Race Towards Environmental Sustainability in the G-20 Countries: Do Green Finance and Political Stability Play a Crucial Role

Race Towards Environmental Sustainability in the G-20 Countries: Do Green Finance and Political Stability Play a Crucial Role | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Race Towards Environmental Sustainability in the G-20 Countries: Do Green Finance and Political Stability Play a Crucial Role Mücahit Çitil, Metin Ilbasmis, Victoria Olushola Olanrewaju, Abdulkadir Barut, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2345689/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2023 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract As the negative repercussions of environmental devastation, such as global warming and climate change, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. Thus, the current study assesses the effect of green investments, institutional quality, and political stability on air quality in the G-20 countries for the period 2004–2020. The stationarity of the variables was examined with the Pesaran ( 2007 ) CADF, the long-term relationship between the variables by Westerlund (2007), the long-run relationship coefficients with the MMQR method proposed by Machado & Silva (2019), and the causality relationship between the variables by Dumitrescu & Hurlin (2012) panel causality. The study findings revealed that green finance investments, institutional quality and political stability increased the air quality, while total output and energy consumption decreased air quality. The panel causality reveals a unidirectional causality from green finance investments, total output, energy consumption and political stability to air quality, and a bidirectional causality between institutional quality and air quality. According to these findings, it has been found that in the long term, green finance investments, total output, energy consumption, political stability, and institutional quality affect air quality. Based on these results, policies implications were proposed. Green Finance Investments Total Output Energy Consumption Political Stability Air Quality Figures Figure 1 Figure 2 Figure 3 1. Introduction The impacts of consumption and production on the ecosystem and air have evolved as a result of the industrial revolution and shifting patterns (Sterpu et al. 2018 ). Although established enterprises have the ability to produce on a vast scale, they also create waste and carbon emissions, which increases their contribution to environmental contamination (Brimblecombe, 1978 ). Today, with the increase in competition, air pollution is continuing at an incredible pace (Adebayo et al. 2022a ; Balsalobre-Lorente et al. 2019 ). According to the Global Air 2020 report, it has caused the deaths of over 6 million people worldwide (State of Global Air, 2020 ). Certainly, these changes not only have adverse effects on human life, but also cause severe damage to the environment and air (Mehmood, 2021 ). In addition to industrialization, increasing urbanization has also exacerbated the problems and caused people to seek new ways of achieving environmental sustainability (Adebayo et al., 2022c ; Alqaralleh, 2021 ). Institutional quality is an essential factor that can affect air pollution, because a strong understanding of institutional quality increases the efficiency of public finances, helps law enforcement, reduces corruption, and eliminates the military's influence on politics (Farhan, Shazia, Amber, & Aiza, 2022 ). In a country where institutional quality increases, adherence to the law increases, and the enforcement of environmental laws can reduce carbon emissions and help improve air quality (Hao, Guo, Guo, Wu, & Ren, 2020 ), (Huynh & Ho, 220), (Shahbaz, Sharma, Sinha, & Jiao, 2021 ). In this context, institutional quality helps countries reach their sustainable goals by reducing ecological degradation. Political stability in a country also affects the sustainability of natural resources and the preservation of ecological balance (Samimi, Ahmadpour, & Ghaderi, 2022 ). There are various paths from political stability to the prevention of environmental degradation. In countries with no political stability, legal regulations regarding environmental rules become more flexible. Therefore, an increase in ecological degradation such as an increase in air pollution and the misuse of natural resources in countries lacking political stability can be expected. On the other hand, the decisions taken to protect the environment and investments made (like green energy investments) in this regard require investments that take decades. However, long-term investments always present significant risks to investors. Among the most important of these risks is political instability. For this reason, investors may be hesitant to make investments aimed at protecting the environment in countries where political stability is absent or under threat. Al-Mulali and Ozturk ( 2015 ) examined the factors that can affect environmental degradation in 14 MENA countries and found that political stability is essential in improving ecological quality (Al-Mulali & Ozturk, 2015 ). In their study on Pakistan, Sohail et al. ( 2022 ) found that political stability reduces environmental damage by reducing carbon emissions (Sohail, Majeed, Shaikh, & Andlib, 2022 ). The relationship between institutional quality and environmental degradation is quite indirect. However, there are more direct ways to stop and reverse environmental degradation. Green finance is just one of these direct paths. Green finance has huge potential to reduce air pollutant emissions such as greenhouse gases and carbon emissions (Eyraud, Clements, & Wane, 2013 ). Countries tend to use green energy sources such as sun and wind instead of fossil fuels (oil, coal, natural gas, etc.). However, using renewable energy sources to replace fossil fuels requires large investments. All these investments are defined as green finance in the literature. More precisely, funds allocated to investments that do not pollute the environment and protect the balance of nature that are realized or planned can be considered as green finance. Studies have found that green investments have positive effects on the environment (Eyraud, Clements, & Wane, 2013 ), (Karásek & Pavlica, 2016 ), ( Sachs et al., 2019 ), ( Heine et al., 2019), (Nassiry, 2019 ), (Zahan & Chuanmin, 2021 ). It is anticipated that this research will make important contributions to the literature. Air pollution, one of the greatest contemporary problems, negatively affects both people's health and the ecological structure. In this context, in this research, the effect of institutional quality and green finance on air quality will be examined. However, the concept of institutional quality not among the main discussion topics in low-income, underdeveloped, or developing countries. Similarly, replacing renewable energy sources with fossil fuel-based energy sources that cause carbon emissions requires significant investments. These two facts suggest that the sample needed to test the impact of GF and governance on air quality should include high-income countries. For this reason, we have used the G-20 countries for the sample of this study. These countries, which comprises more than 50% of the global economy, also make a significant contribution to the deterioration of the environment. Therefore, the scope of the analysis consists of the G-20 countries. Selecting these countries will help to solve many environmental problems based on the results. Except for Sohail et al. (2021), the effect of political stability on air quality has not been mentioned in other studies. However, in this study, both political stability and institutional quality were added to the model. When the existing literature is examined, it is seen that institutional quality or political stability alone are used in the models. However, in this study, political stability, a determinant of institutional quality, was included separately in the model and not in the institutional quality components. The reason for this is that it reveals the effect of political stability on environmental regulations. On the other hand, current econometric test such as (MMQR, Machado & Silva,2019) quantile regression was used in this study. This technique can capture the effect of the regressors on CO2 at different quantiles (0.1–0.90). Another innovation was the inclusion of green finance into the model. In this way, the effect of institutional quality, political stability, and green investments on air quality was examined. Furthermore, although the scope of the study consists of the G-20 countries, the findings can be generalized to all countries because political stability and institutional quality reflect a country's view of the environment. It is thought that the results obtained in this context may therefore be valid for all countries. In this context, it would be helpful to describe the structure of this study. After the introduction, the relevant literature will be summarized, and the hypotheses to be tested will be established. Then, the estimation model and the data used in the model will be introduced. Details of the estimation model will be presented in the methodology section. The results obtained from the estimations are reported in the Findings and Discussion section. This study will end with concluding evaluations and policy recommendations. 2. Related Literature And Hypothesizing 2.1. Green Finance and Carbon Emission When we examine the literature on the relationship between financial development and carbon emissions, we see that the literature is gathered into two groups. In the first group, some studies show that financial development (FD) has a negative effect on CO2, as it increases financial opportunities, and therefore, investment and production. For example, one of the studies frequently mentioned in the literature is (Sadorsky, 2010 ). In this study, data from 22 developing countries were used, covering the period between 1990 and 2006. According to the results, there is a linear interrelationship between FD and energy usage and nonrenewable resources. On the other hand, FD can also contribute to the reduction in carbon emissions, as they can lead to projects that produce renewable energy and are therefore sensitive to the environment. This approach constitutes the second group of literature on the relationship between FD and CO2. It can be said that the developing body of literature on the interrelationship between GF and CO2 is also included in the second group. When we review the literature examining the interrelationship between GF and CO2 as a part of FD, it is seen that studies have been conducted in recent years, predominantly in Asian countries. China is one of the countries that has faced the most criticism in the world regarding carbon emissions. For this reason, China's efforts to sensitize its production activities to the environment and its short, medium, and long-term results has been one of the issues that has particularly attracted the attention of researchers in recent years. For example, (Zhou, Tang, & Zhang, 2020 ) investigated the interrelationship between GF and CO2 for 30 provinces of China using annual data from 2010–2017. Green credit, green bond and green investment were used as green finance indicators. Before the estimation, green finance indicators and principal components analysis were made, and these data were indexed. According to the results, there as a strong relationship between green finance and environmental quality. However, this relationship differed according to the development level of the units (cities). Another important result is that green finance played a role in supporting development by increasing environmental quality. Similar results were obtained for the period 2005–2018 (Chen & Chen, 2021 ), (Xiong & Sun, 2022 ) as well as 2000–2019 (Guo, Zhao, Song, Tang, & Li, 2022 ) One green finance practice used around the world is the green credit policy. (Liu, Xia, Fan, Lin, & Wu, 2017 ) conducted analysis within the framework of China's paper, chemical, cement, iron, and steel industries to measure the effectiveness of this policy. The amount of credit allocated to energy-intensive industries was used as the green credit indicator. According to the results, the authors underlined that while the green credit application effectively suppressed investments in energy-intensive industries, it had low efficiency in transforming the structure of the sector in question. Also, one of the most important conclusions of the study is that even though this practice is effective at changing how industries use energy, it is bad for the economy as a whole. In addition to studies focusing on one country, such as China, there are also multi-country studies. Examples of these studies include (De Haas & Popov, 2019 ) for the period 1990–2013 and (Meo & Karim, 2022 ) for 2008–2019. The results also reveal the carbon emission reducing effect of green finance indicators. However, there are also studies, such as (Sun 2021 ), which argue that it is difficult to demonstrate a meaningful relationship between green finance and carbon emissions. Therefore, one of the hypotheses tested in this study is as follows: $${H}_{\text{1,0}}: Development in green finance will reduce carbon emission and upgrade air quality$$ 2.2. Governance and Carbon Emission Considering the relationship between governance and carbon emissions, an essential part of the studies is company performance, which can be regarded as micro-governance. For example (Kılıç & Kuzey, 2019 ) used data from 2011 to 2015 for non-financial companies traded on Istanbul Stock Exchange (Turkey), (Elsayih, Datt, & Tang, 2021 ) used data from 2010 to 2018, 425 companies operating in Australia and (Nasih, Harymawan, Paramitasari, & Handayani, 2019 ) using data from 2011 to 2016 for companies in Indonesia have found a negative interrelationship between CO2 and governance quality in their studies It is also possible to find studies with macroeconomic variables in the literature for specific countries. For example, according to the study of (Omri, Kahia, & Kahouli, 2021 ) covering the period 1996–2016 for Saudi Arabia and (Khan, Oubaih, & Elgourrami, 2022 ) covering the period 1983–2020 for Morocco, good governance is a factor that reduces carbon emissions. For example (Gani, 2012 ) used data from approximately 100 developing countries for the period between 1998 and 2007, (Mehmood, 2021 ) used data from India, Pakistan, Sri Lanka, and Bangladesh for the period between 1996 and 2019, and (Sethi & Dash, 2022 ). It has been revealed that the quality of governance is a factor that influence the reduction in carbon emissions, which significantly determines the air quality. $${H}_{\text{2,0}}:Better governance performance will reduce carbon emissions and upgrade air quality$$ 2.3. Political Stability and Carbon Emission Political stability is the last component of this study. Carbon emissions are generally evaluated together with economic variables such as industrial production or consumption. However, political stability is a parameter that can have consequences at many levels, including economic, financial, and environmental. Political stability may encourage production and investment, cause more energy consumption and environmental pollution, trigger environmentally-friendly technological developments, and initiate a carbon emission reduction process (Sulemana, James, & Rikoon, 2017 ). In which country and at what time these forces will dominate has been examined in different ways in the literature. Since political stability is a macro-level concept, it is seen that the studies in the literature are carried out with a data set consisting of many countries. These studies (Galinato & Galinato, 2012 ) used data from 22 Asian and Latin American countries between 1990 and 2013, (Al-Mulali & Ozturk, 2015 ) using data from 14 MENA countries between 1996 and 2012, (Zhang & Chiu, 2020 ) using the data