\\sum\\:}_{i=0}^{p}{\\varphi\\:}_{i}{\\Delta\\:}C{O}_{2t-i}+{\\sum\\:}_{i=0}^{p}{\\theta\\:}_{i}{\\Delta\\:}IN{F}_{t-i}+{\\sum\\:}_{i=0}^{p}{\\gamma\\:}_{i}{\\Delta\\:}GD{P}_{t-i}+{\\sum\\:}_{i=0}^{p}{\\pi\\:}_{i}{\\Delta\\:}C{V}_{t-i}+{\\beta\\:}_{1}C{O}_{2,t-1}+{\\beta\\:}_{2}IN{F}_{t-1}+{\\beta\\:}_{3}GD{P}_{t-1}+{\\beta\\:}_{4}C{V}_{t-1}+{e}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the long-run coefficient while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{i},\\:{\\theta\\:}_{i},\\:{\\gamma\\:}_{i}\\:and\\:{\\pi\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the short-run coefficients. After determination of respective coefficient value, Wald test being employed to check the evidence of long-run effect. Suppose to check the long-run effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IN{F}_{t}\\)\u003c/span\u003e\u003c/span\u003e: the null hypothesis will be \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}/{\\beta\\:}_{1}=0\\)\u003c/span\u003e\u003c/span\u003e, No long-run effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IN{F}_{t}\\)\u003c/span\u003e\u003c/span\u003e. Each long-run coefficient normalize to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e (Ullah et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The hypothesis of no short-run effect is just tested by using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{i},\\:{\\theta\\:}_{i},\\:{\\gamma\\:}_{i}\\:and\\:{\\pi\\:}_{i}=0\\)\u003c/span\u003e\u003c/span\u003e. Here this model allowed the model having different integration properties as well.\u003c/p\u003e \u003cp\u003eAfter the identification of long and short-run association, it is important whether the positive/negative change in independent variable symmetrically affecting the CO2 level of not. Thus the Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e could be modified as follow for the Non-linear ARDL specification:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\Delta\\:}C{O}_{2t}=c0+{\\sum\\:}_{i=0}^{p}{\\varphi\\:}_{i}{\\Delta\\:}C{O}_{2t-i}+{\\sum\\:}_{i=0}^{p}{\\theta\\:}_{i}^{+}{\\Delta\\:}IN{F}_{t-i}^{+}+{\\sum\\:}_{i=0}^{p}{\\theta\\:}_{i}^{-}{\\Delta\\:}IN{F}_{t-i}^{-}+{\\sum\\:}_{i=0}^{p}{\\gamma\\:}_{i}^{+}{\\Delta\\:}GD{P}_{t-i}^{+}+{\\sum\\:}_{i=0}^{p}{\\gamma\\:}_{i}^{-}{\\Delta\\:}GD{P}_{t-i}^{-}+{\\sum\\:}_{i=0}^{p}{\\pi\\:}_{i}^{+}{\\Delta\\:}C{V}_{t-i}^{+}+{\\sum\\:}_{i=0}^{p}{\\pi\\:}_{i}^{-}{\\Delta\\:}C{V}_{t-i}^{-}+{\\beta\\:}_{1}C{O}_{2,t-1}+{\\beta\\:}_{2}^{+}IN{F}_{t-1}^{+}+{\\beta\\:}_{2}^{-}IN{F}_{t-1}^{-}+{\\beta\\:}_{3}^{+}GD{P}_{t-1}^{+}+{\\beta\\:}_{3}^{-}GD{P}_{t-1}^{-}+{\\beta\\:}_{4}^{+}C{V}_{t-1}^{+}+{\\beta\\:}_{4}^{-}C{V}_{t-1}^{-}+{e}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all the independent variable is decomposing into positive and negative shock. Suppose, considered only one variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IN{F}_{t}\\)\u003c/span\u003e\u003c/span\u003e, the specification could be as follow:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:IN{F}_{t}^{+}={\\sum\\:}_{q=1}^{t}{\\Delta\\:}IN{F}_{t}^{+}={\\sum\\:}_{q=1}^{t}\\text{m}\\text{a}\\text{x}({\\Delta\\:}IN{F}_{t},\\:0)\\:IN{F}_{t}^{-}={\\sum\\:}_{q=1}^{t}{\\Delta\\:}IN{F}_{t}^{-}={\\sum\\:}_{q=1}^{t}\\text{m}\\text{i}\\text{n}({\\Delta\\:}IN{F}_{t},\\:0)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eLike the Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the it could be decomposing all the selected independent variable into positive and negative specification. Again the Wald-test is employed to examine the asymmetric effect assuming there is no asymmetric effect in long- as well as short-run. The specification for the short-run asymmetric test is (for the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IN{F}_{t})\\)\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[{\\sum\\:}_{i=0}^{p}{\\theta\\:}_{i}^{+}={\\sum\\:}_{i=0}^{p}{\\theta\\:}_{i}^{+}\\right]\\)\u003c/span\u003e\u003c/span\u003e while for the long-run specification \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:[{\\beta\\:}_{2}^{-}/{\\beta\\:}_{1}={\\beta\\:}_{2}^{+}/{\\beta\\:}_{1}]\\)\u003c/span\u003e\u003c/span\u003e (No asymmetric long-run effect).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Result Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistic\u003c/h2\u003e \u003cp\u003eBefore applying any statistical or econometric tools to analyze the time-series data, it is essential to understand the basic characteristics of the variables through descriptive statistics. The average inflation rate over the sample period is 7.25%, with values ranging from a low of -1.65% to a high of 17.83% observed in 1973. A closer examination of the inflation series reveals that inflation remained relatively low during the periods 1999\u0026ndash;2003 and 2014\u0026ndash;2019. However, in recent years\u0026mdash;particularly from 2020 to 2022\u0026mdash;inflation has shown a noticeable upward trend, indicating renewed macroeconomic pressure in the post-pandemic period. The average annual GDP growth rate over the study period was 5.36%, with a peak of 9.69% and a trough of -5.78%, the latter occurring in 2020 due to the economic disruption caused by the COVID-19 pandemic. Descriptive statistics indicate that natural gas consumption exhibits the highest level of volatility, as reflected in its substantial standard deviation, as well as elevated skewness and kurtosis values. Among the variables analyzed, all except TNR and GDP are positively skewed, suggesting a consistent pattern of positive growth across the sample period. Normality tests show that approximately half of the variables satisfy the assumption of normal distribution. However, the presence of non-normality in key variables\u0026mdash;particularly the dependent variable, CO₂ emissions\u0026mdash;supports the need for non-linear modeling techniques, as linear models may not adequately capture the complex relationships among the variables The descriptive statistic of the present study demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of Descriptive Statistic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eJB Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.83***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.12***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectCon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGasCon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-22.