Renewable energy,  carbon emissions and economic growth : Evidence from renewable energy-consuming countries

Renewable energy, carbon emissions and economic growth : Evidence from renewable energy-consuming countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Renewable energy, carbon emissions and economic growth : Evidence from renewable energy-consuming countries olfa ifaoui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8569186/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper examines the dynamic relationship between renewable energy consumption, carbon dioxide emissions, and economic growth in a panel of 15 major renewable energy consuming countries over the period 1990–2020. To account for cross-country heterogeneity and long-run dynamics, the study applies advanced panel econometric techniques, including panel unit root tests (ADF and PP), panel cointegration tests (Kao, Pedroni, and Westerlund), and long-run estimators such as the Pooled Mean Group (PMG) and Fully Modified Ordinary Least Squares (FMOLS). The empirical findings provide strong evidence of a long-run cointegration relationship among renewable energy consumption, carbon emissions, and economic growth. Moreover, the results indicate that renewable energy consumption promotes economic growth while contributing to the reduction of carbon emissions in the long run. These findings underscore the importance of renewable energy development as a key policy instrument for achieving sustainable economic growth and mitigating environmental degradation. The study offers relevant policy implications for energy transition strategies aimed at reconciling economic development JEL Classification: Q42, Q43, Q53, O44 renewable energy carbon emissions economic growth 1. Introduction The global push towards sustainable development has intensified the focus on renewable energy as a critical component in mitigating climate change and fostering economic growth. In recent years, the urgency to reduce carbon emissions has prompted countries worldwide to invest heavily in renewable energy sources such as wind, solar, hydro, and biomass. This transition is not only driven by environmental concerns but also by the potential economic benefits associated with renewable energy adoption, including job creation, energy security, and long-term economic stability. The relationship between energy consumption, carbon emissions, and economic growth has been extensively studied over the past few decades. Early work by Kraft and Kraft (1978) laid the foundation for understanding the linkages between energy and economic growth. Subsequent studies, such as those by Masih and Masih (1998) and Wolde-Rufael (2005), have expanded this analysis to include different countries and regions, providing a broader perspective on the energy-growth nexus. Soytas and Sari (2003), Chontanawat et al. (2008), and Huang et al. (2008) have further explored the environmental implications of energy consumption, particularly focusing on carbon emissions. Lee et al. (2008), Ozturk and Acaravci (2010), and Payne (2010) contributed to the growing body of literature examining the causality between renewable energy consumption and economic growth. More recent studies by Sadorsky (2012), Hamdi and Sbia (2013), and Omri (2014) have incorporated advanced econometric techniques to better understand these complex relationships. Omri et al. (2015a, 2015b), Gozgor et al. (2018), and Rathnayaka et al. (2018) have provided further empirical evidence, reinforcing the critical role of renewable energy in achieving sustainable economic development. This study aims to build on these foundational works by exploring the intricate relationship between renewable energy consumption, carbon emissions, and economic growth in 15 major renewable energy-consuming countries. These countries, which include both developed and developing nations, have been at the forefront of renewable energy deployment and serve as pivotal case studies for understanding the broader implications of renewable energy on a global scale. The core objectives of this research are threefold. Firstly, it seeks to analyze the impact of renewable energy consumption on carbon emissions, thereby assessing the effectiveness of renewable energy in reducing the carbon footprint of these nations. Secondly, it examines the influence of renewable energy consumption on economic growth, providing insights into how green energy investments correlate with economic performance. Lastly, the study investigates the dynamic interplay between economic growth and carbon emissions in the context of increased renewable energy use. By employing robust econometric techniques and comprehensive data analysis, this study contributes to the existing literature on sustainable development and energy economics. The findings are expected to offer valuable policy implications for governments, stakeholders, and international bodies committed to promoting renewable energy and achieving environmental and economic sustainability. 2. Literature review The relationship between renewable energy consumption, carbon emissions, and economic growth has been extensively studied across various contexts and countries. This review synthesizes key findings from the literature to provide a comprehensive understanding of the dynamic interactions among these variables. There are a lot of empirical studies on the link between REC, environment (CO2 emissions), and economic growth, which could be divided into three strands of literature. The first one focuses on the effect of renewable energy on economic growth. For example, Pao and Fu (2013) examined the relationship between GDP, renewable energy consumption (REC), and energy consumption in Brazil. Their VECM results revealed bidirectional causality between GDP and REC, with a one-way causality from economic growth to REC and energy consumption in the long run. Their findings suggest that GDP is crucial for providing resources for sustainable development and that Brazil's economy is energy independent. Similarly, Lin and Mubarak (2014) investigated the interaction between REC and economic growth in China. Their Granger causality test results indicated a long-run bidirectional causality between REC and economic growth, but no evidence of causality between CO2 emissions and REC in either the short or long run. Leitao (2014) analyzed the Portuguese economy, exploring the link between economic growth, CO2 emissions, and REC. Using a GMM model, the study found a positive relationship between these variables, and the Granger causality test indicated a unidirectional causality from REC to economic growth. Shahbaz et al. (2015) studied the relationship between REC and economic growth in Pakistan using the VECM Granger causality technique. Their findings revealed a feedback relationship between the two variables. In Iran, Khoshnevis and Shakouri (2017) examined the relationships among economic growth, REC, energy, gross fixed capital formation, globalization, trade openness, and urbanization. Their results indicated that these variables are cointegrated, showing long-run relationships among them. The Granger causality test revealed bidirectional causality between REC, globalization, financial development, and real GDP. The second one focuses on the impact of renewable energy on environmental quality. The studies analyzing the link between REC and CO2 emissions have reached different results. For instance, Sadorsky (2009) presents two empirical models analyzing the relationship between renewable energy consumption (REC) and income for a panel of emerging economies. He finds that per capita GDP growth has a positive and statistically significant impact on per capita REC. In the long run, a 1% increase in GDP per capita leads to an approximately 3.5% increase in per capita REC in these economies. For European and Eurasian countries, Tiwari (2011) shows that non-renewable energy (NRE) negatively impacts GDP growth and increases CO2 emissions, while renewable energy (RE) positively affects GDP growth. Payne (2012) investigates the link between REC, real GDP, and CO2 emissions, finding that renewable energy legislation and policies adopted since 1978 have significantly and positively influenced REC. The results also suggest that CO2 emissions have positively impacted REC. In another study, Silva et al. (2012) use a structural vector autoregressive (SVAR) model to examine the link between REC, real GDP, and CO2 emissions. Their findings reveal that an increase in renewable energy leads to a decrease in per capita CO2 emissions. The third one focuses on reviewing the studies on the relationship between REC, economic growth, and environmental quality (CO2 emissions). For example, Lu (2017) investigates the causal relationships between emissions, renewable energy consumption (REC), and economic growth. The results suggest a long-run bidirectional causality among REC, GDP, and emissions. In OECD countries, Zaghdoudi (2017) analyzes the link between oil prices, REC, CO2 emissions, and GDP. The empirical results indicate a long-run quadratic relationship between emissions and economic growth, confirming the Environmental Kuznets Curve (EKC) hypothesis. The Granger causality results also show a bidirectional relationship between emissions and REC in both the short and long run. In a recent study, Soukiazis et al. (2017) found that REC is a significant factor in explaining the level of sustainable development in the countries studied. They note that REC largely depends on the increase of human capital and is crucial for reducing CO2 emissions. 