Technological Innovation and Renewable Energy Consumption as Determinants of Environmental Pollution: Panel Evidence from BRICS Countries

Technological Innovation and Renewable Energy Consumption as Determinants of Environmental Pollution: Panel Evidence from BRICS 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 Technological Innovation and Renewable Energy Consumption as Determinants of Environmental Pollution: Panel Evidence from BRICS Countries Alibay MAMMADLI, Farrukh MAMMADZADA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8315644/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 study empirically investigates the impact of technological innovation and renewable energy consumption on environmental pollution, specifically CO₂ emissions, by controlling for the effect of economic growth in a panel of BRICS (Brazil, Russian federation, India, China, South Africa) countries over the period 1996–2021. Employing panel data analysis techniques, the study aims to uncover the distinct roles of innovation and clean energy in shaping environmental outcomes in a group of rapidly industrializing and technologically advancing economies. The empirical findings indicate that technological innovation has a positive and statistically significant relationship with CO₂ emissions, suggesting that, in the context of the BRICS countries, innovation may still be closely linked to carbon-intensive industrial processes. In contrast, renewable energy consumption exhibits a negative and statistically significant association with CO₂ emissions, highlighting its potential as an effective instrument for mitigating environmental degradation. These results underscore the need to align innovation strategies with environmental objectives and to increase the share of clean energy in the energy mix to promote sustainable development across the BRICS economies. JEL classification: O33, Q55, C33 Technological Innovation Renewable Energy CO₂ Emissions BRICS 1. Introduction In the last five decades, environmental pollution and climate change have become major global challenges that threaten not only ecosystems but also the well-being of all living organisms (Bayar, Y., & Şaşmaz, M. Ü., 2016). The rapid and often unregulated expansion of economic and social activities has significantly intensified environmental degradation, creating a reciprocal relationship where environmental damage, in turn, impedes sustainable economic progress. A substantial portion of global energy demand continues to be met through the use of fossil fuels. This reliance leads to the emission of harmful gases, such as carbon dioxide (CO₂)—which accounts for approximately 60% of total greenhouse gas emissions—and methane (CH₄), resulting in environmental issues such as global warming, ozone layer depletion, and air pollution (Nihat, I., & Kılıç, E., 2014 ). According to NASA reports, CO₂ has become the most environmentally damaging greenhouse gas due to its growing concentration in the atmosphere. The accumulation of such gases has been identified as a critical driver of rising global temperatures and other environmental crises. Alarmingly, scientists have warned that by 2050, life on Earth could become unsustainable due to the accelerating pace of environmental degradation (Zhang, Y.J., Peng, Y.L., Ma, C.Q. ve Shen, B., 2017). In recent years, both national governments and international organizations have introduced various regulatory frameworks and signed agreements aimed at reducing CO₂ emissions and combating climate change. At the same time, economic instruments have increasingly been utilized as tools for environmental policy. Among the most prominent of these instruments are renewable energy consumption and technological innovation (Altıntaş, H., & Kassouri, Y., 2020 ). Several countries have succeeded in significantly reducing harmful gas emissions—such as CO₂—by increasing their level of technological advancement and focusing on the development of environmentally oriented patents (Cheng, C., Ren, X., Dong, K., Dong, X. & Wang, Z., 2021 ). Against this backdrop, the primary aim of this study is to analyze the impact of technological innovation and renewable energy consumption on environmental pollution in selected BRICS countries. As a secondary objective, the study also explores how economic growth may influence CO₂ emissions, both positively and negatively. In this context, renewable energy consumption and technological innovation are treated as the main explanatory variables, while economic growth is considered a control variable. The study employs the panel data analysis method, which is increasingly used in environmental and energy economics literature, to examine the period 1996–2021 using annual data. The structure of the study is as follows: The first section provides an overview of environmental pollution and its underlying causes. The second section presents a comprehensive review of the related literature. The third section details the methodology and data used for the econometric analysis. In the fourth section, the empirical results are evaluated. Finally, the fifth section offers conclusions and policy implications, followed by the list of references. 