of 111 countries between 1985 and 2014 (Benlemlih, Assaf, & El Ouadghiri, 2022) using the data of 145 countries, (Hassan, Song, & Kirikkaleli, 2022 ) using the data set between 1990–2020 for Regional Comprehensive Economic Cooperation (RCEP) countries (China, India, Southern Korea, Laos, Myanmar, Indonesia, Philippines, Thailand, and Cambodia) and (Zhang, Ozturk, & Ullah, 2022 ) using the dataset from 1996–2019 for the BRICs countries found that political stability is a reducing factor on carbon emission. $${H}_{\text{3,0}}:Provide political stability will reduce carbon emissions and upgrade air quality$$ 3. Model And The Data 3.1. Data The country group of this study is mainly G-20 countries. The data set covers the 2004–2020 period. However, green investment data, which is critical variable of this study, is not available for each G-20 country. Thus, the sample of the study consists of Germany, USA, Australia, Brazil, China, Indonesia, France, South Korea, India, United Kingdom, Italy, Japan, Canada, and Mexico data. In this respect, it can be said that this study covers both developed and developing countries. In addition, the reason for the analysis to start in 2004 is that the data obtained started from this date. The study used carbon emissions (AirQ) as the air quality variable and GDP (in constant 2015 US $ ) as the total output variable. Investments made for the transition to renewable energy as green finance (GF) variable, REC and NREC as energy consumption (EC), and institutional quality variable (IQ) were calculated as the average of 5 different indexes calculated by the World Bank ( Winful, Sarpong, & Ntiamoah, 2016 ). These; include accountability, the role of law, effectiveness of government, quality of regulation, and control of corruption. The institutional quality index varies between − 2.5 and + 2.5. The best institutional quality index is + 2.5, while the worst is -2.5. Accordingly, the institutional quality index improves as it approaches + 2.5 ( Winful, Sarpong, & Ntiamoah, 2016 ). Political stability (PS) is also an indicator of institutional quality, and it was added to the model because it was considered necessary in this study. All these variables and their abbreviations and sources are shown in Table 1 , and the flow of analysis is depicted in Fig. 1 . Table 1 Variables Description Variable Abbreviation Source Air Quality AirQ Ourworldindata.org Green Finance GF Bloomberg Institutional Quality Variable IQ Worldbank.org Political stability PS Worldbank.org Energy Consumption EC Ourworldindata.org Total Output GDP Worldbank.org 3.2. Model specification In the study, model (1) was created by the following (Gholipour, Arjomandi, & Yam, 2022 ); $${AirQ}_{i,t}= {\beta }_{1}{GF}_{i,t}+{\beta }_{2}{IQ}_{i,t}+{\beta }_{3}{PS}_{i,t}+{\beta }_{4}{X}_{i,t}+{\omega }_{i,t}$$ 1 Here, AirQ represents the amount of carbon per capita, and the increases here indicate an increase in carbon emissions and, accordingly, a decrease in air quality. GF represents green investments, IQ represents the country's institutional quality, and PS represents political stability. X represents control variables. Natural logarithms of GF, GDP, CO2 and EC have been used in the study. 4. Econometrİcs Methodology 4.1. Econometrics Methodology 4.1.1. Cross-sectional dependence (CSD) and slope heterogeneity tests Whether or not the cross-sectional dependence between the series is taken into account significantly affects the results (Breusch & Pagan, 1980 ), (Pesaran M., 2004 ). Therefore, before starting the analysis, it is necessary to test the existence of cross-sectional dependence in the series and the cointegration equation, because this situation should be taken into consideration when choosing the unit root and cointegration tests to be made. Otherwise, the analyses may give erroneous results. The cross-sectional dependence of the variables in the study was measured with the Peseran (2004) test. It is necessary to test whether the long-term slope coefficients show homogeneity before starting both the preliminary tests and the estimation of the long-term coefficients regarding the panel data analysis. The delta (Δ) test, developed by ( Pesaran & Yamagata, Testing slope homogeneity in large panels, 2008) and developed over the (Swamy, 1970 ) test, can perform this test. ( Pesaran & Yamagata, 2008 ) developed Eq. ( 2 ) to test homogeneity for a large sample and Eq. ( 3 ) to test homogeneity for a small sample. 2 3 4.1.2. Panel unit root tests The (Pesaran, 2007 ) CADF (Cross-sectional Augmented Dickey-Fuller) test, which is one of the 2nd generation panel unit root tests is used in this study. Before this test, stationarity is calculated separately for each section (country, company, etc.), and then the stationarity of the panel is calculated with the arithmetic average of these sections. The obtained panel test statistics and CADF statistics are compared with the critical values calculated by Peseran (2007). When the essential values are more significant than the test statistics, the basic hypothesis is rejected. Rejecting the basic hypothesis means that at least one unit in the panel is stationary. In the CADF test, firstly, unit root parameters are calculated for each unit, then unit root test statistics (CIPS) valid for the general panel are reached. The equation is shown in (4). $$CIPS= \frac{1}{N }{\sum }_{i=1}^{N}pi \left(4\right)$$ 4.1.3. Panel cointegration test Conventional cointegration approaches can produce erroneous results in case of breaks and cross-sectional dependence. In this study, (Westerlund, 2008 ) which takes into account the fractures and cross-sectional dependence, was used. For this test, 4 panel cointegration tests are recommended for error correction. On the basis of the tests, the existence of cointegration is tested by deciding that each unit does not have its own error correction. Thus, when the "no error correction" hypothesis is rejected, the "no cointegration" hypothesis is also rejected. The procedure of this test has been carried out with the help of the following equations. The data generation process of this test is included in Eq. 5. $$\varDelta {Y}_{it}= {\delta }_{i}^{{\prime }}{d}_{t}+{{\alpha }_{i}(Y}_{it-1}-{\beta }_{i}^{{\prime }}{x}_{it-1})+{\sum }_{j=1}^{Pi}{a}_{ij}\varDelta {Y}_{it-j}+{\sum }_{j=0}^{Pi}{\rho }_{ij}\varDelta {x}_{it-j}+{\epsilon }_{it} (5)$$ In Eq. 5, the indices \(t=\text{1,2},\dots ,T\) and ve \(i=\text{1,2},\dots ,N\) stand for time series and cross section units, respectively. \({d}_{t}\) includes deterministic components, which can be examined under three cases: (i) In the \({d}_{t}=0\) case, there is no deterministic component in the equation, (ii) The \({d}_{t}=1\) state \(\varDelta {Y}_{it}\) is generated by a constant, (iii) The case of \({d}_{t}={(1,t)}^{{\prime }}\) shows that \(\varDelta {Y}_{it}\) is produced both with a constant and trend. \({x}_{it}\) is a K-dimensional vector and is assumed to be suitable for pure random walk. Finally, the error terms are independent of both \(i\) and \(t\) . Eq. 5 was rewritten and Eq. 6 was obtained. $$\varDelta {Y}_{it}= {\delta }_{i}^{{\prime }}{d}_{t}+{{\alpha }_{i}Y}_{it-1}+{\gamma }_{i}^{{\prime }}{x}_{it-1}+{\sum }_{j=1}^{Pi}{a}_{ij}\varDelta {Y}_{it-j}+{\sum }_{j=0}^{Pi}{\rho }_{ij}\varDelta {x}_{it-j}+{\epsilon }_{it} \left(6\right)$$ It is defined as \({\gamma }_{i}^{{\prime }}=-{\alpha }_{i}{\beta }_{i}^{{\prime }}\) in Eq. 6. The null hypothesis of this test is that \({a}_{i}=0\) there is no cointegration for all units, while the alternative hypothesis is that there is a cointegration relationship for all units \({a}_{i}<0\) . In order to examine the cointegration relationship between the variables, group mean statistics should be obtained. Group mean statistics are obtained in three steps. In the first step, Eq. 6 is estimated by least squares (LS) for each unit \(i\) . The second step involves obtaining the variables \({\widehat{\epsilon }}_{it}\) ve \({\widehat{\rho }}_{ij}\) . This situation is examined with the help of Eq. 7. $${\widehat{u}}_{it}= {\sum }_{j=0}^{Pi}{\widehat{\rho }}_{ij}\varDelta {x}_{it-j}+{\widehat{\epsilon }}_{it} \left(7\right)$$ The third step is to obtain the group mean statistics as follows: $${G}_{\tau }=\frac{1}{N}{\sum }_{i=1}^{N}\frac{{\widehat{a}}_{i}}{SE\left({\widehat{a}}_{i}\right)} \to N\left(\text{0,1}\right), \left(8\right)$$ $${G}_{a}=\frac{1}{N}{\sum }_{i=1}^{N}\frac{T{\widehat{a}}_{i}}{{\widehat{a}}_{i}\left(1\right)} \to N\left(\text{0,1}\right) \left(9\right)$$ In Eq. 8, \(SE\left({\widehat{a}}_{i}\right)\) represents the conventional standard errors of \({\widehat{a}}_{i}\) . Westerlund (2007) cointegration test conforms to the left-tailed standard normal distribution. In this context, when the null hypothesis is rejected, it is revealed that there is a cointegration relationship between all the units that make up the panel. Thus, the statistical panel procedure is discussed by following Westerlund (2007) and Persyn and Westerlund ( 2008 ). The panel statistics, like the mean group statistics, consists of three steps. The first step includes the estimation process of Eq. 6, as in the group mean statistics. The second step includes the common error correction parameter \(\alpha\) , but also uses \(\varDelta {\widehat{y}}_{it}\) and \({\widehat{y}}_{it-1}\) to estimate its standard error. The following equations 10 and 11 are used for this: $$\widehat{\alpha }= {\left({\sum }_{i=1}^{N}{\sum }_{t=2}^{T}{\tilde{y}}_{it-1}^{2}\right)}^{-1}{\sum }_{i=1}^{N}{\sum }_{t=2}^{T}\frac{1}{{\widehat{a}}_{i}\left(1\right)}{\tilde{y}}_{it-1}\varDelta {\tilde{y}}_{it} \left(10\right)$$ The standard error of \(\widehat{\alpha }\) is; $$SE\left(\widehat{\alpha }\right)= {\left({\left({\widehat{S}}_{N}^{2}\right)}^{-1}{\sum }_{i=1}^{N}{\sum }_{t=2}^{T}{\tilde{y}}_{it-1}^{2}\right)}^{-1/2} \left(11\right)$$ Where \({\widehat{S}}_{N}^{2}= \frac{1}{N}{\sum }_{i=1}^{N}{\widehat{S}}_{i}^{2}\) . Let \({\widehat{\sigma }}_{i}\) denote the standard error of the estimated regression in Eq. 6. The quantity \({\widehat{S}}_{i}^{2}\) is defined as \({\widehat{\sigma }}_{i}\) / \({\widehat{a}}_{i}\left(1\right)\) ; this is a consistent estimate of the population response \({\sigma }_{i}\) / \({a}_{i}\left(1\right)\) , the long-run standard deviation of \(\varDelta {y}_{it}\) , based on all current and past values of \(\varDelta {x}_{it}\) . The third step includes the final stage of the panel statistics; that is, the following equations express how this statistic is obtained. The equations are \({P}_{\tau }=\frac{\widehat{\alpha }}{SE\left(\widehat{\alpha }\right)}\) and \({P}_{a}=T\widehat{\alpha }\) 4.1.4. Panel Estimation Procedures In the previous part, the originality and novelty of the study was emphasized that the relationship between green finance, carbon emissions, air quality, and various macro and institutional quality variables was estimated with panel cointegration and quantile panel regression models using balanced panel data. For this purpose, different estimators have been used for the robustness of the empirical findings to be obtained. In this framework, following the study of Kao and Chiang (1999), fully modified ordinary least squares (FM-OLS), dynamic ordinary least squares (D-OLS), and fixed effect ordinary least square (FE-OLS) methods have been used for panel estimation. In this way, the consistency and sensitivity of the obtained findings are also taken into consideration. Driscoll and Kraay (1998) extended and updated the FE-OLS method with Driscoll and Kraay's standard errors. In addition, this method is a robust estimator regarding autocorrelation, heterogeneity, and cross-section dependence (Le et al., 2020; Pedroni, 2004). In order to eliminate the heterogeneity problem in the panel model of dynamic cointegration, the average difference and variations within the sections are adjusted according to the cointegration balance. FM-OLS is effective enough to overcome these problems (Pedroni, 2004). In addition, the D-OLS method produces a more robust estimation based on Monte Carlo simulation in a finite sample compared to FE-OLS and FM-OLS methods (Kao & Chiang, 1999; Kao & Chiang, 2001). On the other hand, when the D-OLS method is compared with other estimators, it allows overcoming the internality problem by expanding the leading and lagging differentials. Koenker and Bassett (1978) proposed a panel quantile regression model in the literature. This method allows it to be applied to evaluate the dependent variance and the conditional mean according to the value of the explanatory parameters. Although the panel quantile regression method produces more robust results even in the presence of outliers in the data set, it cannot take into account the unobserved heterogeneity among the panel sections. In this context, the Method of Moments Quantile Regression (MMQR) proposed by Machado & Silva (2019) was used in this study. MMQR is a more appropriate method when the model has internal explanatory variables and the panel data is characterized by individual effects (Sarkodie &Strezov, 2019). The \({Q}_{\tau }\left(\tau \right|X)\) conditional quantile estimation for the spatial-scale variable model is explained with the help of the following equation: $${Y}_{it}={\alpha }_{i}+{X´}_{it} \beta +\left({\delta }_{i}+{Z´}_{it} \varUpsilon \right){U}_{it} \left(12\right)$$ Probability P{ \({\delta }_{I}\) + \({Z´}_{it} \varUpsilon >0\}\) = 1. The (α, β', δ, ϒ')' parameters, on the other hand, must be estimated. Discrete \(i\) fixed effects \(({\alpha }_{i}, {\delta }_{i})\) ( \(i = 1, \dots , n\) ) are shown. \(Z\) is the K-vector of the known (selected, recognized) elements of \(X\) with differentiable transformations with the element l given by Eq. 13. $${Z}_{l}={Z}_{l}\left(X\right) where l=1,\dots ,k \left(13\right)$$ \({X}_{it}\) , time t and i stand for any constant and show independent and identically distribute (i.i.d). \({U}_{it}\) is i.i.d. and \({X}_{it}\) is also orthogonal between t-time and i-sections, which satisfies Machado and Santos Silva's (2019) moment conditions. And Eq. 14 reads as follows (Machado & Silva, 2019): \({Q}_{y}\) ( \(\tau\) | \({X}_{it}\) ) = \({(\alpha }_{i}+{\delta }_{i} q\left(\tau \right))+{X´}_{it} \beta +{Z´}_{it} \varUpsilon q (\tau \left) \right(14)\) In Eq. 11, \({X}_{it}\) represents the vector of independent variables. These variables are green investments (GF) , the institutional quality of the country (IQ) , and the political stability of the country (PS). Last but not least, Z represents control variables. The quantile distribution of the Y dependent variable is shown with the help of the independent variables and the \({Q}_{y}\) ( \(\tau\) | \({X}_{it}\) ) conditionally expressed to the \({}_{X{\prime }it}\) position. \({\alpha }_{i} \left(\tau \right) \equiv\) \({\alpha }_{i} + {\delta }_{i}q\left(\tau \right)\) is the scaling coefficient reporting the fixed effects of quantile τ for an individual i . Unlike common least square fixed effects, the individual effect does not present the intersection shift. Since the parameters do not change with time, modification of heterogeneous effects and conditional distribution between quantiles is allowed. \(q \left(\tau \right)\) denotes the sample quantiles measured \(\left(\tau -th\right)\) by deciding the resulting optimization problem. $${min }_{q}{\varSigma }_{i}{\varSigma }_{t}{\rho }_{\tau }\left({R}_{it}-\left({\delta }_{i}+{Z´}_{it}\varUpsilon \right)q\right) \left(15\right)$$ where \({\rho }_{\tau }\left(A\right)\) = ( \(\tau -1\left) AI\right\{A\le 0\}+TAI \{A>0\}\) . Figure-1 was followed for the estimation process. 