Uncovering the role of clean fuel energy technology in reducing CO2 emissions in G20 countries: A panel nonparametric analysis

Uncovering the role of clean fuel energy technology in reducing CO2 emissions in G20 countries: A panel nonparametric analysis | 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 Article Uncovering the role of clean fuel energy technology in reducing CO2 emissions in G20 countries: A panel nonparametric analysis Reena Kumari, Md Absar Alam, Balkeshwar Singh, Gyanendra Kumar Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7345767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract The use of clean fuel energy plays a critical role in reducing CO 2 emissions and environmental degradation in G20 countries, which comprise about 4.69 billion people (55%) of the world's population and 85% of the global GDP. There is a lack of analytical studies on the driving forces of emissions, mainly the use of cooking fuels as a green technology that households in G20 countries use. In this study, we intend to examine the effect of clean fuel energy technology on CO 2 emissions over 2000-2021 using a cross-country panel of G20 countries based on data sourced from the World Bank. For this purpose, we used CO 2 emissions per capita as a dependent variable and the use of fuel technology as an independent variable. We also considered electricity consumption, urbanisation, GDP per capita, trade openness, population, urbanisation, foreign direct investment (FDI), and clean fuel as possible determinants of CO 2 emissions. The study employs panel data stationary models, including random and fixed effects models. However, after a diagnostics check, the paper finds that the data has cross-sectional dependency; therefore, it uses a second-generation panel model. This study recommends that the coverage of LPG use encourages the promotion of a cleaner cooking fuel program that can fulfill the objectives of the Sustainable Development Goals. Physical sciences/Energy science and technology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Clean fuel CO2 emissions Urbanisation FDI GDP per capita Environment Figures Figure 1 Figure 2 1. Introduction The United Nations Development Programme (UNDP) introduced the Sustainable Development Goals (SDGs) in 2015, setting forth 17 objectives to address global challenges such as poverty, inequality, climate change, environmental degradation, peace, and justice. Among these, SDG-7 aims to ensure universal access to modern, affordable, reliable, and sustainable energy by 2030. Energy is a critical determinant of a nation’s prosperity, and its accessibility directly influences the living standards of people worldwide. Despite significant efforts by nations to enhance energy accessibility, a substantial portion of the global population still lacks clean and affordable energy. Kaygusuz (2012) estimated that 1.4 billion people lack access to renewable energy, with approximately 2.7 billion people relying on biomass sources, such as fuelwood, for cooking. This reliance on inefficient and hazardous energy sources not only endangers environmental sustainability but also exacerbates public health risks and prolongs poverty. Spalding-Fecher (2005) and Abebaw (2007) emphasized that access to clean cooking energy is essential for sustainable development, as conventional biomass-based cooking methods are environmentally harmful and inefficient. The World Bank (2020) reported that nearly 2.8 billion people globally still lack access to clean cooking fuels. Traditional cooking practices are a major contributor to indoor air pollution, emitting harmful pollutants such as particulate matter, carbon monoxide, and volatile organic compounds, which pose significant health and environmental risks (Juntarawijit & Juntarawijit, 2019). Prior research has analyzed the environmental and economic consequences of greenhouse gas emissions (Xiang et al., 2021; Bese, 2019; Aung et al., 2017; Zhang & Cheng, 2009). However, studies specifically linking clean cooking energy to environmental degradation remain limited. Swain and Mishra (2020) found that access to LPG improved women's standard of living in India by enhancing their health, social interactions, and economic engagement, while also reducing reliance on forest timber and lowering indoor air pollution. Gould and Urpelainen (2018) demonstrated that LPG adoption significantly reduced indoor air pollution in India, although high costs, accessibility challenges, and the affordability of traditional fuels remained obstacles. Dash et al. (2018) examined fuelwood collection patterns in Odisha, India, identifying key factors such as landholding size, education, and employment as determinants of fuel choice. Their study recommended social forestry programs, public education, and the promotion of fuel-efficient stoves to reduce fuelwood dependency. Murshed (2020) investigated the role of information and communication technology (ICT) in renewable energy adoption across South Asian countries, concluding that ICT advancements improve clean cooking fuel access and reduce CO 2 emissions. Similarly, Hanif (2018) analyzed panel data from 34 emerging economies and found that solid fuel consumption significantly contributes to CO 2 emissions, supporting the Environmental Kuznets Curve (EKC) hypothesis for middle-income and developing nations. Empirical research on the relationship between environmental degradation, economic growth, and per capita energy consumption remains inconclusive. Policymakers in G-20 nations face trade-offs between economic expansion and environmental sustainability, raising concerns about whether to prioritize cost-efficient energy strategies or invest in eco-friendly initiatives. Given the lack of comprehensive studies on the role of clean cooking energy in reducing carbon emissions, this research aims to fill this gap through a macroeconomic analysis. However, over-reliance on fossil fuels can be harmful to health and a contributing factor to poverty (Ekholm et al. 2010). According to the energy ladder theory, households move from traditional biomass fuels to more contemporary and effective energy sources like LPG, electricity, and biogas as family earnings rise. The energy ladder theory proposes that families switch from more conventional biomass fuels to more contemporary and effective energy sources like LPG, electricity, and biogas as family earnings rise. This development is not always simple, though. stacking, which refers to their approach to cooking by combining several fuels and burners. Many households have historically relied on dirty fuels, such as wood, charcoal, dung, and crop residues, for cooking. The use of these fuels has significant adverse impacts on both the environment and human health. Studies by Ingale et al. (2013), Bihari et al. (2014), and Ngahane et al. (2015) highlight the various health problems associated with exposure to smoke from these fuels. In addition to the health impacts, the use of dirty fuels for cooking also contributes to environmental degradation. This includes deforestation, soil erosion, and increased greenhouse gas (GHG) emissions, which exacerbate climate change. Clean cooking fuels significantly reduce greenhouse gas emissions and improve public health, especially for women who are most affected by traditional fuel use. Studies by Pradhan et al. (2021) and Sarker et al. (2023) highlight clean energy as an environmentally sustainable alternative. Research quantifies CO 2 emissions from conventional fuels: 1 kg of firewood emits 1.83 kg CO 2 (Khanal & Bajracharya, 2010), 1 kg of dry cow dung emits 2.5 kg CO 2 (Chakrabarty et al., 2013), 1 liter of kerosene emits 3.15 kg CO 2 (Seeckt & Scholz, 2009), while 1 kg of LPG emits only 1.492 kg CO 2 (Steed, 2011), making LPG a cleaner option. Clean fuels also save time and promote gender equality and economic development. CO 2 emissions continue to rise globally, with energy-related emissions growing by 0.9% (321 Mt) in 2022, reaching 36.8 GT (IEA, 2023). The largest increase was from electricity and heat generation, rising by 1.8%. While China's emissions remained flat, emissions in emerging Asian markets (excluding China) increased by 4.2% (206 Mt) in 2022 (IEA, 2023). Multilateral organizations emphasize renewable energy and clean fuel adoption to curb emissions. G20, comprising 19 countries and two regional blocs (EU and AU), represents 57.5% of the global population and 85% of global GDP. Its New Delhi declaration aims to triple renewable energy capacity. Despite progress, a large population still relies on polluting fuels for cooking. A disparity remains between developed and developing G20 nations in access to clean fuel technology, as illustrated in Table 1 and Fig. 1, which track progress from 2000-2022. Cooking with solid fuels delays the progression of multiple SDGs, including good health and well-being, gender equality, affordable and clean energy, climate action, and life on land (Abishek et. al. 2023). An annual investment of USD 150 billion in modern energy cooking services (MECS) is estimated to provide universal access to clean cooking fuel. This represents a fraction of the overall cost to indicators of health, gender, and the environment. Figure 2 brings out the carbon intensity in terms of milligrams of CO 2 per US$ of GDP for G-20 countries. The trend suggests that most of the G-20 countries have a declining trend over time. Our contribution to the expanding body of literature on development economics and environmental economics comes in multiple forms. As far as current research is concerned, no study has examined the impact of clean cooking fuels on carbon emissions in G-20 countries. The current study is anticipated to offer significant insights in the same vein. The remaining portions of the paper are divided into the following manner. The next section deals with the theoretical background for the study followed by information regarding data and methodology utilised for empirical analysis. Thereafter, section contains a quick interpretation of the empirical results as well as a discussion of them and finally key policy recommendations are included in the final section. 2. Theoretical background This section examines the theoretical links between key explanatory variables and environmental degradation. CO 2 , emissions per capita is used as the indicator of GHG emission. The primary variable, access to clean cooking fuels (LNAC), is expected to have a negative correlation with emissions, as supported by Hanif ( 2018 ), who found that reliance on solid fuels increases pollution. Electricity consumption per capita (LNEC) impacts emissions based on the energy mix. If renewable energy use is high, the effect is negative; otherwise, fossil-fuel reliance exacerbates degradation (Pradhan et al., 2022 ; Gasimli et al., 2019). Urbanization (LNURB) has an ambiguous effect. Some studies indicate it accelerates emissions (Katircioğlu & Katircioğlu, 2018 ; Yasin et al., 2021 ), while others suggest efficiency gains can mitigate pollution (Gasimli et al., 2019; Tupy, 2018 ). The literature also highlights varying impacts based on income levels (Poumanyvong & Kaneko, 2010 ; Zhang et al., 2017 ). Income per capita (LNPCI) captures the nexus between development and emissions, while foreign direct investment (LNFDI) has contrasting effects. The 'Pollution Halo' hypothesis suggests FDI fosters cleaner technologies (Balsalobre-Lorente et al., 2019 ; Singhania & Saini, 2021 ), whereas the 'Pollution Haven' hypothesis argues that lax regulations attract polluting industries (Millimet & Roy, 2016 ; Guzel & Okumus, 2020 ). Empirical studies show FDI-driven economic growth can contribute to emissions (Rana & Sharma, 2019 ). Trade openness (LNTO) also presents mixed results. Weak environmental regulations enable pollution-intensive trade (Essandoh et al., 2020 ), while technologically advanced trade partners enhance environmental standards (Sarkodie & Strezov, 2019 ). These factors collectively shape the complex relationship between energy, economic growth, and environmental sustainability. 3. Empirical Model and Methodology 3.1 Empirical Model The objective of this paper is to find out the effect of clean fuel technology on CO 2 emissions. For this purpose, we use the environmental impact model used to identify the determinants of CO 2 emission (York et al., 2003 ; Raskin, 1995 ). The model suggests that the environmental impact here expressed in terms of CO 2 emission, is caused by an increase in population, per capita income, and technology. Further, researchers also included trade, energy consumption, and urbanisation as the determinants of CO 2 emissions. Independent or explanatory variables are chosen based on their impact on CO 2 emission levels through a literature review. The paper aims to estimate the causal relationship between dependent and explanatory variables as per Eq. 1 . $$\:{LNCO2PC}_{it}={\:}_{it}+{\beta\:}_{1}{LNACT}_{it}+{\beta\:}_{2}{LNAE}_{it}+{\beta\:}_{3}{LLNURB}_{it}+{\beta\:}_{4}{LNGDP}_{it}+{\beta\:}_{5}{LNFDI}_{it}+{\beta\:}_{6}{LNTO}_{it}+u$$ 1 Where, i = 1,2,…., G20 countries and t = 1, 2, ….., denotes the years over the period 2000–2021. \(\:LNCO2PC\) = Natural log of CO 2 emission per capita \(\:LNACT\) = Natural log of access to clean fuels and technologies for cooking (% of population) \(\:LNAE\) = Natural log of energy use as % of the total population \(\:LNURB\) = Natural log of urban population (% of total population) \(\:LNGDP\) = Natural log of GDP per capita, PPP (constant 2017 international $ ) \(\:LNFDI\) = Natural log of FDI (% of GDP) \(\:LNTO\) = Natural log of Trade (% of GDP) \(\:u\) = error term In Eq. 1 the variable \(\:{LNCO2PC}_{it}\) stands for CO 2 emission per capita and is used as the dependent reflecting an indicator of environmental degradation. \(\:LNACT\) stands for access to clean fuels and technologies for cooking and refers to the use of sustainable energy development and empowerment of women in society. \(\:LNAE\) indicates energy used per capita indicating fast economic development with environmental degradation. \(\:LNURB\) reflects a high level of economic development of the G20 countries. \(\:LNGDP\) stands for GDP per capita, which is used as an economic indicator of G20 countries, taken in terms of US dollars. The variable \(\:LNFDI\) is used to examine the impact of foreign investment on environmental quality in G20 countries. In the equation, a 0 is the constant term; and \(\:{\beta\:}_{1}\) , \(\:{\beta\:}_{2}\) , \(\:{\beta\:}_{3}\) , \(\:{\beta\:}_{4}\:\:{\beta\:}_{5}\:\) and \(\:\:\:{\beta\:}_{6}\:\) represent the regression coefficients. 