Does Renewable Energy Improve Environmental Quality? Evidence from RECAI Countries

Does Renewable Energy Improve Environmental Quality? 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Evidence from RECAI Countries Chandrashekar Raghutla, Yeliyya Kolati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2466940/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2023 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract Since 1990, the ecological footprints have been increasing significantly with a continuous increase rate, which led to challenges to environmental quality. The basis for Economic growth was said to be the shift of energy and environmental strategies toward a sustainable future. Indeed, it became a matter of proclaimed acceptance that environmental challenges nurtured expansion, innovation, and competitiveness. Climate change is the most pressing issue being faced by the world due to an increase in ecological footprint from 7.0 billion GHA to 20.6 billion GHA. It indicates the seriousness of environmental degradation; therefore, the nations need to ensure environmental sustainability. Keeping this in mind, the present research main aims to examine the impact of renewable energy utilization on the ecological footprints of RECAI economies, spanning the period 1990 to 2020. To significantly achieve the research objective, we utilized panel econometric methods for empirical analysis. The results of long-run elasticities indicate that both the renewable energy utilization as well as trade openness significantly controls the ecological footprints, while higher conventional energy utilization and economic growth significantly impede the environmental sustainability. The empirical findings provide new insights for policymakers on renewable energy for the betterment of environmental quality in RECAI countries. Renewable Energy Environmental Quality Trade Openness Panel analysis RECAI countries Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The basis for economic growth was said to be the shift of energy and environmental strategies toward a sustainable future. Indeed, it became a matter of proclaimed acceptance that environmental challenges nurtured expansion, innovation, and competitiveness. The debate cemented the way for market solutions and a faith that competition would create ‘green’ jobs. The rising Ecological Footprint (EP) is characterized by increasing fossil fuel usage and the civilization process. Since 1961, humanity's ecological footprints have shown a considerable upward trend growth with an average annual of 2.1%. However, in 1961, it climbed by almost 7.0 billion GHA, while in 2014, it increased by 20.6 billion GHA (National Footprint Accounts). The increasing ecological footprint poses great challenges to environmental quality. The majority of energy use is directly dependent on the traditional energy which is causing environmental degradation and climate change. Particularly, more than one-third of environmental pollution is caused by utilization of conventional energy (World Resources Institute). The growth of global pollution releases has continuously elevated from 21.4Gt in 1990 to 34.2Gt in 2020 (IEA, 2021). Further, the global carbon emissions and global economic output have increased by nearly 6% and 5.9% in 2021, respectively. The excessive use of conventional energy and human activities leads to environmental degradation. Renewable energy generation aids to minimize the negative effects on the quality of environment. Further, the negative environmental quality (degradation) can be eliminated by replacing of conventional energy techniques and its utilization (Pata, 2021 ). To overcome this problem renewable energy generation is only option to enhance environmental quality by reduction of emissions and ecological footprints. High-level hopes were placed on fuel cells in the early 2000s, as they became an archetype of eco-modern machinery by ecological modernization advocates. Augmentation of the population is accelerated the resource consumption, and the waste assimilation capacity (Wackernagel & Rees, 1998 ). These accumulated human-producing wastes are arduous to the environment for the waste assimilation process, which causes the extension of the ecological footprints. Conventional energy utilization not only produces emissions but also significantly destroys human health (Nathaniel et al., 2020 ). The environmental quality decreases as a result of fossil fuel-driven and economic expansion; which cause an increase in ecological footprints. On the other hand, utilizing the full capacity of renewable (clean) energy production can help to decrease the environmental footprints (Sharif et al., 2020 ). Given, both degradation of environment and environmental footprints, the present level of renewable (clean) energy use is insufficient to maintain environmental quality. Therefore, increase in share of renewable (clean) energy utilization tends to decrease the environmental footprints in over time, further it can also promote economic growth and transition to the green economy. Technological upgradation in industry can also be used as a strategy to dampen environmental quality degradation issues significantly (Ansari et al., 2021 ). Therefore, the energy resources namely geothermal energy, solar energy, hydropower, biomass energy, and wind energy serve as the leading sources of renewable energy in many nations. These renewable energy sources do not produce emissions and hence lead to a decrease in the ecological footprints. From 1990 to 2020, world countries have significantly witnessed an increase in economic growth while maintaining CO 2 emissions at the same level. We have observed that all countries together recorded an average output of 756.829 billion dollars in the year of 1990, this value is increased by 1.747 trillion dollars in 2020. In total energy consumption of all the selected countries together, the average the non-renewable energy consumption was nearly 76.95% as recorded in 1990. The percentage of using non-renewable energy consumption has decreased by around 74.39% in 2020. Although the average rate of conventical energy may be declined, it can indicate that conventional energy is directly responsible for the majority of energy use. In contrast, between 1990 and 2020, renewable energy consumption climbed from 19.07–22.69% respectively. The environmental degradation statistics display that the usage of conventional energy gradually shifted to more renewable energy. While exports and imports are playing a key role to achieve environmental quality; further foreign trade is also positively and negatively correlated to the ecological footprint from a globalization perspective. The average percentage of export increased from 26.29% in 1990 to 39.50% in 2020, and the average percentage of imports also increased from 26.80–37.12% during 1990 to 2020. Based on these significant reasons, this is really important to analyse their utilization of renewable (clean) energy potentiality and environmental footprints from the perspectives of environmental sustainability. In non-conventional energy, particularly geothermal and biomass energy systems produce much lower pollution than conventional energy, however, non-conventional energy sources mainly are available plenty in nature and it is naturally replenished so that renewable energy avoids human waste assimilation problem to control the ecological footprints. Renewable energy sources can maintain the environmental sustainability and quality of the environment as well. Renewable energy not only significantly decreases the ecological footprints, it also, directly and indirectly, influences the economic and social factors like an increase in humans’ life expectancy and also maintaining sustainability. The installation and maintenance of renewable energy technology cost charges are significantly low compared to conventional energy. This significantly extends the access across the nations, hence the renewable energy utilization decreases the ecological footprints. It is possible through only an energy transition of conventional energy to renewable energy. A study found that renewable energy is the only source to decrease the ecological footprints (Nathaniel et al., 2020 ). Whenever a country shifts its strategies related to the conventional energy consumption towards renewable energy consumption, such a policy shift has a very favourable impact on quality of environment. Furthermore, the increase in the important of renewable (clean) energy utilization improves health and environmental quality by lowering environmental pollution (Alola et al., 2019 ). At present, across the globe, the major question is how to decrease the ecological footprints from the environment and what is the effective way to improve the sustainability of environment. Under these circumstances, renewable (clean) energy utilization is the only alternative solution to improve the environment quality and environmental sustainability. Therefore, the primary intention of present research is to examine the empirical impact of consumption of renewable (clean) energy on ecological footprints by seeing significant role of economic growth, conventional energy usage and trade openness in ecological footprints function for Renewable Energy Consumption Attractive Index (RECAI) nations, spanning the period 1990–2020. The main contribution of this research is as follows. First, this is the first research article to investigate the empirical nexus between renewable energy usage and ecological footprints in RECAI countries, spanning the period 1990–2020. However, previously one of author tried to investigate only 22 RECAI countries and the study fail to address the majority of the RECAI countries; and fail to analyse few RECAI countries which are significantly producing and consuming renewable energy. Furthermore, it is important to analyse and explore, to what extent the individual RECAI countries are utilizing renewable energy resources. Moreover, it is only possible through renewable energy consumption and its utilization, we can allow environmental sustainability, improves environmental quality and by significantly impede ecological footprints as well as emissions. Therefore, our study considered 39 countries for the empirical analysis and owing to the non-availability of all empirical data namely EF, R, Y, NR and T variables during the study period, therefore, we have not considered the Taiwan nation for the empirical analysis. It is more important to understand policymakers for taking the significant decision regarding the future renewable energy production and its utilization. Second, RECAI countries have significantly occupied the energy market, particularly renewable energy production and consumption. How it can motivate the many rests of the nations for renewable energy adoption as well as utilization. Moreover, there is a necessity to adopt renewable energy utilization across the globe. Therefore, it is important to study the RECAI countries from a global perspective to understand the importance of renewable energy and its impact. Third, this study provides the importance of environmental quality and its determinants. Further, it also helps the rest of the nations to understand the significance of renewable energy utilization and production. Fourth, around the globe, climate change and environmental quality are the most pressing issues facing many confront, therefore, the study will provide policy recommendations to reduce ecological footprints from the environment and allow environmental sustainability and quality. Finally, the study applied advanced panel econometric methods for empirical investigation. The rest of the research work is organized as follows. Section 2 presented a relevant review of related literature on use of renewable energy and ecological footprints in the study, Section 3 chalks out the sources of data and empirical methodology, Section 4 describes the results and discussion in the study and Section 5 illustrates the conclusion of study and policy implications. 2. Review Of Literature In the world, mainly use of energy is directly dependent on traditional energy i.e., coal and oil which directly leads to an elevate in environmental degradation and carbon emissions as well. Solving this environmental problem is only possible by impeding emissions. The main reason is conventional energy use; therefore, it must be replaced with renewable energy resources namely wind, solar, biomass, hydropower, etc. In the energy literature, pioneer researchers confirmed that traditional energy utilization has a direct considerable impact on environment; it suggests that the usage of conventional energy increase emissions, particularly (Raghutla & Chittedi, 2020 ) for BRICS economies, (Salahuddin et al., 2015 ) for GCC nations, (Chindo et al., 2015 ) for European nations, (Abbas et al., 2021 ) and (M. K. Khan et al., 2020 ) for Pakistan, (Saidi & Hammami, 2015 ) for Global panel nations, (Ozturk & Acaravci, 2010 ) for Turkey, (Shafiei & Salim, 2014 ) for OECD (Qi et al., 2014 ) for China, (Boontome et al., 2017 ) for Thailand, (Menyah & Wolde-Rufael, 2010 ) for the US, (Nguyen & Kakinaka, 2019 ) for 107 countries, and (Tang & Tan, 2015 ) for Vietnam. Similarly, another group of authors also found that traditional energy utilization has a inverse considerable impact on environment; it suggests that the mainly usage of conventional energy impedes emissions, particularly (Acheampong, 2018 ) for MENA, (Li et al., 2020 ) for China, (Svedberg, 2021 ) for OECD economies, and (Zou & Zhang, 2020 ) for China. Renewable energy and emissions nexus are positively and negatively associated due to the adoption of renewable energy. (Sahoo & Sahoo, 2020 ) for India, indicated that the renewable (clean) energy utilization has a direct considerable impact on environment; it shows that the usage of renewable (clean) energy added carbon emissions to environment. In contrast, some of the pioneer authors found that substantial inverse association between the renewable (clean) energy utilization and CO 2 emissions; it is a sign that renewable energy lever CO 2 emissions, a research by (Bilgili et al., 2016 ) for OECD economies, (Mehmood, 2021 ) for G11 nations, and (Cheng et al., 2019 ) for BRICS nations, (S. A. R. Khan et al., 2020 ) for Nordic nations, (Panwar et al., 2011 ) and (Sharif et al., 2020 ) for top 10 polluted nations. A research by (Zaidi et al., 2018 ) established that adoption renewable (clean) energy use has no favourable impact on environment in the case of Pakistan. Direction of causality evidence mix, as per the (Ajmi et al., 2015 ) study for G7 countries, there is a one-way causal association between consumption of renewable energy to carbon emissions. Similarly, (M. T. I. Khan et al., 2018 ) for 107 nations, (Ben Jebli et al., 2019 ) for South American countries, (Jebli & Youssef, 2017) for North African countries and (Hu et al., 2021 ) for India. While, environmental pollution and utilization of renewable energy is one-way causality (Irandoust, 2016 ) for Nordic nations (Radmehr et al., 2021 ) for EU nations, (Amin et al., 2020 ) European countries. A study by (Dogan & Seker, 2016 ) for European Union nations, authors established the utilization of renewable energy and environmental emissions is significantly bidirectional causality. A similar result is reported by (Saidi & Omri, 2020 ) for fifteen major clean energy-consuming nations. Finally, the authors also argued that renewable (clean) energy utilization and carbon (CO 2 ) emissions, both are independent, more specifically, a study by (Khoshnevis Yazdi & Shakouri, 2018 ) for EU countries. Ecological footprint and consumption of renewable energy nexus literature survey found both