5. Findings And Discussion 5.1. Preliminary Test Outcomes According to the results shown in Table 2 , all variables include cross-sectional dependence. For this reason, the stationarity of the variables was examined with the Peseran (2007) test, which takes into account cross-sectional dependence. According to the results, it was determined that all the variables were stationary at the I(1) level. The outcomes of the slope heterogeneity test (SH) also revealed the SH issue, which supports the use of second-generation panel techniques (see Table 3 ). Table 2 CIPS and CSD Outcomes CSD CIPS LM Pesaran CD I(0) I(I) AirQ 59.121* 2.6443* -1.417 -3.009* GDP 80.812* 28.571* -1.989 -3.615* GF 44.943* 6.0306* -2.197 -4.020* CG 83.514* 23.836* -1.177 -4.401* IQ 23.739* 5.9764* -2.160 -5.725* PS 10.378* 9.9408* -1.847 -5.270* Note: *P < 0.01 Table 3 Slope Homogeneity Outcomes Test Value P-value \(\widehat{\varDelta }\) 9.863* 0.000 \({\widehat{\widehat{\varDelta }}}_{adjusted}\) 8.184* 0.000 Note: *P < 0.01 5.2. Cointegration Outcomes According to the results shown in Table 4 , there is a cointegration relationship between green investments, political stability, institutional quality, energy consumption and total output and air quality. Table 4 Cointegration Outcomes Statistics. Value Z-value. Pvalue. Robust pvalue. Gt -4.069 -2.457 0.007 0.009 Ga -12.642 -0.132 0.379 0.010 Pt -9.830 -2.239 0.011 0.039 Pa -10.328 -0.073 0.503 0.149 5.3. Method of Moments Quantile Regression Results The results of the method of MMQR with fixed effects are presented in Table 5 . MMQR can be used to evaluate the impact of conditional heterogeneity on CO2 emissions, not only by examining shifts in means but also considering the effects of individual determinants. Our first variable of interest is green finance and its relationship with CO2 emissions. An indicator of green finance identifies green innovations that are attributed to commitments to green development, which the OECD defines as improvements to product processes, marketing methods, organizational structures, and institutional arrangements, which are made unintentionally/intentionally in order to reduce the negative effects of emissions. Several empirical studies have been carried out around the world using a variety of datasets that have measured green innovation using different variables. Most have found a negative relationship between green innovation and CO 2 . Based on our empirical results, we can confirm the findings of the literature. From the first to sixth quantiles, GF has a significant negative impact on CO2, while CO 2 emissions are not impacted by green finance in the seventh, eighth, and ninth quantiles. The significant negative coefficient indicates that as the volume of green finance increases, the amount of CO2 emissions decreases, which results in improved air quality, thus confirming the results of (Meo & Karim, 2022 ) and (De Haas & Popov, 2019 ). However, green finance does not reduce CO 2 emissions to improve air quality at higher quantiles. An insignificant coefficient of green finance on air quality at higher quantiles could be related to the state of the economy. As shown by (Doda, 2014), York (2012), and Heutel (2012), CO 2 emissions have a pro-cyclical behavior, rising during periods of economic expansion and declining during recessions. Additionally, Table 5 reports a positive relationship between energy consumption and CO 2 emissions at higher quantiles, indicating that more energy is consumed during economic expansion. Because the 7th, 8th, and 9th quantiles of CO 2 emissions correspond to economic expansion phases, insignificant estimates of green finance suggest that nonrenewable energy rather than green energy is consumed during expansion periods. Furthermore, if we assume the reverse is also true and lower CO 2 emissions are associated with economies in recession, the negative green energy coefficients at lower quantiles indicate that green energy consumption during recessions is sufficiently large to reduce CO 2 emissions and improve air quality. The results in the table support our first hypothesis. In this study, the next potential driver of CO 2 emissions is the quality of institutions. According to institutional theory, economic development, as well as environmental quality, benefit from higher institutional quality, in order to promote environmental protection, government departments have created effective and systematic solutions to constrain enterprise structure and behavior. Enterprises and governments are influenced by their institutional environment and institutional changes with regard to green innovation and CO2 mitigation. The empirical results presented in Table 5 are consistent with the institutional theory. The coefficient estimates for this variable are negative and significant at all quantiles. Among the quantiles, there are no insignificant coefficient estimates, highlighting the importance of institutional quality for CO2 emissions and air quality. Our results suggest that CO 2 emissions, regardless of their level, are sensitive to a country's institutional quality, thus supporting our second hypothesis. When a nation's institutional quality is poor, corporations frequently conceal or underreport pollution emissions due to lax governmental oversight and corruption controls (Goel et al. 2013). Additionally, Fredriksson and Svensson (2003) claimed that political instability and corruption correlate with a softer environmental protection policy because politicians spend most of their time dealing with business and political matters, ignoring the demands of their constituents for ecological protection in the process. When ecological protection is not governed by the rule of law, it is difficult to absorb the negative externalities brought on by economic growth since the marginal societal cost will be greater than the marginal private cost. Consequently, businesses focus more on money and disregard the environment. it is easier to solve environmental pollution issues if good governance is in place, which leads us to hypothesize that CO 2 emissions will be reduced as political stability increases. The relationship between political stability and CO 2 emissions is significantly negative from the 3rd to 9th quantiles, whereas CO 2 emissions are not related to political stability in lower quantiles such as the 1st and 2nd quantiles. Further, the political stability coefficient estimate is also larger when CO 2 emissions increase, establishing a clearer link between political stability and CO 2 emissions. The results show that political stability is most beneficial during periods of high CO 2 emissions. Accordingly, the third hypothesis is also supported. Table 5 Method of Moments Quantile Regression Outcomes 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 GDP 1.4263* 1.3366* 1.2599* 1.1929* 1.1059* 1.0512* 0.9882* 0.9171* 0.8821* GF -0.1895** -0.1495** -0.1152** -0.0853** -0.0464** -0.0219*** 0.0062 0.0380 0.0536 IQ -0.5121** -0.4924** -0.4755** -0.4607* -0.4416* -0.4295* -0.4156* -0.4000* -0.3922* EC 0.2808 0.2445 0.2135*** 0.1865*** 0.1513** 0.1292** 0.1037** 0.0725** 0.0591** PS -0.0720 -0.0307 -0.0046*** -0.0354*** -0.0755** -0.1007** -0.1297* -0.1624* -0.1785* C -13.0550 -11.7852 -10.6991 -9.7509 -8.5192 -7.7441 -6.8519 -5.8461 -5.3505 Obs 238 238 238 238 238 238 238 238 238 Note: *P < 0.01, *P < 0.05 and ***P < 0.10 5.4. Long-run Estimators Outcomes Our next step is to analyze the long-term relationships between the variables of interest and CO2 emissions using the FMOLS, DOLS, and FE-OLS methods. Table 6 summarizes the results of the FMOLS, DOLS, and FE-OLS estimation procedures. The table demonstrates that all three estimation procedures give coefficient estimates that support our findings. According to the table, all coefficient estimates are of similar magnitude, sign, and significance across all models, which makes the results robust. Green finance, institutional quality, and political stability variables all have significant negative relationships with CO 2 in the long run while total output and EC impact CO 2 positively in the long run. Table 6 Long-run Estimators (FMOLS, DOLS and FE-OLS) FMOLS DOLS FE-OLS Coefficients T-statistics Coefficients T-statistics Coefficients T-statistics GDP 0.9459 10.472* 0.6743 9.0480* 1.0744 6.9439* GF -0.0682 -4.7762* -0.0564 -4.4203* -0.0353 -3.1385* IQ -0.4076 -2.5019** -0.3853 -2.6211** -0.2569 -5.0634* EC 0.3076 2.5019** 0.1889 2.5546** 0.2507 4.7312* PS -0.1000 1.7697*** -0.0455 -2.2441** -0.0593 -1.8971*** R 2 0.98 0.95 0.93 Adj-R 2 0.97 0.94 0.92 Note: *P < 0.01, *P < 0.05 and ***P < 0.10 5.5. Comparison of MMQR and DOLS, FMOLS and FE-OLS outcomes Figure 2 compares the estimated coefficients for all applied approaches, including DOLS, FMOLS, MMQR, and FE. The coefficients of MMQR are diverse and provide a dynamic image in all quantiles in contrast to the fixed FMOLS, FE and DOLS coefficients. Figure 2 shows that the coefficient of economic growth declined from the lower to the upper tails, indicating that a substantial increase in economic progress degrades the environmental quality of the G-20 countries. In addition, the devasting effect of the economic upsurge is more pronounced in the lower tails compared to the higher tails. On the other hand, the damaging impact of economic progress on environmental quality decreases with the quantile level. This outcome implies that economic expansion leads to higher emissions in nations with lower pollution, whereas it has a very weak emission-increasing influence in nations with a higher level of pollution (as seen in Adebayo et al. 2022c ; Miao et al. 2022 ). Furthermore, for green finance, interesting findings are obtained. In the lower and middle tails, we observe that green finance enhances air quality, while in the higher tails, green finance dampens air quality. The results show that green finance promotes air quality in nations with lower and middle levels of pollution, while green finance reduces air quality in nations with high pollution (Meo & Karim, 2022 ). Third, institutional quality (IQ) lessens CO 2 at all quantile levels. This result shows that the decreasing effect of IQ on emissions is constant in each quantile, thereby leading to improvement in air quality in the G-20 nations. These outcomes are consistent with the institutional theory in each tail. The coefficient estimates for this variable are negative and significant at all quantiles. In addition, the emission-mitigating effect of IQ decreases with the quantile level. In summary, from a statistical viewpoint, the IQ influence is negative, with the coefficient decreasing from around 0.51 at the 10th quantile to 0.39 at the 90th quantile. This finding implies that IQ has a small but significant impact on lessening CO2 in nations with lower levels of pollution compared to those with higher pollution levels. In each quantile, the effect of energy use (EC) on CO 2 is positive, although the magnitude of the coefficient decreases as we move to the higher quantiles. This implies that a substantial increase in EC degrades the G-20 environmental quality. In addition, the devasting effect of EC is more pronounced in the lower tails compared to the higher tails. Nevertheless, the damaging effect of EC on environmental quality decreases with the quantile level (Awosusi et al., 2022 ; Adebayo, 2022a ; Xie et al., 2022 ). This outcome implies that EC leads to higher emissions in nations with lower pollution. In contrast, it has a very weak emissions-increasing influence in nations with higher levels of pollution. Lastly, PS contributes to an increase in emissions in the lower, quantiles, suggesting that PS has an emissions-increasing effect in countries with lower levels of pollution (Adebayo, 2022b ). However, in middle and upper quantiles, a negative effect of PS on CO 2 is observed, indicating an emissions-reducing effect in nations with medium and higher levels of pollution. As a consequence, MMQR is a simple and effective strategy for evaluating all panel estimators to offer a thorough explanation of the relationship between variables. Fıgure 3 presents a summary of the findings obtained from the four estimators. 5.5. Panel Causality Outcomes The Dumitrescu and Hurlin (2012) test results are reported in Table 7 , which show a unidirectional causality from green finance investments, total output, energy consumption and political stability to air quality. Furthermore, a bidirectional causality was found between institutional quality and air quality. The findings of the studies by Cetin et al. (2018), Hossein (2011), Zhang and Cheng (2009), Ang (2007), and Apergis and Payne (2010) support the results of our research. Table 7 Dumitrescu Hurlin Panel Causality Outcomes Causality Path W-Stat. Zbar-Stat. Prob. Conclusion EC → AirQ 2.19861 1.97133 0.0487 Unidirectional Causality AirQ →LEC 0.88524 -0.57500 0.5653 GDP →LAirQ 4.59096 6.60959 0.0000 Unidirectional Causality AirQ →GDP 1.71862 1.04074 0.2980 GF → AirQ 2.04772 1.67880 0.0932 Unidirectional Causality AirQ →GF 1.63186 0.87253 0.3829 PS → AirQ 2.49805 2.55189 0.0107 Unidirectional Causality AirQ →PS 1.95889 1.50658 0.1319 IQ → AirQ 2.62030 2.78891 0.0053 Bidirectional Causality AirQ →IQ 2.26459 2.09927 0.0358 6. Conclusion And Policy Directions 6.1. Conclusion Mankind has benefited from nature since its existence. Particularly since the industrial revolution, with the opportunities provided by science, the dimensions of this benefit have expanded considerably, and natural resources and the environment have begun to be used uncontrollably. The damages caused by this unlimited use were not initially considered due to the perception that nature has the ability to renew itself, and it was even thought that the environment would eliminate this pollution over time. The quantitative and qualitative increase in the pollution emitted into the environment over time has exceeded the ability of the environment to renew itself and the ecological balance has started to deteriorate rapidly. This situation has caused academic researchers in the fields of both science and the social sciences to accelerate their studies on this topic. In this context, in this study, the effect of green investments, institutional quality and political stability on the air quality in G-20 countries for the period 2004–2020 was examined. In the study, it was first examined whether the variables included horizontal sections. Since the variables included cross-sections, the economic models were continued with tests that focus on the independence of the cross-sections. The stationarity of the variables was examined with the Pesaran ( 2007 ) CADF, the long-term relationship between the variables by Westerlund (2007), the long-run relationship coefficients with the MMQR method proposed by Machado & Silva (2019), and the causality interrelationship between the variables by the Dumitrescu & Hurlin (2012) panel causality test. The results from the FMOLS, DOLS, FE-OLS and MMQR showed that green finance, institutional quality and political stability contribute to environmental sustainability, while economic growth and energy consumption mitigate environmental sustainability in the G-20 nations. The results of the panel causality test also revealed that all the regressors can predict environmental sustainability in the G-20 nations. 