3. Data and Methodology Table 2 presents the summary of the variables used for the analysis. The data for these variables are taken for G20 countries excluding the European Union and the African Union, as a group of countries from the World Bank’s World Development Indicators (WDI) database for the period from 2000 to 2022. Table 2 Description of the variables S.N. Variable name Variable name Expected impact Dependent variable 1 CO 2 Emission per Capita CO 2 PC Independent variables 1 Access to clean fuels and technologies for cooking (% of population) LNACT - 2 Access to Electricity as % of Population LNAE + 3 Urban population (% of total population) LNURB + 4 GDP per capita, PPP (constant 2017 international $ ) LNPCI + 5 FDI % of GDP LNFDI +/- 6 Trade (% of GDP) LNTO +/- 7 Population in numbers LNPOP + Table 3 presents an analysis of descriptive statistics of the variables that are being examined, along with a statistical summary that includes the mean, minimum, and maximum values, as well as the standard deviation. The table also presents the normality test of the dataset. Table 3 Descriptive statistics Variable Obs Mean Std. dev. Pr(skewness) Pr(kurtosis) Joint test ----- Adjchi2(2) Prob > chi2 CO 2 PC 437 8.59 5.34 0.00 0.00 45.97 0.00 LNACT 437 4.44 0.39 0.00 0.00 260.82 0.00 LNAE 437 4.58 0.08 0.00 0.00 256.56 0.00 LNFDI 437 0.42 1.01 0.00 0.00 161.91 0.00 LNPCI 437 10.25 0.73 0.00 0.38 43.60 0.00 LNTO 437 3.89 0.36 0.00 0.07 14.48 0.00 LNURB 437 4.27 0.26 0.00 0.00 132.95 0.00 LNPOP 437 18.45 1.11 0.00 0.14 43.75 0.00 In Table 4 , we present the values of the correlation coefficients of the variables included in our empirical model. The high correlation coefficient values of variables like LNACT, LNURB, and LNGDP show a linear relationship among these independent variables, validating the possibility of multicollinearity in the model. Table 4 Correlation matrix and variation inflation factor Variable CO2PC LNACT LNAE LNFDI LNPCI LNTO LNURB LNPOP CO2PC 1 LNACT 0.44 1 LNAE 0.32 0.66 1 LNFDI -0.05 0.01 0.05 1 LNPCI 0.67 0.76 0.66 -0.07 1 LNTO 0.12 0.01 0.08 0.05 0.17 1 LNURB 0.46 0.79 0.69 -0.04 0.81 -0.01 1 LNPOP -0.43 -0.52 -0.32 0.07 -0.64 -0.35 -0.71 1 The paper follows the analysis to estimate the basic panel model, i.e. Pooled OLS followed by the Fixed and Random Effect models and estimation strategy for panel data analysis based on the equation which is summarized in Eq. 2. The equation presents CO 2 emission per capita as a function of economic activities which also include provision of sustainable energy and clean fuel technology. CO 2 PC = f(Economic Activities)-------------------------------------------------------------------- (2) Equation 1 also includes an error term. It is also well established that the error term is associated with the panel fixed effect model as expressed in Eq. 3. U it = αi + γ t + µ it -------------------------------------------------------------------------------------- (3) Where α i corresponds to the unobservable specific cross-section effects, γ t refers to the unobserved specific time effects and heterogenous factor loading, and µ it is the mutual cross-section time series effect. The heterogenous coefficients are randomly distributed around a common mean such that βi = β + vi, vi ∼ IID(0,Ω v ), where Ω v is the variance–covariance matrix. Therefore, CO 2it − CO 2i = β1(X it − X it ) + (u it − u i ) ⇐⇒ CO 2it = β 1 X it + u it ----------------------------------- (4) Where β1 refers to the parameter of interest to estimate. Notice that standard RE and FE estimators assume that panel members are cross-sectionally independent. Therefore, a cross-sectional dependency test was taken up, which is summarised in Table 5 using the following method as proposed by Pesaran (2004). Table 5 Cross-sectional dependence Tests Pesaran CD Test P-Value Friedman Test P-Value RE Model 65.13 *** 0.000 392.55 *** 0.01 FE Model 65.33 *** 0.000 388.78 *** 0.01 ***, **, and * represent 1%, 5%, and 10% significance levels, respectively Once the data is tested for stationarity and found that the model has cross-sectional dependency, it is suggested that the Common Correlated Effect Model is used for empirical estimation. the Mean Group (MG) estimator from Pesaran and Smith (1995). It allows for more flexible assumptions in a panel data framework: the intercepts, slope coefficients, and error variances to differ across countries (Pesaran and Smith, 1995). Based on the above, the model expressed in Eq. 1 was estimated, and the results as discussed below. While examining Eq. 1 , several studies point out that there is cross-sectional dependency among variables across countries (Alam et al. 2017; Chang, 2015). As a diagnostic test, we also used a cross-sectional dependency test. Two tests are used to see the cross-sectional dependency, namely the Pesaran CD test and the Friedman test. The test statistics are given in Table 5 for both fixed and random effect models. The null hypothesis of the test is that the data have cross-sectional independence. Both models suggest that their test statistics are significant at 1% significance level, rejecting the null hypotheses and accepting that there is cross-sectional dependency in the dataset. This implies that Pooled OLS is not an appropriate model for estimation. Therefore, stationary panel models are used for further analysis. The data has the issue of cross-sectional dependency. Therefore, we follow the Pesaran (2007) CIPS test of unit root to check the stationarity of the dataset. Besides, we also used Im-Pesaran-Shin (IPS) and the Levin-Lin-Chu test to check the same (Table 6 ). The results are presented in Table 6 , indicating that all the variables are stationary. Table 6 Panel unit root test Variable Pesaran Panel Unit Root Test with lag (2) Levin–Lin–Chu unit-root test Im-Pesaran-Shin Test for Unit Root CIPS* CIPS CV Unadjusted t Adjusted t* p-value t-bar t-tilde-bar Z-t-tilde-bar P-Value LNCO 2 -5.279 -2.88*** -15.2919 -4.7537 0.0000 -4.3 -3.1 -10.5 0.000 LNACT -6.325 -2.88*** -18.0418 -11.6695 0.0000 -3.3 -2.7 -8.0 0.000 LNAE -4.215 -2.88*** -16.6742 -9.7519 0.0000 -4.0 -3.0 -9.9 0.000 LNFDI -4.441 -2.88*** -15.1731 -8.8746 0.0000 -3.6 -2.7 -8.5 0.000 LNPCI -5.039 -2.88*** -22.2859 -14.6801 0.0000 -4.3 -3.0 -10.4 0.000 LNTO -4.078 -2.88*** -14.4488 -2.1668 0.0151 -4.1 -2.9 -9.6 0.000 LNURB -4.604 -2.88*** -15.3175 -8.4972 0.0000 -3.1 -2.6 -7.3 0.000 LNPOP -4.034 -2.88*** -17.1149 -7.8703 0.0000 -4.5 -3.1 -10.8 0.000 ***, **, and * represent 1%, 5%, and 10% significance levels, respectively The Wooldridge test is also used for autocorrelation in panel data, indicating that the test statistic F is 184.523 with a P-value of 0.000. The Wooldridge test suggests that there exists autocorrelation of the first order in the dataset. With the presence of cross-sectional dependence and heteroscedasticity, the panel regression models are not used for further analysis. Table 7 presents the results of Fixed and Random Effect models along with the Hausman Test for choosing between the two models. In these models, we considered the dependent variable as level values and the independent variables as their natural log. Table 7 Fixed and Random Effect Model Independent Variable CO2PC as Dependent Variable FE Model RE Model Coefficient Coefficient LNACT -0.03 -0.44 LNAE -9.53** -9.13** LNFDI 0.09 0.09 LNPCI 7.04*** 6.97*** LNTO -0.56 -1.01 LNURB -4.75** -5.45*** LNPOP -0.15 -0.45 _cons 5.38 16.40 ***, **, and * represent 1%, 5%, and 10% significance levels, respectively As indicated earlier that both FE and RE models are stationary models for panel data analysis. However, the presence of cross-sectional dependency needs further investigation using an appropriate model. Therefore, we used the CCE model for better results. The next task is to see the long-run equilibrium relationship among the variables in Eq. ( 1 ). Considering the cross-sectional dependency, we used the panel cointegration model suggested by Westerlund (2007). The result indicates that all variables are cointegrated in the long run (Table 8 ). Table 8 Test for Cointegration Pedroni test for cointegration Westerlund test Kao test Statistics P-Value Statistics P-Value Statistics P-Value Modified Phillips–Perron t 5.91 0.00 Variance ratio 2.2 0.01 Modified Dickey–Fuller t -1.99 0.02 Phillips–Perron t -2.55 0.01 Dickey–Fuller t -7.14 0.00 Augmented Dickey–Fuller t -1.65 0.05 Augmented Dickey–Fuller t -4.96 0.00 Moreover, the long run association between variables is analysed using Peseran (2006) common correlated effects method. Table 9 presents the result of the CCE model as per Eq. 2. Table 9 CCE Model for CO 2 PC as Dependent Variable CO 2 PC (Co 2 emission) Coefficient Std. err. Z P > z LNACT(Access to clean energy) 3.57 1.85 1.93 0.05 LNAE (Access to electricity) 33.46 9.44 3.55 0.00 LNFDI (Foreign direct investment) -0.09 0.14 -0.63 0.53 LNPCI(Per capita income) 2.56 0.66 3.87 0.00 LNTO (Trade openness) -0.40 0.48 -0.84 0.40 LNURB (Urbanisation) 6.40 4.28 1.50 0.13 LNPOP (Population) 7.31 3.65 2.00 0.05 CO 2 PC_csa 0.99 0.01 78.40 0.00 LNACT_csa -4.25 2.48 -1.71 0.09 LNAE_csa -34.65 11.22 -3.09 0.00 LNFDI_csa 0.18 0.18 0.99 0.32 LNPCI_csa -2.52 0.79 -3.20 0.00 LNTO_csa 0.46 0.56 0.83 0.41 LNURB_csa -5.43 4.22 -1.29 0.20 LNPOP_csa -7.28 3.67 -1.99 0.05 Constant 3.21 28.49 0.11 0.91 Source: Authors' estimates The model brings out that access to clean fuel technology is significantly and negatively affecting if taken as average of all cross sections. Similarly, the average of all cross sections in access to electricity has a negative and significant correlation. FDI, Trade openness, and Urbanisation are not significantly affecting if taken as the average of all cross sections. Therefore, we further used Pesaran and Smith (1995) mean group estimator to estimate the averages of all coefficients, showing a better estimate. The results are given in Table 10 . Table 10 Long Run Second Generation Panel Model CO 2 PC (Co 2 emission) Coefficient Std. err. z P > z LNACT(Access to clean energy) -11.12 2.47 -4.50 0.00 LNAE (Access to electricity) -19.41 2.40 -8.08 0.00 LNFDI (Foreign direct investment) -0.09 0.24 -0.38 0.70 LNPCI(Per capita income) 8.34 0.21 39.30 0.00 LNTO (Trade openness) -0.39 0.50 -0.79 0.43 LNURB (Urbanisation) 0.75 1.02 0.73 0.46 LNPOP (Population) -0.01 0.15 -0.09 0.93 _cons 60.82 19.50 3.12 0.00 Source: Authors' estimates The Pesaran and Smith (1995) test plays a crucial role in panel data analysis by addressing heterogeneity across cross-sectional units. This test is designed to evaluate the presence of heterogeneous slope coefficients in panel data models, which is a common issue in real-world datasets where different entities may respond differently to explanatory variables. By allowing for individual-specific slopes, the test provides more accurate and reliable inferences compared to traditional fixed or random effects models that assume homogeneity. Its applicability spans various fields such as economics, finance, and social sciences, where accounting for individual differences is essential for robust policy implications and forecasting. The Pesaran and Smith approach enhances model specification, ensuring that the diversity of cross-sectional units is adequately captured in the analysis. 