positive (direct) and negative (inverse) effects. In a research by (Salari et al., 2021 ) for emerging ecological footprints countries, the authors stated that adoption of renewable (clean) energy has a substantial direct impact on environment; it implies that utilization of renewable (clean) energy added ecological footprint to environment. In contrast, another group of authors argued that renewable energy usage has negatively affected the environment; it significantly indicates utilization of renewable energy abates environmental footprint. (Ansari et al., 2021 ), (Pata, 2021 ), and (Ullah et al., 2021 ). Therefore, it is significant to shift the use of traditional energy to renewable (clean) energy which improves quality of environment and reduces ecological footprint. In general, use of traditional energy sources has increased due to less availability of modern energy. Furthermore, it is the main cause of higher emissions and environmental degradation; as a result, it hampers environmental sustainability. Therefore, countries are highly looking into replacing the traditional energy with to renewable energy, which not only curbs the ecological footprint and emissions; but it is also important to improve environmental quality and sustainability. In the literature, renewable energy and ecological footprints nexus are unclear. Furthermore, there is no researcher has considered RECAI countries for analysis; because those countries are significantly generating more renewable energy in the world. Therefore, we have to know to what extent RECAI countries curb ecological footprints, and while what extent improve or ensure environmental quality. Based on the given argument our research aims mainly to examine the empirical impact of renewable (clean) energy use on quality of environment of RECAI nations during 1990–2020. Our study fulfils the literature gap by investigating the 39 RECAI countries and adding different combinations of the variables which are not considered by previous authors for empirical analysis purposes. Table 1 Renewable energy consumption and ecological footprint -related studies Sl. No Author(s) Period Countries Methodology Key finding(s) 1 (Ansari et al., 2021 ) 1991–2016 RECAI listed 22 countries Panel analysis (Cointegration test, FM-OLS, DOLS) RE reduces EP 2 (Ullah et al., 2021 ) 1996–2018 WorldsTop15 REC Countries Panel analysis (PSTR) RE reduces EP 3 (Pata, 2021 ) 1980–2016 USA Time series analysis, Cointegration test, FMOLS, CCR, DOLS, VECM RE reduces EP 4 (Ulucak & Khan, 2020 ) 1992–2016 BRICS Countries Panel analysis (FMOLS, DOLS) RE reduces EP 5 (Alola et al., 2019 ) 1997–2014 16 EU Countries Panel analysis (PMG-ARDL) RE reduces EP 6 (Pata, 2021 ) 1971–2016 BRIC Countries Panel analysis (panel ARDL test, Causality) RE reduces EP 7 (Naqvi et al., 2021 ) 1990–2017 155 Countries based on income Groups Panel analysis (Westerlund cointegration, D-H panel test) RE reduces EP 8 (Nathaniel et al., 2020 ) 1990–2016 ASEAN Countries Panel analysis (First- and Second-Generation unit root test, cointegration test, STIRPAT model) RE reduces EP 9 (Zhang et al., 2021 ) 1990–2018 Remittance receiving countries Panel analysis (CPI, ECM, FMOLS, Dumitrescu-Hurlin, CUP-FM, CUP-BC) RE reduces EP 10 (Sharma et al., 2021 ) 1990–2015 Southeast Asia countries Panel analysis (CADF, CS-ARDL, Weserlund, Brusch Pagans test) RE reduces EP 11 (M. Usman & Makhdum, 2021 ) 1990–2018 BRICS-T Countries Panel analysis (MG, AMG, CCEMG, FMOLS, D-H Causality test) RE reduces EP 12 (M. Usman et al., 2021 ) 1985–2014 USA Time series analysis (unit root test, cointegration root tests, ARDL) RE reduces EP 13 (Nathaniel et al., 2020 ) 1990–2014 CIVETS Countries Panel analysis (First- and Second-Generation unit root test, Cointegration test.) RE increases EP 14 (Sharif et al., 2020 ) 1965–2017 Turkey Time series analysis, (QRDL, Granger-causality) RE decreases EP 15 (Ahmed et al., 2020 ) 1985–2017 G7 Countries Panel analysis (CUP FM, Breusch-Pagan LM, Pesaran CD, CIPS, CADF, IPS, D-H Causality test) RE decreases EP 16 (M. Usman et al., 2021 ) 1990–2017 15 Highest Emitting Nations Panel analysis (AMG, CCEMG, D-H Causality test) RE decreases EP 3. Theoretical Framework, Data, And Methodology This research significantly examines the empirical relationship between ecological footprints (EF), consumption of renewable energy (R) and non-renewable (conventional) energy (NR), per capita income (Y), as well as trade openness (T) for Renewable Energy Consumption Attractive Index (RECAI) countries. As discussed in the review of literature the empirical relationship between ecological footprints and consumption of renewable energy may be positively and negatively affected, it depends upon the percentage of renewable energy use. Even though most of the studies stated that there is a negative relationship between EF and RE; it implies that RE uses significantly reduces EF, more particularly (Ansari et al., 2021 ) for RECAI listed 22 countries, (Ullah et al., 2021 ) for World top15 REC nations, (Pata, 2021 ) for the USA, (Ulucak & Khan, 2020 ) for BRICS Countries. Economic growth positively affects the ecological footprints; it suggests that Economic growth increases ecological footprints which were reported in the literature by (Hassan et al., 2019 ) for the case of Pakistan, (Galli et al., 2012 ) for India and China, and (Ahmed et al., 2020 ) for China. As stated, by previous authors affirmed that utilization of non-renewable energy unfavorable and positively affected the environment; it significantly indicates that the usage of non-renewable energy increases ecological footprints, particularly (Raghutla & Chittedi, 2020 ) for BRICS nations, (Salahuddin et al., 2015 ) for GCC nations, (Chindo et al., 2015 ) for EU nations, (Abbas et al., 2021 ) for Pakistan, (Saidi & Hammami, 2015 ) for Global panel countries, (M. K. Khan et al., 2020 ) for Pakistan, (Ozturk & Acaravci, 2010 ) for Turkey. Trade openness negatively effects ecological footprints, increasing trade openness leads to a decrease in the ecological footprints (Destek & Sinha, 2020 ) for economic cooperation and developed countries, (Destek et al., 2018 ) for EU nations, (Charfeddine, 2017 ) for GCC and MENA countries. Based on previous arguments we have formulated regression equations as follows. $$EF=f({Y_{it}},{R_{it}},N{R_{it}},{T_{it}})$$ 1 $$EF={Y_{it}}+{R_{it}}+N{R_{it}}+{T_{it}}+{u_{it}}$$ 2 $$EF=Y_{{it}}^{{\phi 1i}}R_{{it}}^{{\phi 2i}}NR_{{it}}^{{\phi 3i}}T_{{it}}^{{\phi 4i}}$$ 3 $$\ln EF={\beta _0}+{\beta _1}\ln {Y_{it}}+{\beta _2}\ln {R_{it}}+{\beta _3}\ln N{R_{it}}+{\beta _4}\ln {T_{it}}+{u_{it}}$$ 4 Where \(E{F_{it}}\) represents ecological footprint, \({Y_{it}}\) indicates per capita income, \({R_{it}}\) represents renewable energy, \(N{R_{it}}\) denotes non-renewable energy, \({T_{it}}\) indicates trade openness, indicates 39 renewable energy consumption attractive index (RECAI) countries, and denotes a period of 1990–2020. ln the natural logarithm, \({\beta _0},\) \({\beta _1},\) \({\beta _2},\) \({\beta _3},\) and \({\beta _4}\) are the intercept and slope coefficients, respectively. \({u_{it}}\) is the random term. We have used time-series data of RECAI countries during 1990–2020. The data on the ecological footprint (consumption of per capita), income per capita (constants 2015 US $ ), both consumption of renewable energy and non-renewable energy (percentage of consumption of total energy) and trade openness (percentage of GDP). Data is collected from both the “ Global Footprint Networks and World Development Indicators ” published by the GFN and World Bank, respectively. This research employed the panel ARDL method to analyse the long-term as well as short-term association among the ecological footprints, economic growth, both consumption of renewable as well as non-renewable energy and trade openness variables for RECAI nations. Moreover, this panel ARDL approach provides reliable estimations by significantly removing the problem of endogeneity in the model. The empirical equation can be written as follows: $$\begin{gathered} \Delta E{F_{it}}={\alpha _0}+\sum\limits_{{i=1}}^{q} {{\alpha _{1i}}} \Delta E{F_{i,t - 1}}+\sum\limits_{{i=1}}^{q} {{\alpha _{2i}}\Delta } {Y_{i,t - 1}}+\sum\limits_{{i=1}}^{q} {{\alpha _{3i}}\Delta } {R_{i,t - 1}},\sum\limits_{{i=1}}^{q} {{\alpha _{4i}}\Delta } N{R_{i,t - 1}}+\sum\limits_{{i=1}}^{q} {{\alpha _{5i}}\Delta } {T_{i.t - 1}} \hfill \\ +{\alpha _6}E{F_{i,t - 1}}+{\alpha _7}{Y_{i,t - 1}}+{\alpha _8}{R_{i,t - 1}}+{\alpha _9}N{R_{i,t - 1}}+{\alpha _{10}}{T_{i,t - 1}}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 5 where and are the lag order and error term, while represents the first difference operator. Equation-5 can be transformed into an Error Correction Model to equation-4 as follows: $$\begin{gathered} \Delta E{F_{it}}={\alpha _0}+\sum\limits_{{i=1}}^{q} {{\alpha _{1i}}} \Delta E{F_{i,t - 1}}+\sum\limits_{{i=1}}^{q} {{\alpha _{2i}}\Delta } {Y_{i,t - 1}}+\sum\limits_{{i=1}}^{q} {{\alpha _{3i}}\Delta } {R_{i,t - 1}},\sum\limits_{{i=1}}^{q} {{\alpha _{4i}}\Delta } N{R_{i,t - 1}}+\sum\limits_{{i=1}}^{q} {{\alpha _{5i}}\Delta } {T_{i.t - 1}} \hfill \\ +\varpi (E{F_{i,t - 1}}+{\theta _1}{Y_{i,t - 1}}+{\theta _2}{R_{i,t - 1}}+{\theta _3}N{R_{i,t - 1}}+{\theta _4}{T_{i,t - 1}})+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 6 where indicates the parameter of speed adjustment, while and are long-run coefficients of economic growth, both the renewable energy and non-renewable energy usage and trade openness, individually. Equation-5 can be transformed into short-run, and represent the short-run coefficients. Therefore, the panel ARDL and models are as follows: $$\begin{gathered} \Delta E{F_{it}}={\eta _0}+\sum\limits_{{i=1}}^{p} {{\eta _{1i}}} \Delta E{F_{i,t - 1}}+\sum\limits_{{i=1}}^{q} {{\eta _{2i}}\Delta } {Y_{i,t - 1}}+\sum\limits_{{i=1}}^{k} {{\eta _{3i}}\Delta } {R_{i,t - 1}},\sum\limits_{{i=1}}^{g} {{\eta _{4i}}\Delta } N{R_{i,t - 1}}+\sum\limits_{{i=1}}^{f} {{\eta _{5i}}\Delta } {T_{i.t - 1}} \hfill \\ +\varpi (E{F_{i,t - 1}}+{\theta _1}{Y_{i,t - 1}}+{\theta _2}{R_{i,t - 1}}+{\theta _3}N{R_{i,t - 1}}+{\theta _4}{T_{i,t - 1}})+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 7 4. Empirical Results And Discussion 4.1. Unit root tests Table 2 exhibits the empirical outcomes of the IPS unit root tests, we applied to verify the stationarity of our dataset. The empirical results show that variables like EF, Y, R, NR, and T are stationary at the level as well as first difference. More precisely, Y, NR, and T are stationary at their levels, while ecological footprint and renewable energy are significantly stationary at their I(1) order. The IPS panel unit root tests considerably show that EF, Y, R, NR, and T have followed both the orders namely at levels and first-order difference, which significantly implies that there may be a long-term relationship among EF, Y, R, NR, and T variables. Therefore, we are going to estimate the panel ARDL model in the next section to identify the long-term equilibrium association between the EF, Y, R, NR, and T variables. Table 2 Results of panel unit root tests IPS test Variable Level Prob. First Difference Prob. EF -0.939 0.173 -32.863* 0.000 Y -7.570* 0.000 -8.454 0.000 R -1.161 0.122 -24.555* 0.000 NR -5.0183* 0.000 -26.544 0.000 T -4.725* 0.000 -23.725 0.000 Note : * indicates one percentage significant level 4.2. Panel ARDL Analysis Table 3 displays the result of the panel ARDL approach. Particularly, a 1% improvement in lnR leads to reduces lnEF by 0.065%. It indicates that using renewable (clean) energy has a negative considerable impact on environment. Using various types of non-conventional (clean) energies namely solar, wind, biomass, water, and geothermal energy, all helpful to minimize CO 2 emissions, decrease the ecological footprints as well as promote environmental quality. This result is the same as the result of (Mehmood, 2021 ) for the 15 Highest Emitting Nations, (Sharif et al., 2020 ) for Turkey, (O. Usman et al., 2020 ) for USA, (Sharma et al., 2021 ) for Southeast Asia nations, (Zhang et al., 2021 ) for remittance receiving countries, and (Nathaniel & Khan, 2020 ) for ASEAN nations. The findings confirmed that the utilization of non-renewable energy considerably increases the ecological footprint. Precisely, a 1% intensification in lnNR contributes to an increase in lnEF by 0.131%. It indicates that ecological footprints and non-renewable energy, both have a positive association. This similar finding is recognized by (Raghutla & Chittedi, 2020 ) for BRICS nations, (Salahuddin et al., 2015 ) for GCC nations, and (Chindo et al., 2015 ) for EU countries, (Abbas et al., 2021 ) for Pakistan, (Saidi & Hammami, 2015 ) for Global panel nations, (Ulucak & Khan, 2020 ) for Pakistan, and (Ozturk & Acaravci, 2010 ) for Turkey. The estimated ARDL coefficients shows that both economic growth as well as ecological footprints are positively associated, this result indicates that higher economic growth increases more ecological footprints. The analysis revealed that a 1% rise in lnY result in a 0.250% increase in lnEF. Because most of the countries are directly dependent on the conventional energy use and this leads to produces more ecological footprints along with growth. RECAI countries are highly using conventional energy (i.e., oil and coal) in the different production industries which is leads to higher in ecological footprints. Therefore, the countries need to lower the usage of conventional (traditional) energy not only in the production industries but also require to extend the other sectors as well. The same results were found by previous authors namely (Hassan et al., 2019 ) for Pakistan, (Galli et al., 2012 ) for China and India, (Ahmed et al., 2020 ) for China, and (Kirikkaleli et al., 2021 ) for Turkey. Finally, this result also affirmed that trade openness has a significant harmful effect on the environment, a 1% improvement in lnT result in a 0.136% decrease in lnEF. International trade removes the barriers to transferring technology which is provided with the nations to access the cleaner technologies. It reduced the level of ecological footprint; as a result, trade openness significantly curbs the ecological footprints. The same results were found by previous authors namely (Destek & Sinha, 2020 ) for economic cooperation and developed countries, (Destek et al., 2018 ) for EU nations, (Charfeddine, 2017 ) for GCC and MENA countries, and (Lu, 2020 ) for Asian countries. The ECM coefficient is negative in the short term, with a value of -0.389 and 1% level of significance. In the short-term, particularly, the findings display that both renewable (clean) energy and non-renewable energy utilization have a negative and substantial impact on the ecological footprint but its coefficients values are not significant. In RECAI countries, if economic growth improvement by 1%, ecological footprints will increase by 0.674%, moreover, coefficients are significant particularly at 1% level. This finding also proposes that ecological footprints and trade openness have a negative relationship, particularly, if trade openness improvement by 1%, ecological footprint will cause to increase by 0.210% and coefficients are significant at a 1% level in the short-term. In the RECAI nations, the ARDL method, results clearly show that renewable energy use as well as trade openness improve quality of environment but conventional energy usage and economic growth do not. This could be due to the extensive utilization of conventional energy in the process of industrial production and other based needs. Overcome this problem is only possible by the reduction of conventional energy use; therefore, RECAI countries need to replace it with non-conventional energy resources like solar, biomass, wind, geothermal and hydropower etc. The governments of RECAI countries have to take the necessary actions particularly fund allocation for renewable energy projects and also encourage private participation in renewable energy projects by providing various incentives. Along with this, RECAI countries need to bring the environmental policies and reframe their energy policies which are need to give more importance to renewable energy projects. Further, it can dampen the ecological footprint in RECAI countries and considerably enhance the quality of environment. Table 3 Results of panel ARDL model \(EF=f(Y,R,NR,T)\) Long-run results Variable Coef. Std. Error t-stat Prob. R -0.065* 0.004 -14.454 0.000 NR 0.131* 0.020 6.555 0.000 Y 0.250* 0.016 15.471 0.000 T -0.136* 0.014 -9.226 0.000 Short-run results Variable Coef. Std. Error t-stat Prob. COINTEQ01 -0.389 0.108 -3.592 0.000* D(EF(-1)) -0.135 0.091 -1.485 0.138 D(EF(-2)) 0.022 0.075 0.298 0.765 D(EF(-3)) -0.187 0.250 -0.745 0.456 D(R) -0.213 0.153 -1.394 0.164 D(R(-1)) -0.049 0.111 -0.441 0.658 D(R(-2)) 0.206 0.174 1.184 0.237 D(R(-3)) 0.072 0.123 0.584 0.559 D(NR) 0.685 0.431 1.589 0.112 D(NR(-1)) 0.251 0.845 0.297 0.766 D(NR(-2)) 1.432 1.341 1.067 0.286 D(NR(-3)) 1.336 0.943 1.417 0.157 D(Y) 0.674* 0.253 2.663 0.008 D(Y(-1)) 0.083 0.286 0.290 0.772 D(Y(-2)) 0.675*** 0.381 1.771 0.077 D(Y(-3)) 0.074 0.212 0.352 0.724 D(T) 0.210* 0.085 2.454 0.014 D(T(-1)) 0.113 0.068 1.645 0.100 D(T(-2)) 0.006 0.082 0.084 0.932 D(T(-3)) -0.018 0.063 -0.292 0.770 C -1.780 0.555 -3.207 0.001* Note *, *** indicates one and ten percentage significant levels. 