3. Data and econometric models 3.1. Data We use data for 15 major renewable energy-consuming countries (Brazil, Canada, China, Denmark, France, Germany, Italy, India, Japan, Poland, Portugal, Spain, Sweden, the United Kingdom, and the United States). over the 1990–2020 period. The data on all variables are sourced from publications by the World Bank. GDP per capita (GDP) is measured in constant 2015 US $ , and CO2 emissions (CE) are measured in metric tons per capita. Gross fixed capital formation (GFCF) in constant 2015 US dollars is used as a proxy of domestic capital. Trade openness (TO) is measured as a percentage of export and import values as a share of GDP, and urbanization (URB) is measured as the percentage of the urban population as a share of the total population. Renewable energy consumption (REC) is measured by the share of renewable energy in total final energy consumption. 3.2. Econometric models In this study, we examine the effect of REC on CO2 emissions and GDP per capita in the case of 15 countries. To achieve this objective, we consider two models (GDP: growth function and CE: environmental function), which are specified as follows: GDP t = f (REC t ; GFEF t ; URB t ) (1) In Eq. (1), GDP, REC, GFCF, and URB stand, respectively, for economic growth, renewable energy, trade openness, Gross fixed capital formation, and urbanization. The above Eq. (1) states that REC, GFEF, and LF are the driving forces of economic growth. The panel version of Eq. (1) can be written as follows: LnGDP = α 0 + α 1 LnREC + α 2 LnGFEF + α 3 URB + ε it (2) Where i designates countries; t represents the period; α0 represents the fixed country effect, and ε is the white noise. Ln is the natural logarithm of all variables. The parameters α1, α2, and α3, are, respectively, the output elasticities of REC, GFEF, and URB. CE t = f (GDP t ; REC t ; TO t ; URB t ) (3) In Eq. (3), CE, GDP, REC, TO, and URB stand, respectively, for CO2 emissions, economic growth, renewable energy, trade openness, and urbanization. Several variables affect CO2 emissions, such as economic growth (Hossain, 2012; Akin, 2014; KEHO, 2015). GDP measures the indicator of economic growth and was included. Furthermore, urbanization (URB: Measured the urban population) is another factor that is used in the environmental function (Mohapatra and Giri, 2015; Shahbaz et al., 2016). Finally, REC, as previously mentioned, is the renewable energy consumption (Sekar and Sohngen, 2014), and trade, which is the trade openness, will moreover be used in the CO2 Emission model. Considering the logarithmic form of it, we can write: LnCE it = β0 + β1 LnGDP it + β2 LnREC it + β3 LnTO it + β4 URB it + ε it (4) Where the country, t i is the period, and εitis the error term. The parameters β 1 , β 2 , and β 3 represent the long-run elasticity estimates of GDP, REC, TO, and URB, respectively. 4. Estimation method 4.1. Panel unit root test As a part of this, understanding the stationarity of variables is very important in any study. As a result, we used the panel stationary test to determine the level and first-difference order of integration in variables according to Augmented Dickey& Fuller (1979) and Phillips & Perron(1988). These are particularly helpful tests when the time dimension is very constrained. The paper provides tests for the presence of a unit root based on Fisher-ADF and Fisher-PP statistics with generalized higher critical values introduced by the authors. The Dickey-Fuller test examines whether a unit root is present in the data-generating process. This test incorporates lags into the model to control for autocorrelation. In contrast, the Phillips and Perron (1988) test employs nonparametric statistical methods to account for autocorrelation of errors without adding lags. 4.2. Panel cointegration test The panel cointegration test is employed to determine whether a long-term equilibrium relationship exists between variables in a panel data setting. Among the most widely used tests are those developed by Kao, Pedroni, and Westerlund: Kao Test : This test is based on the Engle-Granger two-step method and assumes homogeneity in the cointegration vectors across the cross-sections. It provides a straightforward approach to testing for cointegration in panel data. Pedroni Test : Pedroni’s test allows for heterogeneity in the cointegration relationship across different cross-sections. It offers multiple test statistics to evaluate the null hypothesis of no cointegration, making it flexible and robust for various panel data structures. Westerlund Test : The Westerlund test addresses potential issues of cross-sectional dependence and employs error-correction models to test for cointegration. This test focuses on the adjustment process towards the long-run equilibrium rather than solely on the residual properties. These tests are crucial for identifying the existence of a stable long-term relationship among variables in panel data analysis, enabling researchers to draw meaningful inferences about the dynamics of the data under study. 4.3. Pooled Mean Group (PMG) The Pooled Mean Group (PMG) panel estimator is a sophisticated technique used in dynamic panel data analysis to investigate both long-run and short-run relationships among variables. This estimator is particularly beneficial due to the following characteristics: Combination of MG and DFE Features: The PMG estimator combines elements of the Mean Group (MG) and Dynamic Fixed Effects (DFE) estimators. It permits heterogeneity in short-run dynamics and error variances across cross-sections while enforcing homogeneity on long-run coefficients. Long-Run Homogeneity: By assuming a uniform long-run relationship across different cross-sections, the PMG estimator achieves more efficient and consistent long-run coefficient estimates compared to the MG estimator, which allows all coefficients to vary. Short-Run Dynamics: The PMG estimator accommodates heterogeneous short-run dynamics and adjustment speeds. This flexibility is advantageous when different units (e.g., countries, regions) exhibit distinct short-run responses to changes in explanatory variables. 4.4. Panel long-run elasticities (FMOLS) The fully modified OLS (FMOLS) takes into account also unobserved heterogeneity in the cointegration relationship, endogeneity of explanatory variables, and serial correlation property inherent to dynamic panels (Pedroni 1996; 2000). The use of different time periods could be employed to mitigate for common factors that are affecting all countries in the sample similarly, like global shocks. Pedroni shows that FMOLS is a consistent estimator that also has a normal modification term, asymptotically unbiased & normally distributed with autocorrelation-free and endogenous idiosyncratic feedback effects. For small samples, Monte Carlo simulations show that the FMOLS coefficients and t-statistics are biased when the cross-sectional dimension is greater than the time-series dimension of the panel. With a fixed cross-sectional dimension, this bias declines to zero as the time dimension grows (Pedroni 1996; 2000). There are two versions of the FMOLS panel estimator, called the grouped mean and pooled. Pedroni (2000), however, suggests applying the group mean FMOLS estimator for three main rationales in practical applications. (1) It provides a consistent test of the null hypothesis of a homogeneous cointegrating vector versus an alternative where the vectors are heterogeneous, which testing is not possible with the pooled estimator. The second theorem in this paper claims that when the true cointegrating vector is heterogeneous, the group mean FMOLS estimator will supply consistent point estimates whose sample means are H-ME co-integration vectors. Finally, it shows much better properties in small samples compared to the pooled estimator, even if there exist heterogeneity correction errors, individual fixed effects, and endogeneity. 