Trade, Energy Consumption, Economic Growth and Ecological Quality: An Empirical Evidence from D-8 and G-7 Countries

While pursuing sustainable growth there is a need to inculcate the procedure of a sustainable environment for coming generations. This study investigates the association between carbon dioxide emissions (CO 2 ) and gross domestic product (GDP), disaggregate and aggregate energy usage, trade, using yearly data for the time component of 1980–2019 for D-8 and G-7 countries. The study employs second-generation unit root tests namely cross sectionally augmented Dickey Fuller (CADF) and cross sectionally augmented IPS (CIPS). Further, Kao, Pedroni, and Westerlund cointegration tests are employed to test for cointegration. To estimate models, fully modied least squares (FMOLS), dynamic ordinary least squares (DOLS) and heterogeneous panel estimators (MG, AMG, CCEMG, DCCEMG) are used. Finally, to verify the causality among the study variables the Dumitrescu and Hurlin, (2012) causality test is used. The ndings of the study reveal that income, aggregate, and disaggregate energy consumptions (oil, coal, gas), and trade exacerbate ecological quality in D-8 countries while income and trade improve it in G-7 economies. However, oil, coal, and gas usage are detrimental for ecological quality in G-7 economies. The study validates an inverted U-shaped environmental Kuznets curve in D-8 while U-shaped in G-7 countries. It is recommended that both groups of countries need to adopt green strategies and sustainable patterns for growth and development.


Introduction
Ecological degradation is a very primitive topic that has been under discussion since the 20th century. An increasing rate of global carbon emissions is considered the major determinant of deteriorating ecological quality. The agglomeration of CO 2 emissions in the atmosphere has intensi ed by almost 45 percent in the previous 130 years (Carbon footprint, 2018). British Petroleum (2018) highlighted that level of CO 2 emissions intensi ed from 29,714.2 million tons in 2009 to 33,444.0 million tons in 2017. Global climate change persistently threatening the lives and assets, thus restraining the climate changes is a concern of urgency.
Resultantly, climate change has become a momentous global concern. The implication of mitigating CO 2 for opposing the worldwide ecological changes vulnerabilities, via re ning ecological conditions, has been properly recognized in the international perspective. As per the execution of the Kyoto protocol in 1997, under the United Nations Convention on climate change, has stressed curbing CO 2 emissions globally. IPCC (2018) projected that to maintain the worldwide temperature at 1.5 _C above the pre-industrial stages, the worldwide emissions have to be fetched down by about 45% from the level where it was in 2010 till 2030 and secondly, totally bump off it by 2050. According to a recent global climate risk index report, countries largely affected by climate change are mainly developing economies (Eckstein et al., 2019).
Energy is a fundamental factor used in the manufacturing course and enhancing the industrial expansion and economic progress, but the high level of energy consumption increases CO 2 emissions which further deteriorate environmental quality (Alkhatlan & Javid, 2013). In the current era, many countries are now predominantly based on the utilization of fossil fuel which include coal, oil, and natural gas and they bring severe changes in the ecological condition worldwide. The rapidity of securing energy sources leads to 2. Literature Review The theoretical framework for this study is based on the environmental Kuznets curve (EKC) theory which suggest that initially economic growth degrades the environment but after a certain level of growth, environmental degradation starts declining. The EKC has been substantially studied but the empirical validity of EKC is mixed. Since the pioneering work by Grossman & Krueger (1991), Holtz-Eakin & Selden (1995), Narayan & Narayan (2010), Ahmed et al. (2016) supported inverted U-shaped relationships. Studies have typically adopted different types of data sets in analyzing the EKC hypothesis (e.g. Panayotou, 1993;Apergis et al., 2010;Kahia et al., 2019). These studies provide mixed outcomes. Apergis et al. (2010) did not con rm the hypothesis as there was a positive and linear effect of growth on emissions.

Energy Consumption and Ecological Quality
In this recent age of advancement, energy is considered an indispensable factor for economic expansion but also a vital strategic reserve for an economy. Similarly, sustainable economic progress relies on energy consumption (Kraft & Kraft, 1978). The study of Ozokeu and Ozedmir (2017) indicated that a surge in energy use will be pernicious for ecological quality in both developed and emerging states. Empirical evidence of Majeed & Mazhar (2019b) demonstrated the impact of energy consumption on CO 2 emissions by using data of 131 economies . The results suggested that energy consumption is deleterious for the environment. Similar ndings have been highlighted by Kohler, (2013) in South Africa, and Baek, (2015) in Arctic economies. The study by Dogan et al. (2017) examined the nexus between energy usage and ecological degradation using data  of OECD economies. The results con rmed that energy consumption is a signi cant indicator in deteriorating ecological quality. Furthermore, the study of kasman & Duman (2015) on EU members economies, concluded that energy usage is detrimental for environment.
The study of Gours & Aydin (2019) examined the causal relationships among the carbon dioxide emissions, and energy consumption. However, energy consumption cause escalation in CO 2 emissions in the case of MENA countries.