4.3. D-H panel causality Analysis Table 4 illustrates the results of the D-H panel causality approach. To estimate the D-H panel causality tests, the dataset should be a first difference; therefore, researchers have converted the dataset namely EF, Y, R, NR and T into I(1) order. The D-H panel non-causality test empirical results discovered a unidirectional (one-way) causality relation from both trade openness and utilization of non-renewable energy to ecological footprint. Nevertheless, we could not establish any causality relation between the clean or renewable energy usage, environmental footprint and economic growth. Entire causality test empirical results indicate that conventual energy use and trade openness have a substantial short-run influence on environment. Table 4 Results of D-H panel causality tests Variables Zbar-Stat Prob. R→EF 5.707 1.E-08 EF→R 5.497 4.E-08 NR→EF 2.779 0.005* EF→NR 5.721 1.E-08 T→EF 3.877 0.000* EF→T 1.242 0.2139 Y→EF 8.211 2.E-16 EF→Y 0.303 0.7612 Note * indicates one percentage significant level. 4.4. Long-run Analysis of Individuals Nations Table 5 demonstrates the result of individual nations which is estimated by utilizing the DOLS method. The main reason for individual countries' analysis, is we have to know the performance of individual countries towards reduction of ecological footprints which is more useful for policymakers of nations. RECAI countries mainly consume and produce renewable energy and we have to know the significant level of utilization and to what extent those nations have curbed their ecological footprints. Therefore, we estimated individual nations, long-run elasticities. Particularly, ecological footprints in relation to growth are considerable positive for Chile (0.183343), China (1.001764), Denmark (0.053561), Greece (0.360591), Ireland (0.155179), Japan (0.073294), Korea Rep. (0.362029), Morocco (0.567448), Netherlands (0.599342), Romania (0.237314), Saudi Arabia (1.026827) and Turkey (0.192589). For these 12 countries, economic growth improvement has a substantial direct impact on ecological footprints. This empirical analysis indicates that economic growth will significantly add more ecological footprints to the environment due to more conventional energy usage in the process of production. This finding is the same (Hassan et al., 2019 ) for Pakistan, (Galli et al., 2012 ) for India and China, (Ahmed et al., 2020 ) for China, and (Kirikkaleli et al., 2021 ) for Turkey. However, long-run ecological footprints elasticities also disclose the substantial negative impact of growth of RECAI nations on the environmental quality which is revealed for seven countries namely Australia (-0.171622), Austria (-0.254069), Canada (-0.029541), Germany (-0.511752), Norway (-0.230284), South Africa (-0.382278) and United States (-0.240143). This result shows that these seven nations started utilization of renewable energy in their nations in place of conventional energy in their production processes, which has improved quality of environment. This evidence is same to that of (Raghutla et al., 2021 ) for major investment nations and (Hu et al., 2021 ) for India. Furthermore, the economic growth has both the positive and negative impact, but statistically insignificant impact on ecological footprints for 20 countries, particularly, for Belgium (0.106391), Brazil (0.024040), Czech Rep. (0.074502), India (0.035208), Mexico (0.307027), Peru (0.026872), Poland (0.181681), Portugal (0.091672), Slovenia (0.128535), Spain (0.204402), Sweden (0.031784), Ukraine (0.158640), Bulgaria (-0.235712), Finland (-0.301248), France (-0.022195), Israel (-0.006967), Italy (-0.131862), Kenya (-0.081147), Thailand (-0.559202) and United Kingdom (-0.076811). This evidence substantially indicates that utilization of renewable energy is at early stage in the process of production and also follows the energy mix. The long-run elasticities of ecological footprints in relation to utilization of renewable energy, are significant negative for Australia (-0.665953), Belgium (-0.094025), Chile (-0.200933), Denmark (-0.239666), France (-0.192670), Greece (-0.625309), India (-0.541907), Ireland (-0.221611), Korea Rep. (-0.074882), Morocco (-0.179446), Netherlands (-0.219322), Peru (-0.364033), Portugal (-0.342057), Romania (-0.288313), South Africa (-0.312063), Spain (-0.609967), Turkey (-0.303971) and Ukraine (-0.185441). For 18 RECAI countries, renewable energy usage has an inverse considerable effect on environmental footprints. The usage of renewable energy, according to this empirical analysis, will greatly improve environmental quality by reducing ecological footprints. This result is consistent with earlier research namely (Mehmood, 2021 ) for the 15 Highest Emitting Countries, (Sharif et al., 2020 ) for the case of Turkey, (O. Usman et al., 2020 ) for USA, (Sharma et al., 2021 ) for Southeast Asia nations, (Zhang et al., 2021 ) for Remittance receiving countries, and (Nathaniel & Khan, 2020 ) for ASEAN Countries. However, the long-run elasticities show the direct effect of renewable (clean) energy utilization on environmental footprints, which is affirmed for Austria (0.458583) and Norway (0.845168). These two countries still depend on the more conventional energy sources which leads to an increase in ecological footprints. Therefore, these two countries need to replace particularly conventional energy in place of renewable energy sources to reduction of ecological footprints. This analysis is the same as that of (Nathaniel et al., 2020 ) for CIVETS countries. In addition, the renewable energy usage has a direct and negative impact on environmental footprints in 19 countries, but coefficients values are statistically insignificant, particularly, Bulgaria (0.098833), Finland (1.722640), Germany (0.084060), Kenya (0.073561), Mexico (0.087784), Thailand (0.749077) and United States (0.114915), while, Brazil (-0.067405), Canada (-0.015636), China (-0.064087), Czech Rep. (-0.994379), Israel (-0.052705), Italy (-0.027891), Japan (-0.103397), Poland (-0.056451), Saudi Arabia (-0.062993), Slovenia (-0.410748), Sweden (-0.071500) and United Kingdom (-0.047636). This analysis evidence that these 19 nations early stage of renewable energy utilization. The long-run elasticities, particularly ecological footprints in relation to conventional or non-renewable energy usage, are significant for six countries namely Australia (0.767181), Canada (0.554320), France (0.651401), Germany (3.591729), South Africa (2.558199) and United States (1.910811), this suggests that utilization of non-renewable energy will considerably generate the ecological footprints. Therefore, to reduce their ecological footprints, these six countries must expand their use of renewable energy. This evidence is recognized by (Raghutla & Chittedi, 2020 ) for BRICS nations, (Salahuddin et al., 2015 ) for the gulf cooperation council, and (Chindo et al., 2015 ) for EU nations, (Abbas et al., 2021 ) for Pakistan, (Saidi & Hammami, 2015 ) for Global panel countries, (Ulucak & Khan, 2020 ) for Pakistan, and (Ozturk & Acaravci, 2010 ) for the case of Turkey economy. In contrast, the long-run elasticities show the negative impact of utilization of non-renewable or conventional energy on ecological footprints, which is significantly affirmed for ten different countries namely Belgium (-1.080421), Chile (-0.583265), China (-6.200239), Greece (-1.483880), Korea Rep. (-1.781583), Morocco (-2.712848), Netherlands (-2.837685), Romania (-0.828651), Saudi Arabia (-5.976091) and Turkey (-0.642183). It is indicating that these ten countries have not only significantly adopted carbon capture technology but have also increase their usage of renewable energy which significantly lowers the ecological footprints. This result is the same as earlier findings that of (Acheampong, 2018 ) for MENA, (Li et al., 2020 ) for China, (Zou & Zhang, 2020 ) for China, and (Svedberg, 2021 ) for OECD countries. The non-renewable or traditional energy usage has a both positive and negative but statistically insignificant effect on ecological footprints for 23 countries mainly Austria (0.407919), Brazil (0.240165), Bulgaria (1.519572), Czech Rep. (0.110697), Denmark (0.000467), Finland (1.049226), India (0.239507), Israel (0.092793), Italy (1.124164), Japan (0.045196), Kenya (0.432077), Norway (0.659952), Peru (0.187332), Portugal (0.103684) Sweden (0.170439), Thailand (2.265330), and United Kingdom (1.025801), while Ireland (-0.363577), Mexico (-1.667034), Poland (-0.586811), Slovenia (-0.513299), Spain (-0.661970) and Ukraine (-0.511889). This analysis suggests that these 23 countries can significantly shows the quality of environment improvement when increase in renewable energy share which is a major strategy for climate change and environmental sustainability. The long-run elasticities show the positive considerable effect of trade openness on ecological footprints, which is considerably affirmed for nine countries namely Australia (1.521857), Belgium (0.776539), Canada (0.176009), Greece (0.113260), Israel (0.437041), Norway (0.498667), Peru (0.158328), Saudi Arabia (0.292289) and Sweden (0.175702). This empirical evidence implies that trade openness increases ecological footprints. This finding is similar to (Al-Mulali et al., 2015 ) for ninety three nations, (Al-mulali et al., 2016 ) for 58 countries, (Aşici & Acar, 2015 ) for 116 nations, (Kongbuamai et al., 2020 ) for Thailand and (Ozturk et al., 2016 ) for 144 countries. However, the long-run empirical elasticities of ecological footprints in relation to trade openness, are significant negative for four countries particularly Brazil (-0.140466), Japan (-0.219299), Morocco (-0.343332), and Turkey (-0.135102). This analysis evidence that increasing trade openness reduces environmental impact via the exchange of technological innovations. This empirical evidence same as that of (Destek & Sinha, 2020 ) for economic cooperation and developed countries, (Destek et al., 2018 ) for EU nations, (Charfeddine, 2017 ) for GCC and MENA countries, (Aydin & Turan, 2020 ) for BRICS countries. For 26 countries, trade openness significantly has both beneficial and unfavorable effects in the environment, but these effects are statistically insignificant, specifically, Austria (0.249136), Bulgaria (0.076275), Czech Rep. (0.414014), Denmark (0.337752), France (0.051734), Germany (0.105519), India (0.007031), Italy (0.117378), Kenya (0.177215), Slovenia (0.418659), South Africa (0.199467), Spain (0.072806), and Thailand (0.795743), while Chile (-0.063882), China (-0.173344), Finland (-0.079236), Ireland (-0.080919), Korea Rep. (-0.123008), Mexico (-0.055472), Netherlands (-0.337172), Poland (-0.154949), Portugal (-0.084911), Romania (-0.155023), Ukraine (-0.130195), United Kingdom (-0.206886) and United States (-0.048103). This analysis suggests that these 26 countries need to improve their trade openness for reduction of ecological footprints. Table 5 Results of long-run ecological footprint elasticities using the DOLS Model (Dependent variable: ecological footprints). Variable Y R NR T Australia -0.171622* -0.665953* 0.767181* 1.521857* Austria -0.254069*** 0.458583* 0.407919 0.249136 Belgium 0.106391 -0.094025* -1.080421** 0.776539* Brazil 0.024040 -0.067405 0.240165 -0.140466* Bulgaria -0.235712 0.098833 1.519572 0.076275 Canada -0.029541*** -0.015636 0.554320* 0.176009** Chile 0.183343* -0.200933** -0.583265** -0.063882 China 1.001764* -0.064087 -6.200239** -0.173344 Czech Rep. 0.074502 -0.994379 0.110697 0.414014 Denmark 0.053561*** -0.239666* 0.000467 0.337752 Finland -0.301248 1.722640 1.049226 -0.079236 France -0.022195 -0.192670* 0.651401* 0.051734 Germany -0.511752* 0.084060 3.591729* 0.105519 Greece 0.360591* -0.625309* -1.483880* 0.113260** India 0.035208 -0.541907* 0.239507 0.007031 Ireland 0.155179** -0.221611* -0.363577 -0.080919 Israel -0.006967 -0.052705 0.092793 0.437041* Italy -0.131862 -0.027891 1.124164 0.117378 Japan 0.073294** -0.103397 0.045196 -0.219299* Kenya -0.081147 0.073561 0.432077 0.177215 Korea Rep. 0.362029* -0.074882* -1.781583* -0.123008 Mexico 0.307027 0.087784 -1.667034 -0.055472 Morocco 0.567448* -0.179446* -2.712848* -0.343332** Netherlands 0.599342* -0.219322** -2.837685* -0.337172 Norway -0.230284** 0.845168** 0.659952 0.498667* Peru 0.026872 -0.364033* 0.187332 0.158328* Poland 0.181681 -0.056451 -0.586811 -0.154949 Portugal 0.091672 -0.342057* 0.103684 -0.084911 Romania 0.237314* -0.288313* -0.828651* -0.155023 Saudi Arabia 1.026827* -0.062993 -5.976091* 0.292289** Slovenia 0.128535 -0.410748 -0.513299 0.418659 South Africa -0.382278* -0.312063* 2.558199* 0.199467 Spain 0.204402 -0.609967* -0.661970 0.072806 Sweden 0.031784 -0.071500 0.170439 0.175702*** Thailand -0.559202 0.749077 2.265330 0.795743 Turkey 0.192589* -0.303971* -0.642183** -0.135102** Ukraine 0.158640 -0.185441* -0.511889 -0.130195 United Kingdom -0.076811 -0.047636 1.025801 -0.206886 United States -0.240143* 0.114915 1.910811* -0.048103 Note *, ** and *** indicates one, five and ten percentage significant levels. Conclusion And Policy Implications Globally, the majority of countries are striving for economic growth, and most of them have seen considerable growth, particularly from 1990 onwards. At the same span, their ecological footprints have also increased along with their economic growth due to the significant use of conventional energy resources. Further, it represents a threat to the future generations of humans, environmental quality, and sustainability as well. Therefore, without disturbing the production capacity; using clean energy in the production process can contribute to achieve environmental quality. Given this context, this research investigates the empirical impact of renewable energy utilization on ecological footprints by seeing significance of economic growth, trade openness, and utilization of non-renewable or traditional energy in ecological footprints function for RECAI countries, spanning the period 1990 to 2020. According to the study, renewable (clean) energy usage and trade openness, these significantly improve environmental quality over time, whereas non-renewable energy usage and