4. Results and Discussions The results from the CCE model provide crucial statistical insights into the short-run and long-run determinants of CO 2 emissions (CO 2 PC) across panel data. By addressing cross-sectional dependencies, the model captures the complex interactions between socio-economic factors and environmental outcomes. In the short run, as per Table 9 , the statistical findings highlight several key relationships. Access to clean energy (LNACT) has a positive and statistically significant impact on CO 2 emissions, suggesting that an increase in access to clean energy leads to an increase in CO 2 emissions. This counterintuitive result could indicate that while cleaner energy sources are being adopted, overall energy consumption is rising at a faster rate. Similarly, access to electricity (LNAE) has a strongly positive and highly significant impact, implying that expanding electricity access leads to greater emissions. One of the reasons for this would be the fact that electricity generation is still sourced through non-renewable energy sources. Foreign direct investment (LNFDI) shows a negative but statistically insignificant impact in the short run. This may indicate that while FDI influences industrial activity, its environmental impact is either negligible or offset by technological improvements. In contrast, per capita income (LNPCI) has a positive and highly significant relationship, supporting the Environmental Kuznets Curve (EKC) hypothesis, which suggests that economic growth initially drives emissions upward before potentially decreasing at higher income levels. Trade openness (LNTO) has a negative but insignificant impact on emissions in the short run. The effect may depend on whether trade facilitates cleaner technologies or carbon-intensive industries. Urbanization (LNURB) has a positive coefficient but is not statistically significant impact in the short term on emissions whereas population size (LNPOP) has a positive and significant impact. The cross-sectional averages in the model help control for unobserved common factors. Notably, the significantly negative coefficients for LNAE and LNPCI suggest that when accounting for shared influences across units, increased access to electricity and income may contribute to lower emissions. This finding implies that some regions or countries manage emissions effectively through energy efficiency improvements and cleaner technologies. The results from the Pesaran and Smith (1995) long-run panel model provide further insights into the sustained effects of socio-economic factors on CO 2 emissions. Access to clean energy has a highly significant and negative effect on CO 2 emissions, confirming that clean energy access reduces the level of emissions in the long run. This reinforces the importance of investing in renewable energy infrastructure. It also highlights that to tackle the emission problem through access to clean energy, long-term policies would result in desirable outcomes. Similarly, access to electricity is negatively and significantly associated with emissions, suggesting that as more people gain access to electricity, cleaner energy sources and improved efficiency contribute to lower emissions. It also highlights the gradual transformation of the source of electricity generation from non-renewable sources to renewable sources. For example, in India, electricity using renewable sources accounted for 0.16% in 2005-06, which increased to 2.22% in 2023-24. However, the share is still low and needs to be improved in the long run to tackle the issue of emissions in the country. For G-20 countries, it remains important to increase the share of renewable sources in electricity generation. Foreign direct investment remains negative but statistically insignificant, indicating that its long-term environmental impact is neutral or context-dependent. Per capita income retains a strong positive and statistically significant relationship with CO 2 emissions in the long run. This is an important aspect for the economy to achieve sustainable development goals through the decoupling of economic activities from the level of emissions in the long run. Trade openness has a negative but statistically insignificant impact on the level of emissions in the long run, and urbanization has a positive but statistically insignificant impact. Population size shows no significant long-run effect on CO 2 emissions. 5. Conclusion and Policy Recommendations This study examines the impact of clean fuel technology, per capita energy consumption, FDI, trade openness, and urbanization on CO 2 emissions in G20 countries using a stationary panel model framework. The analysis presents both short-run and long-run impacts of clean fuel technology, electricity consumption, and economic growth etc. Results of the analysis in the short and long run contradict each other. Most of the policies related to clean fuel technologies and the generation of electricity in G-20 countries are eco-friendly in the long run. The findings reveal that access to clean fuel technology, such as the provision of LPG for household cooking, has a negative impact on CO 2 emissions in the long run. This suggests that policies promoting clean fuel adoption can significantly contribute to emissions reduction in G20 economies. However, other variables, including GDP per capita, FDI, and urbanization, exhibit a positive and significant effect on emissions, highlighting the challenge of decoupling economic activities from environmental degradation. Given the global influence of the G20, policy recommendations derived from the long-run second-generation panel model are particularly crucial. G20 nations collectively account for a substantial share of global emissions, making their policy choices critical for achieving sustainable development. To mitigate emissions effectively, these countries should prioritize the transition to clean energy, recognizing their varying levels of development. Developed economies such as Germany, the U.S., and Japan must take the lead by fostering technological innovations, providing financial assistance, and supporting clean energy investments in developing members like India, Indonesia, and South Africa. Expanding access to electricity in countries where it remains limited should focus on integrating renewable sources, enhancing grid infrastructure, and implementing energy efficiency measures, such as smart grids and demand-side management. FDI plays a vital role in G20 economies, and its environmental impact must be carefully managed. To ensure FDI supports sustainable development, regulatory frameworks should incentivize investments in green technologies and low-carbon industries. Countries that attract significant FDI, such as China and Brazil, would benefit from stricter environmental regulations and financial incentives that direct investment toward sustainable projects. Similarly, trade openness should be leveraged to promote environmentally friendly technologies and practices. International trade agreements among G20 nations should incorporate strong environmental provisions, including harmonized carbon pricing mechanisms and reduced tariffs on green technologies, to facilitate the transition toward sustainability. Economic growth remains a key driver of emissions within G20 countries. The persistent positive relationship between per capita income and CO 2 emissions reinforces the need for policies that integrate green initiatives to break the Environmental Kuznets Curve (EKC) trajectory. Strategies such as carbon pricing, subsidies for clean industries, and circular economy initiatives can help mitigate the environmental impact of rising income levels. High-income members, including the European Union and Canada, should lead the way by implementing ambitious carbon neutrality targets and expanding green finance initiatives to support lower-income G20 members in their sustainability efforts. Urbanization, particularly in rapidly growing economies like China, India, and Brazil, presents both challenges and opportunities for emissions reduction. Effective urban planning strategies, such as investing in energy-efficient buildings, expanding public transportation networks, and increasing green spaces, can mitigate the negative environmental impacts of urbanization. Collaboration within the G20 can facilitate the sharing of best practices and promote large-scale, sustainable urban development initiatives. Population growth also plays a significant role in emissions dynamics. While some G20 countries, such as India, must focus on enhancing resource efficiency and promoting sustainable consumption patterns, others with aging populations, like Japan and Germany, should emphasize maximizing resource productivity and sustainable economic participation. The study also highlights the importance of managing the energy transition carefully. The divergent short- and long-run impacts of energy access highlight the need for a balanced approach. While expanding electricity access initially increases emissions, investments in renewable energy and efficiency improvements can mitigate this effect over time. The long-run findings confirm that access to clean fuel and electricity significantly reduces emissions, reinforcing the need for policies that promote sustainable energy solutions. Finally, while trade openness and FDI do not show immediate direct impacts on emissions, their long-term potential for technological spillovers and green industrial development should not be overlooked. Encouraging environmentally responsible investment policies and trade agreements that facilitate the diffusion of cleaner technologies can accelerate progress toward sustainability. Additionally, given the significant influence of population growth on emissions, urban infrastructure and planning policies must prioritize sustainability, integrating efficient public transportation, energy-saving technologies, and green urban development initiatives. A coordinated approach to emissions reduction is essential within the G20. By leveraging their collective influence, these nations can drive global progress toward sustainability. Through joint commitments to clean energy transitions, sustainable economic policies, and international cooperation on environmental standards, the G20 can play a pivotal role in mitigating climate change and promoting a more sustainable future for all. Declarations Funding: No funding for this study ETHICAL APPROVAL & INFORMED CONSENT STATEMENTS Ethical Approval : This article does not contain any studies with human participants performed by any of the authors Informed Consent: This article does not contain any studies with human participants performed by any of the authors. References Abebaw, D. (2007). Household determinants of fuelwood choice in urban Ethiopia: a case study of Jimma Town. The Journal of Developing Areas , 117-126. Abhishek Kar, Roshan Wathore, Arunabha Ghosh, Shruti Sharma, Emily Floess, Andrew Grieshop, Rob Bailis, and Nitin Labhasetwar (2023), Promoting the Use of LPG for Household Cooking in Developing Countries, Policy Brief, CEEW. Ajmi, A. N., Hammoudeh, S., Nguyen, D. K., & Sato, J. R. (2015). On the relationships between CO 2 emissions, energy consumption and income: The importance of time variation. Energy Economics, 49, 629–638. doi:10.1016/j.eneco.2015.02.007 Aung TS, Saboori B, Rasoulinezhad E (2017) Economic growth and environmental pollution in Myanmar: an analysis of environmental Kuznets curve. Environ Sci Pollut Res 24(25):20487–20501. Balsalobre-Lorente D, Gokmenoglu KK, Taspinar N, Cantos-Cantos JM (2019) An approach to the pollution haven and pollution halo hypotheses in MINT countries. Environ Sci Pollut Res 26(22):23010–23026. Bese E (2019). Testing the environmental Kuznets curve hypothesis: evidence from Egypt, Kenya and Turkey. 670216917. Bihari V, Iqbal SM, Srivastava LP, Kesavachandran C (2014). Menstrual impairment among adolescents exposed to biomass fuel smoke. J Environ Biol 35(1):273. Chakrabarty S, Boksh FM, Chakraborty A (2013). Economic viability of biogas and green self-employment opportunities. Renew Sustain Energy Rev, 28:757–766. Dash M, Behera B, Rahut DB (2018) Understanding the factors that influence household use of clean energy in the S imilipal T iger R eserve, I ndia. In Natural Resources Forum (Vol. 42, No. 1, pp. 3–18). Oxford, UK: Blackwell Publishing Ltd. Ekholm, T., Krey, V., Pachauri, S., & Riahi, K. (2010). Determinants of household energy consumption in India. Energy policy , 38 (10), 5696-5707. Essandoh OK, Islam M, Kakinaka M (2020) Linking international trade and foreign direct investment to CO2 emissions: any differences between developed and developing Gasimli O, Haq IU, Naradda Gamage SK, Shihadeh F, Rajapakshe PSK, Shafq M (2019). Energy, trade, urbanization and environmental degradation nexus in Sri Lanka: bounds testing approach. Energies 12(9):1655. Gould CF, Urpelainen J (2018) LPG as a clean cooking fuel: adoption, use, and impact in rural India. Energy Policy 122:395-408. Guzel AE, Okumus İ (2020) Revisiting the pollution haven hypothesis in ASEAN-5 countries: new insights from panel data analysis. Environ Sci Pollut Res 27(15):18157–18167. Hanif I (2018) Impact of economic growth, nonrenewable and renewable energy consumption, and urbanization on carbon emissions in Sub-Saharan Africa. Environ Sci Pollut Res 25(15):15057–15067. He, Z., Xu, S., Shen, W., Long, R., & Chen, H. (2017). Impact of urbanization on energy related CO2 emission at different development levels: Regional difference in China based on panel estimation. Journal of cleaner production , 140 , 1719-1730. IEA, 2023, CO 2 Emission in 2022, available at https://www.iea.org/reports/co2-emissions-in-2022 Ingale LT, Dube KJ, Sarode DB, Attarde SB, Ingle ST (2013). Monitoring and respiratory health assessment of the population exposed to cooking fuel emissions in a rural area of Jalgaon district, India. Asia Pacifc Journal of Public Health 25(6):463–475. Juntarawijit Y, Juntarawijit C (2019). Cooking smoke exposure and respiratory symptoms among those responsible for household cooking: a study in Phitsanulok. Thailand Heliyon 5(5):e01706. Kaygusuz, K. (2012). Energy for sustainable development: A case of developing countries. Renewable