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e131.46***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eThis table reports the descriptive statistic such as Mean, Median, Min, Max, Q1, Q3, Standard deviation (SD), skewness, Kurtosis and the test of normality through Jarque-Bera test (JB) considering the annual data of selected variables. The null hypothesis of JB test is that the series follow the normal distribution while ***, ** and * respectively reported for 1%, 5% and 10% level of significance.\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of Pearson\u0026rsquo;s Correlation Coefficient\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\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eElectCon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNGasCon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGDP2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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 \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\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \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 \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\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\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\u003eTNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eElectCon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eNGasCon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.24*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eGDP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.32**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eThis table reports the result of Pearson\u0026rsquo;s correlation coefficient among the variables and test the null hypothesis that no correlation among the variables over the study period. ***, ** and * respectively reported for 1%, 5% and 10% level of significance.\u003c/p\u003e \u003cp\u003eSources: Authors Calculation\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\u003eAfter conducting the descriptive analysis, it is crucial to examine the correlation among the selected variables to identify potential multicollinearity issues and evaluate the suitability of each variable for inclusion in the model. Initially, GDP per capita was considered as a proxy for national economic growth. However, a very strong correlation was found between GDP per capita and its squared term, indicating a high risk of multicollinearity that could undermine the reliability of the regression estimates. To mitigate this issue, the annual GDP growth rate was chosen instead as a proxy for national growth. This alternative showed a more acceptable correlation of 0.38 between GDP growth and its squared term, suggesting a significantly lower risk of multicollinearity and better model stability for subsequent analysis. The result of cross variable correlation reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It shows significant and inverse relation between CO2 and INF while GDP positively associated with CO2 emission. Indeed, the correlation of ElectCon and NGasCon show the negative relationship with CO2. The rolling correlation (with 5 observation window size) plot of CO2 with all selected variable are showing in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All the figure (except GDP) found to show dynamic correlation with CO2 during time zone. Though initially the GDP negatively correlated with CO2 but after 1980 it reveled significant and positive correlation with the CO2 emission. If we are ignoring the effect of GDP (as positive correlation over the period), the asymmetric effect of other variables again supports the use of non-linear specification for our analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Test of Unit-root\u003c/h2\u003e \u003cp\u003eAfter conducting the correlation analysis, it is essential to test for stationarity, a key assumption in time-series econometrics. In this study, the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests were applied to examine the order of integration of the variables. Both tests were performed under three specifications: without drift, with drift, and with drift and trend. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the ADF test results indicate that all variables, except ElectCon and CO₂ emissions, are stationary at level. However, when first differenced, all variables reject the null hypothesis of a unit root at the 1% significance level, confirming stationarity. The PP test results are largely consistent, with ElectCon found to be stationary at level (under the drift and trend specifications), while CO₂ emissions remain non-stationary in level form\u0026mdash;consistent with the ADF findings. Overall, these results suggest that the dependent variable (CO₂) is integrated of order zero [I(0)], while the independent variables exhibit a mix of integration at level and at first difference [I(0) and I(1)]. Given this combination of integration orders, the Autoregressive Distributed Lag (ARDL) modeling approach is the most appropriate, as it can effectively handle variables with different levels of integration and is suitable for testing long-run cointegration relationships.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Unit Root Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAt Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eAt First Difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \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\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrift \u0026amp; Trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDrift \u0026amp; Trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDrift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDrift \u0026amp; Trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDrift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eDrift \u0026amp; Trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-13.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.82*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-24.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-45.71***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-45.82***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.76***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.81***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-35.