economic expansion do not improve the quality of environment. Trade openness and economic expansion exacerbate environmental damage in the short-run. Causality results document a one-way (unidirectional) causality relation from both trade openness as well as non-renewable energy usage to the ecological footprint. Overall, the findings indicated that the usage of non-renewable or traditional energy, as well as economic expansion, both causes environmental degradation in RECAI countries. The RECAI countries are reliant on traditional energy in the industrial production process, which has resulted in increased environmental degradation, both directly and indirectly. Furthermore, globally most of their production units are directly dependent on traditional energy, which witnesses an increase in environmental degradation. Therefore, those countries need to use renewable energy instead of using traditional energy to ensure environmental quality. Policymakers and governments should focus to enhancement the renewable energy sources through the installation of solar PV, the development of hydropower and wind energy projects. It is only possible by providing incentives and subsidies in the energy sector, particularly for renewable energy projects. The household can significantly play an important role, by making accessible renewable energy sources to the household and they may give better results particularly reduction in ecological footprint. Furthermore, public industries and government should follow renewable energy production methods; so that it will significantly promote and motivates others as well. In most countries, there is no proper awareness of using renewable energy in the countryside, making it accessible to the rural areas by providing special subsidies and creating awareness through social policies will encourage them to use clean energy such as solar PV and wind power. In extension to this, the government need to create awareness of utilization of renewable energy; especially in, the industrial, service, agriculture sector and other basic needs as well. Given this background, RECAI countries should take initiatives to make renewable energy accessible to all, so that renewable energy usage will increase and hence it will pave the way to replace the traditional energy. Finally, it can significantly lower the ecological footprint and improve environmental quality. Taking into account today’s environmental condition, where our global-ecological structure once again appears to be dealing with resources disastrously, it is important to reanalyse and arbitrate in the conversation about how energy and environmental policies are sustainably rooted. Declarations Ethical Approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Authors’ contributions: Chandrashekar Raghutla: Conceptualization, Writing-Original Draft, Methodology, Software, Supervision.. Yeliyya Kolati: Writing-Original Draft, Review & Editing, Resources, Methodology, Funding: Not applicable (we are not received any funding). Competing interests: The authors declare that there are no conflicts of interest regarding the publication of this paper. Data availability: Data will be available upon request. References Abbas, S., Kousar, S., & Pervaiz, A. (2021). 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Salahuddin, M., Gow, J., & Ozturk, I. (2015). Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust? Renewable and Sustainable Energy Reviews , 51 , 317–326. Salari, T. E., Roumiani, A., & Kazemzadeh, E. (2021). Globalization, renewable energy consumption, and agricultural production impacts on ecological footprint in emerging countries: using quantile regression approach. Environmental Science and Pollution Research , 28 (36), 49627–49641. Shafiei, S., & Salim, R. A. (2014). Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis. Energy Policy , 66 , 547–556. Sharif, A., Baris-Tuzemen, O., Uzuner, G., Ozturk, I., & Sinha, A. (2020). Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from Quantile ARDL approach. Sustainable Cities and Society , 57 , 102138. Sharma, R., Sinha, A., & Kautish, P. (2021). Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia. Journal of Cleaner Production , 285 , 124867. Svedberg, S. (2021). The impact of non-fossil energy consumption on environmental quality: Investigating ecological footprints . Tang, C. F., & Tan, B. W. (2015). The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam. Energy , 79 , 447–454. Ullah, A., Ahmed, M., Raza, S. A., & Ali, S. (2021). A threshold approach to sustainable development: Nonlinear relationship between renewable energy consumption, natural resource rent, and ecological footprint. Journal of Environmental Management , 295 , 113073. Ulucak, R., & Khan, S. U.-D. (2020). Determinants of the ecological footprint: role of renewable energy, natural resources, and urbanization. Sustainable Cities and Society , 54 , 101996. 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The role of renewable and non-renewable energy consumption in CO2 emissions: a disaggregate analysis of Pakistan. Environmental Science and Pollution Research , 25 (31), 31616–31629. Zhang, L., Yang, B., & Jahanger, A. (2021). The role of remittance inflow and renewable and non-renewable energy consumption in the environment: Accounting ecological footprint indicator for top remittance-receiving countries. Environmental Science and Pollution Research , 1–16. Zou, S., & Zhang, T. (2020). CO2 emissions, energy consumption, and economic growth nexus: evidence from 30 provinces in China. Mathematical Problems in Engineering , 2020 . Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2023 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 17 Feb, 2023 Reviewers agreed at journal 30 Jan, 2023 Reviewers invited by journal 30 Jan, 2023 Editor invited by journal 27 Jan, 2023 Editor assigned by journal 20 Jan, 2023 First submitted to journal 17 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2466940","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":171644397,"identity":"b3d1f119-4792-4a0a-8b24-b70f22084821","order_by":0,"name":"Chandrashekar Raghutla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYDACCSDmAWIDBjYwX46BGSbFTKQWYwZmZhK1JDbgVAoF/LObnz14u8fO3py9LfFzBcO99O3s/Mce8zDYyTOw8x7AasmdY+aGc54lJ+7sOXZY8gxDce7OZmZ2Yx6GZMMGZr4EbFoMJBLMpHkOMCcY3EhvkGxgSMjdcJiZTZqHgTmBgZnHALuW9G9ALfX2BvefN/8Eakk3gGipx6MlB2TLYcYNN9iOgWxJgGo5jFOLxI2cMsk5B44nbjiTlmbZYJBgCHQY0HcGxw3bcGjhn5G+TeLNgWp7g+PHjG82VCTIG5w/+OzBm4pqeX7+M1i1oLsTTLIxgcxnI0I9HLAx/iBF+SgYBaNgFAx3AAB0DlL31MEz8AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9041-3981","institution":"NIT Puducherry: National Institute of Technology Puducherry","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chandrashekar","middleName":"","lastName":"Raghutla","suffix":""},{"id":171644398,"identity":"9ba05d9a-7fc4-47b5-87c0-220284211a7a","order_by":1,"name":"Yeliyya Kolati","email":"","orcid":"","institution":"National Institute of Technology Puducherry","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yeliyya","middleName":"","lastName":"Kolati","suffix":""}],"badges":[],"createdAt":"2023-01-11 11:31:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2466940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2466940/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-023-29402-y","type":"published","date":"2023-08-28T15:09:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":32293265,"identity":"e2d47139-b8bb-44a3-a11f-fd113ad87841","added_by":"auto","created_at":"2023-01-31 20:48:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":532639,"visible":true,"origin":"","legend":"\u003cp\u003eEconomic growth and ecological footprints\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2466940/v1/f64cacabd4aacbd85e712988.jpg"},{"id":32293266,"identity":"d06d85f7-fc33-4939-a64d-507acaaebe97","added_by":"auto","created_at":"2023-01-31 20:48:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":531412,"visible":true,"origin":"","legend":"\u003cp\u003eRenewable energy and ecological footprints\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2466940/v1/32a28c72b5d06cf67dba46eb.jpg"},{"id":32293561,"identity":"73f24ebe-ba5c-4fcc-a45c-1ed08a47ab17","added_by":"auto","created_at":"2023-01-31 20:56:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":489585,"visible":true,"origin":"","legend":"\u003cp\u003eNon-renewable energy and ecological footprints\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2466940/v1/bb89e5b1e4ac978b0b705494.jpg"},{"id":32293267,"identity":"9175dbf8-8877-443c-994a-f2318e5caac5","added_by":"auto","created_at":"2023-01-31 20:48:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":532350,"visible":true,"origin":"","legend":"\u003cp\u003eTrade Openness and ecological footprints\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2466940/v1/68ad9656b3a591c572a141f6.jpg"},{"id":42782400,"identity":"43c01b78-e7bb-400b-85f6-c0075ebb835e","added_by":"auto","created_at":"2023-09-07 15:17:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":928960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2466940/v1/40d84dad-6450-4da3-a78b-bf6b392c417c.pdf"}],"financialInterests":"","formattedTitle":"Does Renewable Energy Improve Environmental Quality? Evidence from RECAI Countries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe basis for economic growth was said to be the shift of energy and environmental strategies toward a sustainable future. Indeed, it became a matter of proclaimed acceptance that environmental challenges nurtured expansion, innovation, and competitiveness. The debate cemented the way for market solutions and a faith that competition would create \u0026lsquo;green\u0026rsquo; jobs. The rising Ecological Footprint (EP) is characterized by increasing fossil fuel usage and the civilization process. Since 1961, humanity's ecological footprints have shown a considerable upward trend growth with an average annual of 2.1%. However, in 1961, it climbed by almost 7.0\u0026nbsp;billion GHA, while in 2014, it increased by 20.6\u0026nbsp;billion GHA (National Footprint Accounts). The increasing ecological footprint poses great challenges to environmental quality. The majority of energy use is directly dependent on the traditional energy which is causing environmental degradation and climate change. Particularly, more than one-third of environmental pollution is caused by utilization of conventional energy (World Resources Institute). The growth of global pollution releases has continuously elevated from 21.4Gt in 1990 to 34.2Gt in 2020 (IEA, 2021). Further, the global carbon emissions and global economic output have increased by nearly 6% and 5.9% in 2021, respectively. The excessive use of conventional energy and human activities leads to environmental degradation. Renewable energy generation aids to minimize the negative effects on the quality of environment. Further, the negative environmental quality (degradation) can be eliminated by replacing of conventional energy techniques and its utilization (Pata, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To overcome this problem renewable energy generation is only option to enhance environmental quality by reduction of emissions and ecological footprints. High-level hopes were placed on fuel cells in the early 2000s, as they became an archetype of eco-modern machinery by ecological modernization advocates.\u003c/p\u003e \u003cp\u003eAugmentation of the population is accelerated the resource consumption, and the waste assimilation capacity (Wackernagel \u0026amp; Rees, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). These accumulated human-producing wastes are arduous to the environment for the waste assimilation process, which causes the extension of the ecological footprints. Conventional energy utilization not only produces emissions but also significantly destroys human health (Nathaniel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The environmental quality decreases as a result of fossil fuel-driven and economic expansion; which cause an increase in ecological footprints. On the other hand, utilizing the full capacity of renewable (clean) energy production can help to decrease the environmental footprints (Sharif et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given, both degradation of environment and environmental footprints, the present level of renewable (clean) energy use is insufficient to maintain environmental quality. Therefore, increase in share of renewable (clean) energy utilization tends to decrease the environmental footprints in over time, further it can also promote economic growth and transition to the green economy. Technological upgradation in industry can also be used as a strategy to dampen environmental quality degradation issues significantly (Ansari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the energy resources namely geothermal energy, solar energy, hydropower, biomass energy, and wind energy serve as the leading sources of renewable energy in many nations. These renewable energy sources do not produce emissions and hence lead to a decrease in the ecological footprints.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2020, world countries have significantly witnessed an increase in economic growth while maintaining CO\u003csub\u003e2\u003c/sub\u003e emissions at the same level. We have observed that all countries together recorded an average output of 756.829\u0026nbsp;billion dollars in the year of 1990, this value is increased by 1.747 trillion dollars in 2020. In total energy consumption of all the selected countries together, the average the non-renewable energy consumption was nearly 76.95% as recorded in 1990. The percentage of using non-renewable energy consumption has decreased by around 74.39% in 2020. Although the average rate of conventical energy may be declined, it can indicate that conventional energy is directly responsible for the majority of energy use. In contrast, between 1990 and 2020, renewable energy consumption climbed from 19.07\u0026ndash;22.69% respectively. The environmental degradation statistics display that the usage of conventional energy gradually shifted to more renewable energy. While exports and imports are playing a key role to achieve environmental quality; further foreign trade is also positively and negatively correlated to the ecological footprint from a globalization perspective. The average percentage of export increased from 26.29% in 1990 to 39.50% in 2020, and the average percentage of imports also increased from 26.80\u0026ndash;37.12% during 1990 to 2020. Based on these significant reasons, this is really important to analyse their utilization of renewable (clean) energy potentiality and environmental footprints from the perspectives of environmental sustainability.