5. Empirical results and discussions Table 3 shows the results of the ADF (Augmented Dickey Fuller) and PP unit root test. These tests are conducted on the GDP per capita, REC, and CO2 emissions variables in level form or first differences. The test results indicate that the null hypothesis is rejected, which means none of the variables are stationary at levels. But they are Stationary on the First Differences of these series too, so we reject this null hypothesis. Put another way, all series are of order unity. Table 3 represents the results of the ADF and PP unit root tests. We present each test for the level and first difference in GDP per capita, REC, and CO2 emissions variables. Therefore, the two-unit root tests reveal that the variables are not stationary at the level. Nevertheless, they are stationary at the first difference, indicating a rejection of the null hypothesis. However, they are stationary at first difference, meaning that all series are integrated of order one. Level Form, For most variables, the p-values of the ADF and PP tests are greater than 0.05. This means the tests do not reject the null hypothesis of a unit root. In other words, these variables are not stationary at their levels. A non-stationary variable at the level form indicates that its values evolve with a deterministic or stochastic trend, making forecasts less reliable and potentially inaccurate. First Differences, The p-values for all variables in the first differences are very low (equal to 0.0000). This means the null hypothesis of a unit root is rejected for all variables. In other words, the variables become stationary when considering their first differences. A series that is stationary in first differences means that while the absolute values of the variables may follow a trend, the successive changes in these values (differences) are constant and predictable. Table 4 presents the results of the cointegration analysis, which examines the long-term equilibrium relationships between variables in two models: Model 1 (lnGDP) and Model 2 (lnCE). Cointegration tests are crucial in determining whether non-stationary time series variables move together in the long run, indicating a stable relationship despite short-term fluctuations. The table includes results from three cointegration tests: Kao, Pedroni, and Westerlund. Model 1 (lnGDP): Economic growth, as measured by GDP per capita, has a long-term equilibrium relationship with the included variables (likely renewable energy consumption, labor force, etc.). Model 2 (lnCE): CO2 emissions are also cointegrated with the variables included in this model, suggesting that changes in CO2 emissions are associated with long-term changes in these variables. For policymakers, these findings underscore the importance of considering long-term impacts when designing policies related to economic growth and environmental sustainability. For instance, policies aimed at promoting renewable energy consumption may have lasting effects on GDP growth and CO2 emissions. Table 5 presents the results of a Pooled Mean Group (PMG) regression, which is used to analyze the short- and long-term relationships between variables in a panel data setting. The dependent variable is the growth rate of GDP per capita (DLGDP), and the key explanatory variables include the growth rate of renewable energy consumption (DLREC), the growth rate of gross fixed capital formation (DLGFCF), and the urbanization rate (LURB). The results are separated into error correction (EC) terms and short-run (SR) dynamics. Long-Run Coefficients (Error Correction Term), The coefficient for DLREC is negative and marginally significant at the 10% level (p = 0.060). This suggests that in the long run, an increase in renewable energy consumption is associated with a slight decrease in GDP growth. This could be due to the transition costs or initial inefficiencies associated with shifting to renewable energy sources. The coefficient for DLGFCF is positive and highly significant (p = 0.000). This indicates that an increase in capital investment is strongly associated with higher GDP growth in the long run. This is consistent with economic theory, as capital formation is a key driver of economic growth. the coefficient for urbanization is negative but not statistically significant (p = 0.508). This suggests that urbanization does not have a significant long-term effect on GDP growth in this sample. Short-Run Coefficients (SR Dynamics), The error correction term is highly significant and close to 1 (p = 0.000). This indicates that deviations from the long-term equilibrium are quickly corrected, with almost 96% of the disequilibrium adjusted within one period. In the short run, an increase in renewable energy consumption is associated with a slight decrease in GDP growth, and this effect is statistically significant (p = 0.010). This might reflect short-term adjustment costs or disruptions during the transition to renewable energy. In the short run, an increase in capital investment leads to higher GDP growth, and this effect is highly significant (p = 0.000). This reinforces the importance of capital investment in driving economic growth. In the short run, urbanization has a positive and significant effect on GDP growth (p = 0.045). This suggests that short-term increases in urbanization can boost economic growth, possibly due to improved productivity and economic activities associated with urban areas. The Pooled Mean Group (PMG) regression analysis indicates that renewable energy consumption has a slight negative impact on GDP growth both in the short and long run, reflecting potential transition costs. Capital investment significantly boosts GDP growth, underscoring its importance for economic development. Urbanization positively affects GDP growth in the short run but not in the long run. The significant error correction term suggests that the economy quickly returns to long-term equilibrium after short-term deviations. Policymakers should focus on mitigating the short-term costs of renewable energy transition while promoting investment in physical capital and leveraging urbanization's short-term benefits to sustain economic growth. Table 6 presents the results of a Pooled Mean Group (PMG) regression analysis, examining the short- and long-term relationships between CO2 emissions (DLCE) and various economic factors, including GDP growth (DLGDP), renewable energy consumption (DLREC), trade openness (DLTO), and urbanization (LURB). Long-Run Coefficients (Error Correction Term), GDP growth has a strong positive and significant long-term impact on CO2 emissions. This suggests that as the economy grows, CO2 emissions increase, indicating that economic activities are still reliant on carbon-intensive processes. Renewable energy consumption has a significant negative long-term effect on CO2 emissions, suggesting that increasing the share of renewable energy in the energy mix can help reduce emissions over time. Trade openness positively impacts CO2 emissions in the long run, indicating that increased trade activities are associated with higher emissions, possibly due to increased production and transportation. Urbanization has a positive but marginally significant effect on CO2 emissions in the long run. This suggests that urbanization may lead to higher emissions, potentially due to increased energy demand and consumption in urban areas. Short-Run Coefficients (SR Dynamics). The significant error correction term indicates that short-term deviations from the long-term equilibrium are quickly corrected, implying a stable adjustment process towards the equilibrium. In the short run, GDP growth positively impacts CO2 emissions, reflecting the immediate increase in economic activities and energy use. Renewable energy consumption significantly reduces CO2 emissions in the short run, reinforcing the role of renewable energy in mitigating emissions. Trade openness has a positive and significant short-term effect on CO2 emissions, similar to its long-term impact. Urbanization's short-term effect on CO2 emissions is positive but not statistically significant, suggesting no immediate impact of urbanization on emissions. The analysis reveals that GDP growth and trade openness significantly increase CO2 emissions both in the short and long run, while renewable energy consumption significantly reduces emissions in both periods. Urbanization's impact is marginally significant in the long run and insignificant in the short run. These findings highlight the need for policies that balance economic growth and trade with environmental sustainability, emphasizing the importance of promoting renewable energy to mitigate CO2 emissions. Table 7 presents the results of the Fully Modified Ordinary Least Squares (FMOLS) regression analysis, examining the long-term relationships between economic growth (lnGDP) and various economic factors, including renewable energy consumption (lnREC), government final expenditure (lnGFEF), CO2 emissions (lnCE), trade openness (lnOPEN), and urbanization (lnURB). Model 1, Renewable energy consumption has a negative coefficient, suggesting a slight negative impact on GDP. However, the p-value of 0.086 indicates that this relationship is not statistically significant at the 5% level. This implies that renewable energy consumption does not have a strong or clear effect on GDP in the long run. Government final expenditure has a positive and highly significant impact on GDP, with a p-value of 0.000. This indicates that increased government spending is strongly associated with higher GDP, reflecting the critical role of government expenditure in stimulating economic growth. Urbanization has a small but negative and highly significant impact on GDP. The negative coefficient suggests that increased urbanization may lead to a slight reduction in GDP, potentially due to the costs associated with urban infrastructure development and management. Model 2, Economic growth has a positive and highly significant impact on CO2 emissions, with a p-value of 0.000. This indicates that higher GDP is strongly associated with increased CO2 emissions, highlighting the carbon-intensive nature of economic activities. Renewable energy consumption has a negative and highly significant impact on CO2 emissions. This suggests that higher consumption of renewable energy effectively reduces CO2 emissions, underscoring the environmental benefits of transitioning to cleaner energy sources. Urbanization has a negative but not statistically significant impact on CO2 emissions, with a p-value of 0.148. This indicates that urbanization does not have a clear or significant effect on emissions in the long run. Government final expenditure has a positive and statistically significant impact on CO2 emissions. This suggests that higher government spending may lead to increased emissions, possibly due to higher energy consumption and infrastructure development. 