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.73***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.73***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-6.69***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-54.88***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-54.95***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-55.13***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.69***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-53.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-55.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-6.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-67.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-67.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-67.15***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.99**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.70*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-50.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-50.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-50.60***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectCon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-37.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-37.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.02***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.07***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-60.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-60.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-60.39***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGasCon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-49.90***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-50.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.98***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.91***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.88***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-65.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-65.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-65.29***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.98***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.61***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.30**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-47.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-50.91***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-6.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-65.98***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-65.92***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-65.87***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eThis table reports the result of test of stationarity such as ADF and PP respectively represent for the Augmented-Dickey Fuller test and Phillips-Perron test of unit root with the null hypothesis of series having unit-root. On the view point of robustness, we test the unit-root based on no-drift, with drift and drift and trend. The optimum lag order selected based on the BIC criteria. ***, ** and * respectively reported rejecting the null hypothesis at 1%, 5% and 10% significance level.\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\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\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResult of ARDL\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAfter accessing the level of integration, we use the ARDL model to examine the long run effect of selected variable on CO2 emission. We fixed the maximum order of lag for the ARDL model is two, because of small sample and use Bayesian Information Criterion (BIC) to select the appropriate lag for the analysis as this criterion is most suitable in case of small sample (Li et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The summary\u003csup\u003e1\u003c/sup\u003e result of ARDL model with five different cases are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The specification of different cases is given in Appendix 2. The result of long-run bound test show the long-run cointegration in all the cases (except case 1) in both the 5% and 10% level of significance but not significant at 1% level, which indicate further examining the long-run effect with different specification. The positive long-run effects of GDP (0.0355) and electricity consumption (0.0167) on CO₂ emissions suggest that economic growth contributes to increased emissions through channels such as infrastructure expansion, industrial development, and rising trade activities. These findings highlight the environmental cost associated with sustained economic growth, particularly in rapidly developing economies like India. On the other hand, the positive long-run effect of electricity consumption on CO2 emission indicate major part of the production of electricity depends on the non-rentable sources. If the production of electricity mostly from renewable sources, it could show negative effect of ElectCon towards CO2 emission. From the EKC hypothesis prospective, though the square-GDP found to be negative at case 4th and 5th but not significant. The result reviles the rejection of EKC hypothesis in case of Indian economy.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of ARDL UECM\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\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCase-4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCase-5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong-run Cointegration: Bound Test\u003c/b\u003e\u003c/p\u003e \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\u003e10% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong Run Effect: Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0355**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0355**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNR_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectCon_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0167***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0167***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGasCon_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual Diagnostics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG_SC_lm_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.46*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.98*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.98*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM_ARCH_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormality_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRESET_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eThis table demonstrate the results of ARDL model following the eq-2. Due to the small numbers of observations, we set the maximum lag for the all the independent variable is two and here only results of long-run coefficient part is reported. ***, ** and * respectively reported for 1%, 5% and 10% level of significance.