\u003c/p\u003e \u003cp\u003eIn non-conventional energy, particularly geothermal and biomass energy systems produce much lower pollution than conventional energy, however, non-conventional energy sources mainly are available plenty in nature and it is naturally replenished so that renewable energy avoids human waste assimilation problem to control the ecological footprints. Renewable energy sources can maintain the environmental sustainability and quality of the environment as well. Renewable energy not only significantly decreases the ecological footprints, it also, directly and indirectly, influences the economic and social factors like an increase in humans\u0026rsquo; life expectancy and also maintaining sustainability. The installation and maintenance of renewable energy technology cost charges are significantly low compared to conventional energy. This significantly extends the access across the nations, hence the renewable energy utilization decreases the ecological footprints. It is possible through only an energy transition of conventional energy to renewable energy. A study found that renewable energy is the only source to decrease the ecological footprints (Nathaniel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Whenever a country shifts its strategies related to the conventional energy consumption towards renewable energy consumption, such a policy shift has a very favourable impact on quality of environment. Furthermore, the increase in the important of renewable (clean) energy utilization improves health and environmental quality by lowering environmental pollution (Alola et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At present, across the globe, the major question is how to decrease the ecological footprints from the environment and what is the effective way to improve the sustainability of environment. Under these circumstances, renewable (clean) energy utilization is the only alternative solution to improve the environment quality and environmental sustainability. Therefore, the primary intention of present research is to examine the empirical impact of consumption of renewable (clean) energy on ecological footprints by seeing significant role of economic growth, conventional energy usage and trade openness in ecological footprints function for Renewable Energy Consumption Attractive Index (RECAI) nations, spanning the period 1990\u0026ndash;2020.\u003c/p\u003e \u003cp\u003eThe main contribution of this research is as follows. First, this is the first research article to investigate the empirical nexus between renewable energy usage and ecological footprints in RECAI countries, spanning the period 1990\u0026ndash;2020. However, previously one of author tried to investigate only 22 RECAI countries and the study fail to address the majority of the RECAI countries; and fail to analyse few RECAI countries which are significantly producing and consuming renewable energy. Furthermore, it is important to analyse and explore, to what extent the individual RECAI countries are utilizing renewable energy resources. Moreover, it is only possible through renewable energy consumption and its utilization, we can allow environmental sustainability, improves environmental quality and by significantly impede ecological footprints as well as emissions. Therefore, our study considered 39 countries for the empirical analysis and owing to the non-availability of all empirical data namely EF, R, Y, NR and T variables during the study period, therefore, we have not considered the Taiwan nation for the empirical analysis. It is more important to understand policymakers for taking the significant decision regarding the future renewable energy production and its utilization. Second, RECAI countries have significantly occupied the energy market, particularly renewable energy production and consumption. How it can motivate the many rests of the nations for renewable energy adoption as well as utilization. Moreover, there is a necessity to adopt renewable energy utilization across the globe. Therefore, it is important to study the RECAI countries from a global perspective to understand the importance of renewable energy and its impact. Third, this study provides the importance of environmental quality and its determinants. Further, it also helps the rest of the nations to understand the significance of renewable energy utilization and production. Fourth, around the globe, climate change and environmental quality are the most pressing issues facing many confront, therefore, the study will provide policy recommendations to reduce ecological footprints from the environment and allow environmental sustainability and quality. Finally, the study applied advanced panel econometric methods for empirical investigation.\u003c/p\u003e \u003cp\u003eThe rest of the research work is organized as follows. Section 2 presented a relevant review of related literature on use of renewable energy and ecological footprints in the study, Section 3 chalks out the sources of data and empirical methodology, Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the results and discussion in the study and Section 5 illustrates the conclusion of study and policy implications.\u003c/p\u003e"},{"header":"2. Review Of Literature","content":"\u003cp\u003eIn the world, mainly use of energy is directly dependent on traditional energy i.e., coal and oil which directly leads to an elevate in environmental degradation and carbon emissions as well. Solving this environmental problem is only possible by impeding emissions. The main reason is conventional energy use; therefore, it must be replaced with renewable energy resources namely wind, solar, biomass, hydropower, etc. In the energy literature, pioneer researchers confirmed that traditional energy utilization has a direct considerable impact on environment; it suggests that the usage of conventional energy increase emissions, particularly (Raghutla \u0026amp; Chittedi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for BRICS economies, (Salahuddin et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for GCC nations, (Chindo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for European nations, (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and (M. K. Khan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Pakistan, (Saidi \u0026amp; Hammami, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for Global panel nations, (Ozturk \u0026amp; Acaravci, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for Turkey, (Shafiei \u0026amp; Salim, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for OECD (Qi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for China, (Boontome et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for Thailand, (Menyah \u0026amp; Wolde-Rufael, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for the US, (Nguyen \u0026amp; Kakinaka, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for 107 countries, and (Tang \u0026amp; Tan, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for Vietnam. Similarly, another group of authors also found that traditional energy utilization has a inverse considerable impact on environment; it suggests that the mainly usage of conventional energy impedes emissions, particularly (Acheampong, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for MENA, (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for China, (Svedberg, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for OECD economies, and (Zou \u0026amp; Zhang, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for China.\u003c/p\u003e \u003cp\u003eRenewable energy and emissions nexus are positively and negatively associated due to the adoption of renewable energy. (Sahoo \u0026amp; Sahoo, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for India, indicated that the renewable (clean) energy utilization has a direct considerable impact on environment; it shows that the usage of renewable (clean) energy added carbon emissions to environment. In contrast, some of the pioneer authors found that substantial inverse association between the renewable (clean) energy utilization and CO\u003csub\u003e2\u003c/sub\u003e emissions; it is a sign that renewable energy lever CO\u003csub\u003e2\u003c/sub\u003e emissions, a research by (Bilgili et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for OECD economies, (Mehmood, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for G11 nations, and (Cheng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for BRICS nations, (S. A. R. Khan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Nordic nations, (Panwar et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and (Sharif et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for top 10 polluted nations. A research by (Zaidi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) established that adoption renewable (clean) energy use has no favourable impact on environment in the case of Pakistan. Direction of causality evidence mix, as per the (Ajmi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) study for G7 countries, there is a one-way causal association between consumption of renewable energy to carbon emissions. Similarly, (M. T. I. Khan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for 107 nations, (Ben Jebli et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for South American countries, (Jebli \u0026amp; Youssef, 2017) for North African countries and (Hu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for India. While, environmental pollution and utilization of renewable energy is one-way causality (Irandoust, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for Nordic nations (Radmehr et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for EU nations, (Amin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) European countries. A study by (Dogan \u0026amp; Seker, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for European Union nations, authors established the utilization of renewable energy and environmental emissions is significantly bidirectional causality. A similar result is reported by (Saidi \u0026amp; Omri, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for fifteen major clean energy-consuming nations. Finally, the authors also argued that renewable (clean) energy utilization and carbon (CO\u003csub\u003e2\u003c/sub\u003e) emissions, both are independent, more specifically, a study by (Khoshnevis Yazdi \u0026amp; Shakouri, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for EU countries.\u003c/p\u003e \u003cp\u003eEcological footprint and consumption of renewable energy nexus literature survey found both positive (direct) and negative (inverse) effects. In a research by (Salari et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for emerging ecological footprints countries, the authors stated that adoption of renewable (clean) energy has a substantial direct impact on environment; it implies that utilization of renewable (clean) energy added ecological footprint to environment. In contrast, another group of authors argued that renewable energy usage has negatively affected the environment; it significantly indicates utilization of renewable energy abates environmental footprint. (Ansari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), (Pata, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and (Ullah et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it is significant to shift the use of traditional energy to renewable (clean) energy which improves quality of environment and reduces ecological footprint. In general, use of traditional energy sources has increased due to less availability of modern energy. Furthermore, it is the main cause of higher emissions and environmental degradation; as a result, it hampers environmental sustainability. Therefore, countries are highly looking into replacing the traditional energy with to renewable energy, which not only curbs the ecological footprint and emissions; but it is also important to improve environmental quality and sustainability.\u003c/p\u003e \u003cp\u003eIn the literature, renewable energy and ecological footprints nexus are unclear. Furthermore, there is no researcher has considered RECAI countries for analysis; because those countries are significantly generating more renewable energy in the world. Therefore, we have to know to what extent RECAI countries curb ecological footprints, and while what extent improve or ensure environmental quality. Based on the given argument our research aims mainly to examine the empirical impact of renewable (clean) energy use on quality of environment of RECAI nations during 1990\u0026ndash;2020. Our study fulfils the literature gap by investigating the 39 RECAI countries and adding different combinations of the variables which are not considered by previous authors for empirical analysis purposes.\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\u003eRenewable energy consumption and ecological footprint -related studies\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\u003eSl. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethodology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKey finding(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ansari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1991\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRECAI listed 22 countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(Cointegration test, FM-OLS, DOLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ullah et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1996\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorldsTop15 REC Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(PSTR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Pata, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1980\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime series analysis, Cointegration test, FMOLS, CCR, DOLS, VECM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ulucak \u0026amp; Khan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1992\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRICS Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(FMOLS, DOLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Alola et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1997\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 EU Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(PMG-ARDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Pata, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1971\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRIC Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(panel ARDL test, Causality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Naqvi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 Countries based on income Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(Westerlund cointegration, D-H panel test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Nathaniel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASEAN Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis (First- and Second-Generation unit root test, cointegration test, STIRPAT model)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRemittance receiving countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(CPI, ECM, FMOLS, Dumitrescu-Hurlin, CUP-FM, CUP-BC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Sharma et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoutheast Asia countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(CADF, CS-ARDL, Weserlund, Brusch Pagans test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(M. Usman \u0026amp; Makhdum, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRICS-T Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(MG, AMG, CCEMG, FMOLS, D-H Causality test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(M. Usman et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1985\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime series analysis\u003c/p\u003e \u003cp\u003e(unit root test, cointegration root tests, ARDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE reduces EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Nathaniel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIVETS Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis (First- and Second-Generation unit root test, Cointegration test.