Coal, Oil, Gas and Ecological Quality
The consumption of coal, oil, and gas has escorted severe changes in the ecological condition worldwide. A report published by the European Union Joint Research Centre, elaborated that the combustion of fossil fuel accounted for about 90 percent of total worldwide CO 2 emissions (Oliver et al., 2012). Every non-renewable source either gas, coal, or oil has a calamitous effect on the environment at both levels, whether it is extraction or consumption of the resource. A report by British petroleum (2020) highlighted that at the global level energy usage upsurges the level of emissions by 2.3 percent. Moreover, elevation in oil consumption by 0.9 percent, gas consumption and production 2 and 3.4 percent was observed especially in emerging countries. But globally there was a reduction in coal consumption by 0.6 percent other than the case of OECD economies where a reduction in its demand was observed. The recent literature suggests that disaggregated research at regional or national levels is more felicitous (Li et al., 2018).
Empirical evidence supports that raising the level of oil, coal, and consumption are exacerbating ecological quality. According to a study by Alkhathlan & Javid (2013) using autoregressive distributive lag (ARDL) over the period from 1980-2011 analyzed that oil, coal, and gas usage are positively associated with CO 2 emissions. The study by Khan et al. (2019) concludes that coal, oil, and natural gas consumption escalate environmental degradation. Likewise, using the data of South Asian economies Munir and Riaz (2019) concluded that coal, oil, and gas usage are increasing the level of CO 2 emissions in the region. Similarly, another study by Valadkhani et al. (2019) examined the threshold effects of oil, coal, and gas on CO 2 emissions for the panel data set of 79 countries from 1965 to 2017. The results of the study con rmed that an increase in the consumption of oil, coal, and gas intensi es the level of CO 2 emissions. A recent study by Munir & Riaz (2020) investigated the impact of oil, coal, gas, and electricity consumption by applying the econometric technique non-linear autoregressive distributive lag (NARDL) for the time from 1975 to 2018 in the case of the United States of America, China and Australia on ecological degradation. The results indicate that oil, coal, and gas are signi cant factors in enhancing the CO 2 emissions in the USA, in Australia, oil and coal are the main contributors to escalating emissions, and lastly in china oil, and gas are key factors that increasing the level of CO 2 emissions. Shabaz et al. (2013) documented that coal consumption is a major factor in raising emissions in South Africa.