6.2. Policy Recommendations A variety of policy implications stem from our findings. Firstly, increasing energy use from fossil fuels and economic expansion both worsen air quality. Therefore, the G-20 countries should adopt more practical measures to uphold their pledge to curb fossil fuel-intensive economic operations. This can be accomplished by including clean, low-carbon sources of energy in the long-term sustainable development plans of the G-20 member countries, providing financial incentives for the use of alternative energy sources, and placing a high priority on energy efficiency. Policymakers should also launch creative initiatives and support the R & D of eco-friendly practices. Secondly, a key policy instrument for slowing down environmental deterioration is the employment of renewable energy in place of non-renewable energy in economic sectors. Government officials in the G-20 countries could employ low interest grants, loans, fiscal incentives and subsidies to promote the penetration of renewable energy sources. To explicitly support investments in renewable energy projects, the governments can establish regional or global green energy funds. By upgrading old equipment and technology, energy infrastructure investments that increase energy efficiency would reduce the use of conventional energy sources. Third, an emphasis on cleaner and greener technologies will enhance environmental quality and help achieve sustainable growth. A green financing model is a practical choice for financial and environmental authorities. Our research also suggests that the institutional involvement could promote pollution abatement. Improved institutions will foster growth and enhance the surrounding conditions. Consequently, a robust institutional structure is required to enable governments to better control CO 2 emissions. Lastly, the study also established that political stability enhances environmental quality. This lends credence to the claim that political stability facilitates the creation of laws that help slow down environmental deterioration. This inverse relationship between political stability and ecological degradation demonstrates that the governments of the G-20 countries are capable of enforcing environmental laws and regulations that raise environmental standards. Therefore, maintaining a stable political climate is crucial for the G-20 countries to reduce environmental deterioration. 6.3. Limitations of the Study and Future Direction This investigation can be furthered in a number of different ways. To determine whether this association changes amongst the nations in our sample, future studies should first examine the connection between renewable energy, institutional quality, green finance and CO 2 for each nation independently. Other studies might evaluate the impact of the stock market, technological innovation, economic complexity on air quality. Future studies should also examine whether the use of renewable energy promotes the usage of green energy or whether it has an indirect impact on CO 2 emissions through GDP growth. Declarations Author contribution : MC: Validation; Visualization; Data curation Mİ: Conceptualization; Visualization VOO: Methodology, Writing - original draft AB: Conceptualization; Formal analysis; and Corresponding. SK: Writing, Validation MA: ; Visualization; Supervision Data availability Data set used in the study can be obtained by a reasonable request from the corresponding author Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Neural Computing and Applications , 1-15. doi:10.1007/s00521-021-06514-5 Swamy, P. A. (1970). Efficient Inference in a Random Coefficient Regression Model. Econometrica, 38 (2), 311-323. doi:https://doi.org/10.2307/1913012 Westerlund, J. (2008). Testing for Error Correction in Panel Data. The STATA journal, 8 (2), 232-241. doi: https://doi.org/10.1111/j.1468-0084.2007.00477.x Winful, E., Sarpong, D., & Ntiamoah, J. (2016). Relationship between institutional quality and stock market performance: Evidence from emerging economies. The African Journal of Business Management, 10 (19), 469-484. doi:https://doi.org/10.5897/AJBM2016.8153 Xie, Q., Adebayo, T. S., Irfan, M., & Altuntaş, M. (2022). Race to environmental sustainability: Can renewable energy consumption and technological innovation sustain the strides for China?. Renewable Energy . Xiong, Q., & Sun, D. (2022). Infuence analysis of green fnance development impact on carbon emissions: an exploratory study based on fsQCA. Environmental Science and Pollution Research , 1-12. doi:https://doi.org/10.1007/s11356-021-18351-z Younas, Z. I., Qureshi, A., & Al-Faryan, M. A. S. (2022). Financial inclusion, the shadow economy and economic growth in developing economies. Structural Change and Economic Dynamics . https://doi.org/10.1016/j.strueco.2022.03.011 Zahan, I., & Chuanmin, S. (2021). Towards a green economic policy framework in China: role of green investment in fostering clean energy consumption and environmental sustainability. Environmental Science and Pollution Research, 28 , 43618–43628. doi:https://doi.org/10.1007/s11356-021-13041-2 Zhang, D., Ozturk, I., & Ullah, S. (2022). Institutional factors-environmental quality nexus in BRICS: a strategic pillar of governmental performance. Economic Research-Ekonomska Istraživanja , 1-13. doi:https://doi.org/10.1080/1331677X.2022.2037446 Zhang, W., & Chiu, Y.-B. (2020). Do country risks influence carbon dioxide emissions? A non-linear perspective. Energy, 206 , 118048. doi:https://doi.org/10.1016/j.energy.2020.118048. Zhou, X., Tang, X., & Zhang, R. (2020). Impact of green finance on economic development and environmental quality: a study based on provincial panel data from China. Environmental Science and Pollution Research, 27 , 19915–19932. doi:https://doi.org/10.1007/s11356-020-08383-2 Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2023 Read the published version in Environmental Science and Pollution Research → Version 1 posted Reviewers agreed at journal 23 Dec, 2022 Reviewers invited by journal 23 Dec, 2022 Editor invited by journal 23 Dec, 2022 Editor assigned by journal 13 Dec, 2022 First submitted to journal 09 Dec, 2022 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. 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Aksaray Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Metin","middleName":"","lastName":"Ilbasmis","suffix":""},{"id":162572156,"identity":"3327434f-48bb-4020-93dc-bdd1d5a999d0","order_by":2,"name":"Victoria Olushola Olanrewaju","email":"","orcid":"","institution":"Cyprus International University: Uluslararasi Kibris Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"Olushola","lastName":"Olanrewaju","suffix":""},{"id":162572157,"identity":"7cdfb69e-fadb-4e4f-8c0c-b2d5e39fc3fe","order_by":3,"name":"Abdulkadir Barut","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACCQYGgwOMDSAm8zFkUWK0sLGloYji1cIA0cJjRpwWyfbDGw9X7rDLN7jf8+3Bzz335M0ZmA/e5mH4k49LizRPWsHBs2eSLTcc491u2POs2HBnA1uyNQ+DgWUDDi1yDDkGBxvbmA0MjvFuk+A5kMC44QCPmTRQC06XyfG/AWmpB2rheSb550CC/YYD/N/wapGWANtyGKSFTRpoSyLQFja8WiRnPCs42HjmuIHksTQzaZkDCckbDrMZW84xMMapReJ88uaPjTuqDfgOH34m+eZAgu2G480Pb7ypkMMdypiAGUSQomEUjIJRMApGAQYAAOJ+VVTuzm0SAAAAAElFTkSuQmCC","orcid":"","institution":"Harran University: Harran Universitesi","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Abdulkadir","middleName":"","lastName":"Barut","suffix":""},{"id":162572158,"identity":"c26c0cfd-c3ee-4985-b4a0-4116c62e51a6","order_by":4,"name":"Sadık Karaoğlan","email":"","orcid":"","institution":"Izmir Katip Celebi Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sadık","middleName":"","lastName":"Karaoğlan","suffix":""},{"id":162572159,"identity":"2787aa2c-93b1-4aa5-a905-b3a86aaf340a","order_by":5,"name":"Muhammad Ali","email":"","orcid":"","institution":"Iqra University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2022-12-05 11:22:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2345689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2345689/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-023-26016-2","type":"published","date":"2023-03-04T19:03:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":30899650,"identity":"841ccfef-a4f1-4852-886e-5155d3fdd6c1","added_by":"auto","created_at":"2022-12-29 18:38:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27527,"visible":true,"origin":"","legend":"\u003cp\u003eFlow of Analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2345689/v1/846f48b905afbb97623bb2e7.png"},{"id":30899413,"identity":"e894f6e5-8933-4ccb-a9dd-8fd1753aabaa","added_by":"auto","created_at":"2022-12-29 18:30:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57342,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the MMQR, DOLS, \u0026nbsp;\u0026nbsp;FE-OLS and FMOLS Estimates\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2345689/v1/1c2f7fed14a7ea68839b6f76.png"},{"id":30899415,"identity":"f710b4c7-1e53-4141-a340-614d5e93b940","added_by":"auto","created_at":"2022-12-29 18:30:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24891,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of Findings from MMQR, DOLS, FE-OLS and FMOLS\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2345689/v1/8418ff2283212d6868a1ab08.png"},{"id":44720475,"identity":"13d398f2-66f5-43da-bf3f-de2510ed7e46","added_by":"auto","created_at":"2023-10-16 19:11:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":674657,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2345689/v1/14c22386-5ece-4f34-ae35-61201a7db9e1.pdf"}],"financialInterests":"","formattedTitle":"Race Towards Environmental Sustainability in the G-20 Countries: Do Green Finance and Political Stability Play a Crucial Role","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe impacts of consumption and production on the ecosystem and air have evolved as a result of the industrial revolution and shifting patterns (Sterpu et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although established enterprises have the ability to produce on a vast scale, they also create waste and carbon emissions, which increases their contribution to environmental contamination (Brimblecombe, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Today, with the increase in competition, air pollution is continuing at an incredible pace (Adebayo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Balsalobre-Lorente et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the Global Air 2020 report, it has caused the deaths of over 6\u0026nbsp;million people worldwide (State of Global Air, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Certainly, these changes not only have adverse effects on human life, but also cause severe damage to the environment and air (Mehmood, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition to industrialization, increasing urbanization has also exacerbated the problems and caused people to seek new ways of achieving environmental sustainability (Adebayo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e; Alqaralleh, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInstitutional quality is an essential factor that can affect air pollution, because a strong understanding of institutional quality increases the efficiency of public finances, helps law enforcement, reduces corruption, and eliminates the military's influence on politics (Farhan, Shazia, Amber, \u0026amp; Aiza, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In a country where institutional quality increases, adherence to the law increases, and the enforcement of environmental laws can reduce carbon emissions and help improve air quality (Hao, Guo, Guo, Wu, \u0026amp; Ren, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Huynh \u0026amp; Ho, 220), (Shahbaz, Sharma, Sinha, \u0026amp; Jiao, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this context, institutional quality helps countries reach their sustainable goals by reducing ecological degradation.\u003c/p\u003e \u003cp\u003ePolitical stability in a country also affects the sustainability of natural resources and the preservation of ecological balance (Samimi, Ahmadpour, \u0026amp; Ghaderi, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are various paths from political stability to the prevention of environmental degradation. In countries with no political stability, legal regulations regarding environmental rules become more flexible. Therefore, an increase in ecological degradation such as an increase in air pollution and the misuse of natural resources in countries lacking political stability can be expected. On the other hand, the decisions taken to protect the environment and investments made (like green energy investments) in this regard require investments that take decades. However, long-term investments always present significant risks to investors. Among the most important of these risks is political instability. For this reason, investors may be hesitant to make investments aimed at protecting the environment in countries where political stability is absent or under threat. Al-Mulali and Ozturk (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) examined the factors that can affect environmental degradation in 14 MENA countries and found that political stability is essential in improving ecological quality (Al-Mulali \u0026amp; Ozturk, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In their study on Pakistan, Sohail et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that political stability reduces environmental damage by reducing carbon emissions (Sohail, Majeed, Shaikh, \u0026amp; Andlib, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between institutional quality and environmental degradation is quite indirect. However, there are more direct ways to stop and reverse environmental degradation. Green finance is just one of these direct paths. Green finance has huge potential to reduce air pollutant emissions such as greenhouse gases and carbon emissions (Eyraud, Clements, \u0026amp; Wane, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Countries tend to use green energy sources such as sun and wind instead of fossil fuels (oil, coal, natural gas, etc.). However, using renewable energy sources to replace fossil fuels requires large investments. All these investments are defined as green finance in the literature. More precisely, funds allocated to investments that do not pollute the environment and protect the balance of nature that are realized or planned can be considered as green finance. Studies have found that green investments have positive effects on the environment (Eyraud, Clements, \u0026amp; Wane, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), (Kar\u0026aacute;sek \u0026amp; Pavlica, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), ( Sachs et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), ( Heine et al., 2019), (Nassiry, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), (Zahan \u0026amp; Chuanmin, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is anticipated that this research will make important contributions to the literature. Air pollution, one of the greatest contemporary problems, negatively affects both people's health and the ecological structure. In this context, in this research, the effect of institutional quality and green finance on air quality will be examined. However, the concept of institutional quality not among the main discussion topics in low-income, underdeveloped, or developing countries. Similarly, replacing renewable energy sources with fossil fuel-based energy sources that cause carbon emissions requires significant investments. These two facts suggest that the sample needed to test the impact of GF and governance on air quality should include high-income countries. For this reason, we have used the G-20 countries for the sample of this study. These countries, which comprises more than 50% of the global economy, also make a significant contribution to the deterioration of the environment. Therefore, the scope of the analysis consists of the G-20 countries. Selecting these countries will help to solve many environmental problems based on the results. Except for Sohail et al. (2021), the effect of political stability on air quality has not been mentioned in other studies. However, in this study, both political stability and institutional quality were added to the model. When the existing literature is examined, it is seen that institutional quality or political stability alone are used in the models. However, in this study, political stability, a determinant of institutional quality, was included separately in the model and not in the institutional quality components. The reason for this is that it reveals the effect of political stability on environmental regulations. On the other hand, current econometric test such as (MMQR, Machado \u0026amp; Silva,2019) quantile regression was used in this study. This technique can capture the effect of the regressors on CO2 at different quantiles (0.1\u0026ndash;0.90). Another innovation was the inclusion of green finance into the model. In this way, the effect of institutional quality, political stability, and green investments on air quality was examined. Furthermore, although the scope of the study consists of the G-20 countries, the findings can be generalized to all countries because political stability and institutional quality reflect a country's view of the environment. It is thought that the results obtained in this context may therefore be valid for all countries.