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE increases EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Sharif et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1965\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime series analysis, (QRDL, Granger-causality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE decreases EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1985\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG7 Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(CUP FM, Breusch-Pagan LM, Pesaran CD, CIPS, CADF, IPS, D-H Causality test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE decreases EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(M. Usman et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1990\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 Highest Emitting Nations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePanel analysis\u003c/p\u003e \u003cp\u003e(AMG, CCEMG, D-H Causality test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRE decreases EP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. Theoretical Framework, Data, And Methodology","content":"\u003cp\u003eThis research significantly examines the empirical relationship between ecological footprints (EF), consumption of renewable energy (R) and non-renewable (conventional) energy (NR), per capita income (Y), as well as trade openness (T) for Renewable Energy Consumption Attractive Index (RECAI) countries. As discussed in the review of literature the empirical relationship between ecological footprints and consumption of renewable energy may be positively and negatively affected, it depends upon the percentage of renewable energy use. Even though most of the studies stated that there is a negative relationship between EF and RE; it implies that RE uses significantly reduces EF, more particularly (Ansari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for RECAI listed 22 countries, (Ullah et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for World top15 REC nations, (Pata, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the USA, (Ulucak \u0026amp; Khan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for BRICS Countries. Economic growth positively affects the ecological footprints; it suggests that Economic growth increases ecological footprints which were reported in the literature by (Hassan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for the case of Pakistan, (Galli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for India and China, and (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for China. As stated, by previous authors affirmed that utilization of non-renewable energy unfavorable and positively affected the environment; it significantly indicates that the usage of non-renewable energy increases ecological footprints, particularly (Raghutla \u0026amp; Chittedi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for BRICS nations, (Salahuddin et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for GCC nations, (Chindo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for EU nations, (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Pakistan, (Saidi \u0026amp; Hammami, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for Global panel countries, (M. K. Khan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Pakistan, (Ozturk \u0026amp; Acaravci, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for Turkey. Trade openness negatively effects ecological footprints, increasing trade openness leads to a decrease in the ecological footprints (Destek \u0026amp; Sinha, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for economic cooperation and developed countries, (Destek et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for EU nations, (Charfeddine, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for GCC and MENA countries. Based on previous arguments we have formulated regression equations as follows.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$EF=f({Y_{it}},{R_{it}},N{R_{it}},{T_{it}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$EF={Y_{it}}+{R_{it}}+N{R_{it}}+{T_{it}}+{u_{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$EF=Y_{{it}}^{{\\phi 1i}}R_{{it}}^{{\\phi 2i}}NR_{{it}}^{{\\phi 3i}}T_{{it}}^{{\\phi 4i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\ln EF={\\beta _0}+{\\beta _1}\\ln {Y_{it}}+{\\beta _2}\\ln {R_{it}}+{\\beta _3}\\ln N{R_{it}}+{\\beta _4}\\ln {T_{it}}+{u_{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(E{F_{it}}\\) \u003c/span\u003e \u003c/span\u003e represents ecological footprint, \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({Y_{it}}\\) \u003c/span\u003e \u003c/span\u003e indicates per capita income, \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({R_{it}}\\) \u003c/span\u003e \u003c/span\u003e represents renewable energy, \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(N{R_{it}}\\) \u003c/span\u003e \u003c/span\u003e denotes non-renewable energy, \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({T_{it}}\\) \u003c/span\u003e \u003c/span\u003e indicates trade openness, \u003cspan class=\"InlineEquation\"\u003e \u003c/span\u003e indicates 39 renewable energy consumption attractive index (RECAI) countries, and \u003cspan class=\"InlineEquation\"\u003e \u003c/span\u003e denotes a period of 1990\u0026ndash;2020. ln the natural logarithm, \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({\\beta _0},\\) \u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({\\beta _1},\\) \u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({\\beta _2},\\) \u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({\\beta _3},\\) \u003c/span\u003e \u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({\\beta _4}\\) \u003c/span\u003e \u003c/span\u003e are the intercept and slope coefficients, respectively. \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\({u_{it}}\\) \u003c/span\u003e \u003c/span\u003e is the random term. We have used time-series data of RECAI countries during 1990\u0026ndash;2020. The data on the ecological footprint (consumption of per capita), income per capita (constants 2015 US\u003cspan\u003e$\u003c/span\u003e), both consumption of renewable energy and non-renewable energy (percentage of consumption of total energy) and trade openness (percentage of GDP). Data is collected from both the \u0026ldquo;\u003cem\u003eGlobal Footprint Networks and World Development Indicators\u003c/em\u003e\u0026rdquo; published by the GFN and World Bank, respectively.\u003c/p\u003e \u003cp\u003eThis research employed the panel ARDL method to analyse the long-term as well as short-term association among the ecological footprints, economic growth, both consumption of renewable as well as non-renewable energy and trade openness variables for RECAI nations. Moreover, this panel ARDL approach provides reliable estimations by significantly removing the problem of endogeneity in the model. The empirical equation can be written as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\begin{gathered} \\Delta E{F_{it}}={\\alpha _0}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{1i}}} \\Delta E{F_{i,t - 1}}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{2i}}\\Delta } {Y_{i,t - 1}}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{3i}}\\Delta } {R_{i,t - 1}},\\sum\\limits_{{i=1}}^{q} {{\\alpha _{4i}}\\Delta } N{R_{i,t - 1}}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{5i}}\\Delta } {T_{i.t - 1}} \\hfill \\\\ +{\\alpha _6}E{F_{i,t - 1}}+{\\alpha _7}{Y_{i,t - 1}}+{\\alpha _8}{R_{i,t - 1}}+{\\alpha _9}N{R_{i,t - 1}}+{\\alpha _{10}}{T_{i,t - 1}}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e are the lag order and error term, while \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e represents the first difference operator. Equation-5 can be transformed into an Error Correction Model to equation-4 as follows:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\begin{gathered} \\Delta E{F_{it}}={\\alpha _0}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{1i}}} \\Delta E{F_{i,t - 1}}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{2i}}\\Delta } {Y_{i,t - 1}}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{3i}}\\Delta } {R_{i,t - 1}},\\sum\\limits_{{i=1}}^{q} {{\\alpha _{4i}}\\Delta } N{R_{i,t - 1}}+\\sum\\limits_{{i=1}}^{q} {{\\alpha _{5i}}\\Delta } {T_{i.t - 1}} \\hfill \\\\ +\\varpi (E{F_{i,t - 1}}+{\\theta _1}{Y_{i,t - 1}}+{\\theta _2}{R_{i,t - 1}}+{\\theta _3}N{R_{i,t - 1}}+{\\theta _4}{T_{i,t - 1}})+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e indicates the parameter of speed adjustment, while \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e are long-run coefficients of economic growth, both the renewable energy and non-renewable energy usage and trade openness, individually. Equation-5 can be transformed into short-run, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e represent the short-run coefficients. Therefore, the panel ARDL \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e models are as follows:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\begin{gathered} \\Delta E{F_{it}}={\\eta _0}+\\sum\\limits_{{i=1}}^{p} {{\\eta _{1i}}} \\Delta E{F_{i,t - 1}}+\\sum\\limits_{{i=1}}^{q} {{\\eta _{2i}}\\Delta } {Y_{i,t - 1}}+\\sum\\limits_{{i=1}}^{k} {{\\eta _{3i}}\\Delta } {R_{i,t - 1}},\\sum\\limits_{{i=1}}^{g} {{\\eta _{4i}}\\Delta } N{R_{i,t - 1}}+\\sum\\limits_{{i=1}}^{f} {{\\eta _{5i}}\\Delta } {T_{i.t - 1}} \\hfill \\\\ +\\varpi (E{F_{i,t - 1}}+{\\theta _1}{Y_{i,t - 1}}+{\\theta _2}{R_{i,t - 1}}+{\\theta _3}N{R_{i,t - 1}}+{\\theta _4}{T_{i,t - 1}})+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Empirical Results And Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Unit root tests\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e exhibits the empirical outcomes of the IPS unit root tests, we applied to verify the stationarity of our dataset. The empirical results show that variables like EF, Y, R, NR, and T are stationary at the level as well as first difference. More precisely, Y, NR, and T are stationary at their levels, while ecological footprint and renewable energy are significantly stationary at their I(1) order. The IPS panel unit root tests considerably show that EF, Y, R, NR, and T have followed both the orders namely at levels and first-order difference, which significantly implies that there may be a long-term relationship among EF, Y, R, NR, and T variables. Therefore, we are going to estimate the panel ARDL model in the next section to identify the long-term equilibrium association between the EF, Y, R, NR, and T variables.\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\u003e\u003cem\u003eResults of panel unit root tests\u003c/em\u003e\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\u003eIPS test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-32.863*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.570*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-24.555*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.0183*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-26.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.725*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: * indicates one percentage significant level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Panel ARDL Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the result of the panel ARDL approach. Particularly, a 1% improvement in lnR leads to reduces lnEF by 0.065%. It indicates that using renewable (clean) energy has a negative considerable impact on environment. Using various types of non-conventional (clean) energies namely solar, wind, biomass, water, and geothermal energy, all helpful to minimize CO\u003csub\u003e2\u003c/sub\u003e emissions, decrease the ecological footprints as well as promote environmental quality. This result is the same as the result of (Mehmood, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the 15 Highest Emitting Nations, (Sharif et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Turkey, (O. Usman et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for USA, (Sharma et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Southeast Asia nations, (Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for remittance receiving countries, and (Nathaniel \u0026amp; Khan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for ASEAN nations. The findings confirmed that the utilization of non-renewable energy considerably increases the ecological footprint. Precisely, a 1% intensification in lnNR contributes to an increase in lnEF by 0.131%. It indicates that ecological footprints and non-renewable energy, both have a positive association. This similar finding is recognized by (Raghutla \u0026amp; Chittedi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for BRICS nations, (Salahuddin et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for GCC nations, and (Chindo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for EU countries, (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Pakistan, (Saidi \u0026amp; Hammami, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for Global panel nations, (Ulucak \u0026amp; Khan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Pakistan, and (Ozturk \u0026amp; Acaravci, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for Turkey. The estimated ARDL coefficients shows that both economic growth as well as ecological footprints are positively associated, this result indicates that higher economic growth increases more ecological footprints. The analysis revealed that a 1% rise in lnY result in a 0.250% increase in lnEF. Because most of the countries are directly dependent on the conventional energy use and this leads to produces more ecological footprints along with growth. RECAI countries are highly using conventional energy (i.e., oil and coal) in the different production industries which is leads to higher in ecological footprints. Therefore, the countries need to lower the usage of conventional (traditional) energy not only in the production industries but also require to extend the other sectors as well. The same results were found by previous authors namely (Hassan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for Pakistan, (Galli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for China and India, (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for China, and (Kirikkaleli et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Turkey. Finally, this result also affirmed that trade openness has a significant harmful effect on the environment, a 1% improvement in lnT result in a 0.136% decrease in lnEF. International trade removes the barriers to transferring technology which is provided with the nations to access the cleaner technologies. It reduced the level of ecological footprint; as a result, trade openness significantly curbs the ecological footprints. The same results were found by previous authors namely (Destek \u0026amp; Sinha, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for economic cooperation and developed countries, (Destek et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for EU nations, (Charfeddine, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for GCC and MENA countries, and (Lu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Asian countries.\u003c/p\u003e \u003cp\u003eThe ECM coefficient is negative in the short term, with a value of -0.389 and 1% level of significance. In the short-term, particularly, the findings display that both renewable (clean) energy and non-renewable energy utilization have a negative and substantial impact on the ecological footprint but its coefficients values are not significant. In RECAI countries, if economic growth improvement by 1%, ecological footprints will increase by 0.674%, moreover, coefficients are significant particularly at 1% level. This finding also proposes that ecological footprints and trade openness have a negative relationship, particularly, if trade openness improvement by 1%, ecological footprint will cause to increase by 0.210% and coefficients are significant at a 1% level in the short-term.