and sustainable energy reviews , 16 (2), 1116-1126. Katircioğlu S, Katircioğlu S (2018). Testing the role of urban development in the conventional environmental Kuznets curve: evidence from Turkey. Appl Econ Lett 25(11):741-746. Khanal RC, Bajracharya L (2010). Improved cooking stove (ICS) and its impact on firewood consumptions and reducing carbon dioxide emission: a case study from TMJ area. Nepal IUCN Nepal 26:1-3. Liu, Meihan, Shi Baisheng, Majed Alharthi, Muhammad Shahid Hassan, Imran Hanif (2023). The role of natural resources, clean energy and technology in mitigating carbon emissions in top populated countries, Resources Policy, 83, 103705, https://doi.org/10.1016/j.resourpol.2023.103705. Millimet DL, Roy J (2016) Empirical tests of the pollution haven hypothesis when environmental regulation is endogenous. J Appl Economet 31(4):652-677. Ngahane BHM, Ze EA, Chebu C, Mapoure NY, Temfack E, Nganda M, Luma NH (2015) Effects of cooking fuel smoke on respiratory symptoms and lung function in semi-rural women in Cameroon. Int J Occup Environ Health 21(1):61–65. https:// doi.org/10.1179/2049396714Y.0000000090. Poumanyvong, P., Kaneko, S., 2010. Does urbanization lead to less energy use and lower CO 2 emissions? A cross-country analysis. Ecol. Econ. 70 (2), 434e444. Pradhan AK, Rout S, Khan IA (2021). Does market concentration affect wholesale electricity prices? An analysis of the Indian electricity sector in the COVID-19 pandemic context. Utilities Policy, 73:101305. Pradhan, A. K., Sachan, A., Sahu, U. K., & Mohindra, V. (2022). Do foreign direct investment inflows affect environmental degradation in BRICS nations? Environmental Science and Pollution Research. doi:10.1007/s11356-021-15678-5. Rana, R., & Sharma, M. (2019). International trade, foreign direct investment, economic growth and CO2 emissions: a study of India. Interdisciplinary Environmental Review , 20 (1), 73-82. Raskin, P. D. (1995). Methods for estimating the population contribution to environmental change. Ecological economics , 15 (3), 225-233. Sarker PK, Lau CKM, Pradhan AK (2023) Asymmetric efects of climate policy uncertainty and energy prices on bitcoin prices. Innov Green Devt 2(2):100048. Sarkodie SA, Strezov V (2019) Efect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Sci Total Environ 646:862–871. Seeckt K, Scholz D (2009) Jet versus prop, hydrogen versus kerosene for a regional freighter aircraft. In Deutscher Luft-und Raumfahrtkongress (Vol. 121195). Singhania M, Saini N (2021) Demystifying pollution haven hypothesis: Role of FDI. J Bus Res 123:516-528. Spalding-Fecher, R. (2005). Health benefits of electrification in developing countries: a quantitative assessment in South Africa. Energy for Sustainable Development , 9 (1), 53-62. Sreeja, A.T., Dhengle, A., Kumar, D. et al. Does access to clean cooking fuels reduce environmental degradation? Evidence from BRICS nations. Environ Sci Pollut Res 30 , 78948–78958 (2023). https://doi.org/10.1007/s11356-023-27619-5 Steed C (2011) Business Carbon Footprint (BCF) Report Commentary and Methodology. UK Power Networks. Retrieved from https:// www.ukpowernetworks.co.uk/internet/en/about-us/documents/ bcf-commentary-2011-fnal-1.pdf. Swain SS, Mishra P (2020) Determinants of adoption of cleaner cooking energy: experience of the Pradhan Mantri Ujjwala Yojana in rural Odisha. India J Clean Prod 248:119223. Tupy M (2018) Urbanization Is Good for the Environment. 2017. Retrieved from https://www.cato.org/blog/urbanization-goodenvironment. World Bank, 2020, The State of Access to Modern Energy Cooking Service, Washington D. C. available at https://documents1.worldbank.org/curated/en/937141600195758792/pdf/The-State-of-Access-to-Modern-Energy-Cooking-Services.pdf , page 1 Xiang L, Chen X, Su S, Yin Z (2021) Time-varying impact of economic growth on carbon emission in BRICS countries: new evidence from wavelet analysis. Front Environ Sci 280. Yahong, W., Cai, Y., Khan, S. et al. How do clean fuels and technology-based energy poverty affect carbon emissions? New evidence from eighteen developing countries. Environ Sci Pollut Res 30 , 37396–37414 (2023). https://doi.org/10.1007/s11356-022-24798-5 Yao, C., Feng, K., & Hubacek, K. (2015). Driving forces of CO2 emissions in the G20 countries: An index decomposition analysis from 1971 to 2010. Ecological Informatics, 26, 93–100. doi:10.1016/j.ecoinf.2014.02.003 Yasin I, Ahmad N, Chaudhary MA (2021) The impact of financial development, political institutions, and urbanization on environmental degradation: evidence from 59 less-developed economies. Environ Dev Sustain 23(5):6698-6721. York, R., Rosa, E. A., & Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological economics , 46 (3), 351-365. Zhang XP, Cheng XM (2009) Energy consumption, carbon emissions, and economic growth in China. Ecol Econ, 68(10):2706–2712. Zhang, N., Yu, K., Chen, Z., 2017. How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis. Energy Pol., 107, 678e687. Zhengxia He, Shichun Xu, Wenxing Shen, Ruyin Long, Hong Chen. (2017). Impact of urbanization on energy-related CO2 emission at different development levels: Regional difference in China based on panel estimation, Journal of Cleaner Production, 140(3), 1719-1730. https://doi.org/10.1016/j.jclepro.2016.08.155. Table 1 Table 1 is not available with this version. Additional Declarations No competing interests reported. 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17:19:00","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160039,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7345767/v1/6e1986078e6db5083f2e791f.html"},{"id":93258076,"identity":"e16ff1f5-50dc-496e-b6c9-29fbf59f7fa5","added_by":"auto","created_at":"2025-10-10 17:19:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrend of Access to clean fuels and technologies for cooking (% of population)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7345767/v1/c1e049c3483b04e2337f4f70.png"},{"id":93258867,"identity":"3951174d-1162-43c3-965f-712313b48bf3","added_by":"auto","created_at":"2025-10-10 17:27:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntensity of CO2 emission per US$ GDP\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7345767/v1/164ca5a9ecda5b272050e0bf.png"},{"id":93259033,"identity":"376103c2-b3a1-4f44-b16c-cab524ec73f3","added_by":"auto","created_at":"2025-10-10 17:35:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1194578,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7345767/v1/43bff028-057d-4d91-a614-0eea9ffec647.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering the role of clean fuel energy technology in reducing CO2 emissions in G20 countries: A panel nonparametric analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe United Nations Development Programme (UNDP) introduced the Sustainable Development Goals (SDGs) in 2015, setting forth 17 objectives to address global challenges such as poverty, inequality, climate change, environmental degradation, peace, and justice. Among these, SDG-7 aims to ensure universal access to modern, affordable, reliable, and sustainable energy by 2030. Energy is a critical determinant of a nation\u0026rsquo;s prosperity, and its accessibility directly influences the living standards of people worldwide. Despite significant efforts by nations to enhance energy accessibility, a substantial portion of the global population still lacks clean and affordable energy. Kaygusuz (2012) estimated that 1.4 billion people lack access to renewable energy, with approximately 2.7 billion people relying on biomass sources, such as fuelwood, for cooking. This reliance on inefficient and hazardous energy sources not only endangers environmental sustainability but also exacerbates public health risks and prolongs poverty. Spalding-Fecher (2005) and Abebaw (2007) emphasized that access to clean cooking energy is essential for sustainable development, as conventional biomass-based cooking methods are environmentally harmful and inefficient. The World Bank (2020) reported that nearly 2.8 billion people globally still lack access to clean cooking fuels.\u003c/p\u003e\n\u003cp\u003eTraditional cooking practices are a major contributor to indoor air pollution, emitting harmful pollutants such as particulate matter, carbon monoxide, and volatile organic compounds, which pose significant health and environmental risks (Juntarawijit \u0026amp; Juntarawijit, 2019). Prior research has analyzed the environmental and economic consequences of greenhouse gas emissions (Xiang et al., 2021; Bese, 2019; Aung et al., 2017; Zhang \u0026amp; Cheng, 2009). However, studies specifically linking clean cooking energy to environmental degradation remain limited. Swain and Mishra (2020) found that access to LPG improved women\u0026apos;s standard of living in India by enhancing their health, social interactions, and economic engagement, while also reducing reliance on forest timber and lowering indoor air pollution. Gould and Urpelainen (2018) demonstrated that LPG adoption significantly reduced indoor air pollution in India, although high costs, accessibility challenges, and the affordability of traditional fuels remained obstacles.\u003c/p\u003e\n\u003cp\u003eDash et al. (2018) examined fuelwood collection patterns in Odisha, India, identifying key factors such as landholding size, education, and employment as determinants of fuel choice. Their study recommended social forestry programs, public education, and the promotion of fuel-efficient stoves to reduce fuelwood dependency. Murshed (2020) investigated the role of information and communication technology (ICT) in renewable energy adoption across South Asian countries, concluding that ICT advancements improve clean cooking fuel access and reduce CO\u003csub\u003e2\u003c/sub\u003e emissions. Similarly, Hanif (2018) analyzed panel data from 34 emerging economies and found that solid fuel consumption significantly contributes to CO\u003csub\u003e2\u003c/sub\u003e emissions, supporting the Environmental Kuznets Curve (EKC) hypothesis for middle-income and developing nations.\u003c/p\u003e\n\u003cp\u003eEmpirical research on the relationship between environmental degradation, economic growth, and per capita energy consumption remains inconclusive. Policymakers in G-20 nations face trade-offs between economic expansion and environmental sustainability, raising concerns about whether to prioritize cost-efficient energy strategies or invest in eco-friendly initiatives. Given the lack of comprehensive studies on the role of clean cooking energy in reducing carbon emissions, this research aims to fill this gap through a macroeconomic analysis.\u003c/p\u003e\n\u003cp\u003eHowever, over-reliance on fossil fuels can be harmful to health and a contributing factor to poverty (Ekholm et al. 2010). According to the energy ladder theory, households move from traditional biomass fuels to more contemporary and effective energy sources like LPG, electricity, and biogas as family earnings rise. The energy ladder theory proposes that families switch from more conventional biomass fuels to more contemporary and effective energy sources like LPG, electricity, and biogas as family earnings rise. This development is not always simple, though. stacking, which refers to their approach to cooking by combining several fuels and burners.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMany households have historically relied on dirty fuels, such as wood, charcoal, dung, and crop residues, for cooking. The use of these fuels has significant adverse impacts on both the environment and human health. Studies by Ingale et al. (2013), Bihari et al. (2014), and Ngahane et al. (2015) highlight the various health problems associated with exposure to smoke from these fuels. In addition to the health impacts, the use of dirty fuels for cooking also contributes to environmental degradation. This includes deforestation, soil erosion, and increased greenhouse gas (GHG) emissions, which exacerbate climate change.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClean cooking fuels significantly reduce greenhouse gas emissions and improve public health, especially for women who are most affected by traditional fuel use. Studies by Pradhan et al. (2021) and Sarker et al. (2023) highlight clean energy as an environmentally sustainable alternative. Research quantifies CO\u003csub\u003e2\u003c/sub\u003e emissions from conventional fuels: 1 kg of firewood emits 1.83 kg CO\u003csub\u003e2\u003c/sub\u003e (Khanal \u0026amp; Bajracharya, 2010), 1 kg of dry cow dung emits 2.5 kg CO\u003csub\u003e2\u003c/sub\u003e (Chakrabarty et al., 2013), 1 liter of kerosene emits 3.15 kg CO\u003csub\u003e2\u003c/sub\u003e (Seeckt \u0026amp; Scholz, 2009), while 1 kg of LPG emits only 1.492 kg CO\u003csub\u003e2\u003c/sub\u003e (Steed, 2011), making LPG a cleaner option. Clean fuels also save time and promote gender equality and economic development.\u003c/p\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emissions continue to rise globally, with energy-related emissions growing by 0.9% (321 Mt) in 2022, reaching 36.8 GT (IEA, 2023). The largest increase was from electricity and heat generation, rising by 1.8%. While China\u0026apos;s emissions remained flat, emissions in emerging Asian markets (excluding China) increased by 4.2% (206 Mt) in 2022 (IEA, 2023). Multilateral organizations emphasize renewable energy and clean fuel adoption to curb emissions.