\u003c/p\u003e \u003cp\u003eIn this context, it would be helpful to describe the structure of this study. After the introduction, the relevant literature will be summarized, and the hypotheses to be tested will be established. Then, the estimation model and the data used in the model will be introduced. Details of the estimation model will be presented in the methodology section. The results obtained from the estimations are reported in the \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003eFindings and Discussion\u003c/span\u003e section. This study will end with concluding evaluations and policy recommendations.\u003c/p\u003e"},{"header":"2. Related Literature And Hypothesizing","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Green Finance and Carbon Emission\u003c/h2\u003e \u003cp\u003eWhen we examine the literature on the relationship between financial development and carbon emissions, we see that the literature is gathered into two groups. In the first group, some studies show that financial development (FD) has a negative effect on CO2, as it increases financial opportunities, and therefore, investment and production. For example, one of the studies frequently mentioned in the literature is (Sadorsky, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, data from 22 developing countries were used, covering the period between 1990 and 2006. According to the results, there is a linear interrelationship between FD and energy usage and nonrenewable resources. On the other hand, FD can also contribute to the reduction in carbon emissions, as they can lead to projects that produce renewable energy and are therefore sensitive to the environment. This approach constitutes the second group of literature on the relationship between FD and CO2. It can be said that the developing body of literature on the interrelationship between GF and CO2 is also included in the second group.\u003c/p\u003e \u003cp\u003eWhen we review the literature examining the interrelationship between GF and CO2 as a part of FD, it is seen that studies have been conducted in recent years, predominantly in Asian countries. China is one of the countries that has faced the most criticism in the world regarding carbon emissions. For this reason, China's efforts to sensitize its production activities to the environment and its short, medium, and long-term results has been one of the issues that has particularly attracted the attention of researchers in recent years. For example, (Zhou, Tang, \u0026amp; Zhang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) investigated the interrelationship between GF and CO2 for 30 provinces of China using annual data from 2010\u0026ndash;2017. Green credit, green bond and green investment were used as green finance indicators. Before the estimation, green finance indicators and principal components analysis were made, and these data were indexed. According to the results, there as a strong relationship between green finance and environmental quality. However, this relationship differed according to the development level of the units (cities). Another important result is that green finance played a role in supporting development by increasing environmental quality. Similar results were obtained for the period 2005\u0026ndash;2018 (Chen \u0026amp; Chen, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), (Xiong \u0026amp; Sun, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as well as 2000\u0026ndash;2019 (Guo, Zhao, Song, Tang, \u0026amp; Li, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOne green finance practice used around the world is the green credit policy. (Liu, Xia, Fan, Lin, \u0026amp; Wu, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) conducted analysis within the framework of China's paper, chemical, cement, iron, and steel industries to measure the effectiveness of this policy. The amount of credit allocated to energy-intensive industries was used as the green credit indicator. According to the results, the authors underlined that while the green credit application effectively suppressed investments in energy-intensive industries, it had low efficiency in transforming the structure of the sector in question. Also, one of the most important conclusions of the study is that even though this practice is effective at changing how industries use energy, it is bad for the economy as a whole.\u003c/p\u003e \u003cp\u003eIn addition to studies focusing on one country, such as China, there are also multi-country studies. Examples of these studies include (De Haas \u0026amp; Popov, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for the period 1990\u0026ndash;2013 and (Meo \u0026amp; Karim, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for 2008\u0026ndash;2019. The results also reveal the carbon emission reducing effect of green finance indicators. However, there are also studies, such as (Sun \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which argue that it is difficult to demonstrate a meaningful relationship between green finance and carbon emissions. Therefore, one of the hypotheses tested in this study is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${H}_{\\text{1,0}}: Development in green finance will reduce carbon emission and upgrade air quality$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Governance and Carbon Emission\u003c/h2\u003e \u003cp\u003eConsidering the relationship between governance and carbon emissions, an essential part of the studies is company performance, which can be regarded as micro-governance. For example (Kılı\u0026ccedil; \u0026amp; Kuzey, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) used data from 2011 to 2015 for non-financial companies traded on Istanbul Stock Exchange (Turkey), (Elsayih, Datt, \u0026amp; Tang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used data from 2010 to 2018, 425 companies operating in Australia and (Nasih, Harymawan, Paramitasari, \u0026amp; Handayani, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) using data from 2011 to 2016 for companies in Indonesia have found a negative interrelationship between CO2 and governance quality in their studies\u003c/p\u003e \u003cp\u003eIt is also possible to find studies with macroeconomic variables in the literature for specific countries. For example, according to the study of (Omri, Kahia, \u0026amp; Kahouli, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) covering the period 1996\u0026ndash;2016 for Saudi Arabia and (Khan, Oubaih, \u0026amp; Elgourrami, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) covering the period 1983\u0026ndash;2020 for Morocco, good governance is a factor that reduces carbon emissions. For example (Gani, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) used data from approximately 100 developing countries for the period between 1998 and 2007, (Mehmood, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used data from India, Pakistan, Sri Lanka, and Bangladesh for the period between 1996 and 2019, and (Sethi \u0026amp; Dash, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been revealed that the quality of governance is a factor that influence the reduction in carbon emissions, which significantly determines the air quality.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${H}_{\\text{2,0}}:Better governance performance will reduce carbon emissions and upgrade air quality$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Political Stability and Carbon Emission\u003c/h2\u003e \u003cp\u003ePolitical stability is the last component of this study. Carbon emissions are generally evaluated together with economic variables such as industrial production or consumption. However, political stability is a parameter that can have consequences at many levels, including economic, financial, and environmental. Political stability may encourage production and investment, cause more energy consumption and environmental pollution, trigger environmentally-friendly technological developments, and initiate a carbon emission reduction process (Sulemana, James, \u0026amp; Rikoon, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In which country and at what time these forces will dominate has been examined in different ways in the literature.\u003c/p\u003e \u003cp\u003eSince political stability is a macro-level concept, it is seen that the studies in the literature are carried out with a data set consisting of many countries. These studies (Galinato \u0026amp; Galinato, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) used data from 22 Asian and Latin American countries between 1990 and 2013, (Al-Mulali \u0026amp; Ozturk, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) using data from 14 MENA countries between 1996 and 2012, (Zhang \u0026amp; Chiu, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) using the data of 111 countries between 1985 and 2014 (Benlemlih, Assaf, \u0026amp; El Ouadghiri, 2022) using the data of 145 countries, (Hassan, Song, \u0026amp; Kirikkaleli, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using the data set between 1990\u0026ndash;2020 for Regional Comprehensive Economic Cooperation (RCEP) countries (China, India, Southern Korea, Laos, Myanmar, Indonesia, Philippines, Thailand, and Cambodia) and (Zhang, Ozturk, \u0026amp; Ullah, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using the dataset from 1996\u0026ndash;2019 for the BRICs countries found that political stability is a reducing factor on carbon emission.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${H}_{\\text{3,0}}:Provide political stability will reduce carbon emissions and upgrade air quality$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Model And The Data","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data\u003c/h2\u003e \u003cp\u003eThe country group of this study is mainly G-20 countries. The data set covers the 2004\u0026ndash;2020 period. However, green investment data, which is critical variable of this study, is not available for each G-20 country. Thus, the sample of the study consists of Germany, USA, Australia, Brazil, China, Indonesia, France, South Korea, India, United Kingdom, Italy, Japan, Canada, and Mexico data. In this respect, it can be said that this study covers both developed and developing countries. In addition, the reason for the analysis to start in 2004 is that the data obtained started from this date. The study used carbon emissions (AirQ) as the air quality variable and GDP (in constant 2015 US\u003cspan\u003e$\u003c/span\u003e) as the total output variable. Investments made for the transition to renewable energy as green finance (GF) variable, REC and NREC as energy consumption (EC), and institutional quality variable (IQ) were calculated as the average of 5 different indexes calculated by the World Bank ( Winful, Sarpong, \u0026amp; Ntiamoah, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These; include accountability, the role of law, effectiveness of government, quality of regulation, and control of corruption. The institutional quality index varies between \u0026minus;\u0026thinsp;2.5 and +\u0026thinsp;2.5. The best institutional quality index is +\u0026thinsp;2.5, while the worst is -2.5. Accordingly, the institutional quality index improves as it approaches\u0026thinsp;+\u0026thinsp;2.5 ( Winful, Sarpong, \u0026amp; Ntiamoah, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Political stability (PS) is also an indicator of institutional quality, and it was added to the model because it was considered necessary in this study. All these variables and their abbreviations and sources are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the flow of analysis is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables Description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAirQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOurworldindata.org\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional Quality Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorldbank.org\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorldbank.org\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOurworldindata.org\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Output\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorldbank.org\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Model specification\u003c/h2\u003e \u003cp\u003eIn the study, model (1) was created by the following (Gholipour, Arjomandi, \u0026amp; Yam, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e);\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${AirQ}_{i,t}= {\\beta }_{1}{GF}_{i,t}+{\\beta }_{2}{IQ}_{i,t}+{\\beta }_{3}{PS}_{i,t}+{\\beta }_{4}{X}_{i,t}+{\\omega }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, AirQ represents the amount of carbon per capita, and the increases here indicate an increase in carbon emissions and, accordingly, a decrease in air quality. GF represents green investments, IQ represents the country's institutional quality, and PS represents political stability. X represents control variables. Natural logarithms of GF, GDP, CO2 and EC have been used in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Econometrİcs Methodology","content":"\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e4.1. Econometrics Methodology\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec11\"\u003e\n \u003ch2\u003e4.1.1. Cross-sectional dependence (CSD) and slope heterogeneity tests\u003c/h2\u003e\n \u003cp\u003eWhether or not the cross-sectional dependence between the series is taken into account significantly affects the results (Breusch \u0026amp; Pagan, \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e), (Pesaran M., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). Therefore, before starting the analysis, it is necessary to test the existence of cross-sectional dependence in the series and the cointegration equation, because this situation should be taken into consideration when choosing the unit root and cointegration tests to be made. Otherwise, the analyses may give erroneous results. The cross-sectional dependence of the variables in the study was measured with the Peseran (2004) test. It is necessary to test whether the long-term slope coefficients show homogeneity before starting both the preliminary tests and the estimation of the long-term coefficients regarding the panel data analysis. The delta (Δ) test, developed by ( Pesaran \u0026amp; Yamagata, Testing slope homogeneity in large panels, 2008) and developed over the (Swamy, \u003cspan class=\"CitationRef\"\u003e1970\u003c/span\u003e) test, can perform this test.\u003c/p\u003e\n \u003cp\u003e( Pesaran \u0026amp; Yamagata, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) developed Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) to test homogeneity for a large sample and Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) to test homogeneity for a small sample.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\u003cimg 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\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003e4.1.2. Panel unit root tests\u003c/h2\u003e\n \u003cp\u003eThe (Pesaran, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) CADF (Cross-sectional Augmented Dickey-Fuller) test, which is one of the 2nd generation panel unit root tests is used in this study. Before this test, stationarity is calculated separately for each section (country, company, etc.), and then the stationarity of the panel is calculated with the arithmetic average of these sections. The obtained panel test statistics and CADF statistics are compared with the critical values calculated by Peseran (2007). When the essential values are more significant than the test statistics, the basic hypothesis is rejected. Rejecting the basic hypothesis means that at least one unit in the panel is stationary.