\u003c/p\u003e \u003cp\u003eIn the RECAI nations, the ARDL method, results clearly show that renewable energy use as well as trade openness improve quality of environment but conventional energy usage and economic growth do not. This could be due to the extensive utilization of conventional energy in the process of industrial production and other based needs. Overcome this problem is only possible by the reduction of conventional energy use; therefore, RECAI countries need to replace it with non-conventional energy resources like solar, biomass, wind, geothermal and hydropower etc. The governments of RECAI countries have to take the necessary actions particularly fund allocation for renewable energy projects and also encourage private participation in renewable energy projects by providing various incentives. Along with this, RECAI countries need to bring the environmental policies and reframe their energy policies which are need to give more importance to renewable energy projects. Further, it can dampen the ecological footprint in RECAI countries and considerably enhance the quality of environment.\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\u003e\u003cem\u003eResults of panel ARDL model\u003c/em\u003e\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\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(EF=f(Y,R,NR,T)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLong-run results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.065*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.131*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.250*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.136*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eShort-run results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOINTEQ01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EF(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EF(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EF(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(R(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(R(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(R(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(NR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(NR(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(NR(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(Y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.674*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(Y(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(Y(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(Y(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.210*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(T(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(T(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(T(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001*\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 \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e*, *** indicates one and ten percentage significant levels.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3. D-H panel causality Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the results of the D-H panel causality approach. To estimate the D-H panel causality tests, the dataset should be a first difference; therefore, researchers have converted the dataset namely EF, Y, R, NR and T into I(1) order. The D-H panel non-causality test empirical results discovered a unidirectional (one-way) causality relation from both trade openness and utilization of non-renewable energy to ecological footprint. Nevertheless, we could not establish any causality relation between the clean or renewable energy usage, environmental footprint and economic growth. Entire causality test empirical results indicate that conventual energy use and trade openness have a substantial short-run influence on environment.\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\u003e\u003cem\u003eResults of D-H panel causality tests\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZbar-Stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026rarr;EF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u0026rarr;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR\u0026rarr;EF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u0026rarr;NR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026rarr;EF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u0026rarr;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u0026rarr;EF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.E-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u0026rarr;Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7612\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 \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e* indicates one percentage significant level.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Long-run Analysis of Individuals Nations\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrates the result of individual nations which is estimated by utilizing the DOLS method. The main reason for individual countries' analysis, is we have to know the performance of individual countries towards reduction of ecological footprints which is more useful for policymakers of nations. RECAI countries mainly consume and produce renewable energy and we have to know the significant level of utilization and to what extent those nations have curbed their ecological footprints. Therefore, we estimated individual nations, long-run elasticities. Particularly, ecological footprints in relation to growth are considerable positive for Chile (0.183343), China (1.001764), Denmark (0.053561), Greece (0.360591), Ireland (0.155179), Japan (0.073294), Korea Rep. (0.362029), Morocco (0.567448), Netherlands (0.599342), Romania (0.237314), Saudi Arabia (1.026827) and Turkey (0.192589). For these 12 countries, economic growth improvement has a substantial direct impact on ecological footprints. This empirical analysis indicates that economic growth will significantly add more ecological footprints to the environment due to more conventional energy usage in the process of production. This finding is the same (Hassan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for Pakistan, (Galli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for India and China, (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for China, and (Kirikkaleli et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Turkey. However, long-run ecological footprints elasticities also disclose the substantial negative impact of growth of RECAI nations on the environmental quality which is revealed for seven countries namely Australia (-0.171622), Austria (-0.254069), Canada (-0.029541), Germany (-0.511752), Norway (-0.230284), South Africa (-0.382278) and United States (-0.240143). This result shows that these seven nations started utilization of renewable energy in their nations in place of conventional energy in their production processes, which has improved quality of environment. This evidence is same to that of (Raghutla et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for major investment nations and (Hu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for India. Furthermore, the economic growth has both the positive and negative impact, but statistically insignificant impact on ecological footprints for 20 countries, particularly, for Belgium (0.106391), Brazil (0.024040), Czech Rep. (0.074502), India (0.035208), Mexico (0.307027), Peru (0.026872), Poland (0.181681), Portugal (0.091672), Slovenia (0.128535), Spain (0.204402), Sweden (0.031784), Ukraine (0.158640), Bulgaria (-0.235712), Finland (-0.301248), France (-0.022195), Israel (-0.006967), Italy (-0.131862), Kenya (-0.081147), Thailand (-0.559202) and United Kingdom (-0.076811). This evidence substantially indicates that utilization of renewable energy is at early stage in the process of production and also follows the energy mix.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe long-run elasticities of ecological footprints in relation to utilization of renewable energy, are significant negative for Australia (-0.665953), Belgium (-0.094025), Chile (-0.200933), Denmark (-0.239666), France (-0.192670), Greece (-0.625309), India (-0.541907), Ireland (-0.221611), Korea Rep. (-0.074882), Morocco (-0.179446), Netherlands (-0.219322), Peru (-0.364033), Portugal (-0.342057), Romania (-0.288313), South Africa (-0.312063), Spain (-0.609967), Turkey (-0.303971) and Ukraine (-0.185441). For 18 RECAI countries, renewable energy usage has an inverse considerable effect on environmental footprints. The usage of renewable energy, according to this empirical analysis, will greatly improve environmental quality by reducing ecological footprints. This result is consistent with earlier research namely (Mehmood, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the 15 Highest Emitting Countries, (Sharif et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for the case of Turkey, (O. Usman et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for USA, (Sharma et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Southeast Asia nations, (Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Remittance receiving countries, and (Nathaniel \u0026amp; Khan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for ASEAN Countries. However, the long-run elasticities show the direct effect of renewable (clean) energy utilization on environmental footprints, which is affirmed for Austria (0.458583) and Norway (0.845168). These two countries still depend on the more conventional energy sources which leads to an increase in ecological footprints. Therefore, these two countries need to replace particularly conventional energy in place of renewable energy sources to reduction of ecological footprints. This analysis is the same as that of (Nathaniel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for CIVETS countries. In addition, the renewable energy usage has a direct and negative impact on environmental footprints in 19 countries, but coefficients values are statistically insignificant, particularly, Bulgaria (0.098833), Finland (1.722640), Germany (0.084060), Kenya (0.073561), Mexico (0.087784), Thailand (0.749077) and United States (0.114915), while, Brazil (-0.067405), Canada (-0.015636), China (-0.064087), Czech Rep. (-0.994379), Israel (-0.052705), Italy (-0.027891), Japan (-0.103397), Poland (-0.056451), Saudi Arabia (-0.062993), Slovenia (-0.410748), Sweden (-0.071500) and United Kingdom (-0.047636). This analysis evidence that these 19 nations early stage of renewable energy utilization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe long-run elasticities, particularly ecological footprints in relation to conventional or non-renewable energy usage, are significant for six countries namely Australia (0.767181), Canada (0.554320), France (0.651401), Germany (3.591729), South Africa (2.558199) and United States (1.910811), this suggests that utilization of non-renewable energy will considerably generate the ecological footprints. Therefore, to reduce their ecological footprints, these six countries must expand their use of renewable energy. This evidence is recognized by (Raghutla \u0026amp; Chittedi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for BRICS nations, (Salahuddin et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for the gulf cooperation council, and (Chindo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for EU nations, (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Pakistan, (Saidi \u0026amp; Hammami, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for Global panel countries, (Ulucak \u0026amp; Khan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Pakistan, and (Ozturk \u0026amp; Acaravci, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for the case of Turkey economy. In contrast, the long-run elasticities show the negative impact of utilization of non-renewable or conventional energy on ecological footprints, which is significantly affirmed for ten different countries namely Belgium (-1.080421), Chile (-0.583265), China (-6.200239), Greece (-1.483880), Korea Rep. (-1.781583), Morocco (-2.712848), Netherlands (-2.837685), Romania (-0.828651), Saudi Arabia (-5.976091) and Turkey (-0.642183). It is indicating that these ten countries have not only significantly adopted carbon capture technology but have also increase their usage of renewable energy which significantly lowers the ecological footprints. This result is the same as earlier findings that of (Acheampong, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for MENA, (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for China, (Zou \u0026amp; Zhang, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for China, and (Svedberg, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for OECD countries. The non-renewable or traditional energy usage has a both positive and negative but statistically insignificant effect on ecological footprints for 23 countries mainly Austria (0.407919), Brazil (0.240165), Bulgaria (1.519572), Czech Rep. (0.110697), Denmark (0.000467), Finland (1.049226), India (0.239507), Israel (0.092793), Italy (1.124164), Japan (0.045196), Kenya (0.432077), Norway (0.659952), Peru (0.187332), Portugal (0.103684) Sweden (0.170439), Thailand (2.265330), and United Kingdom (1.025801), while Ireland (-0.363577), Mexico (-1.667034), Poland (-0.586811), Slovenia (-0.513299), Spain (-0.661970) and Ukraine (-0.511889). This analysis suggests that these 23 countries can significantly shows the quality of environment improvement when increase in renewable energy share which is a major strategy for climate change and environmental sustainability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe long-run elasticities show the positive considerable effect of trade openness on ecological footprints, which is considerably affirmed for nine countries namely Australia (1.521857), Belgium (0.776539), Canada (0.176009), Greece (0.113260), Israel (0.437041), Norway (0.498667), Peru (0.158328), Saudi Arabia (0.292289) and Sweden (0.175702). This empirical evidence implies that trade openness increases ecological footprints. This finding is similar to (Al-Mulali et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for ninety three nations, (Al-mulali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for 58 countries, (Aşici \u0026amp; Acar, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for 116 nations, (Kongbuamai et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for Thailand and (Ozturk et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for 144 countries. However, the long-run empirical elasticities of ecological footprints in relation to trade openness, are significant negative for four countries particularly Brazil (-0.140466), Japan (-0.219299), Morocco (-0.343332), and Turkey (-0.135102). This analysis evidence that increasing trade openness reduces environmental impact via the exchange of technological innovations. This empirical evidence same as that of (Destek \u0026amp; Sinha, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for economic cooperation and developed countries, (Destek et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for EU nations, (Charfeddine, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for GCC and MENA countries, (Aydin \u0026amp; Turan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for BRICS countries. For 26 countries, trade openness significantly has both beneficial and unfavorable effects in the environment, but these effects are statistically insignificant, specifically, Austria (0.249136), Bulgaria (0.076275), Czech Rep. (0.414014), Denmark (0.337752), France (0.051734), Germany (0.105519), India (0.007031), Italy (0.117378), Kenya (0.177215), Slovenia (0.418659), South Africa (0.199467), Spain (0.072806), and Thailand (0.795743), while Chile (-0.063882), China (-0.173344), Finland (-0.079236), Ireland (-0.080919), Korea Rep. (-0.123008), Mexico (-0.055472), Netherlands (-0.337172), Poland (-0.154949), Portugal (-0.084911), Romania (-0.155023), Ukraine (-0.130195), United Kingdom (-0.206886) and United States (-0.048103). This analysis suggests that these 26 countries need to improve their trade openness for reduction of ecological footprints.