\u003c/p\u003e\n\u003cp\u003eG20, comprising 19 countries and two regional blocs (EU and AU), represents 57.5% of the global population and 85% of global GDP. Its New Delhi declaration aims to triple renewable energy capacity. Despite progress, a large population still relies on polluting fuels for cooking. A disparity remains between developed and developing G20 nations in access to clean fuel technology, as illustrated in Table 1 and Fig. 1, which track progress from 2000-2022.\u003c/p\u003e\n\u003cp\u003eCooking with solid fuels delays the progression of multiple SDGs, including good health and well-being, gender equality, affordable and clean energy, climate action, and life on land (Abishek et. al. 2023). An annual investment of USD 150 billion in modern energy cooking services (MECS) is estimated to provide universal access to clean cooking fuel. This represents a fraction of the overall cost to indicators of health, gender, and the environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 brings out the carbon intensity in terms of milligrams of CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eper US$ of GDP for G-20 countries. The trend suggests that most of the G-20 countries have a declining trend over time. Our contribution to the expanding body of literature on development economics and environmental economics comes in multiple forms. As far as current research is concerned, no study has examined the impact of clean cooking fuels on carbon emissions in G-20 countries. The current study is anticipated to offer significant insights in the same vein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe remaining portions of the paper are divided into the following manner. The next section deals with the theoretical background for the study followed by information regarding data and methodology utilised for empirical analysis. \u0026nbsp;Thereafter, section contains a quick interpretation of the empirical results as well as a discussion of them and finally key policy recommendations are included in the final section.\u003c/p\u003e"},{"header":"2. Theoretical background","content":"\u003cp\u003eThis section examines the theoretical links between key explanatory variables and environmental degradation. CO\u003csub\u003e2\u003c/sub\u003e, emissions per capita is used as the indicator of GHG emission. The primary variable, access to clean cooking fuels (LNAC), is expected to have a negative correlation with emissions, as supported by Hanif (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who found that reliance on solid fuels increases pollution.\u003c/p\u003e\u003cp\u003eElectricity consumption per capita (LNEC) impacts emissions based on the energy mix. If renewable energy use is high, the effect is negative; otherwise, fossil-fuel reliance exacerbates degradation (Pradhan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gasimli et al., 2019). Urbanization (LNURB) has an ambiguous effect. Some studies indicate it accelerates emissions (Katircioğlu \u0026amp; Katircioğlu, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yasin et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while others suggest efficiency gains can mitigate pollution (Gasimli et al., 2019; Tupy, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The literature also highlights varying impacts based on income levels (Poumanyvong \u0026amp; Kaneko, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIncome per capita (LNPCI) captures the nexus between development and emissions, while foreign direct investment (LNFDI) has contrasting effects. The 'Pollution Halo' hypothesis suggests FDI fosters cleaner technologies (Balsalobre-Lorente et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singhania \u0026amp; Saini, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), whereas the 'Pollution Haven' hypothesis argues that lax regulations attract polluting industries (Millimet \u0026amp; Roy, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Guzel \u0026amp; Okumus, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Empirical studies show FDI-driven economic growth can contribute to emissions (Rana \u0026amp; Sharma, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTrade openness (LNTO) also presents mixed results. Weak environmental regulations enable pollution-intensive trade (Essandoh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while technologically advanced trade partners enhance environmental standards (Sarkodie \u0026amp; Strezov, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These factors collectively shape the complex relationship between energy, economic growth, and environmental sustainability.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3. Empirical Model and Methodology\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e3.1 Empirical Model\u003c/h2\u003e\u003cp\u003eThe objective of this paper is to find out the effect of clean fuel technology on CO\u003csub\u003e2\u003c/sub\u003e emissions. For this purpose, we use the environmental impact model used to identify the determinants of CO\u003csub\u003e2\u003c/sub\u003e emission (York et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Raskin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The model suggests that the environmental impact here expressed in terms of CO\u003csub\u003e2\u003c/sub\u003e emission, is caused by an increase in population, per capita income, and technology. Further, researchers also included trade, energy consumption, and urbanisation as the determinants of CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\u003cp\u003eIndependent or explanatory variables are chosen based on their impact on CO\u003csub\u003e2\u003c/sub\u003e emission levels through a literature review. The paper aims to estimate the causal relationship between dependent and explanatory variables as per Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{LNCO2PC}_{it}={\\:}_{it}+{\\beta\\:}_{1}{LNACT}_{it}+{\\beta\\:}_{2}{LNAE}_{it}+{\\beta\\:}_{3}{LLNURB}_{it}+{\\beta\\:}_{4}{LNGDP}_{it}+{\\beta\\:}_{5}{LNFDI}_{it}+{\\beta\\:}_{6}{LNTO}_{it}+u$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,2,\u0026hellip;., G20 countries and \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip;.., denotes the years over the period 2000\u0026ndash;2021.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNCO2PC\\)\u003c/span\u003e\u003c/span\u003e = Natural log of CO\u003csub\u003e2\u003c/sub\u003e emission per capita\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNACT\\)\u003c/span\u003e\u003c/span\u003e = Natural log of access to clean fuels and technologies for cooking (% of population)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNAE\\)\u003c/span\u003e\u003c/span\u003e = Natural log of energy use as % of the total population\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNURB\\)\u003c/span\u003e\u003c/span\u003e = Natural log of urban population (% of total population)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNGDP\\)\u003c/span\u003e\u003c/span\u003e = Natural log of GDP per capita, PPP (constant 2017 international \u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNFDI\\)\u003c/span\u003e\u003c/span\u003e = Natural log of FDI (% of GDP)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNTO\\)\u003c/span\u003e\u003c/span\u003e = Natural log of Trade (% of GDP)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:u\\)\u003c/span\u003e\u003c/span\u003e = error term\u003c/p\u003e\u003cp\u003eIn Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{LNCO2PC}_{it}\\)\u003c/span\u003e\u003c/span\u003e stands for CO\u003csub\u003e2\u003c/sub\u003e emission per capita and is used as the dependent reflecting an indicator of environmental degradation. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNACT\\)\u003c/span\u003e\u003c/span\u003e stands for access to clean fuels and technologies for cooking and refers to the use of sustainable energy development and empowerment of women in society. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNAE\\)\u003c/span\u003e\u003c/span\u003e indicates energy used per capita indicating fast economic development with environmental degradation. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNURB\\)\u003c/span\u003e\u003c/span\u003e reflects a high level of economic development of the G20 countries. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNGDP\\)\u003c/span\u003e\u003c/span\u003e stands for GDP per capita, which is used as an economic indicator of G20 countries, taken in terms of US dollars. The variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LNFDI\\)\u003c/span\u003e\u003c/span\u003e is used to examine the impact of foreign investment on environmental quality in G20 countries. In the equation, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is the constant term; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{4}\\:\\:{\\beta\\:}_{5}\\:\\)\u003c/span\u003e\u003c/span\u003eand\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:{\\beta\\:}_{6}\\:\\)\u003c/span\u003e\u003c/span\u003erepresent the regression coefficients.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Data and Methodology","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the summary of the variables used for the analysis. The data for these variables are taken for G20 countries excluding the European Union and the African Union, as a group of countries from the World Bank\u0026rsquo;s World Development Indicators (WDI) database for the period from 2000 to 2022.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of the variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.N.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpected impact\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e Emission per Capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to clean fuels and technologies for cooking (% of population)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to Electricity as % of Population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban population (% of total population)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNURB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita, PPP (constant 2017 international \u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFDI % of GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+/-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrade (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+/-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation in numbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents an analysis of descriptive statistics of the variables that are being examined, along with a statistical summary that includes the mean, minimum, and maximum values, as well as the standard deviation. The table also presents the normality test of the dataset.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStd. dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePr(skewness)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePr(kurtosis)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eJoint test -----\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjchi2(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNURB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, we present the values of the correlation coefficients of the variables included in our empirical model. The high correlation coefficient values of variables like LNACT, LNURB, and LNGDP show a linear relationship among these independent variables, validating the possibility of multicollinearity in the model.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation matrix and variation inflation factor\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCO2PC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNACT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNAE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNPCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNTO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNURB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO2PC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNURB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe paper follows the analysis to estimate the basic panel model, i.e. Pooled OLS followed by the Fixed and Random Effect models and estimation strategy for panel data analysis based on the equation which is summarized in Eq. 2. The equation presents CO\u003csub\u003e2\u003c/sub\u003e emission per capita as a function of economic activities which also include provision of sustainable energy and clean fuel technology.\u003c/p\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ePC\u0026thinsp;=\u0026thinsp;f(Economic Activities)-------------------------------------------------------------------- (2)\u003c/p\u003e\n\u003cp\u003eEquation \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e also includes an error term. It is also well established that the error term is associated with the panel fixed effect model as expressed in Eq. 3.