\u003c/p\u003e\n \u003cp\u003eIn the CADF test, firstly, unit root parameters are calculated for each unit, then unit root test statistics (CIPS) valid for the general panel are reached. The equation is shown in (4).\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equd\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$CIPS= \\frac{1}{N }{\\sum }_{i=1}^{N}pi \\left(4\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec13\"\u003e\n \u003ch2\u003e4.1.3. Panel cointegration test\u003c/h2\u003e\n \u003cp\u003eConventional cointegration approaches can produce erroneous results in case of breaks and cross-sectional dependence. In this study, (Westerlund, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) which takes into account the fractures and cross-sectional dependence, was used. For this test, 4 panel cointegration tests are recommended for error correction. On the basis of the tests, the existence of cointegration is tested by deciding that each unit does not have its own error correction. Thus, when the \"no error correction\" hypothesis is rejected, the \"no cointegration\" hypothesis is also rejected. The procedure of this test has been carried out with the help of the following equations. The data generation process of this test is included in Eq.\u0026nbsp;5.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Eque\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\varDelta {Y}_{it}= {\\delta }_{i}^{{\\prime }}{d}_{t}+{{\\alpha }_{i}(Y}_{it-1}-{\\beta }_{i}^{{\\prime }}{x}_{it-1})+{\\sum }_{j=1}^{Pi}{a}_{ij}\\varDelta {Y}_{it-j}+{\\sum }_{j=0}^{Pi}{\\rho }_{ij}\\varDelta {x}_{it-j}+{\\epsilon }_{it} (5)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn Eq. 5, the indices \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t=\\text{1,2},\\dots ,T\\)\u003c/span\u003e\u003c/span\u003e and ve \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i=\\text{1,2},\\dots ,N\\)\u003c/span\u003e\u003c/span\u003e stand for time series and cross section units, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{t}\\)\u003c/span\u003e\u003c/span\u003eincludes deterministic components, which can be examined under three cases: \u003cem\u003e(i)\u003c/em\u003e In the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{t}=0\\)\u003c/span\u003e\u003c/span\u003e case, there is no deterministic component in the equation, \u003cem\u003e(ii)\u003c/em\u003e The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{t}=1\\)\u003c/span\u003e\u003c/span\u003e state \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {Y}_{it}\\)\u003c/span\u003e\u003c/span\u003e is generated by a constant, \u003cem\u003e(iii)\u003c/em\u003e The case of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{t}={(1,t)}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e shows that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {Y}_{it}\\)\u003c/span\u003e\u003c/span\u003e is produced both with a constant and trend. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a K-dimensional vector and is assumed to be suitable for pure random walk. Finally, the error terms are independent of both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e. Eq.\u0026nbsp;5 was rewritten and Eq.\u0026nbsp;6 was obtained.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equf\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e$$\\varDelta {Y}_{it}= {\\delta }_{i}^{{\\prime }}{d}_{t}+{{\\alpha }_{i}Y}_{it-1}+{\\gamma }_{i}^{{\\prime }}{x}_{it-1}+{\\sum }_{j=1}^{Pi}{a}_{ij}\\varDelta {Y}_{it-j}+{\\sum }_{j=0}^{Pi}{\\rho }_{ij}\\varDelta {x}_{it-j}+{\\epsilon }_{it} \\left(6\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIt is defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma }_{i}^{{\\prime }}=-{\\alpha }_{i}{\\beta }_{i}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e in Eq. 6. The null hypothesis of this test is that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}=0\\)\u003c/span\u003e\u003c/span\u003e there is no cointegration for all units, while the alternative hypothesis is that there is a cointegration relationship for all units \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\u0026lt;0\\)\u003c/span\u003e\u003c/span\u003e. In order to examine the cointegration relationship between the variables, group mean statistics should be obtained. Group mean statistics are obtained in three steps. In the first step, Eq. 6 is estimated by least squares (LS) for each unit \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e. The second step involves obtaining the variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{\\epsilon }}_{it}\\)\u003c/span\u003e\u003c/span\u003e ve \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{\\rho }}_{ij}\\)\u003c/span\u003e\u003c/span\u003e. This situation is examined with the help of Eq.\u0026nbsp;7.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equg\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e$${\\widehat{u}}_{it}= {\\sum }_{j=0}^{Pi}{\\widehat{\\rho }}_{ij}\\varDelta {x}_{it-j}+{\\widehat{\\epsilon }}_{it} \\left(7\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe third step is to obtain the group mean statistics as follows:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equh\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e$${G}_{\\tau }=\\frac{1}{N}{\\sum }_{i=1}^{N}\\frac{{\\widehat{a}}_{i}}{SE\\left({\\widehat{a}}_{i}\\right)} \\to N\\left(\\text{0,1}\\right), \\left(8\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Equation\" id=\"Equi\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e$${G}_{a}=\\frac{1}{N}{\\sum }_{i=1}^{N}\\frac{T{\\widehat{a}}_{i}}{{\\widehat{a}}_{i}\\left(1\\right)} \\to N\\left(\\text{0,1}\\right) \\left(9\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Eq. 8, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(SE\\left({\\widehat{a}}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the conventional standard errors of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{a}}_{i}\\)\u003c/span\u003e\u003c/span\u003e. Westerlund (2007) cointegration test conforms to the left-tailed standard normal distribution. In this context, when the null hypothesis is rejected, it is revealed that there is a cointegration relationship between all the units that make up the panel. Thus, the statistical panel procedure is discussed by following Westerlund (2007) and Persyn and Westerlund (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The panel statistics, like the mean group statistics, consists of three steps. The first step includes the estimation process of Eq. 6, as in the group mean statistics. The second step includes the common error correction parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e, but also uses \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {\\widehat{y}}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{y}}_{it-1}\\)\u003c/span\u003e\u003c/span\u003e to estimate its standard error. The following equations 10 and 11 are used for this:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equj\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e$$\\widehat{\\alpha }= {\\left({\\sum }_{i=1}^{N}{\\sum }_{t=2}^{T}{\\tilde{y}}_{it-1}^{2}\\right)}^{-1}{\\sum }_{i=1}^{N}{\\sum }_{t=2}^{T}\\frac{1}{{\\widehat{a}}_{i}\\left(1\\right)}{\\tilde{y}}_{it-1}\\varDelta {\\tilde{y}}_{it} \\left(10\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe standard error of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{\\alpha }\\)\u003c/span\u003e\u003c/span\u003e is;\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equk\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e$$SE\\left(\\widehat{\\alpha }\\right)= {\\left({\\left({\\widehat{S}}_{N}^{2}\\right)}^{-1}{\\sum }_{i=1}^{N}{\\sum }_{t=2}^{T}{\\tilde{y}}_{it-1}^{2}\\right)}^{-1/2} \\left(11\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{S}}_{N}^{2}= \\frac{1}{N}{\\sum }_{i=1}^{N}{\\widehat{S}}_{i}^{2}\\)\u003c/span\u003e\u003c/span\u003e. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{\\sigma }}_{i}\\)\u003c/span\u003e\u003c/span\u003e denote the standard error of the estimated regression in Eq. 6. The quantity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{S}}_{i}^{2}\\)\u003c/span\u003e\u003c/span\u003e is defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{\\sigma }}_{i}\\)\u003c/span\u003e\u003c/span\u003e/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{a}}_{i}\\left(1\\right)\\)\u003c/span\u003e\u003c/span\u003e; this is a consistent estimate of the population response \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{i}\\)\u003c/span\u003e\u003c/span\u003e/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\left(1\\right)\\)\u003c/span\u003e\u003c/span\u003e, the long-run standard deviation of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {y}_{it}\\)\u003c/span\u003e\u003c/span\u003e, based on all current and past values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta {x}_{it}\\)\u003c/span\u003e\u003c/span\u003e. The third step includes the final stage of the panel statistics; that is, the following equations express how this statistic is obtained. The equations are \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{\\tau }=\\frac{\\widehat{\\alpha }}{SE\\left(\\widehat{\\alpha }\\right)}\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{a}=T\\widehat{\\alpha }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec14\"\u003e\n \u003ch2\u003e4.1.4. Panel Estimation Procedures\u003c/h2\u003e\n \u003cp\u003eIn the previous part, the originality and novelty of the study was emphasized that the relationship between green finance, carbon emissions, air quality, and various macro and institutional quality variables was estimated with panel cointegration and quantile panel regression models using balanced panel data. For this purpose, different estimators have been used for the robustness of the empirical findings to be obtained. In this framework, following the study of Kao and Chiang (1999), fully modified ordinary least squares (FM-OLS), dynamic ordinary least squares (D-OLS), and fixed effect ordinary least square (FE-OLS) methods have been used for panel estimation. In this way, the consistency and sensitivity of the obtained findings are also taken into consideration. Driscoll and Kraay (1998) extended and updated the FE-OLS method with Driscoll and Kraay's standard errors. In addition, this method is a robust estimator regarding autocorrelation, heterogeneity, and cross-section dependence (Le et al., 2020; Pedroni, 2004). In order to eliminate the heterogeneity problem in the panel model of dynamic cointegration, the average difference and variations within the sections are adjusted according to the cointegration balance. FM-OLS is effective enough to overcome these problems (Pedroni, 2004). In addition, the D-OLS method produces a more robust estimation based on Monte Carlo simulation in a finite sample compared to FE-OLS and FM-OLS methods (Kao \u0026amp; Chiang, 1999; Kao \u0026amp; Chiang, 2001). On the other hand, when the D-OLS method is compared with other estimators, it allows overcoming the internality problem by expanding the leading and lagging differentials.\u003c/p\u003e\n \u003cp\u003eKoenker and Bassett (1978) proposed a panel quantile regression model in the literature. This method allows it to be applied to evaluate the dependent variance and the conditional mean according to the value of the explanatory parameters. Although the panel quantile regression method produces more robust results even in the presence of outliers in the data set, it cannot take into account the unobserved heterogeneity among the panel sections. In this context, the Method of Moments Quantile Regression (MMQR) proposed by Machado \u0026amp; Silva (2019) was used in this study. MMQR is a more appropriate method when the model has internal explanatory variables and the panel data is characterized by individual effects (Sarkodie \u0026amp;Strezov, 2019). The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{\\tau }\\left(\\tau \\right|X)\\)\u003c/span\u003e\u003c/span\u003e conditional quantile estimation for the spatial-scale variable model is explained with the help of the following equation:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equl\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e$${Y}_{it}={\\alpha }_{i}+{X´}_{it} \\beta +\\left({\\delta }_{i}+{Z´}_{it} \\varUpsilon \\right){U}_{it} \\left(12\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eProbability P{\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }_{I}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z´}_{it} \\varUpsilon \u0026gt;0\\}\\)\u003c/span\u003e\u003c/span\u003e = 1. The \u003cem\u003e(α, β', δ, ϒ')'\u003c/em\u003e parameters, on the other hand, must be estimated. Discrete \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e fixed effects \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(({\\alpha }_{i}, {\\delta }_{i})\\)\u003c/span\u003e\u003c/span\u003e ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i = 1, \\dots , n\\)\u003c/span\u003e\u003c/span\u003e) are shown. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Z\\)\u003c/span\u003e\u003c/span\u003e is the K-vector of the known (selected, recognized) elements of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(X\\)\u003c/span\u003e\u003c/span\u003e with differentiable transformations with the element l given by Eq. 13.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equm\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equm\" name=\"EquationSource\"\u003e$${Z}_{l}={Z}_{l}\\left(X\\right) where l=1,\\dots ,k \\left(13\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e, time t and i stand for any constant and show independent and identically distribute \u003cem\u003e(i.i.d).\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{it}\\)\u003c/span\u003e\u003c/span\u003e is \u003cem\u003ei.i.d.\u003c/em\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is also orthogonal between t-time and i-sections, which satisfies Machado and Santos Silva's (2019) moment conditions. And Eq. 14 reads as follows (Machado \u0026amp; Silva, 2019):\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({Q}_{y}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003e(\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\tau\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003e|\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003e) =\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(\\alpha }_{i}+{\\delta }_{i} q\\left(\\tau \\right))+{X´}_{it} \\beta +{Z´}_{it} \\varUpsilon q (\\tau \\left) \\right(14)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eIn Eq. 11, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u003c/span\u003e represents the vector of independent variables. These variables are green investments \u003cem\u003e(GF)\u003c/em\u003e, the institutional quality of the country \u003cem\u003e(IQ)\u003c/em\u003e, and the political stability of the country \u003cem\u003e(PS).\u003c/em\u003e Last but not least, Z represents control variables. The quantile distribution of the Y dependent variable is shown with the help of the independent variables and the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{y}\\)\u003c/span\u003e\u003c/span\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\tau\\)\u003c/span\u003e\u003c/span\u003e|\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u003c/span\u003e) conditionally expressed to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({}_{X{\\prime }it}\\)\u003c/span\u003e\u003c/span\u003e position. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{i} \\left(\\tau \\right) \\equiv\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{i} + {\\delta }_{i}q\\left(\\tau \\right)\\)\u003c/span\u003e\u003c/span\u003e is the scaling coefficient reporting the fixed effects of quantile τ for an individual \u003cem\u003ei\u003c/em\u003e. Unlike common least square fixed effects, the individual effect does not present the intersection shift. Since the parameters do not change with time, modification of heterogeneous effects and conditional distribution between quantiles is allowed. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(q \\left(\\tau \\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the sample quantiles measured \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\tau -th\\right)\\)\u003c/span\u003e\u003c/span\u003e by deciding the resulting optimization problem.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equn\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equn\" name=\"EquationSource\"\u003e$${min }_{q}{\\varSigma }_{i}{\\varSigma }_{t}{\\rho }_{\\tau }\\left({R}_{it}-\\left({\\delta }_{i}+{Z´}_{it}\\varUpsilon \\right)q\\right) \\left(15\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{\\tau }\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\tau -1\\left) AI\\right\\{A\\le 0\\}+TAI \\{A\u0026gt;0\\}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFigure-1 was followed for the estimation process.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Findings And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003e5.1. Preliminary Test Outcomes\u003c/h2\u003e\n \u003cp\u003eAccording to the results shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, all variables include cross-sectional dependence. For this reason, the stationarity of the variables was examined with the Peseran (2007) test, which takes into account cross-sectional dependence. According to the results, it was determined that all the variables were stationary at the I(1) level. The outcomes of the slope heterogeneity test (SH) also revealed the SH issue, which supports the use of second-generation panel techniques (see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCIPS and CSD Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCIPS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLM\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePesaran CD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eI(0)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eI(I)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.121*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6443*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.812*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.571*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.615*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.943*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0306*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.514*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.836*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.401*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.739*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9764*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.725*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.378*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9408*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.270*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eNote: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSlope Homogeneity Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{\\varDelta }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.863*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{\\widehat{\\varDelta }}}_{adjusted}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.184*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNote: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003e5.2. Cointegration Outcomes\u003c/h2\u003e\n \u003cp\u003eAccording to the results shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, there is a cointegration relationship between green investments, political stability, institutional quality, energy consumption and total output and air quality.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCointegration Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistics.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ-value.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePvalue.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRobust pvalue.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003e5.3. Method of Moments Quantile Regression Results\u003c/h2\u003e\n \u003cp\u003eThe results of the method of MMQR with fixed effects are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. MMQR can be used to evaluate the impact of conditional heterogeneity on CO2 emissions, not only by examining shifts in means but also considering the effects of individual determinants.\u003c/p\u003e\n \u003cp\u003eOur first variable of interest is green finance and its relationship with CO2 emissions. An indicator of green finance identifies green innovations that are attributed to commitments to green development, which the OECD defines as improvements to product processes, marketing methods, organizational structures, and institutional arrangements, which are made unintentionally/intentionally in order to reduce the negative effects of emissions. Several empirical studies have been carried out around the world using a variety of datasets that have measured green innovation using different variables. Most have found a negative relationship between green innovation and CO\u003csub\u003e2\u003c/sub\u003e. Based on our empirical results, we can confirm the findings of the literature.\u003c/p\u003e\n \u003cp\u003eFrom the first to sixth quantiles, GF has a significant negative impact on CO2, while CO\u003csub\u003e2\u003c/sub\u003e emissions are not impacted by green finance in the seventh, eighth, and ninth quantiles. The significant negative coefficient indicates that as the volume of green finance increases, the amount of CO2 emissions decreases, which results in improved air quality, thus confirming the results of (Meo \u0026amp; Karim, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and (De Haas \u0026amp; Popov, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, green finance does not reduce CO\u003csub\u003e2\u003c/sub\u003e emissions to improve air quality at higher quantiles. An insignificant coefficient of green finance on air quality at higher quantiles could be related to the state of the economy. As shown by (Doda, 2014), York (2012), and Heutel (2012), CO\u003csub\u003e2\u003c/sub\u003e emissions have a pro-cyclical behavior, rising during periods of economic expansion and declining during recessions. Additionally, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e reports a positive relationship between energy consumption and CO\u003csub\u003e2\u003c/sub\u003e emissions at higher quantiles, indicating that more energy is consumed during economic expansion. Because the 7th, 8th, and 9th quantiles of CO\u003csub\u003e2\u003c/sub\u003e emissions correspond to economic expansion phases, insignificant estimates of green finance suggest that nonrenewable energy rather than green energy is consumed during expansion periods. Furthermore, if we assume the reverse is also true and lower CO\u003csub\u003e2\u003c/sub\u003e emissions are associated with economies in recession, the negative green energy coefficients at lower quantiles indicate that green energy consumption during recessions is sufficiently large to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions and improve air quality. The results in the table support our first hypothesis.\u003c/p\u003e\n \u003cp\u003eIn this study, the next potential driver of CO\u003csub\u003e2\u003c/sub\u003e emissions is the quality of institutions. According to institutional theory, economic development, as well as environmental quality, benefit from higher institutional quality, in order to promote environmental protection, government departments have created effective and systematic solutions to constrain enterprise structure and behavior. Enterprises and governments are influenced by their institutional environment and institutional changes with regard to green innovation and CO2 mitigation. The empirical results presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e are consistent with the institutional theory. The coefficient estimates for this variable are negative and significant at all quantiles. Among the quantiles, there are no insignificant coefficient estimates, highlighting the importance of institutional quality for CO2 emissions and air quality. Our results suggest that CO\u003csub\u003e2\u003c/sub\u003e emissions, regardless of their level, are sensitive to a country\u0026apos;s institutional quality, thus supporting our second hypothesis.\u003c/p\u003e\n \u003cp\u003eWhen a nation\u0026apos;s institutional quality is poor, corporations frequently conceal or underreport pollution emissions due to lax governmental oversight and corruption controls (Goel et al. 2013). Additionally, Fredriksson and Svensson (2003) claimed that political instability and corruption correlate with a softer environmental protection policy because politicians spend most of their time dealing with business and political matters, ignoring the demands of their constituents for ecological protection in the process. When ecological protection is not governed by the rule of law, it is difficult to absorb the negative externalities brought on by economic growth since the marginal societal cost will be greater than the marginal private cost. Consequently, businesses focus more on money and disregard the environment. it is easier to solve environmental pollution issues if good governance is in place, which leads us to hypothesize that CO\u003csub\u003e2\u003c/sub\u003e emissions will be reduced as political stability increases. The relationship between political stability and CO\u003csub\u003e2\u003c/sub\u003e emissions is significantly negative from the 3rd to 9th quantiles, whereas CO\u003csub\u003e2\u003c/sub\u003e emissions are not related to political stability in lower quantiles such as the 1st and 2nd quantiles. Further, the political stability coefficient estimate is also larger when CO\u003csub\u003e2\u003c/sub\u003e emissions increase, establishing a clearer link between political stability and CO\u003csub\u003e2\u003c/sub\u003e emissions. The results show that political stability is most beneficial during periods of high CO\u003csub\u003e2\u003c/sub\u003e emissions. Accordingly, the third hypothesis is also supported.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMethod of Moments Quantile Regression Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4263*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3366*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2599*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1929*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1059*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0512*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9882*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9171*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8821*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1895**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1495**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1152**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0853**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0464**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0219***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5121**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4924**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4755**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4607*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4416*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4295*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4156*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3922*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2135***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1865***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1513**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1292**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1037**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0725**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0591**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0046***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0354***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0755**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1297*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1624*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1785*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.0550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.7852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.6991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.7509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.5192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.7441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.8519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.8461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.3505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"10\"\u003e\n \u003cp\u003eNote: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003e5.4. Long-run Estimators Outcomes\u003c/h2\u003e\n \u003cp\u003eOur next step is to analyze the long-term relationships between the variables of interest and CO2 emissions using the FMOLS, DOLS, and FE-OLS methods. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the results of the FMOLS, DOLS, and FE-OLS estimation procedures. The table demonstrates that all three estimation procedures give coefficient estimates that support our findings. According to the table, all coefficient estimates are of similar magnitude, sign, and significance across all models, which makes the results robust. Green finance, institutional quality, and political stability variables all have significant negative relationships with CO\u003csub\u003e2\u003c/sub\u003e in the long run while total output and EC impact CO\u003csub\u003e2\u003c/sub\u003e positively in the long run.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLong-run Estimators (FMOLS, DOLS and FE-OLS)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFMOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFE-OLS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.472*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0480*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9439*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.7762*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.4203*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.1385*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5019**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.6211**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.0634*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5019**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5546**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7312*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7697***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.2441**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8971***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdj-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eNote: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec20\"\u003e\n \u003ch2\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e5.5.