\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\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 next step, we are altering the independent variable in the system equation to verify the accuracy of the result of ARDL model. The study fixing the maximum lag to tow and detect the best fit model specification with altering the independent variable which is reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and the summary result in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the coefficient of INF and GDP found to be positive and significant in all the cases in the long-run part (i.e., GDP_1 and INF_1). Case 3 \u0026amp; 5 detected as the best fit model at different specification. The coefficient of square-GDP not significant at long-run while negative but insignificant at short-run, again verify the rejection of EKC hypothesis i.e., initially the level of CO2 emission increases with the economic growth but as the economic growth exceeding a threshold level the, the CO2 emission will negatively have affected by economic growth. The summery result of the ARDL model with variable alteration reported the similar result as previous i.e., evidence of long-run effect at 10% and 5% level of significance and ensure the positive and significance long-run effect of GDP and INF implies the both the economic growth and the high inflation leads high level of CO2 emission over the sample period (in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Surprisingly, the result showing significant only when we only considered GDP and INF as the independent variable. Thus, here after we are eliminating all other variable and considered only GDP and the INF for in case of using non-linear ARDL model.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of Auto ARDL-UECM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eARDL_ECM Details\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBest Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcept: GDP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP,GDP2, Inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInflation \u0026amp; GDP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\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\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0613***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0707***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.386***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.397***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.251***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.275***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00513***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00679***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00971***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00867***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00562*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00835***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00826***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00754***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNR_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0337***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectCon_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00338**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00371**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGasCon_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.CO2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00376***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00446***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00354***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00374***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00472**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00564***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00538***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00502***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Inflation_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00505***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00447***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Inflation_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00249*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00205*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.97E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP2_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00912***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0101***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-Stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.891***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.173***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.984***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.524***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj.R-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eThis table demonstrate the results of ARDL model following the eq-2. Due to the small numbers of observations, we set the maximum lag for the all the independent variable is two and check the appropriate lag based on BIC criteria while the lag for the dependent variable considered one. Here we detect the best fir model by altering the independent variable with different specification such as all variable, all variable except GDO^2, Only GDP, GDP^2 and Inflation, only Inflation and GDP.