\u003c/p\u003e\n\u003cp\u003eU\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026alpha;i\u0026thinsp;+\u0026thinsp;\u0026gamma;\u003csub\u003et\u003c/sub\u003e + \u0026micro;\u003csub\u003eit\u003c/sub\u003e -------------------------------------------------------------------------------------- (3)\u003c/p\u003e\n\u003cp\u003eWhere \u0026alpha;\u003csub\u003ei\u003c/sub\u003e corresponds to the unobservable specific cross-section effects, \u0026gamma;\u003csub\u003et\u003c/sub\u003e refers to the unobserved specific time effects and heterogenous factor loading, and \u0026micro;\u003csub\u003eit\u003c/sub\u003e is the mutual cross-section time series effect. The heterogenous coefficients are randomly distributed around a common mean such that\u003c/p\u003e\n\u003cp\u003e\u0026beta;i\u0026thinsp;=\u0026thinsp;\u0026beta;\u0026thinsp;+\u0026thinsp;vi, vi \u0026sim; IID(0,Ω\u003csub\u003ev\u003c/sub\u003e), where Ω\u003csub\u003ev\u003c/sub\u003e is the variance\u0026ndash;covariance matrix.\u003c/p\u003e\n\u003cp\u003eTherefore,\u003c/p\u003e\n\u003cp\u003eCO\u003csub\u003e2it\u003c/sub\u003e\u0026thinsp;\u0026minus;\u0026thinsp;CO\u003csub\u003e2i\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026beta;1(X\u003csub\u003eit\u003c/sub\u003e \u0026minus; X\u003csub\u003eit\u003c/sub\u003e) + (u\u003csub\u003eit\u003c/sub\u003e \u0026minus; u\u003csub\u003ei\u003c/sub\u003e) \u0026lArr;\u0026rArr; CO\u003csub\u003e2it\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026beta;\u003csub\u003e1\u003c/sub\u003eX \u003csub\u003eit\u003c/sub\u003e + u\u003csub\u003eit\u003c/sub\u003e----------------------------------- (4)\u003c/p\u003e\n\u003cp\u003eWhere \u0026beta;1 refers to the parameter of interest to estimate. Notice that standard RE and FE estimators assume that panel members are cross-sectionally independent. Therefore, a cross-sectional dependency test was taken up, which is summarised in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e using the following method as proposed by Pesaran (2004).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCross-sectional dependence\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTests\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePesaran CD Test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFriedman Test\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/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRE Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e392.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFE Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e388.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e***, **, and * represent 1%, 5%, and 10% significance levels, respectively\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eOnce the data is tested for stationarity and found that the model has cross-sectional dependency, it is suggested that the Common Correlated Effect Model is used for empirical estimation. the Mean Group (MG) estimator from Pesaran and Smith (1995). It allows for more flexible assumptions in a panel data framework: the intercepts, slope coefficients, and error variances to differ across countries (Pesaran and Smith, 1995). Based on the above, the model expressed in Eq. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e was estimated, and the results as discussed below.\u003c/p\u003e\n\u003cp\u003eWhile examining Eq. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, several studies point out that there is cross-sectional dependency among variables across countries (Alam et al. 2017; Chang, 2015). As a diagnostic test, we also used a cross-sectional dependency test. Two tests are used to see the cross-sectional dependency, namely the Pesaran CD test and the Friedman test. The test statistics are given in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e for both fixed and random effect models. The null hypothesis of the test is that the data have cross-sectional independence. Both models suggest that their test statistics are significant at 1% significance level, rejecting the null hypotheses and accepting that there is cross-sectional dependency in the dataset. This implies that Pooled OLS is not an appropriate model for estimation. Therefore, stationary panel models are used for further analysis.\u003c/p\u003e\n\u003cp\u003eThe data has the issue of cross-sectional dependency. Therefore, we follow the Pesaran (2007) CIPS test of unit root to check the stationarity of the dataset. Besides, we also used Im-Pesaran-Shin (IPS) and the Levin-Lin-Chu test to check the same (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, indicating that all the variables are stationary.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePanel unit root test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePesaran Panel Unit Root\u003c/p\u003e\n \u003cp\u003eTest with lag (2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLevin\u0026ndash;Lin\u0026ndash;Chu unit-root test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eIm-Pesaran-Shin Test for Unit Root\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIPS*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIPS CV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted t\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted t*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-bar\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-tilde-bar\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ-t-tilde-bar\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/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.2919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.7537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eLNACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-18.0418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.6695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eLNAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-16.6742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.7519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.1731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.8746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eLNPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.2859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.6801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eLNTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.4488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.1668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eLNURB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.3175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.4972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-17.1149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.8703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e***, **, and * represent 1%, 5%, and 10% significance levels, respectively\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe Wooldridge test is also used for autocorrelation in panel data, indicating that the test statistic F is 184.523 with a P-value of 0.000. The Wooldridge test suggests that there exists autocorrelation of the first order in the dataset. With the presence of cross-sectional dependence and heteroscedasticity, the panel regression models are not used for further analysis. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the results of Fixed and Random Effect models along with the Hausman Test for choosing between the two models. In these models, we considered the dependent variable as level values and the independent variables as their natural log.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFixed and Random Effect Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCO2PC as Dependent Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFE Model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRE Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.13**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.97***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNURB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.45***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e***, **, and * represent 1%, 5%, and 10% significance levels, respectively\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs indicated earlier that both FE and RE models are stationary models for panel data analysis. However, the presence of cross-sectional dependency needs further investigation using an appropriate model. Therefore, we used the CCE model for better results. The next task is to see the long-run equilibrium relationship among the variables in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Considering the cross-sectional dependency, we used the panel cointegration model suggested by Westerlund (2007). The result indicates that all variables are cointegrated in the long run (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest for Cointegration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePedroni test for cointegration\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eWesterlund test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eKao test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStatistics\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/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModified Phillips\u0026ndash;Perron t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVariance ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModified Dickey\u0026ndash;Fuller t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhillips\u0026ndash;Perron t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDickey\u0026ndash;Fuller t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugmented Dickey\u0026ndash;Fuller t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugmented Dickey\u0026ndash;Fuller t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMoreover, the long run association between variables is analysed using Peseran (2006) common correlated effects method. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e presents the result of the CCE model as per Eq. 2.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCCE Model for CO\u003csub\u003e2\u003c/sub\u003ePC as Dependent Variable\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ePC (Co\u003csub\u003e2\u003c/sub\u003e emission)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. err.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNACT(Access to clean energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAE (Access to electricity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI (Foreign direct investment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPCI(Per capita income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNTO (Trade openness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNURB (Urbanisation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP (Population)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ePC_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNACT_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAE_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-34.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPCI_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNTO_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNURB_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP_csa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSource: Authors\u0026apos; estimates\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe model brings out that access to clean fuel technology is significantly and negatively affecting if taken as average of all cross sections. Similarly, the average of all cross sections in access to electricity has a negative and significant correlation. FDI, Trade openness, and Urbanisation are not significantly affecting if taken as the average of all cross sections. Therefore, we further used Pesaran and Smith (1995) mean group estimator to estimate the averages of all coefficients, showing a better estimate. The results are given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLong Run Second Generation Panel Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ePC (Co\u003csub\u003e2\u003c/sub\u003e emission)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. err.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNACT(Access to clean energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAE (Access to electricity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-19.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI (Foreign direct investment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPCI(Per capita income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNTO (Trade openness)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNURB (Urbanisation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP (Population)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\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\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSource: Authors\u0026apos; estimates\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe Pesaran and Smith (1995) test plays a crucial role in panel data analysis by addressing heterogeneity across cross-sectional units. This test is designed to evaluate the presence of heterogeneous slope coefficients in panel data models, which is a common issue in real-world datasets where different entities may respond differently to explanatory variables. By allowing for individual-specific slopes, the test provides more accurate and reliable inferences compared to traditional fixed or random effects models that assume homogeneity. Its applicability spans various fields such as economics, finance, and social sciences, where accounting for individual differences is essential for robust policy implications and forecasting. The Pesaran and Smith approach enhances model specification, ensuring that the diversity of cross-sectional units is adequately captured in the analysis.