Trade and Ecological Quality
The famous theory of trade Hecksher-Ohlin trade recommends that if there is free trade developing economies should specialize in manufacturing and exporting those goods which are plentiful or cheap for instance labor and ample natural resources while on other hand developed nations will lead towards capital intensive affairs. Trade necessitates the transfer of goods from a country to another country. Sometimes it can be for consumption or it can be for further processing. This indicates that pollution is usually generated during production and consumption is in other countries. Now, more engagement of developing economies into foreign trade has made trade openness imperative discussion for the EKC hypothesis (Stern et al., 1994;Lean & Smyth, 2010;Baek, 2015). Sha k and Bandyopadhyay (1992) recommend that an economy that is more open to trade will be less polluted. The reason is when the economies are more open, they face an advanced level of competition and they must invest in advance e cient technologies which in return leads towards lessening pollution. Suri and Chapman (1998) discussed the trade in both form energy-intensive and second is non-energy intensive commodities. For instance, in the case of non-energy intensive production of automobiles needs more pollution-intensive inputs. The industrialized economies usually manage by exporting those more energyintensive goods to curtail the level of pollution. On the other hand, developing economies face the challenge of mounting pollution due to more exports of energy-intensive products. This hypothesis got more interest in the previous literature, which is known as the famous displacement or PHH hypothesis (Kellenberg, 2009;Kearsley & Riddel, 2010).
Trade can ameliorate and degrade ecological quality. To boost growth, production is elevated which needs more energy to bring resurgence in output and repercussion will be high emissions; it is known as scale effect. Similarly, if the process of production is more energy intensive and comprised by primary commodities, it causes more emissions and is said composition effect. Although, advanced technologies or substitution with green technology improve ecological quality and this is called technological effect (Grossman & Krueger 1995;Antweiler et al., 2001). The study of Zafar et al. 2019 concluded that trade can curb ecological degradation if the technological effect is higher than both scale and composition effect.
Secondly, a surge in exports of natural resources like coal, gas especially its extraction through conventional means, deforestation dissipates natural resources, and the repercussion will be an escalation in CO 2 emissions a worse ecological quality (Schmalensee et al., 1998).
The empirical evidence shows mixed results, like trade may worse, improve, or may have an insigni cant effect on ecological quality. In the case of developing economies, a recent study by Van Tran (2020) explored the repercussion of international trade on ecological quality. Two-step system Generalized method of moments (GMM) was employed for the period 1971 to 2016. Results of the study indicate that a 1% surge in trade openness leads to 0.2% proliferation in CO 2 emissions in the case of 66 developing countries. Similarly, the study of Ahemd et al. 2019 in the case of D-8 economies examined how exports affect the environment using panel data for the time 1980-2014. The study employed the FMOLS and the DOLS method and the result con rmed that exports adversely affect ecological quality moreover, similar ndings were concluded by Rahman (2017) in the case of China.
Contrary to this, studies also analyzed that trade may compact on CO 2 emissions. A study by Jayanthakumaran et al. (2012) explored the relationship between trade and CO 2 emissions in the case of China and India. The study used the time series data of India and China for the period from 1971 to 2007. The results of ARDL concludes that international trade tends to reduce CO 2 emissions in the short run.
Furthermore, an empirical examination of the causal association between trade openness and environmental pollution was carried out by (Ahmed et al., 2016). The data of BRICS economies was utilized for the time 1970-2013. The result of FMOLS con rmed that trade openness mitigates CO 2 emissions in the long run as deforestation amongst the main concern.
Aller et al. (2015) using the Three-stage least square (3SLS) technique, indicates that in low-income countries indirect effects of trade harm the ecological conditions while it improves the environmental conditions in high-income countries. In the existing literature, few studies have mixed ndings. The study by Le et al. (2016) observed the data set of 98 countries  utilizing the GMM approach. The study found that trade openness is good for the environment in high-income countries, and it pollutes the environment in developing economies.

Theoretical Framework
Different Studies had discussed the impact of Growth, trade, and FDI on CO 2 emissions. A study by Grossman & Kruger (1991), Examined the EKC which elaborates the relationship between economic development and ecological degradation with an inverted U-Shaped curve. Previously equation used by different studies (Holtz-Eakin & Selden, 1995;Chandran & Tang, 2013) is as:

Empirical Model and variable Description
Generally, in existing literature, carbon emission is a proxy used for ecological degradation (Ozturk & Acarvic, 2013;Ozokeu & Ozdemir, 2017). Therefore, to understand the relationship between environmental distortion and trade, growth, aggregate and disaggregate energy usage, our model speci ed as by Halicioglu & Ketenci (2016) and formulation for the impact of trade on the environment was as: In above eq.1 CO 2it is the CO 2 emissions per capita measured in Metric tons, ENRG is the energy used per capita measured in Kg of oil equivalent, GDP is per capita GDP measured in US$ Constant 2010, GDP 2 is square of GDP following the EKC hypothesis and measure of trade EXP and IMP are measured in current US$. Since to measure estimates log-linear transformation of eq.1 is being made to make interpretation easier in percentage form.
Extensive energy usage of non-renewable sources is pernicious for the environment as well as leads economies towards an unstainable path of development. This study will assess the association between the segregation of non-renewable energy usage namely oil, gas, and coal consumption, speci ed in equation 2: The above model is used for both in the case of D-8 and G-7 countries. Here, in eq.2 ln represents the natural log for each variable. The proxies used for ecological degradation CO2 emissions (Kt), emissions which are originated from the consumption of fossils fuels and the production of cement, which includes the usage of gas, liquid, and solid fuels. GDP is GDP per capita constant 2010 US$ and POC, PGC, PCC are per capita oil, gas, and coal consumption. TR is the sum of merchandise imports current US$ and merchandise exports current US$. All the variables collected are in per capita form except imports and exports; therefore, these two variables are divided by the respective country population to transform them into per capita form. Energy use is measured in kg of oil equivalent per capita.