6. Conclusion and policy implications This paper examines the dynamic relationship between renewable energy consumption, carbon dioxide emissions, and economic growth in the 15 largest renewable energy-consuming countries from 1990 to 2020. Using a set of panel econometric techniques, including panel unit root tests, cointegration tests, PMG and FMOLS estimations, the study confirms the existence of a long-run equilibrium among the three variables. The results reveal three major findings. First, renewable energy consumption is negatively associated with economic growth in the long run, suggesting that the transition towards renewable sources may involve adjustment costs and efficiency challenges in the short term. However, renewable energy significantly reduces CO₂ emissions, highlighting its crucial role in achieving environmental sustainability. Second, economic growth exerts a positive effect on both energy consumption and emissions, which is consistent with the environmental Kuznets curve (EKC) hypothesis in the selected countries. Third, other control variables such as gross fixed capital formation and trade openness are found to influence growth dynamics, underlining the importance of complementary structural and economic factors. From a policy perspective, these findings imply that the expansion of renewable energy should not be viewed solely as an environmental strategy, but also as a long-term investment in sustainable development. Policymakers should adopt a gradual and well-coordinated transition strategy that mitigates short-term growth slowdowns while maximizing environmental benefits. This requires: Enhancing investment in renewable energy technologies to improve efficiency and reduce production costs. Supporting innovation and R&D to accelerate the integration of renewables into national energy systems. Implementing regulatory and financial incentives such as subsidies, feed-in tariffs, or carbon taxes to encourage clean energy adoption. Promoting international cooperation in technology transfer, financing, and capacity building, especially among countries with different development levels. Balancing trade and capital flows to ensure that renewable energy expansion is aligned with broader economic growth strategies. Overall, this study demonstrates that renewable energy is a key driver of carbon mitigation but requires complementary economic and institutional measures to translate environmental benefits into sustained economic growth. Future research could extend this analysis by exploring heterogeneous effects across different income groups or by incorporating additional indicators of digitalization and innovation in the energy sector. The data used in this study are publicly available from the World Development Indicators (World Bank) Declarations Conflict of Interest: The author declares that there is no conflict of interest regarding the publication of this paper. Funding: This research received no external funding. Author Contributions: The author conducted the study, collected and analyzed the data, and wrote the manuscript. Ethical Approval: Not applicable. Data Availability Statement: The data used in this study are publicly available from the World Development Indicators (World Bank). JEL Classification: Q42, Q43, O44 References Apergis, N., Payne, J. E., (2011). On the causal dynamics between renewable and non-renewable energy consumption and economic growth in developed and developing countries, Energy System, 2, 299-312. Aydin, F. F. (2013).CO2 emissions, renewable energy consumption, population density and economic growth in G7 countries. BilgiEkonomisiveYönetimiDergisi, 8 (2). Akin, C. S. (2014). The Impact of Foreign Trade, Energy Consumption and Income on CO2 Emissions. International Journal of Energy Economics and Policy, 4(3), 465. Chontanawat, J., Hunt, L.C., Pierse, R., 2008. 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(2017).The interconnections between Renewable Energy, Economic Development and Environmental Pollution.A simultaneous equation system approach. Sadorsky, P. (2009). Renewable energy consumption and income in emerging economies, Energy Policy, 37 (10), 4021-4028. Sadorsky, P.(2012). Energy consumption, output and trade in South America. Energy Econ.34, 476–488. Sekar, S., &Sohngen, B. (2014).The Effects of Renewable Portfolio Standards on Carbon Intensity in the United States. Resources for the Future Discussion Paper, (14-10). Saidi, K., &Mbarek, M. B. (2016a). Nuclear energy, renewable energy, CO2 emissions, and economic growth for nine developed countries: Evidence from panel Granger causality tests. Progress in Nuclear Energy, 88, 364-374. Saidi, K., &Mbarek, M. B. (2016b). The impact of income, trade, urbanization, and financial development on CO2 emissions in 19 emerging economies. Environmental Science and Pollution Research, 1-10. Shahbaz, M., Loganathan, N., Muzaffar, A. T., Ahmed, K., &Jabran, M. A. (2016). How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model. Renewable and Sustainable Energy Reviews, 57, 83-93. Shahbaz, M., Loganathan, N., Zeshan, M., Zaman, K., (2015). Does renewable energy consumption add in economic growth? An application of autoregressive distributed lag model in Pakistan, Renewable and Sustainable Energy Reviews, 44, 576-585. Soytas, U., Sari, R., 2003. Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Econ. 25, 33–37. Tiwari, A.K. (2011). Comparative performance of renewable and nonrenewable energy source on economic growth and CO2 emissions of Europe and Eurasian countries: A PVAR approach, Economics Bulletin, vol. 31 (3). Wolde-Rufael, Y., 2005. Energy demand and Economic growth: the African experience. J. Pol. Model. 27, 891–903. Zaghdoudi, T. (2017). Oil prices, renewable energy, CO2 emissions and economic growth in OECD countries.Economics Bulletin 37, 1844-1850. Table 1 Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1summarizespreviousconclusionsonthelinkbetweenrenewableenergyconsumption.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8569186","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572942149,"identity":"e009fdf3-39ea-4f66-9db4-dbe025ed07a2","order_by":0,"name":"olfa ifaoui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3PMWrDMBTGcYkHzmJHq73kDAqGJkPJRbq8EOimoXQxNE0MgXTpATqE9Aru4tnmgb0EMpcuPoJHjZU6Fuy4WyH6D+Ib9AOJMZfrPzbiDcNbM0A00zaxA9J+AiAZ3tvhs8fwZAcfQJglzGdtsLfjAhEAvG1wMZF1UGbR8flOvBiik7yTRDuAEHEVSxqv5ENeqzfiKX89fXUSSaIIlxqWGbEbGeWVSg0Bvu8jABpxa8lMB4dKvQ8gnnkYGeJLGaRrlV0i5i/eHLGOI/IwDqtCfRhS9v1FjHbwqfFpMj5TMW3XG3U8U9nopJv8jn7OYvB90+Yvl10ul+tK+gbiG1kdyRCzzQAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"olfa","middleName":"","lastName":"ifaoui","suffix":""}],"badges":[],"createdAt":"2026-01-10 15:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8569186/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8569186/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100108109,"identity":"a1cedf54-eaa6-49d4-8bdb-661341bad838","added_by":"auto","created_at":"2026-01-13 05:48:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39943,"visible":true,"origin":"","legend":"","description":"","filename":"Article4confrence.docx","url":"https://assets-eu.researchsquare.com/files/rs-8569186/v1/bf313e1233a3887dd7c01e6f.docx"},{"id":100108114,"identity":"0b027a4a-a1c3-4142-92ef-f61590759f42","added_by":"auto","created_at":"2026-01-13 05:48:19","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3001,"visible":true,"origin":"","legend":"","description":"","filename":"1c206daf6db74c8e9a29920e2351e3ad.json","url":"https://assets-eu.researchsquare.com/files/rs-8569186/v1/36a9196d627a194e5de0e200.json"},{"id":100108112,"identity":"ce61b596-222f-4f89-915c-2a09f9d821c2","added_by":"auto","created_at":"2026-01-13 05:48:19","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74348,"visible":true,"origin":"","legend":"","description":"","filename":"1c206daf6db74c8e9a29920e2351e3ad1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8569186/v1/0b427ab2bf68c4549d15264e.xml"},{"id":100108111,"identity":"406eb798-0998-494d-ad84-d040ab1ade10","added_by":"auto","created_at":"2026-01-13 05:48:19","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73343,"visible":true,"origin":"","legend":"","description":"","filename":"1c206daf6db74c8e9a29920e2351e3ad1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8569186/v1/31b31fecab5eb2746e8b61f0.xml"},{"id":100108113,"identity":"a7c1df57-4e81-43f5-ace8-87a2e61967a1","added_by":"auto","created_at":"2026-01-13 05:48:19","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83671,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8569186/v1/1e014065757af687e1fff532.html"},{"id":100364735,"identity":"7fcc5f8b-dea1-48db-9aa5-a88a8cd4ba32","added_by":"auto","created_at":"2026-01-16 07:54:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":649395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8569186/v1/dd218b99-76f6-42c4-a1b4-bbbc9ad82738.pdf"},{"id":100108108,"identity":"9d39e8d0-b4c5-4ae2-8b8a-d32f86619014","added_by":"auto","created_at":"2026-01-13 05:48:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16860,"visible":true,"origin":"","legend":"","description":"","filename":"Table1summarizespreviousconclusionsonthelinkbetweenrenewableenergyconsumption.docx","url":"https://assets-eu.researchsquare.com/files/rs-8569186/v1/515ffb8e886bfc99fba26ed2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Renewable energy, carbon emissions and economic growth : Evidence from renewable energy-consuming countries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global push towards sustainable development has intensified the focus on renewable energy as a critical component in mitigating climate change and fostering economic growth. In recent years, the urgency to reduce carbon emissions has prompted countries worldwide to invest heavily in renewable energy sources such as wind, solar, hydro, and biomass. This transition is not only driven by environmental concerns but also by the potential economic benefits associated with renewable energy adoption, including job creation, energy security, and long-term economic stability.