\u003c/p\u003e"},{"header":"4. Results and Discussions","content":"\u003cp\u003eThe results from the CCE model provide crucial statistical insights into the short-run and long-run determinants of CO\u003csub\u003e2\u003c/sub\u003e emissions (CO\u003csub\u003e2\u003c/sub\u003ePC) across panel data. By addressing cross-sectional dependencies, the model captures the complex interactions between socio-economic factors and environmental outcomes.\u003c/p\u003e\u003cp\u003eIn the short run, as per Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the statistical findings highlight several key relationships. Access to clean energy (LNACT) has a positive and statistically significant impact on CO\u003csub\u003e2\u003c/sub\u003e emissions, suggesting that an increase in access to clean energy leads to an increase in CO\u003csub\u003e2\u003c/sub\u003e emissions. This counterintuitive result could indicate that while cleaner energy sources are being adopted, overall energy consumption is rising at a faster rate. Similarly, access to electricity (LNAE) has a strongly positive and highly significant impact, implying that expanding electricity access leads to greater emissions. One of the reasons for this would be the fact that electricity generation is still sourced through non-renewable energy sources.\u003c/p\u003e\u003cp\u003eForeign direct investment (LNFDI) shows a negative but statistically insignificant impact in the short run. This may indicate that while FDI influences industrial activity, its environmental impact is either negligible or offset by technological improvements. In contrast, per capita income (LNPCI) has a positive and highly significant relationship, supporting the Environmental Kuznets Curve (EKC) hypothesis, which suggests that economic growth initially drives emissions upward before potentially decreasing at higher income levels.\u003c/p\u003e\u003cp\u003eTrade openness (LNTO) has a negative but insignificant impact on emissions in the short run. The effect may depend on whether trade facilitates cleaner technologies or carbon-intensive industries. Urbanization (LNURB) has a positive coefficient but is not statistically significant impact in the short term on emissions whereas population size (LNPOP) has a positive and significant impact.\u003c/p\u003e\u003cp\u003eThe cross-sectional averages in the model help control for unobserved common factors. Notably, the significantly negative coefficients for LNAE and LNPCI suggest that when accounting for shared influences across units, increased access to electricity and income may contribute to lower emissions. This finding implies that some regions or countries manage emissions effectively through energy efficiency improvements and cleaner technologies.\u003c/p\u003e\u003cp\u003eThe results from the Pesaran and Smith (1995) long-run panel model provide further insights into the sustained effects of socio-economic factors on CO\u003csub\u003e2\u003c/sub\u003e emissions. Access to clean energy has a highly significant and negative effect on CO\u003csub\u003e2\u003c/sub\u003e emissions, confirming that clean energy access reduces the level of emissions in the long run. This reinforces the importance of investing in renewable energy infrastructure. It also highlights that to tackle the emission problem through access to clean energy, long-term policies would result in desirable outcomes. Similarly, access to electricity is negatively and significantly associated with emissions, suggesting that as more people gain access to electricity, cleaner energy sources and improved efficiency contribute to lower emissions. It also highlights the gradual transformation of the source of electricity generation from non-renewable sources to renewable sources. For example, in India, electricity using renewable sources accounted for 0.16% in 2005-06, which increased to 2.22% in 2023-24. However, the share is still low and needs to be improved in the long run to tackle the issue of emissions in the country. For G-20 countries, it remains important to increase the share of renewable sources in electricity generation.\u003c/p\u003e\u003cp\u003eForeign direct investment remains negative but statistically insignificant, indicating that its long-term environmental impact is neutral or context-dependent. Per capita income retains a strong positive and statistically significant relationship with CO\u003csub\u003e2\u003c/sub\u003e emissions in the long run. This is an important aspect for the economy to achieve sustainable development goals through the decoupling of economic activities from the level of emissions in the long run. Trade openness has a negative but statistically insignificant impact on the level of emissions in the long run, and urbanization has a positive but statistically insignificant impact. Population size shows no significant long-run effect on CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e"},{"header":"5. Conclusion and Policy Recommendations","content":"\u003cp\u003eThis study examines the impact of clean fuel technology, per capita energy consumption, FDI, trade openness, and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions in G20 countries using a stationary panel model framework. The analysis presents both short-run and long-run impacts of clean fuel technology, electricity consumption, and economic growth etc. Results of the analysis in the short and long run contradict each other. Most of the policies related to clean fuel technologies and the generation of electricity in G-20 countries are eco-friendly in the long run. The findings reveal that access to clean fuel technology, such as the provision of LPG for household cooking, has a negative impact on CO\u003csub\u003e2\u003c/sub\u003e emissions in the long run. This suggests that policies promoting clean fuel adoption can significantly contribute to emissions reduction in G20 economies. However, other variables, including GDP per capita, FDI, and urbanization, exhibit a positive and significant effect on emissions, highlighting the challenge of decoupling economic activities from environmental degradation.\u003c/p\u003e\u003cp\u003eGiven the global influence of the G20, policy recommendations derived from the long-run second-generation panel model are particularly crucial. G20 nations collectively account for a substantial share of global emissions, making their policy choices critical for achieving sustainable development. To mitigate emissions effectively, these countries should prioritize the transition to clean energy, recognizing their varying levels of development. Developed economies such as Germany, the U.S., and Japan must take the lead by fostering technological innovations, providing financial assistance, and supporting clean energy investments in developing members like India, Indonesia, and South Africa. Expanding access to electricity in countries where it remains limited should focus on integrating renewable sources, enhancing grid infrastructure, and implementing energy efficiency measures, such as smart grids and demand-side management.\u003c/p\u003e\u003cp\u003eFDI plays a vital role in G20 economies, and its environmental impact must be carefully managed. To ensure FDI supports sustainable development, regulatory frameworks should incentivize investments in green technologies and low-carbon industries. Countries that attract significant FDI, such as China and Brazil, would benefit from stricter environmental regulations and financial incentives that direct investment toward sustainable projects. Similarly, trade openness should be leveraged to promote environmentally friendly technologies and practices. International trade agreements among G20 nations should incorporate strong environmental provisions, including harmonized carbon pricing mechanisms and reduced tariffs on green technologies, to facilitate the transition toward sustainability.\u003c/p\u003e\u003cp\u003eEconomic growth remains a key driver of emissions within G20 countries. The persistent positive relationship between per capita income and CO\u003csub\u003e2\u003c/sub\u003e emissions reinforces the need for policies that integrate green initiatives to break the Environmental Kuznets Curve (EKC) trajectory. Strategies such as carbon pricing, subsidies for clean industries, and circular economy initiatives can help mitigate the environmental impact of rising income levels. High-income members, including the European Union and Canada, should lead the way by implementing ambitious carbon neutrality targets and expanding green finance initiatives to support lower-income G20 members in their sustainability efforts.\u003c/p\u003e\u003cp\u003eUrbanization, particularly in rapidly growing economies like China, India, and Brazil, presents both challenges and opportunities for emissions reduction. Effective urban planning strategies, such as investing in energy-efficient buildings, expanding public transportation networks, and increasing green spaces, can mitigate the negative environmental impacts of urbanization. Collaboration within the G20 can facilitate the sharing of best practices and promote large-scale, sustainable urban development initiatives. Population growth also plays a significant role in emissions dynamics. While some G20 countries, such as India, must focus on enhancing resource efficiency and promoting sustainable consumption patterns, others with aging populations, like Japan and Germany, should emphasize maximizing resource productivity and sustainable economic participation.\u003c/p\u003e\u003cp\u003eThe study also highlights the importance of managing the energy transition carefully. The divergent short- and long-run impacts of energy access highlight the need for a balanced approach. While expanding electricity access initially increases emissions, investments in renewable energy and efficiency improvements can mitigate this effect over time. The long-run findings confirm that access to clean fuel and electricity significantly reduces emissions, reinforcing the need for policies that promote sustainable energy solutions.\u003c/p\u003e\u003cp\u003eFinally, while trade openness and FDI do not show immediate direct impacts on emissions, their long-term potential for technological spillovers and green industrial development should not be overlooked. Encouraging environmentally responsible investment policies and trade agreements that facilitate the diffusion of cleaner technologies can accelerate progress toward sustainability. Additionally, given the significant influence of population growth on emissions, urban infrastructure and planning policies must prioritize sustainability, integrating efficient public transportation, energy-saving technologies, and green urban development initiatives.\u003c/p\u003e\u003cp\u003eA coordinated approach to emissions reduction is essential within the G20. By leveraging their collective influence, these nations can drive global progress toward sustainability. Through joint commitments to clean energy transitions, sustainable economic policies, and international cooperation on environmental standards, the G20 can play a pivotal role in mitigating climate change and promoting a more sustainable future for all.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL \u0026amp; INFORMED CONSENT STATEMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e: This article does not contain any studies with human participants performed by any of the authors\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e This article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbebaw, D. (2007). Household determinants of fuelwood choice in urban Ethiopia: a case study of Jimma Town. \u003cem\u003eThe Journal of Developing Areas\u003c/em\u003e, 117-126.\u003c/li\u003e\n\u003cli\u003eAbhishek Kar, Roshan Wathore, Arunabha Ghosh, Shruti Sharma, Emily Floess, Andrew Grieshop, Rob Bailis, and Nitin Labhasetwar (2023), Promoting the Use of LPG for Household Cooking in Developing Countries, Policy Brief, CEEW. Ajmi, A. N., Hammoudeh, S., Nguyen, D. K., \u0026amp; Sato, J. R. (2015). On the relationships between CO 2 emissions, energy consumption and income: The importance of time variation. Energy Economics, 49, 629\u0026ndash;638. doi:10.1016/j.eneco.2015.02.007 \u003c/li\u003e\n\u003cli\u003eAung TS, Saboori B, Rasoulinezhad E (2017) Economic growth and environmental pollution in Myanmar: an analysis of environmental Kuznets curve. Environ Sci Pollut Res 24(25):20487\u0026ndash;20501.\u003c/li\u003e\n\u003cli\u003eBalsalobre-Lorente D, Gokmenoglu KK, Taspinar N, Cantos-Cantos JM (2019) An approach to the pollution haven and pollution halo hypotheses in MINT countries. Environ Sci Pollut Res 26(22):23010\u0026ndash;23026. \u003c/li\u003e\n\u003cli\u003eBese E (2019). Testing the environmental Kuznets curve hypothesis: evidence from Egypt, Kenya and Turkey. 670216917. \u003c/li\u003e\n\u003cli\u003eBihari V, Iqbal SM, Srivastava LP, Kesavachandran C (2014). Menstrual impairment among adolescents exposed to biomass fuel smoke. J Environ Biol 35(1):273. \u003c/li\u003e\n\u003cli\u003eChakrabarty S, Boksh FM, Chakraborty A (2013). Economic viability of biogas and green self-employment opportunities. Renew Sustain Energy Rev, 28:757\u0026ndash;766.