\u003c/span\u003e Comparison of MMQR and DOLS, FMOLS and FE-OLS outcomes\u003c/h2\u003e\n \u003cp\u003eFigure 2 compares the estimated coefficients for all applied approaches, including DOLS, FMOLS, MMQR, and FE. The coefficients of MMQR are diverse and provide a dynamic image in all quantiles in contrast to the fixed FMOLS, FE and DOLS coefficients. Figure\u0026nbsp;2 shows that the coefficient of economic growth declined from the lower to the upper tails, indicating that a substantial increase in economic progress degrades the environmental quality of the G-20 countries. In addition, the devasting effect of the economic upsurge is more pronounced in the lower tails compared to the higher tails. On the other hand, the damaging impact of economic progress on environmental quality decreases with the quantile level. This outcome implies that economic expansion leads to higher emissions in nations with lower pollution, whereas it has a very weak emission-increasing influence in nations with a higher level of pollution (as seen in Adebayo et al. \u003cspan class=\"CitationRef\"\u003e2022c\u003c/span\u003e; Miao et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFurthermore, for green finance, interesting findings are obtained. In the lower and middle tails, we observe that green finance enhances air quality, while in the higher tails, green finance dampens air quality. The results show that green finance promotes air quality in nations with lower and middle levels of pollution, while green finance reduces air quality in nations with high pollution (Meo \u0026amp; Karim, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Third, institutional quality (IQ) lessens CO\u003csub\u003e2\u003c/sub\u003e at all quantile levels. This result shows that the decreasing effect of IQ on emissions is constant in each quantile, thereby leading to improvement in air quality in the G-20 nations. These outcomes are consistent with the institutional theory in each tail. The coefficient estimates for this variable are negative and significant at all quantiles. In addition, the emission-mitigating effect of IQ decreases with the quantile level. In summary, from a statistical viewpoint, the IQ influence is negative, with the coefficient decreasing from around 0.51 at the 10th quantile to 0.39 at the 90th quantile. This finding implies that IQ has a small but significant impact on lessening CO2 in nations with lower levels of pollution compared to those with higher pollution levels.\u003c/p\u003e\n \u003cp\u003eIn each quantile, the effect of energy use (EC) on CO\u003csub\u003e2\u003c/sub\u003e is positive, although the magnitude of the coefficient decreases as we move to the higher quantiles. This implies that a substantial increase in EC degrades the G-20 environmental quality. In addition, the devasting effect of EC is more pronounced in the lower tails compared to the higher tails. Nevertheless, the damaging effect of EC on environmental quality decreases with the quantile level (Awosusi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Adebayo, \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Xie et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This outcome implies that EC leads to higher emissions in nations with lower pollution. In contrast, it has a very weak emissions-increasing influence in nations with higher levels of pollution. Lastly, PS contributes to an increase in emissions in the lower, quantiles, suggesting that PS has an emissions-increasing effect in countries with lower levels of pollution (Adebayo, \u003cspan class=\"CitationRef\"\u003e2022b\u003c/span\u003e). However, in middle and upper quantiles, a negative effect of PS on CO\u003csub\u003e2\u003c/sub\u003e is observed, indicating an emissions-reducing effect in nations with medium and higher levels of pollution. As a consequence, MMQR is a simple and effective strategy for evaluating all panel estimators to offer a thorough explanation of the relationship between variables. Fıgure 3 presents a summary of the findings obtained from the four estimators.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec21\"\u003e\n \u003ch2\u003e5.5. Panel Causality Outcomes\u003c/h2\u003e\n \u003cp\u003eThe Dumitrescu and Hurlin (2012) test results are reported in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, which show a unidirectional causality from green finance investments, total output, energy consumption and political stability to air quality. Furthermore, a bidirectional causality was found between institutional quality and air quality. The findings of the studies by Cetin et al. (2018), Hossein (2011), Zhang and Cheng (2009), Ang (2007), and Apergis and Payne (2010) support the results of our research.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab7\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDumitrescu Hurlin Panel Causality Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCausality Path\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW-Stat.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZbar-Stat.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConclusion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC \u0026rarr; AirQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnidirectional Causality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirQ \u0026rarr;LEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.57500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP \u0026rarr;LAirQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.59096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.60959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnidirectional Causality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirQ \u0026rarr;GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGF \u0026rarr; AirQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnidirectional Causality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirQ \u0026rarr;GF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePS \u0026rarr; AirQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnidirectional Causality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirQ \u0026rarr;PS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIQ \u0026rarr; AirQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBidirectional Causality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirQ \u0026rarr;IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Conclusion And Policy Directions","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Conclusion\u003c/h2\u003e \u003cp\u003eMankind has benefited from nature since its existence. Particularly since the industrial revolution, with the opportunities provided by science, the dimensions of this benefit have expanded considerably, and natural resources and the environment have begun to be used uncontrollably. The damages caused by this unlimited use were not initially considered due to the perception that nature has the ability to renew itself, and it was even thought that the environment would eliminate this pollution over time. The quantitative and qualitative increase in the pollution emitted into the environment over time has exceeded the ability of the environment to renew itself and the ecological balance has started to deteriorate rapidly. This situation has caused academic researchers in the fields of both science and the social sciences to accelerate their studies on this topic. In this context, in this study, the effect of green investments, institutional quality and political stability on the air quality in G-20 countries for the period 2004\u0026ndash;2020 was examined. In the study, it was first examined whether the variables included horizontal sections. Since the variables included cross-sections, the economic models were continued with tests that focus on the independence of the cross-sections. The stationarity of the variables was examined with the Pesaran (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) CADF, the long-term relationship between the variables by Westerlund (2007), the long-run relationship coefficients with the MMQR method proposed by Machado \u0026amp; Silva (2019), and the causality interrelationship between the variables by the Dumitrescu \u0026amp; Hurlin (2012) panel causality test. The results from the FMOLS, DOLS, FE-OLS and MMQR showed that green finance, institutional quality and political stability contribute to environmental sustainability, while economic growth and energy consumption mitigate environmental sustainability in the G-20 nations. The results of the panel causality test also revealed that all the regressors can predict environmental sustainability in the G-20 nations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Policy Recommendations\u003c/h2\u003e \u003cp\u003eA variety of policy implications stem from our findings. Firstly, increasing energy use from fossil fuels and economic expansion both worsen air quality. Therefore, the G-20 countries should adopt more practical measures to uphold their pledge to curb fossil fuel-intensive economic operations. This can be accomplished by including clean, low-carbon sources of energy in the long-term sustainable development plans of the G-20 member countries, providing financial incentives for the use of alternative energy sources, and placing a high priority on energy efficiency. Policymakers should also launch creative initiatives and support the R \u0026amp; D of eco-friendly practices.\u003c/p\u003e \u003cp\u003eSecondly, a key policy instrument for slowing down environmental deterioration is the employment of renewable energy in place of non-renewable energy in economic sectors. Government officials in the G-20 countries could employ low interest grants, loans, fiscal incentives and subsidies to promote the penetration of renewable energy sources. To explicitly support investments in renewable energy projects, the governments can establish regional or global green energy funds. By upgrading old equipment and technology, energy infrastructure investments that increase energy efficiency would reduce the use of conventional energy sources.\u003c/p\u003e \u003cp\u003eThird, an emphasis on cleaner and greener technologies will enhance environmental quality and help achieve sustainable growth. A green financing model is a practical choice for financial and environmental authorities. Our research also suggests that the institutional involvement could promote pollution abatement. Improved institutions will foster growth and enhance the surrounding conditions. Consequently, a robust institutional structure is required to enable governments to better control CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eLastly, the study also established that political stability enhances environmental quality. This lends credence to the claim that political stability facilitates the creation of laws that help slow down environmental deterioration. This inverse relationship between political stability and ecological degradation demonstrates that the governments of the G-20 countries are capable of enforcing environmental laws and regulations that raise environmental standards. Therefore, maintaining a stable political climate is crucial for the G-20 countries to reduce environmental deterioration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.3. Limitations of the Study and Future Direction\u003c/h2\u003e \u003cp\u003eThis investigation can be furthered in a number of different ways. To determine whether this association changes amongst the nations in our sample, future studies should first examine the connection between renewable energy, institutional quality, green finance and CO\u003csub\u003e2\u003c/sub\u003e for each nation independently. Other studies might evaluate the impact of the stock market, technological innovation, economic complexity on air quality. Future studies should also examine whether the use of renewable energy promotes the usage of green energy or whether it has an indirect impact on CO\u003csub\u003e2\u003c/sub\u003e emissions through GDP growth.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contribution : \u003cstrong\u003eMC:\u0026nbsp;\u003c/strong\u003eValidation; Visualization; Data curation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMİ:\u0026nbsp;\u003c/strong\u003eConceptualization; Visualization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVOO:\u003c/strong\u003e Methodology, Writing - original draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAB:\u0026nbsp;\u003c/strong\u003eConceptualization; Formal analysis; and Corresponding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSK:\u003c/strong\u003e Writing, Validation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMA:\u003c/strong\u003e ; Visualization; Supervision\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e Data set used in the study can be obtained by a reasonable request from the corresponding author \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdebayo, T. 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Impact of green finance on economic development and environmental quality: a study based on provincial panel data from China. \u003cem\u003eEnvironmental Science and Pollution Research, 27\u003c/em\u003e, 19915\u0026ndash;19932. doi:https://doi.org/10.1007/s11356-020-08383-2\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":false,"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":"Green Finance Investments, Total Output, Energy Consumption, Political Stability, Air Quality","lastPublishedDoi":"10.21203/rs.3.rs-2345689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2345689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the negative repercussions of environmental devastation, such as global warming and climate change, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. Thus, the current study assesses the effect of green investments, institutional quality, and political stability on air quality in the G-20 countries for the period 2004\u0026ndash;2020. The stationarity of the variables was examined with the Pesaran (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) CADF, the long-term relationship between the variables by Westerlund (2007), the long-run relationship coefficients with the MMQR method proposed by Machado \u0026amp; Silva (2019), and the causality relationship between the variables by Dumitrescu \u0026amp; Hurlin (2012) panel causality. The study findings revealed that green finance investments, institutional quality and political stability increased the air quality, while total output and energy consumption decreased air quality. The panel causality reveals a unidirectional causality from green finance investments, total output, energy consumption and political stability to air quality, and a bidirectional causality between institutional quality and air quality. According to these findings, it has been found that in the long term, green finance investments, total output, energy consumption, political stability, and institutional quality affect air quality. Based on these results, policies implications were proposed.\u003c/p\u003e","manuscriptTitle":"Race Towards Environmental Sustainability in the G-20 Countries: Do Green Finance and Political Stability Play a Crucial Role","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-29 18:30:34","doi":"10.21203/rs.3.rs-2345689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-12-23T21:29:07+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-12-23T21:03:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2022-12-23T20:57:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-12-14T04:26:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2022-12-09T15:13:30+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"aa8a33c1-e1c9-4f3d-b112-eff81959131b","owner":[],"postedDate":"December 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T19:09:21+00:00","versionOfRecord":{"articleIdentity":"rs-2345689","link":"https://doi.org/10.1007/s11356-023-26016-2","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2023-03-04 19:03:59","publishedOnDateReadable":"March 4th, 2023"},"versionCreatedAt":"2022-12-29 18:30:34","video":"","vorDoi":"10.1007/s11356-023-26016-2","vorDoiUrl":"https://doi.org/10.1007/s11356-023-26016-2","workflowStages":[]},"version":"v1","identity":"rs-2345689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2345689","identity":"rs-2345689","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}