Data and Econometric Tools
The data of CO 2 emissions, GDP per capita, energy consumption, trade volume (imports + exports), and the total population is retrieved from WDI while data regarding disaggregate consumption which includes oil, coal, and gas consumption is taken from British petroleum 2019. D-8 economies include Bangladesh, Egypt, Indonesia, Iran, Malaysia, Nigeria, Pakistan and Turkey. G-7 economies comprise Canada, France, Italy, Japan, United Kingdom and United States.
As the study uses panel time-series data, so different tests of panel unit root which includes Levin, Lin & Chu (LLC), I'm, Pesaran (IPS) and from second-generation unit root tests, CIPs, and CADF tests will be used to examine whether data is stationary or non-stationary (Pesaran, 2007). To test cointegration among variables, Pedroni, Kao, cointegration test, proposed by (Kao, 1999;Pedroni, 1999) and the test suggested by Westerlund (2007) and Westerlund (2008), consider four tests that include two group stats and twopanel statistics. Furthermore, it also allows cross-sectional dependence (CD).
This study used FMOLS, and DOLS to estimates the results. It provides robust ndings and e caciously analyze the small sample size. The FMOLS and DOLS also consider the serial correlation and account endogeneity in the regressors but it does not account CD. However, unlike earlier studies, the current study control for distinct economic and econometric concerns which include (cross-sectional dependence, endogeneity, and heterogeneity) using distinct empirical approaches, thereby producing consistent and robust outcomes for policy designs. Likewise, Mean Group (MG) Pesaran & smith (1995), Augmented Mean Group (AMG) (Bond & Eberhardt, (2009). The study also uses the common correlated effect (CCE) estimation technique proposed by Pesaran (2006), and the dynamic CCE (DCCE) estimator developed by (Chidik & Pesaran, 2015). The Dumitrescu & Hurlin (2012) causality test will be used to examine the causality among the variables.
The descriptive statistics of the variables are presented in Table 1 Moreover, the ndings of the correlation matrix are presented in table 2. All the study variables are correlated as expected.

Results And Discussion
LLC and IPS unit root tests are used initially to examine the stationarity of data. Some variables are stationary at the level and some at the level. Results are presented in appendix table 1(a). cross-sectional dependence (CD) of the variables and model was also checked, and results are presented in Table 3. All test statistics are signi cant, con rms the presence of CD and analysis can proceed second generation unit root test.  Panel and group test con rm the existence of cointegration. The null hypothesis is rejected.  Table 7 column 1-2, & 3-4 explains the ndings of Fully modi ed OLS and dynamic OLS in D-8 and G-7 countries, respectively., the coe cient of GDP per capita is positive and signi cant in the case of D-8 economies as 1% increase in GDP causes CO 2 emissions to escalate by 3.721%, 5.320% as results shown by FMOLS and DOLS respectively. Moreover, the coe cient of square of GDP per capita, as shown by both estimators are negative and signi cant. As 1% increase in square of GDP per capita causes CO 2 emissions to decrease by -0.515%, and -0.703%. Result con rms the existence of the EKC hypothesis in the case of D-8 countries as the initial phases of development are associated with ecological degradation but after a certain threshold level ecological degradation tends to decrease (Hanif et al., 2019;Ozturk & Acaravi, 2013;Grossman & Krueger, 1991). On the other side, GDP per capita coe cient is negatively and statistically signi cant associated with CO 2 emissions in the case of G-7 countries as 1 percent increase in GDP Causes improvements in ecological quality by -2.913, -9.730% respectively (results of FMOLS, DOLS).
Here GDP per capita has a negative and signi cant link with CO 2 emissions and square of GDP per capita is positive and signi cant which supports U-shaped curve similar to the ndings of (Destek & Sarkodie, 2019).
Secondly, the ndings of disaggregate energy consumption suggest that in the case of D-8 economies, the coe cients of coal, oil, gas consumption are positive and signi cant implying that 1 percent surge in oil, coal, gas consumption leads to elevate emissions by 0.04, 0.32, 0.31 percent respectively. Though the magnitude of oil consumption is higher out of the aforementioned non-renewable resources. The ndings of DOLS are same but differs in magnitude. According to a report published by the European Union Joint Research Centre, elaborated that the combustion of fossil fuel accounted for about 90 percent of total worldwide CO 2 emissions (Oliver et al., 2012). However, the outcomes of the present study are similar to the outcomes of (Valadkhani et al.,2019). In the case of G-7 countries, the ndings are similar as a rise by 1 percent raises in coal, oil, and gas consumption escalates emissions by 0.14, 0.05, 0.52 percent respectively and ndings of DOLS estimator is same. The ndings are consistent with the outcomes of (Ahmad et al. 2016). In G-7 economies gas and oil consumption is more detrimental for the environment but lower magnitude than D-8 con rms that G-7 economies are also using green technologies to curb the level of emissions.
Thirdly, estimates obtained from shows that in column 1 & 2, the coe cient of trade is positive and signi cant suggesting that 1% increase in trade causes a surge in the level of emissions by 0.04%, 0.06%. This result is in the line with the results of (Ozturk & Acaravi, 2013;Al-Mulali & Ozturk, 2015;Van Tran, 2020). In developing economies trade can affect ecological quality through various channels like if trade increases, energy demand will also increase, furthermore it will escalate the energy consumption repercussion will be a resurgence in emissions. Contrary to this, the ndings of trade in the case of G-7 are as trade shows negative and signi cant association with CO 2 emissions. Both estimators con rm that one percent surge in trade slackens the emissions by -0.01 percent. In the case developed economies, trade cannot be a source of boosting emissions because they usually use advance and eco-friendly technologies.
Strick rules and regulations are also a hurdle in these economies in polluting environment. our ndings are supported by (Ahmed et al. 2016;Jalil & Mehmood, 2011;Managi et al. 2009;Aller et al. 2015).