\u003c/li\u003e\n\u003cli\u003eDash M, Behera B, Rahut DB (2018) Understanding the factors that influence household use of clean energy in the S imilipal T iger R eserve, I ndia. In Natural Resources Forum (Vol. 42, No. 1, pp. 3\u0026ndash;18). Oxford, UK: Blackwell Publishing Ltd. \u003c/li\u003e\n\u003cli\u003eEkholm, T., Krey, V., Pachauri, S., \u0026amp; Riahi, K. (2010). Determinants of household energy consumption in India. \u003cem\u003eEnergy policy\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(10), 5696-5707.\u003c/li\u003e\n\u003cli\u003eEssandoh OK, Islam M, Kakinaka M (2020) Linking international trade and foreign direct investment to CO2 emissions: any differences between developed and developing Gasimli O, Haq IU, Naradda Gamage SK, Shihadeh F, Rajapakshe PSK, Shafq M (2019). Energy, trade, urbanization and environmental degradation nexus in Sri Lanka: bounds testing approach. Energies 12(9):1655.\u003c/li\u003e\n\u003cli\u003eGould CF, Urpelainen J (2018) LPG as a clean cooking fuel: adoption, use, and impact in rural India. Energy Policy 122:395-408.\u003c/li\u003e\n\u003cli\u003eGuzel AE, Okumus İ (2020) Revisiting the pollution haven hypothesis in ASEAN-5 countries: new insights from panel data analysis. Environ Sci Pollut Res 27(15):18157\u0026ndash;18167. \u003c/li\u003e\n\u003cli\u003eHanif I (2018) Impact of economic growth, nonrenewable and renewable energy consumption, and urbanization on carbon emissions in Sub-Saharan Africa. Environ Sci Pollut Res 25(15):15057\u0026ndash;15067. \u003c/li\u003e\n\u003cli\u003eHe, Z., Xu, S., Shen, W., Long, R., \u0026amp; Chen, H. (2017). Impact of urbanization on energy related CO2 emission at different development levels: Regional difference in China based on panel estimation. \u003cem\u003eJournal of cleaner production\u003c/em\u003e, \u003cem\u003e140\u003c/em\u003e, 1719-1730.\u003c/li\u003e\n\u003cli\u003eIEA, 2023, CO\u003csub\u003e2 \u003c/sub\u003eEmission in 2022, available at https://www.iea.org/reports/co2-emissions-in-2022\u003c/li\u003e\n\u003cli\u003eIngale LT, Dube KJ, Sarode DB, Attarde SB, Ingle ST (2013). Monitoring and respiratory health assessment of the population exposed to cooking fuel emissions in a rural area of Jalgaon district, India. Asia Pacifc Journal of Public Health 25(6):463\u0026ndash;475. \u003c/li\u003e\n\u003cli\u003eJuntarawijit Y, Juntarawijit C (2019). Cooking smoke exposure and respiratory symptoms among those responsible for household cooking: a study in Phitsanulok. Thailand Heliyon 5(5):e01706.\u003c/li\u003e\n\u003cli\u003eKaygusuz, K. (2012). Energy for sustainable development: A case of developing countries. \u003cem\u003eRenewable and sustainable energy reviews\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 1116-1126.\u003c/li\u003e\n\u003cli\u003eKatircioğlu S, Katircioğlu S (2018). Testing the role of urban development in the conventional environmental Kuznets curve: evidence from Turkey. Appl Econ Lett 25(11):741-746.\u003c/li\u003e\n\u003cli\u003eKhanal RC, Bajracharya L (2010). Improved cooking stove (ICS) and its impact on firewood consumptions and reducing carbon dioxide emission: a case study from TMJ area. Nepal IUCN Nepal 26:1-3. \u003c/li\u003e\n\u003cli\u003eLiu, Meihan, Shi Baisheng, Majed Alharthi, Muhammad Shahid Hassan, Imran Hanif (2023). The role of natural resources, clean energy and technology in mitigating carbon emissions in top populated countries, Resources Policy, 83, 103705, https://doi.org/10.1016/j.resourpol.2023.103705.\u003c/li\u003e\n\u003cli\u003eMillimet DL, Roy J (2016) Empirical tests of the pollution haven hypothesis when environmental regulation is endogenous. J Appl Economet 31(4):652-677.\u003c/li\u003e\n\u003cli\u003eNgahane BHM, Ze EA, Chebu C, Mapoure NY, Temfack E, Nganda M, Luma NH (2015) Effects of cooking fuel smoke on respiratory symptoms and lung function in semi-rural women in Cameroon. Int J Occup Environ Health 21(1):61\u0026ndash;65. https:// doi.org/10.1179/2049396714Y.0000000090. \u003c/li\u003e\n\u003cli\u003ePoumanyvong, P., Kaneko, S., 2010. Does urbanization lead to less energy use and lower CO\u003csub\u003e2\u003c/sub\u003e emissions? A cross-country analysis. Ecol. Econ. 70 (2), 434e444. \u003c/li\u003e\n\u003cli\u003ePradhan AK, Rout S, Khan IA (2021). Does market concentration affect wholesale electricity prices? An analysis of the Indian electricity sector in the COVID-19 pandemic context. Utilities Policy, 73:101305. \u003c/li\u003e\n\u003cli\u003ePradhan, A. K., Sachan, A., Sahu, U. K., \u0026amp; Mohindra, V. (2022). Do foreign direct investment inflows affect environmental degradation in BRICS nations?\u003cem\u003e Environmental Science and Pollution Research.\u003c/em\u003e doi:10.1007/s11356-021-15678-5.\u003c/li\u003e\n\u003cli\u003eRana, R., \u0026amp; Sharma, M. (2019). International trade, foreign direct investment, economic growth and CO2 emissions: a study of India. \u003cem\u003eInterdisciplinary Environmental Review\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 73-82.\u003c/li\u003e\n\u003cli\u003eRaskin, P. D. (1995). Methods for estimating the population contribution to environmental change. \u003cem\u003eEcological economics\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 225-233.\u003c/li\u003e\n\u003cli\u003eSarker PK, Lau CKM, Pradhan AK (2023) Asymmetric efects of climate policy uncertainty and energy prices on bitcoin prices. Innov Green Devt 2(2):100048. \u003c/li\u003e\n\u003cli\u003eSarkodie SA, Strezov V (2019) Efect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Sci Total Environ 646:862\u0026ndash;871. \u003c/li\u003e\n\u003cli\u003eSeeckt K, Scholz D (2009) Jet versus prop, hydrogen versus kerosene for a regional freighter aircraft. In Deutscher Luft-und Raumfahrtkongress (Vol. 121195). \u003c/li\u003e\n\u003cli\u003eSinghania M, Saini N (2021) Demystifying pollution haven hypothesis: Role of FDI. J Bus Res 123:516-528.\u003c/li\u003e\n\u003cli\u003eSpalding-Fecher, R. (2005). Health benefits of electrification in developing countries: a quantitative assessment in South Africa. \u003cem\u003eEnergy for Sustainable Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 53-62.\u003c/li\u003e\n\u003cli\u003eSreeja, A.T., Dhengle, A., Kumar, D. \u003cem\u003eet al.\u003c/em\u003e Does access to clean cooking fuels reduce environmental degradation? Evidence from BRICS nations. \u003cem\u003eEnviron Sci Pollut Res\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 78948\u0026ndash;78958 (2023). https://doi.org/10.1007/s11356-023-27619-5\u003c/li\u003e\n\u003cli\u003eSteed C (2011) Business Carbon Footprint (BCF) Report Commentary and Methodology. UK Power Networks. Retrieved from https:// www.ukpowernetworks.co.uk/internet/en/about-us/documents/ bcf-commentary-2011-fnal-1.pdf. \u003c/li\u003e\n\u003cli\u003eSwain SS, Mishra P (2020) Determinants of adoption of cleaner cooking energy: experience of the Pradhan Mantri Ujjwala Yojana in rural Odisha. India J Clean Prod 248:119223. \u003c/li\u003e\n\u003cli\u003eTupy M (2018) Urbanization Is Good for the Environment. 2017. Retrieved from https://www.cato.org/blog/urbanization-goodenvironment.\u003c/li\u003e\n\u003cli\u003eWorld Bank, 2020, The State of Access to Modern Energy Cooking Service, Washington D. C. available at https://documents1.worldbank.org/curated/en/937141600195758792/pdf/The-State-of-Access-to-Modern-Energy-Cooking-Services.pdf , page 1\u003c/li\u003e\n\u003cli\u003eXiang L, Chen X, Su S, Yin Z (2021) Time-varying impact of economic growth on carbon emission in BRICS countries: new evidence from wavelet analysis. Front Environ Sci 280. \u003c/li\u003e\n\u003cli\u003eYahong, W., Cai, Y., Khan, S. \u003cem\u003eet al.\u003c/em\u003e How do clean fuels and technology-based energy poverty affect carbon emissions? New evidence from eighteen developing countries. \u003cem\u003eEnviron Sci Pollut Res\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 37396\u0026ndash;37414 (2023). https://doi.org/10.1007/s11356-022-24798-5\u003c/li\u003e\n\u003cli\u003eYao, C., Feng, K., \u0026amp; Hubacek, K. (2015). Driving forces of CO2 emissions in the G20 countries: An index decomposition analysis from 1971 to 2010. Ecological Informatics, 26, 93\u0026ndash;100. doi:10.1016/j.ecoinf.2014.02.003 \u003c/li\u003e\n\u003cli\u003eYasin I, Ahmad N, Chaudhary MA (2021) The impact of financial development, political institutions, and urbanization on environmental degradation: evidence from 59 less-developed economies. Environ Dev Sustain 23(5):6698-6721.\u003c/li\u003e\n\u003cli\u003eYork, R., Rosa, E. A., \u0026amp; Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. \u003cem\u003eEcological economics\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 351-365.\u003c/li\u003e\n\u003cli\u003eZhang XP, Cheng XM (2009) Energy consumption, carbon emissions, and economic growth in China. Ecol Econ, 68(10):2706\u0026ndash;2712.\u003c/li\u003e\n\u003cli\u003eZhang, N., Yu, K., Chen, Z., 2017. How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis. \u003cem\u003eEnergy Pol.,\u003c/em\u003e 107, 678e687.\u003c/li\u003e\n\u003cli\u003eZhengxia He, Shichun Xu, Wenxing Shen, Ruyin Long, Hong Chen. (2017). Impact of urbanization on energy-related CO2 emission at different development levels: Regional difference in China based on panel estimation, Journal of Cleaner Production, 140(3), 1719-1730. https://doi.org/10.1016/j.jclepro.2016.08.155.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Clean fuel, CO2 emissions, Urbanisation, FDI, GDP per capita, Environment","lastPublishedDoi":"10.21203/rs.3.rs-7345767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7345767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe use of clean fuel energy plays a critical role in reducing CO\u003csub\u003e2 \u003c/sub\u003eemissions and environmental degradation in G20 countries, which comprise about 4.69 billion people (55%) of the world's population and 85% of the global GDP. There is a lack of analytical studies on the driving forces of emissions, mainly the use of cooking fuels as a green technology that households in G20 countries use. In this study, we intend to examine the effect of clean fuel energy technology on CO\u003csub\u003e2 \u003c/sub\u003eemissions over 2000-2021 using a cross-country panel of G20 countries based on data sourced from the World Bank. For this purpose, we used CO\u003csub\u003e2\u003c/sub\u003e emissions per capita as a dependent variable and the use of fuel technology as an independent variable. We also considered electricity consumption, urbanisation, GDP per capita, trade openness, population, urbanisation, foreign direct investment (FDI), and clean fuel as possible determinants of CO\u003csub\u003e2 \u003c/sub\u003eemissions. The study employs panel data stationary models, including random and fixed effects models. However, after a diagnostics check, the paper finds that the data has cross-sectional dependency; therefore, it uses a second-generation panel model.\u0026nbsp; This study recommends that the coverage of LPG use encourages the promotion of a cleaner cooking fuel program that can fulfill the objectives of the Sustainable Development Goals.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Uncovering the role of clean fuel energy technology in reducing CO2 emissions in G20 countries: A panel nonparametric analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 17:18:55","doi":"10.21203/rs.3.rs-7345767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-06T17:55:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T16:28:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T22:32:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T08:42:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8089277938939081328267468810223653349","date":"2025-12-01T04:42:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228056249295688012955776573755505784708","date":"2025-11-29T11:05:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311009240312058657075736243250493436558","date":"2025-11-29T08:36:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11553144633346930048657849557204261550","date":"2025-11-29T05:56:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T14:21:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337265015750746548808919577342814617974","date":"2025-09-29T07:45:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31786172240275735795951296198267979081","date":"2025-09-27T16:46:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-27T09:23:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T09:19:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-27T05:57:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T12:55:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-18T11:17:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ca4145cc-a999-4fa8-9249-13fbd4c84436","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55886159,"name":"Physical sciences/Energy science and technology"},{"id":55886160,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55886161,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-19T08:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 17:18:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7345767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7345767","identity":"rs-7345767","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}