\u003c/p\u003e \u003cp\u003eThe relationship between energy consumption, carbon emissions, and economic growth has been extensively studied over the past few decades. Early work by Kraft and Kraft (1978) laid the foundation for understanding the linkages between energy and economic growth. Subsequent studies, such as those by Masih and Masih (1998) and Wolde-Rufael (2005), have expanded this analysis to include different countries and regions, providing a broader perspective on the energy-growth nexus.\u003c/p\u003e \u003cp\u003eSoytas and Sari (2003), Chontanawat et al. (2008), and Huang et al. (2008) have further explored the environmental implications of energy consumption, particularly focusing on carbon emissions. Lee et al. (2008), Ozturk and Acaravci (2010), and Payne (2010) contributed to the growing body of literature examining the causality between renewable energy consumption and economic growth.\u003c/p\u003e \u003cp\u003eMore recent studies by Sadorsky (2012), Hamdi and Sbia (2013), and Omri (2014) have incorporated advanced econometric techniques to better understand these complex relationships. Omri et al. (2015a, 2015b), Gozgor et al. (2018), and Rathnayaka et al. (2018) have provided further empirical evidence, reinforcing the critical role of renewable energy in achieving sustainable economic development.\u003c/p\u003e \u003cp\u003eThis study aims to build on these foundational works by exploring the intricate relationship between renewable energy consumption, carbon emissions, and economic growth in 15 major renewable energy-consuming countries. These countries, which include both developed and developing nations, have been at the forefront of renewable energy deployment and serve as pivotal case studies for understanding the broader implications of renewable energy on a global scale.\u003c/p\u003e \u003cp\u003eThe core objectives of this research are threefold. Firstly, it seeks to analyze the impact of renewable energy consumption on carbon emissions, thereby assessing the effectiveness of renewable energy in reducing the carbon footprint of these nations. Secondly, it examines the influence of renewable energy consumption on economic growth, providing insights into how green energy investments correlate with economic performance. Lastly, the study investigates the dynamic interplay between economic growth and carbon emissions in the context of increased renewable energy use.\u003c/p\u003e \u003cp\u003eBy employing robust econometric techniques and comprehensive data analysis, this study contributes to the existing literature on sustainable development and energy economics. The findings are expected to offer valuable policy implications for governments, stakeholders, and international bodies committed to promoting renewable energy and achieving environmental and economic sustainability.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe relationship between renewable energy consumption, carbon emissions, and economic growth has been extensively studied across various contexts and countries. This review synthesizes key findings from the literature to provide a comprehensive understanding of the dynamic interactions among these variables. There are a lot of empirical studies on the link between REC, environment (CO2 emissions), and economic growth, which could be divided into three strands of literature. The first one focuses on the effect of renewable energy on economic growth. For example, Pao and Fu (2013) examined the relationship between GDP, renewable energy consumption (REC), and energy consumption in Brazil. Their VECM results revealed bidirectional causality between GDP and REC, with a one-way causality from economic growth to REC and energy consumption in the long run. Their findings suggest that GDP is crucial for providing resources for sustainable development and that Brazil's economy is energy independent. Similarly, Lin and Mubarak (2014) investigated the interaction between REC and economic growth in China. Their Granger causality test results indicated a long-run bidirectional causality between REC and economic growth, but no evidence of causality between CO2 emissions and REC in either the short or long run. Leitao (2014) analyzed the Portuguese economy, exploring the link between economic growth, CO2 emissions, and REC. Using a GMM model, the study found a positive relationship between these variables, and the Granger causality test indicated a unidirectional causality from REC to economic growth. Shahbaz et al. (2015) studied the relationship between REC and economic growth in Pakistan using the VECM Granger causality technique. Their findings revealed a feedback relationship between the two variables. In Iran, Khoshnevis and Shakouri (2017) examined the relationships among economic growth, REC, energy, gross fixed capital formation, globalization, trade openness, and urbanization. Their results indicated that these variables are cointegrated, showing long-run relationships among them. The Granger causality test revealed bidirectional causality between REC, globalization, financial development, and real GDP.\u003c/p\u003e \u003cp\u003eThe second one focuses on the impact of renewable energy on environmental quality. The studies analyzing the link between REC and CO2 emissions have reached different results. For instance, Sadorsky (2009) presents two empirical models analyzing the relationship between renewable energy consumption (REC) and income for a panel of emerging economies. He finds that per capita GDP growth has a positive and statistically significant impact on per capita REC. In the long run, a 1% increase in GDP per capita leads to an approximately 3.5% increase in per capita REC in these economies. For European and Eurasian countries, Tiwari (2011) shows that non-renewable energy (NRE) negatively impacts GDP growth and increases CO2 emissions, while renewable energy (RE) positively affects GDP growth. Payne (2012) investigates the link between REC, real GDP, and CO2 emissions, finding that renewable energy legislation and policies adopted since 1978 have significantly and positively influenced REC. The results also suggest that CO2 emissions have positively impacted REC. In another study, Silva et al. (2012) use a structural vector autoregressive (SVAR) model to examine the link between REC, real GDP, and CO2 emissions. Their findings reveal that an increase in renewable energy leads to a decrease in per capita CO2 emissions.\u003c/p\u003e \u003cp\u003eThe third one focuses on reviewing the studies on the relationship between REC, economic growth, and environmental quality (CO2 emissions). For example, Lu (2017) investigates the causal relationships between emissions, renewable energy consumption (REC), and economic growth. The results suggest a long-run bidirectional causality among REC, GDP, and emissions. In OECD countries, Zaghdoudi (2017) analyzes the link between oil prices, REC, CO2 emissions, and GDP. The empirical results indicate a long-run quadratic relationship between emissions and economic growth, confirming the Environmental Kuznets Curve (EKC) hypothesis. The Granger causality results also show a bidirectional relationship between emissions and REC in both the short and long run. In a recent study, Soukiazis et al. (2017) found that REC is a significant factor in explaining the level of sustainable development in the countries studied. They note that REC largely depends on the increase of human capital and is crucial for reducing CO2 emissions.