FMOLS and DOLS Results with Aggregate Energy Consumption
In Table 8 column 1-2, 3-4 explain the estimates of the same model with aggregate energy consumption.
Results obtained from FMOLS and DOLS, in this case, show that in D-8 countries coe cient of GDP per capita is also positive and signi cant highlighting that, 1% increase in GDP per capita causes CO 2 emissions to escalate by 3.19, 2.03%. However, the result reveals are the same as the ndings of (Zoundi, 2017). The square of GDP per capita con rm the presence of the EKC hypothesis in D-8 countries. The result supports the existence of the EKC hypothesis in the case of D-8 countries (Hanif et al., 2019). However, aggregate energy consumption is a second signi cant contributor that enhances CO 2 emissions by 1.25, 0.91% if there is 1 percent surge in the usage of energy consumption as examined by (Alam et al., 2016). Trade is positively associated with CO 2 emissions concludes that one percent increase in trade elevates CO 2 emissions by 0.05%, 0.11%.
In G-7 economies, according to the estimates of FMOLS, the sign of parameter of GDP per capita is negative highlighting that 1% surge in GDP per capita leads to a decline in emissions by -2.99% and -1.08%.
Furthermore, Square GDP per capita coe cient is positive and signi cant supporting the U-shaped EKC hypothesis. The nding of the current study, in this case, are supported by (Destek & Sarkodie, 2019). Moreover, aggregate energy consumption in G-7 has positive and signi cant coe cient associated with CO 2 emissions. As 1% increment in energy usage leads to boost CO 2 emissions by 0.60%, 0.27%. According to existing literature more energy usage is a signi cant factor in boosting environmental pollution. Thus, the result of the present study are consistent with the ndings of (kasman & Duman, 2015; Acheampong, 2018). The coe cient of trade is negative and signi cant implying that 1 percent increase in decline CO 2 emissions by -0.54and -0.38 percent. Trade ndings in the case G-7 countries show that trade is not a signi cant factor in escalating CO 2 emissions (Jalil & Mehmood, 2009).
From the above discussion, it can be concluded that, the major contributor in escalating carbon emissions include economic growth, disaggregate and aggregate energy use. Trade is also a factor boosting the level of carbon emissions in D-8 economies, but trade is not a major contributor. EKC is valid in D-8 countries. In the case of G-7 economies, trade, growth is not a signi cant factor elevating carbon emissions. But both aggregate and disaggregate energy usage in economies are the major contributor in boosting carbon emissions  To check further robustness of the results study used heterogeneous static and dynamic estimators. The results of MG, AMG, CCEMG, and DCCEMG are presented in table 9, which will elaborate the ndings in the case of both D-8 and G-7 countries. Tables 9, column 1-4 explaining the ndings of MG, AMG, CCEMG, and DCCEMG. In the case of D-8 countries, column 1 elaborates the ndings of MG. The results of columns 1 and 4 show that GDP per capita is a positive factor in deteriorating ecological quality. It also con rms the existence of the EKC hypothesis as discussed in the ndings of previous estimators.
Coal consumption has positive but statistically signi cant as shown in columns 3 and four of table 11.
Similarly, the ndings of columns 1,2, and 3 highlights that oil consumption is also a signi cant factor in boosting carbon emissions. Lastly, the results explain that gas consumption is also contributing to emissions. Trade coe cient is positive and signi cant, and ndings are similar in columns 2, 3 and 4 as well. On contrary, column 5-8 present ndings in the case of G-7 economies as However, the ndings of GDP, the square of GDP, and the results of disaggregated energy consumptions are robust and similar as discussed in previous results (FMOLS, DOLS). Trade is a negative but statistically insigni cant factor in escalating CO 2 emissions and the same results drawn in the study of (Jalil & Mehmood, 2011). The results of country-speci c analysis in G-7 countries indicate that trade has a negative relation in the UK, USA, and positive in Canada and Italy. GDP has a negative and statistically signi cant indicator and con rms the presence of a U-shaped curve in all G-7 countries except Canada. All the indicators of disaggregate energy consumption are damaging environment in G-7 countries. Our ndings to some extend are consistent with the outcomes of (Ike et al., 2020). The study used a second-generation panel causality test to investigate the causality among variables. The result of Dumitrescu and Hurlin, (2012) are presented in table 11. The results reveal that there is unidirectional causality from CO 2 to GDP, CO 2 to trade, CO 2 to PGC, CO 2 to POC, GDP to PCC and bidirectional causality from PCC to CO 2 , in D-8 countries while in the case of G-7 economies ndings are as follows: there is an existence of unidirectional causality from GDP to CO 2 , TR to CO2, PGC to CO 2 and PCC to CO 2 , bidirectional causality from POC to CO 2 .