\u003c/p\u003e \u003cp\u003e \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\u003e\u003cem\u003eResults of long-run ecological footprint elasticities using the DOLS Model (Dependent variable: ecological footprints).\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.171622*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.665953*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767181*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.521857*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.254069***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.458583*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.407919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.106391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.094025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.080421**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776539*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.067405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.240165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.140466*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.235712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.519572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.029541***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.015636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.554320*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.176009**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.183343*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.200933**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.583265**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.063882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001764*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.064087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.200239**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.173344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCzech Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.074502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.994379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.414014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.053561***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.239666*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.301248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.722640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.049226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.079236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.022195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.192670*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.651401*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.511752*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.591729*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.360591*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.625309*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.483880*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113260**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.541907*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.155179**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.221611*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.363577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.080919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsrael\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.006967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.052705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.437041*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.131862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.027891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.124164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.073294**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.103397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.219299*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.432077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKorea Rep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.362029*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.074882*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.781583*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.123008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.307027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.667034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.055472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorocco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.567448*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.179446*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.712848*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.343332**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.599342*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.219322**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.837685*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.337172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.230284**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.845168**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.659952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.498667*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.026872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.364033*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.187332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158328*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.181681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.056451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.586811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.154949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.091672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.342057*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.084911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.237314*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.288313*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.828651*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.155023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.026827*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.062993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.976091*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.292289**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlovenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.128535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.410748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.513299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.418659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.382278*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.312063*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.558199*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.199467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.204402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.609967*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.661970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.071500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.170439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.175702***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.559202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.265330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.795743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.192589*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.303971*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.642183**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.135102**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUkraine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.158640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.185441*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.511889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.130195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.076811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.047636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.025801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.206886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.240143*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.910811*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.048103\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 \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e*, ** and *** indicates one, five and ten percentage significant levels.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion And Policy Implications","content":"\u003cp\u003eGlobally, the majority of countries are striving for economic growth, and most of them have seen considerable growth, particularly from 1990 onwards. At the same span, their ecological footprints have also increased along with their economic growth due to the significant use of conventional energy resources. Further, it represents a threat to the future generations of humans, environmental quality, and sustainability as well. Therefore, without disturbing the production capacity; using clean energy in the production process can contribute to achieve environmental quality. Given this context, this research investigates the empirical impact of renewable energy utilization on ecological footprints by seeing significance of economic growth, trade openness, and utilization of non-renewable or traditional energy in ecological footprints function for RECAI countries, spanning the period 1990 to 2020. According to the study, renewable (clean) energy usage and trade openness, these significantly improve environmental quality over time, whereas non-renewable energy usage and economic expansion do not improve the quality of environment. Trade openness and economic expansion exacerbate environmental damage in the short-run. Causality results document a one-way (unidirectional) causality relation from both trade openness as well as non-renewable energy usage to the ecological footprint. Overall, the findings indicated that the usage of non-renewable or traditional energy, as well as economic expansion, both causes environmental degradation in RECAI countries. The RECAI countries are reliant on traditional energy in the industrial production process, which has resulted in increased environmental degradation, both directly and indirectly. Furthermore, globally most of their production units are directly dependent on traditional energy, which witnesses an increase in environmental degradation. Therefore, those countries need to use renewable energy instead of using traditional energy to ensure environmental quality.\u003c/p\u003e \u003cp\u003ePolicymakers and governments should focus to enhancement the renewable energy sources through the installation of solar PV, the development of hydropower and wind energy projects. It is only possible by providing incentives and subsidies in the energy sector, particularly for renewable energy projects. The household can significantly play an important role, by making accessible renewable energy sources to the household and they may give better results particularly reduction in ecological footprint. Furthermore, public industries and government should follow renewable energy production methods; so that it will significantly promote and motivates others as well. In most countries, there is no proper awareness of using renewable energy in the countryside, making it accessible to the rural areas by providing special subsidies and creating awareness through social policies will encourage them to use clean energy such as solar PV and wind power. In extension to this, the government need to create awareness of utilization of renewable energy; especially in, the industrial, service, agriculture sector and other basic needs as well. Given this background, RECAI countries should take initiatives to make renewable energy accessible to all, so that renewable energy usage will increase and hence it will pave the way to replace the traditional energy. Finally, it can significantly lower the ecological footprint and improve environmental quality. Taking into account today\u0026rsquo;s environmental condition, where our global-ecological structure once again appears to be dealing with resources disastrously, it is important to reanalyse and arbitrate in the conversation about how energy and environmental policies are sustainably rooted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eChandrashekar Raghutla: Conceptualization, Writing-Original Draft, Methodology, Software, Supervision..\u003c/li\u003e\n \u003cli\u003eYeliyya Kolati: Writing-Original Draft, Review \u0026amp; Editing, Resources, Methodology,\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Not applicable (we are not received any funding).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that there are no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e Data will be available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas, S., Kousar, S., \u0026amp; Pervaiz, A. 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CO2 emissions, energy consumption, and economic growth nexus: evidence from 30 provinces in China. \u003cem\u003eMathematical Problems in Engineering\u003c/em\u003e, \u003cem\u003e2020\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Renewable Energy, Environmental Quality, Trade Openness, Panel analysis, RECAI countries","lastPublishedDoi":"10.21203/rs.3.rs-2466940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2466940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSince 1990, the ecological footprints have been increasing significantly with a continuous increase rate, which led to challenges to environmental quality. The basis for Economic growth was said to be the shift of energy and environmental strategies toward a sustainable future. Indeed, it became a matter of proclaimed acceptance that environmental challenges nurtured expansion, innovation, and competitiveness. Climate change is the most pressing issue being faced by the world due to an increase in ecological footprint from 7.0\u0026nbsp;billion GHA to 20.6\u0026nbsp;billion GHA. It indicates the seriousness of environmental degradation; therefore, the nations need to ensure environmental sustainability. Keeping this in mind, the present research main aims to examine the impact of renewable energy utilization on the ecological footprints of RECAI economies, spanning the period 1990 to 2020. To significantly achieve the research objective, we utilized panel econometric methods for empirical analysis. The results of long-run elasticities indicate that both the renewable energy utilization as well as trade openness significantly controls the ecological footprints, while higher conventional energy utilization and economic growth significantly impede the environmental sustainability. The empirical findings provide new insights for policymakers on renewable energy for the betterment of environmental quality in RECAI countries.\u003c/p\u003e","manuscriptTitle":"Does Renewable Energy Improve Environmental Quality? 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