\u003c/p\u003e \u003cp\u003e***, ** and * respectively reported for 1%, 5% and 10% level of significance\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Auto ARDL-UECM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eARDL_ECM Details\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBest Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcept: GDP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP,GDP2, Inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInflation \u0026amp; GDP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong-run Cointegration: Bound Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e# Long Run Effect: Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0386***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0316***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0328**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0275***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNR_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-61.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectCon_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGasCon_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.505\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual Diagnostics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG_SC_lm_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.96**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.18**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM_ARCH_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormality_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRESET_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eThis table reports the cointegration and residual diagnostics part of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For other details follow the notes on Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. ***, ** and * respectively reported for 1%, 5% and 10% level of significance\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Result of Non-linear ARDL Model\u003c/h2\u003e \u003cp\u003eIn this step, we are considering the CO2 as the dependent variable while GDP and the INF as the independent variable. In both the long and short-run system equation, the positive and negative decomposed factor of all the independent variables have included to detect the asymmetric effect on CO2. The result of ARDL indicate that there is a positive effect of INF and the GDP to emission level, means the CO2 will move in the same direction with the levels of INF and GDP. Now the question is does the effect of the change in the emission level is same when the independent variable moves in up and downward? In other words, does a one-unit increase in inflation (INF) have the same magnitude of impact on CO₂ emissions as a one-unit decrease in inflation, but in the opposite direction? The asymmetric effect indicates the different magnitude in two different situations. Similar to the ARDL analysis, this session examines the long- and short-run effect in two step. First, run the NARDL with lag two for each case (i.e., case 1 to 5) considering both the variable are decomposing variable. Second, assume the maximum lag for NARDL is two and run the model altering one variable as the control and other is decompose variable. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e demonstrate the NARDL result and summary respectively considering both INF and GDP decomposed into negative and positive factor. If the Wald test result of difference between the positive and negative coefficient found to be, implies the evidence of asymmetric effect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of Auto NARDL UECM (Both Inflation and GDP are Decompose Variable)\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\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCase 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.11*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.11*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.393***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.393***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.0771*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.0771*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.336***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.336***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0053*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0053*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.00809***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00809***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.00704**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.00663**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.00663**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.00907***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00907***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.CO2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Inflation_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Inflation_pos_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Inflation_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.00487*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.00487*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.Inflation_neg_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP_pos_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.GDP_neg_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u003cb\u003e0.012**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-Stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.407***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.748*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.748*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj.R-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eThis table reports the results of NARDL model with the eq-3 and decompose the inflation and GDP based on eq-4. Based on eq-3 the inflation (GDP) is divided in to two part i.e., Inflation_pos and Inflation_neg (GDP_pos and GDP_neg) respectively. The result of this table demonstrates whether the increasing Inflation (GDP) and decreasing Inflation (GDP) have same impact in CO2 or not. The lag order selected for this model is two and the symbol ***, ** and * respectively reported for 1%, 5% and 10% level of significance\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary Result of Auto NARDL UECM (Both Inflation and GDP are decompose Variable)\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\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCase 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eLong-run Cointegration: Bound Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong Run Effect: Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0241**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0241**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.027**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsymmetric Effect: Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR Asym. effect from: Inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR Asym. effect from: GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR Asym. effect from: Inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR Asym. effect from: GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual Diagnostics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG_SC_lm_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM_ARCH_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormality_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRESET_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eThis table reports the summary results of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei.e., long-run effect, short and long-run asymmetric effect. For other details follow the notes of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The symbol ***, ** and * respectively reported for 1%, 5% and 10% level of significance\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\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\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, long-run prospective, first, the result of long-run bound test reject the evidence of long-run cointegration from the overall decomposing factor to emission level. Second, the coefficient of INF_neg found to be positive and significant in all the cases, similarly the INF_pos also show the similar result as INF_neg except case 1. It conforms the overall change in INF to CO2 is positive. Third, the positive and negative factor of GDP are not significant at all though the value is positive throughout the result table. It revels the similar types of effect from the positive and negative INF to CO2 level. Fortunately, in short-run, the INF_neg reported significant and negative value in case 4 and 5. Overall, it can be concluding that whether there is a upward or downward movement of INF, in long-run inflation leads CO2 emission while there is a certain possibility that the negative shock of inflation could control the emission level. Again the Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e confirm the positive significant effect from INF_pos and INF_neg while no long-run effect of positive and negative GDP factor reported. Non-of the variable reporting either short- or long-run asymmetric effect forwards emission level indicate the sizable effect form the independent variable are quite same even they are moves up or moves down.\u003c/p\u003e \u003cp\u003eIn step two, we are fixing one variable as decomposing (DV) and other as the control variable (CV); the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. First, where DV is INF and the CV is GDP, the long-run factor of GDP (0.0039) is positive and significance while the negative and positive long-run factor of INF (0.00692 \u0026amp; 0.0061) also found to be positive. The result reviles, with the positive long-run effect of GDP, both the INF decompose factor positively affecting emission level. As the GDP growth rate increase leads to money flow to the economy, which shift the price level upward shifting. In the other hand, with the high inflation, the energy consumption of the society moves towards the conventional energy resources and leads emission level. The result also revels the long-run effect both from INF decompose factor and the GDP. Second, where DV is GDP and CV is INF, long-run inflation factor found to be positive and significant while non-of the decomposing factor of GDP are significant. Further, in short-run inflation reported negative effect towards the emission level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of Auto NARDL UECM (With Decompose and Control Variable)\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=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eModel Details\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eResidual Diagnostics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBest Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBG_SC_lm_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecompose Variable (DV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLM_ARCH_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variable (CV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enormality_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e108***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e93.7***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRESET_test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \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\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.336***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.335***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLong-run Cointegration: Bound Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.253***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.279***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDV_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.00692***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDV_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0061***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1% critical value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo Long-run\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.0039**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.00556***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.DV_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLong Run Effect: Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.DV_pos_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDV_pos_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0273**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.DV_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.00405**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDV_neg_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0241**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.DV_neg_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCV_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0154*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0199**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.CV_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.00269*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.00852**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.00924**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAsymmetric Effect\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-Stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.006***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.276**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSR Asym. effect from: DV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLR Asym. effect from: DV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj.R-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eThis table reports the results of NARDL model while considering Inflation as decompose variable (DV) and GDP as the Control Variable (CV) and alternatively. This specification would able to examine the asymmetric effect of one with controlling another. It observe the best fit model is Case 5 in both the specification. The symbol ***, ** and * respectively reported for 1%, 5% and 10% level of significance.\u003c/p\u003e \u003cp\u003e\u003cb\u003eSources\u003c/b\u003e: Authors Calculation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion and policy Implication","content":"\u003cp\u003eIndia has ranked as the third-highest carbon-emitting country for over a decade, following the United States and China. While carbon emissions from the U.S. have gradually declined over the years, China surpassed the U.S. in total emissions in 2005. Similarly, India overtook Japan in 2006 and has continued to show a steady upward trend in emissions since then. A comparison between developed nations such as the U.S. and Japan and emerging economies like India and China reveals a contrasting pattern: while the former have begun to reduce their CO₂ emissions, the latter are experiencing significant increases alongside rapid economic growth. Moreover, inflation plays a critical role in shaping a country's energy consumption patterns. High inflation can influence both the affordability and preference for specific energy sources, while also affecting industrialization patterns\u0026mdash;where low-cost labor and inflation-driven input adjustments may attract global manufacturing. In addition, the consumption of natural resources, electricity, and natural gas directly impacts CO₂ emission levels. Against this backdrop, the present study aims to examine the dynamic effects of GDP growth and inflation on CO₂ emissions in India, covering the period from 1970 to 2022\u0026mdash;an era during which the country has emerged as both a rapidly growing economy and a significant contributor to global carbon emissions.\u003c/p\u003e \u003cp\u003eThis study applies both the Autoregressive Distributed Lag (ARDL) and Non-linear ARDL (NARDL) models to explore the short-run and long-run dynamics of CO₂ emissions in relation to economic growth and inflation in India. The empirical findings yield several important insights. First, the results do not confirm the validity of the EKC hypothesis, which posits an inverted U-shaped relationship between economic growth and environmental degradation (Grossman \u0026amp; Krueger, 1995; Dinda, 2004). This suggests that India has not yet reached the critical income threshold required for economic growth to begin reducing environmental impact. Similar outcomes have been observed in other developing countries where industrialization and urban expansion continue to drive emissions (Shahbaz et al., 2017). Second, both inflation and economic growth are found to have a significant and positive impact on CO₂ emissions, indicating a deterioration in environmental quality. This aligns with the findings of Apergis and Payne (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Zhang et al. (2017), who argue that macroeconomic instability and growth-oriented policies in developing economies often come at the cost of environmental sustainability.\u003c/p\u003e \u003cp\u003eThird, the NARDL results indicate that the effects of inflation and GDP on CO₂ emissions are largely symmetric\u0026mdash;meaning that increases and decreases in these variables have a proportionate impact on emissions. This contrasts with studies that report asymmetric effects in other contexts (e.g., Balcilar et al., 2020), highlighting the uniqueness of India's macroeconomic-environmental relationship. Finally, the results suggest that controlling inflation in the short run may offer a modest yet effective policy tool for improving environmental quality.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Policy implication\u003c/h2\u003e \u003cp\u003eEconomic sustainability is vital for maintaining steady growth, yet environmental sustainability is equally indispensable for ensuring long-term ecological balance and human well-being. The results of this study provide important insights for researchers and policymakers seeking to understand the complex interplay between macroeconomic variables and environmental outcomes within a country-specific context. While directly restricting economic growth is neither practical nor desirable, it is essential for policymakers to implement regulatory frameworks that manage inflation effectively. Controlling inflation can indirectly contribute to lower carbon emissions by influencing production costs, consumption patterns, and the choice of energy sources. In the long run, such measures may encourage a transition from conventional, carbon-intensive energy sources to cleaner, renewable alternatives. As the study finds limited short-run effects, policy efforts must focus on long-term strategies that integrate economic and environmental goals. Developing such forward-looking, sustainable policies is especially critical for emerging economies like India, where rapid growth must be balanced with environmental responsibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Limitation and Future Scope\u003c/h2\u003e \u003cp\u003eThe present study focuses exclusively on India, aligning with its objective to analyze the country-specific relationship between macroeconomic factors and environmental sustainability. While this national focus provides valuable insights, it also limits the generalizability of the findings. Future research could broaden the scope by including other major CO₂-emitting economies, which would offer a more comparative perspective and contribute to global policy discussions on emission