\u003c/p\u003e "},{"header":"3. Data and econometric models","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data\u003c/h2\u003e \u003cp\u003eWe use data for 15 major renewable energy-consuming countries (Brazil, Canada, China, Denmark, France, Germany, Italy, India, Japan, Poland, Portugal, Spain, Sweden, the United Kingdom, and the United States). over the 1990\u0026ndash;2020 period. The data on all variables are sourced from publications by the World Bank. GDP per capita (GDP) is measured in constant 2015 US\u003cspan\u003e$\u003c/span\u003e, and CO2 emissions (CE) are measured in metric tons per capita. Gross fixed capital formation (GFCF) in constant 2015 US dollars is used as a proxy of domestic capital. Trade openness (TO) is measured as a percentage of export and import values as a share of GDP, and urbanization (URB) is measured as the percentage of the urban population as a share of the total population. Renewable energy consumption (REC) is measured by the share of renewable energy in total final energy consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Econometric models\u003c/h2\u003e \u003cp\u003eIn this study, we examine the effect of REC on CO2 emissions and GDP per capita in the case of 15 countries. To achieve this objective, we consider two models (GDP: growth function and CE: environmental function), which are specified as follows:\u003c/p\u003e \u003cp\u003eGDP\u003csub\u003et\u003c/sub\u003e= f (REC\u003csub\u003et\u003c/sub\u003e ; GFEF\u003csub\u003et\u003c/sub\u003e ; URB\u003csub\u003et\u003c/sub\u003e) (1)\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(1), GDP, REC, GFCF, and URB stand, respectively, for economic growth, renewable energy, trade openness, Gross fixed capital formation, and urbanization.\u003c/p\u003e \u003cp\u003eThe above Eq.\u0026nbsp;(1) states that REC, GFEF, and LF are the driving forces of economic growth. The panel version of Eq.\u0026nbsp;(1) can be written as follows:\u003c/p\u003e \u003cp\u003eLnGDP\u0026thinsp;=\u0026thinsp;α\u003csub\u003e0 +\u003c/sub\u003e α\u003csub\u003e1\u003c/sub\u003e LnREC\u0026thinsp;+\u0026thinsp;α\u003csub\u003e2\u003c/sub\u003e LnGFEF\u0026thinsp;+\u0026thinsp;α\u003csub\u003e3\u003c/sub\u003e URB\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e (2)\u003c/p\u003e \u003cp\u003eWhere i designates countries; t represents the period; α0 represents the fixed country effect, and ε is the white noise. Ln is the natural logarithm of all variables. The parameters α1, α2, and α3, are, respectively, the output elasticities of REC, GFEF, and URB.\u003c/p\u003e \u003cp\u003eCE\u003csub\u003et\u003c/sub\u003e = f (GDP\u003csub\u003et\u003c/sub\u003e ; REC\u003csub\u003et\u003c/sub\u003e ; TO\u003csub\u003et\u003c/sub\u003e ; URB\u003csub\u003et\u003c/sub\u003e ) (3)\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(3), CE, GDP, REC, TO, and URB stand, respectively, for CO2 emissions, economic growth, renewable energy, trade openness, and urbanization. Several variables affect CO2 emissions, such as economic growth (Hossain, 2012; Akin, 2014; KEHO, 2015). GDP measures the indicator of economic growth and was included. Furthermore, urbanization (URB: Measured the urban population) is another factor that is used in the environmental function (Mohapatra and Giri, 2015; Shahbaz et al., 2016).\u003c/p\u003e \u003cp\u003eFinally, REC, as previously mentioned, is the renewable energy consumption (Sekar and Sohngen, 2014), and trade, which is the trade openness, will moreover be used in the CO2 Emission model. Considering the logarithmic form of it, we can write:\u003c/p\u003e \u003cp\u003eLnCE\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β0\u0026thinsp;+\u0026thinsp;β1 LnGDP\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β2 LnREC\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β3 LnTO\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β4 URB\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e (4)\u003c/p\u003e \u003cp\u003eWhere the country, t i is the period, and εitis the error term. The parameters β\u003csub\u003e1\u003c/sub\u003e, β\u003csub\u003e2\u003c/sub\u003e, and β\u003csub\u003e3\u003c/sub\u003e represent the long-run elasticity estimates of GDP, REC, TO, and URB, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Estimation method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Panel unit root test\u003c/h2\u003e \u003cp\u003eAs a part of this, understanding the stationarity of variables is very important in any study. As a result, we used the panel stationary test to determine the level and first-difference order of integration in variables according to Augmented Dickey\u0026amp; Fuller (1979) and Phillips \u0026amp; Perron(1988). These are particularly helpful tests when the time dimension is very constrained. The paper provides tests for the presence of a unit root based on Fisher-ADF and Fisher-PP statistics with generalized higher critical values introduced by the authors.\u003c/p\u003e \u003cp\u003eThe Dickey-Fuller test examines whether a unit root is present in the data-generating process. This test incorporates lags into the model to control for autocorrelation. In contrast, the Phillips and Perron (1988) test employs nonparametric statistical methods to account for autocorrelation of errors without adding lags.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Panel cointegration test\u003c/h2\u003e \u003cp\u003eThe panel cointegration test is employed to determine whether a long-term equilibrium relationship exists between variables in a panel data setting. Among the most widely used tests are those developed by Kao, Pedroni, and Westerlund:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eKao Test\u003c/b\u003e: This test is based on the Engle-Granger two-step method and assumes homogeneity in the cointegration vectors across the cross-sections. It provides a straightforward approach to testing for cointegration in panel data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePedroni Test\u003c/b\u003e: Pedroni\u0026rsquo;s test allows for heterogeneity in the cointegration relationship across different cross-sections. It offers multiple test statistics to evaluate the null hypothesis of no cointegration, making it flexible and robust for various panel data structures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWesterlund Test\u003c/b\u003e: The Westerlund test addresses potential issues of cross-sectional dependence and employs error-correction models to test for cointegration. This test focuses on the adjustment process towards the long-run equilibrium rather than solely on the residual properties.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese tests are crucial for identifying the existence of a stable long-term relationship among variables in panel data analysis, enabling researchers to draw meaningful inferences about the dynamics of the data under study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Pooled Mean Group (PMG)\u003c/h2\u003e \u003cp\u003eThe Pooled Mean Group (PMG) panel estimator is a sophisticated technique used in dynamic panel data analysis to investigate both long-run and short-run relationships among variables. This estimator is particularly beneficial due to the following characteristics:\u003c/p\u003e \u003cp\u003eCombination of MG and DFE Features: The PMG estimator combines elements of the Mean Group (MG) and Dynamic Fixed Effects (DFE) estimators. It permits heterogeneity in short-run dynamics and error variances across cross-sections while enforcing homogeneity on long-run coefficients.\u003c/p\u003e \u003cp\u003eLong-Run Homogeneity: By assuming a uniform long-run relationship across different cross-sections, the PMG estimator achieves more efficient and consistent long-run coefficient estimates compared to the MG estimator, which allows all coefficients to vary.\u003c/p\u003e \u003cp\u003eShort-Run Dynamics: The PMG estimator accommodates heterogeneous short-run dynamics and adjustment speeds. This flexibility is advantageous when different units (e.g., countries, regions) exhibit distinct short-run responses to changes in explanatory variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Panel long-run elasticities (FMOLS)\u003c/h2\u003e \u003cp\u003eThe fully modified OLS (FMOLS) takes into account also unobserved heterogeneity in the cointegration relationship, endogeneity of explanatory variables, and serial correlation property inherent to dynamic panels (Pedroni 1996; 2000). The use of different time periods could be employed to mitigate for common factors that are affecting all countries in the sample similarly, like global shocks. Pedroni shows that FMOLS is a consistent estimator that also has a normal modification term, asymptotically unbiased \u0026amp; normally distributed with autocorrelation-free and endogenous idiosyncratic feedback effects. For small samples, Monte Carlo simulations show that the FMOLS coefficients and t-statistics are biased when the cross-sectional dimension is greater than the time-series dimension of the panel. With a fixed cross-sectional dimension, this bias declines to zero as the time dimension grows (Pedroni 1996; 2000).\u003c/p\u003e \u003cp\u003eThere are two versions of the FMOLS panel estimator, called the grouped mean and pooled. Pedroni (2000), however, suggests applying the group mean FMOLS estimator for three main rationales in practical applications. (1) It provides a consistent test of the null hypothesis of a homogeneous cointegrating vector versus an alternative where the vectors are heterogeneous, which testing is not possible with the pooled estimator. The second theorem in this paper claims that when the true cointegrating vector is heterogeneous, the group mean FMOLS estimator will supply consistent point estimates whose sample means are H-ME co-integration vectors. Finally, it shows much better properties in small samples compared to the pooled estimator, even if there exist heterogeneity correction errors, individual fixed effects, and endogeneity.