Conclusion
Ecological degradation is a very pressing topic that has been under discussion since the 20 th century. This study examines the dynamics links among CO 2 emissions, trade, GDP, aggregate, and disaggregate energy consumption for D-8 and G-7 countries utilizing the annual data between 1980 and 2019. The empirical analysis is based on both rst and second-gen panel time series analysis. The study takes care of different econometric concerns such as endogeneity, heterogeneity, and cross-sectional dependence using static and dynamic panel estimators.
The results of both Pedroni, Kao, and Westerlund cointegration test con rm the long-run association among all the study variables. The ndings of FMOLS, DOLS and heterogenous dynamic panel estimators conclude that the GDP per capita, aggregate and disaggregate energy consumption (i-e oil, coal, gas consumption), trade are the signi cant factors of CO 2 emissions in the panel of D-8 countries. The results accept the EKC hypothesis for the panel D-8 economies implying that the existing development policy framework in these countries has the potential to recover the environmental loss caused by growth-oriented policies in the long run. However, a better and improved policy framework can shorten the transitionary spell between for-and pro-environment growth periods.
The results for G-7 countries reveal that GDP and trade have a negative effect on CO 2 emissions. Besides, the existence of a U-shaped curve is validated in G-7 economies. G-7 countries are following strict environmental rules and regulations but at the same time due to economies of scale and large-scale production, they are also facing the challenge of high emissions. The ndings validate that aggregate and disaggregate energy consumption, and GDP are highly emission intensive. Trade deleterious for the environment in D-8 countries.
Findings of the study suggest that D-8 economies need to adopt green strategies, green technologies and must ensure sustainable patterns for production and consumption. Renewable energy has the potential to meet the growing demands of energy with improving ecological quality. The integration of more effective, advanced and green, and clean technologies is mandatory for an effective transition from fossil fuel usage while attaining a decarbonized economy. Public, private sectors should substitute conventional sources with advance, clean and green resources.

Declarations
Authors' contributions This idea was given by Muhammad Tariq Majeed. Muhammad Tariq Majeed supported in all sections of this work and completed the nal write up of the paper. Naveed Asghar analysed the data and discussed the results and drafted initial versions of the other sections. All authors have read and approved the manuscript.
Con ict of Interest: Authors declare that they have no con ict of interest.
Ethical Approval: This article does not contain any studies with human participants or animals performed by any of the authors.
Finding: No funding from any institute is received for this study.
Availability of Data: Data sources are clearly mentioned. Interested person can access the data.
Computational Codes: Computational codes are available on demand.
Consent to Participate: I am free to contact any of the people involved in the research to seek further clari cation and information.

Consent for publication: Not applicable
Declaration of interest statement Declaration of interests The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to in uence the work reported in this paper.