reduction. Additionally, this study does not account for specific environmental policies or regulatory interventions. Future studies could examine the effectiveness of policy measures\u0026mdash;such as carbon pricing, renewable energy mandates, or environmental regulations\u0026mdash;in shaping emission outcomes in India. Moreover, incorporating additional macroeconomic variables, such as foreign direct investment, trade openness, technological innovation, or energy pricing, could provide a more holistic understanding of the determinants of CO₂ emissions and help in designing more targeted and effective sustainability strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThere is no funding to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u0026nbsp;\u003c/strong\u003eSM, SC are responsible for conducting the literature review and drafting the initial manuscript. LP and SM carried out the data analysis. SC, SM and JMB contributed to the manuscript by reviewing and revising it to enhance its quality and coherence. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNo acknowledgements are to be reported.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eApergis, N., \u0026amp; Payne, J. E. (2009). Energy consumption and economic growth: evidence from the Commonwealth of Independent States. Energy economics, 31(5), 641-647\u003c/p\u003e\n\u003cp\u003eAzam, M., \u0026amp; Khan, S. (2022). Threshold effects in the relationship between inflation and economic growth: Further empirical evidence from the developed and developing world. International Journal of Finance \u0026amp; Economics, 27(4), 4224-4243.\u003c/p\u003e\n\u003cp\u003eBarro, R. J. (1995). Inflation and economic growth\u003c/p\u003e\n\u003cp\u003eBenhabib, J., \u0026amp; Spiegel, M. M. (2009). Moderate inflation and the deflation\u0026ndash;depression link. Journal of Money, Credit and Banking, 41(4), 787-798.\u003c/p\u003e\n\u003cp\u003eChakrabarty, S., Nag, B., Dasgupta, P., \u0026amp; Rastogi, S. K. (2016). Determinants and relationships in sectoral trade: A bilateral model for knitwear clothing. Thunderbird International Business Review, 58(6), 565-574.\u003c/p\u003e\n\u003cp\u003eChiu, Y. B. (2012). Deforestation and the environmental Kuznets curve in developing countries: a panel smooth transition regression approach. Canadian Journal of Agricultural Economics/Revue canadienne d\u0026apos;agroeconomie, 60(2), 177-194.\u003c/p\u003e\n\u003cp\u003eDuca-Radu, I., Kenny, G., \u0026amp; Reuter, A. (2021). Inflation expectations, consumption and the lower bound: Micro evidence from a large multi-country survey. 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Journal of cleaner production, 264, 121701.\u003c/p\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The is big in size thus the author can provide the full result in request.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-linear ARDL Model, Emerging Economy, Environmental Kuznets Curve (EKC), CO2 Emission, inflation","lastPublishedDoi":"10.21203/rs.3.rs-8533787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8533787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExponential growth of economic activity and high inflation are the major factors to contribute the decoration of environmental sustainability. The main objective of this study is to examine the effect of economic growth and the inflation on the level of CO2 emission, and secondly, to explore the evidence of Environmental Kuznets Curve (EKC) hypothesis in Indian Scenario over the period from 1970 to 2022. We employed ARDL and Non-linear ARDL (NARDL), which allow to capture both the long as well as short-run interdependency, in addition to that the NARDL able to capture the asymmetric effect of GDP and Inflation on the CO2 emission, with different specification in long and short-run equation. We include some factors which controlling the level of emission such as Consumption of Natural Resources (TNR), Consumption of Natural Gas (NGasCon) and Electricity (ElectCon) to obtain accurate inference about the study. The empirical analysis found the positive association of both the economic development (GDP growth) and the Inflation towards the CO2 emission in long-run. Implies the higher inflation and the substantial economic activities leads to the deteriorate the quality of environment. The result of NARDL reviles, neither short- or long-run asymmetric from negative and positive shock of GDP and Inflation, though some extend significant and negative effect of negative inflation reported. Overall, the hypothesis of EKC i.e., inverted U-shape in the association between economic growth and the quality of environment is not satisfying.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e\u003c/em\u003e: G0, C12, O44\u003c/p\u003e","manuscriptTitle":"Impact of Inflation and Economic Growth on Environmental Sustainability of the Indian Economy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 09:41:53","doi":"10.21203/rs.3.rs-8533787/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"decision","content":"Revision requested","date":"2026-03-19T21:05:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T18:02:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:37:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130867064339637635154460943800152558189","date":"2026-02-02T12:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226727134983379676550241521631994168029","date":"2026-01-30T11:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112148474401798939797806571022390505208","date":"2026-01-29T02:38:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328392285248129940953147645697804157132","date":"2026-01-28T15:06:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-28T11:47:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T09:49:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T11:46:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T17:32:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-01-20T17:25:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8fa3c64-4c71-47b5-bdde-3617c28eab9b","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T18:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 09:41:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8533787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8533787","identity":"rs-8533787","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}