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical results and discussions","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of the ADF (Augmented Dickey Fuller) and PP unit root test. These tests are conducted on the GDP per capita, REC, and CO2 emissions variables in level form or first differences. The test results indicate that the null hypothesis is rejected, which means none of the variables are stationary at levels. But they are Stationary on the First Differences of these series too, so we reject this null hypothesis. Put another way, all series are of order unity.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e represents the results of the ADF and PP unit root tests. We present each test for the level and first difference in GDP per capita, REC, and CO2 emissions variables. Therefore, the two-unit root tests reveal that the variables are not stationary at the level. Nevertheless, they are stationary at the first difference, indicating a rejection of the null hypothesis. However, they are stationary at first difference, meaning that all series are integrated of order one.\u003c/p\u003e\n\u003cp\u003eLevel Form, For most variables, the p-values of the ADF and PP tests are greater than 0.05. This means the tests do not reject the null hypothesis of a unit root. In other words, these variables are not stationary at their levels. A non-stationary variable at the level form indicates that its values evolve with a deterministic or stochastic trend, making forecasts less reliable and potentially inaccurate.\u003c/p\u003e\n\u003cp\u003eFirst Differences, The p-values for all variables in the first differences are very low (equal to 0.0000). This means the null hypothesis of a unit root is rejected for all variables. In other words, the variables become stationary when considering their first differences. A series that is stationary in first differences means that while the absolute values of the variables may follow a trend, the successive changes in these values (differences) are constant and predictable.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the cointegration analysis, which examines the long-term equilibrium relationships between variables in two models: Model 1 (lnGDP) and Model 2 (lnCE). Cointegration tests are crucial in determining whether non-stationary time series variables move together in the long run, indicating a stable relationship despite short-term fluctuations. The table includes results from three cointegration tests: Kao, Pedroni, and Westerlund.\u003c/p\u003e\n\u003cp\u003eModel 1 (lnGDP): Economic growth, as measured by GDP per capita, has a long-term equilibrium relationship with the included variables (likely renewable energy consumption, labor force, etc.).\u003c/p\u003e\n\u003cp\u003eModel 2 (lnCE): CO2 emissions are also cointegrated with the variables included in this model, suggesting that changes in CO2 emissions are associated with long-term changes in these variables.\u003c/p\u003e\n\u003cp\u003eFor policymakers, these findings underscore the importance of considering long-term impacts when designing policies related to economic growth and environmental sustainability. For instance, policies aimed at promoting renewable energy consumption may have lasting effects on GDP growth and CO2 emissions.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of a Pooled Mean Group (PMG) regression, which is used to analyze the short- and long-term relationships between variables in a panel data setting. The dependent variable is the growth rate of GDP per capita (DLGDP), and the key explanatory variables include the growth rate of renewable energy consumption (DLREC), the growth rate of gross fixed capital formation (DLGFCF), and the urbanization rate (LURB). The results are separated into error correction (EC) terms and short-run (SR) dynamics.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eLong-Run Coefficients (Error Correction Term), The coefficient for DLREC is negative and marginally significant at the 10% level (p\u0026thinsp;=\u0026thinsp;0.060). This suggests that in the long run, an increase in renewable energy consumption is associated with a slight decrease in GDP growth. This could be due to the transition costs or initial inefficiencies associated with shifting to renewable energy sources. The coefficient for DLGFCF is positive and highly significant (p\u0026thinsp;=\u0026thinsp;0.000). This indicates that an increase in capital investment is strongly associated with higher GDP growth in the long run. This is consistent with economic theory, as capital formation is a key driver of economic growth. the coefficient for urbanization is negative but not statistically significant (p\u0026thinsp;=\u0026thinsp;0.508). This suggests that urbanization does not have a significant long-term effect on GDP growth in this sample.\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eShort-Run Coefficients (SR Dynamics), The error correction term is highly significant and close to 1 (p\u0026thinsp;=\u0026thinsp;0.000). This indicates that deviations from the long-term equilibrium are quickly corrected, with almost 96% of the disequilibrium adjusted within one period. In the short run, an increase in renewable energy consumption is associated with a slight decrease in GDP growth, and this effect is statistically significant (p\u0026thinsp;=\u0026thinsp;0.010). This might reflect short-term adjustment costs or disruptions during the transition to renewable energy. In the short run, an increase in capital investment leads to higher GDP growth, and this effect is highly significant (p\u0026thinsp;=\u0026thinsp;0.000). This reinforces the importance of capital investment in driving economic growth. In the short run, urbanization has a positive and significant effect on GDP growth (p\u0026thinsp;=\u0026thinsp;0.045). This suggests that short-term increases in urbanization can boost economic growth, possibly due to improved productivity and economic activities associated with urban areas.\u003c/p\u003e\n\u003cp\u003eThe Pooled Mean Group (PMG) regression analysis indicates that renewable energy consumption has a slight negative impact on GDP growth both in the short and long run, reflecting potential transition costs. Capital investment significantly boosts GDP growth, underscoring its importance for economic development. Urbanization positively affects GDP growth in the short run but not in the long run. The significant error correction term suggests that the economy quickly returns to long-term equilibrium after short-term deviations. Policymakers should focus on mitigating the short-term costs of renewable energy transition while promoting investment in physical capital and leveraging urbanization\u0026apos;s short-term benefits to sustain economic growth.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of a Pooled Mean Group (PMG) regression analysis, examining the short- and long-term relationships between CO2 emissions (DLCE) and various economic factors, including GDP growth (DLGDP), renewable energy consumption (DLREC), trade openness (DLTO), and urbanization (LURB).\u003c/p\u003e\n\u003cp\u003eLong-Run Coefficients (Error Correction Term), GDP growth has a strong positive and significant long-term impact on CO2 emissions. This suggests that as the economy grows, CO2 emissions increase, indicating that economic activities are still reliant on carbon-intensive processes. Renewable energy consumption has a significant negative long-term effect on CO2 emissions, suggesting that increasing the share of renewable energy in the energy mix can help reduce emissions over time. Trade openness positively impacts CO2 emissions in the long run, indicating that increased trade activities are associated with higher emissions, possibly due to increased production and transportation. Urbanization has a positive but marginally significant effect on CO2 emissions in the long run. This suggests that urbanization may lead to higher emissions, potentially due to increased energy demand and consumption in urban areas.\u003c/p\u003e\n\u003cp\u003eShort-Run Coefficients (SR Dynamics). The significant error correction term indicates that short-term deviations from the long-term equilibrium are quickly corrected, implying a stable adjustment process towards the equilibrium. In the short run, GDP growth positively impacts CO2 emissions, reflecting the immediate increase in economic activities and energy use. Renewable energy consumption significantly reduces CO2 emissions in the short run, reinforcing the role of renewable energy in mitigating emissions. Trade openness has a positive and significant short-term effect on CO2 emissions, similar to its long-term impact. Urbanization\u0026apos;s short-term effect on CO2 emissions is positive but not statistically significant, suggesting no immediate impact of urbanization on emissions.\u003c/p\u003e\n\u003cp\u003eThe analysis reveals that GDP growth and trade openness significantly increase CO2 emissions both in the short and long run, while renewable energy consumption significantly reduces emissions in both periods. Urbanization\u0026apos;s impact is marginally significant in the long run and insignificant in the short run. These findings highlight the need for policies that balance economic growth and trade with environmental sustainability, emphasizing the importance of promoting renewable energy to mitigate CO2 emissions.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the results of the Fully Modified Ordinary Least Squares (FMOLS) regression analysis, examining the long-term relationships between economic growth (lnGDP) and various economic factors, including renewable energy consumption (lnREC), government final expenditure (lnGFEF), CO2 emissions (lnCE), trade openness (lnOPEN), and urbanization (lnURB).\u003c/p\u003e\n\u003cp\u003eModel 1, Renewable energy consumption has a negative coefficient, suggesting a slight negative impact on GDP. However, the p-value of 0.086 indicates that this relationship is not statistically significant at the 5% level. This implies that renewable energy consumption does not have a strong or clear effect on GDP in the long run. Government final expenditure has a positive and highly significant impact on GDP, with a p-value of 0.000. This indicates that increased government spending is strongly associated with higher GDP, reflecting the critical role of government expenditure in stimulating economic growth. Urbanization has a small but negative and highly significant impact on GDP. The negative coefficient suggests that increased urbanization may lead to a slight reduction in GDP, potentially due to the costs associated with urban infrastructure development and management.\u003c/p\u003e\n\u003cp\u003eModel 2, Economic growth has a positive and highly significant impact on CO2 emissions, with a p-value of 0.000. This indicates that higher GDP is strongly associated with increased CO2 emissions, highlighting the carbon-intensive nature of economic activities. Renewable energy consumption has a negative and highly significant impact on CO2 emissions. This suggests that higher consumption of renewable energy effectively reduces CO2 emissions, underscoring the environmental benefits of transitioning to cleaner energy sources. Urbanization has a negative but not statistically significant impact on CO2 emissions, with a p-value of 0.148. This indicates that urbanization does not have a clear or significant effect on emissions in the long run. Government final expenditure has a positive and statistically significant impact on CO2 emissions. This suggests that higher government spending may lead to increased emissions, possibly due to higher energy consumption and infrastructure development.\u003c/p\u003e"},{"header":"6. Conclusion and policy implications","content":"\u003cp\u003eThis paper examines the dynamic relationship between renewable energy consumption, carbon dioxide emissions, and economic growth in the 15 largest renewable energy-consuming countries from 1990 to 2020. Using a set of panel econometric techniques, including panel unit root tests, cointegration tests, PMG and FMOLS estimations, the study confirms the existence of a long-run equilibrium among the three variables.\u003c/p\u003e \u003cp\u003eThe results reveal three major findings. First, renewable energy consumption is negatively associated with economic growth in the long run, suggesting that the transition towards renewable sources may involve adjustment costs and efficiency challenges in the short term. However, renewable energy significantly reduces CO₂ emissions, highlighting its crucial role in achieving environmental sustainability. Second, economic growth exerts a positive effect on both energy consumption and emissions, which is consistent with the environmental Kuznets curve (EKC) hypothesis in the selected countries. Third, other control variables such as gross fixed capital formation and trade openness are found to influence growth dynamics, underlining the importance of complementary structural and economic factors.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, these findings imply that the expansion of renewable energy should not be viewed solely as an environmental strategy, but also as a long-term investment in sustainable development. Policymakers should adopt a gradual and well-coordinated transition strategy that mitigates short-term growth slowdowns while maximizing environmental benefits. This requires:\u003c/p\u003e \u003cp\u003eEnhancing investment in renewable energy technologies to improve efficiency and reduce production costs.\u003c/p\u003e \u003cp\u003eSupporting innovation and R\u0026amp;D to accelerate the integration of renewables into national energy systems.\u003c/p\u003e \u003cp\u003eImplementing regulatory and financial incentives such as subsidies, feed-in tariffs, or carbon taxes to encourage clean energy adoption.\u003c/p\u003e \u003cp\u003ePromoting international cooperation in technology transfer, financing, and capacity building, especially among countries with different development levels.\u003c/p\u003e \u003cp\u003eBalancing trade and capital flows to ensure that renewable energy expansion is aligned with broader economic growth strategies.\u003c/p\u003e \u003cp\u003eOverall, this study demonstrates that renewable energy is a key driver of carbon mitigation but requires complementary economic and institutional measures to translate environmental benefits into sustained economic growth. Future research could extend this analysis by exploring heterogeneous effects across different income groups or by incorporating additional indicators of digitalization and innovation in the energy sector.\u003c/p\u003e \u003cp\u003eThe data used in this study are publicly available from the World Development Indicators (World Bank)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe author declares that there is no conflict of interest regarding the publication of this paper.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis research received no external funding.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe author conducted the study, collected and analyzed the data, and wrote the manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe data used in this study are publicly available from the World Development Indicators (World Bank).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ42, Q43, O44\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eApergis, N., Payne, J. 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Does renewable energy consumption add in economic growth? An application of autoregressive distributed lag model in Pakistan, Renewable and Sustainable Energy Reviews, 44, 576-585. \u003c/li\u003e\n\u003cli\u003eSoytas, U., Sari, R., 2003. Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Econ. 25, 33\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eTiwari, A.K. (2011). Comparative performance of renewable and nonrenewable energy source on economic growth and CO2 emissions of Europe and Eurasian countries: A PVAR approach, Economics Bulletin, vol. 31 (3).\u003c/li\u003e\n\u003cli\u003eWolde-Rufael, Y., 2005. Energy demand and Economic growth: the African experience. J. Pol. Model. 27, 891\u0026ndash;903.\u003c/li\u003e\n\u003cli\u003eZaghdoudi, T. (2017). Oil prices, renewable energy, CO2 emissions and economic growth in OECD countries.Economics Bulletin 37, 1844-1850. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"renewable energy, carbon emissions, economic growth","lastPublishedDoi":"10.21203/rs.3.rs-8569186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8569186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines the dynamic relationship between renewable energy consumption, carbon dioxide emissions, and economic growth in a panel of 15 major renewable energy consuming countries over the period 1990–2020. To account for cross-country heterogeneity and long-run dynamics, the study applies advanced panel econometric techniques, including panel unit root tests (ADF and PP), panel cointegration tests (Kao, Pedroni, and Westerlund), and long-run estimators such as the Pooled Mean Group (PMG) and Fully Modified Ordinary Least Squares (FMOLS). The empirical findings provide strong evidence of a long-run cointegration relationship among renewable energy consumption, carbon emissions, and economic growth. Moreover, the results indicate that renewable energy consumption promotes economic growth while contributing to the reduction of carbon emissions in the long run. These findings underscore the importance of renewable energy development as a key policy instrument for achieving sustainable economic growth and mitigating environmental degradation. The study offers relevant policy implications for energy transition strategies aimed at reconciling economic development\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification:\u003c/strong\u003e Q42, Q43, Q53, O44\u003c/p\u003e","manuscriptTitle":"Renewable energy, carbon emissions and economic growth : Evidence from renewable energy-consuming countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 05:48:14","doi":"10.21203/rs.3.rs-8569186/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18ac8e8a-41d1-4292-9f80-0948fbe98f79","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T05:48:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 05:48:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8569186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8569186","identity":"rs-8569186","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}