2. Literature Review Environmental pollution and rising emissions, as adverse outcomes of economic growth performance, have become significant concerns for both countries and the academic community. Since the early 20th century, various technological developments aimed at protecting the environment have emerged alongside economic expansion, prompting investigations into their effectiveness. Over the last decade, there has been growing scholarly attention on studies that analyse the evolution of emissions and environmental degradation across different countries and time periods, using indicators such as innovation in energy technologies, R&D intensity, and the number of patents. Notably, several empirical studies have employed patent counts as proxies for technological innovation in their analysis of environmental impacts. Table 1 Summary of studies on environmental pollution Author(s) Country Group Period Methodology Key Findings Johnstone et al. ( 2010 ) 25 High-Income Countries 1978–2003 Panel Regression Analysis Environmental policies significantly promote renewable energy innovation; public R&D spending positively impacts innovation in geothermal, ocean, wind, and solar energy technologies. Hang and Yuan-Sheng ( 2011 ) China 1980–2006 Panel Regression Analysis While the initial impact of new technology on environmental degradation is positive, in later stages, as technology advances, this effect becomes negative. Albino et al. ( 2014 ) Global (All countries) 1971–2010 Patent Data Analysis Innovative activity in energy peaked during oil crises and early 1990s; private sector leads in low-carbon energy innovation; patent numbers in alternative energy remain limited; over half of low-carbon energy patents held by the USA. Fei et al. ( 2014 ) Norway, New Zealand 1971–2010 ARDL, Granger Causality Test A bidirectional causality exists between environmental degradation and technological innovation in Norway, whereas no significant relationship was found in New Zealand. Işık and Kılıç ( 2014 ) OECD Countries 1990–2010 Dynamic Panel Data Analysis R&D expenditures have a negative effect on CO₂ emissions. Akın and Aytun ( 2015 ) Türkiye 1971–2010 Bootstrap Causality Analysis A linear causality relationship was found from higher education levels to CO₂ emissions and energy consumption. Ahmed et al. ( 2016 ) 24 European Countries 1980–2010 ARDL Technological innovation was found to reduce environmental pollution. Samargandi ( 2017 ) Saudi Arabia 1970–2014 ARDL Technological development was found to have no significant effect on environmental degradation. Yii and Geetha ( 2017 ) Malaysia 1971–2013 Error Correction Model (ECM) and Granger Causality Test In the short run, there is a negative relationship between technological innovation and CO₂ emissions; however, no statistically significant relationship exists in the long run. Causality analysis indicates that technological innovation Granger-causes CO₂ emissions in the short run, but no causality is found in the long run. Hashmi and Alem (2019) OECD countries 1999–2014 Panel data analysis and GMM Environment-oriented innovation and environmental tax have negatively affected CO₂ emissions. Fan and Hossain ( 2018 ) China, India 1974–2016 Toda-Yamamoto causality test In China, there is a bidirectional causality between technological innovation and environmental degradation, while in India, a unidirectional causality is found from technological innovation to environmental degradation. Chen and Lee ( 2020 ) 96 countries 1996–2018 Spatial panel data model CO₂ emissions and R&D intensity are spatially correlated across countries. In high-income, high-tech countries, technological innovation reduces both domestic and neighbouring countries’ emissions. In low- and middle-income, low-tech countries, innovation increases emissions both domestically and in neighbouring countries. Bindi ( 2019 ) 47 Countries (Developed and Developing) 1976–2012 Panel Regression Analysis Innovation, measured by climate change-related patent counts, reduces carbon emissions in developed countries. Danish and Ulucak ( 2021 ) USA, China 1980–2016 Dynamic ARDL simulation method Innovation significantly reduced CO₂ emissions in the USA but did not have a significant effect in China. Renewable energy consumption reduced CO₂ emissions in both countries. The impact of income level on CO₂ emissions increased from the short run to the long run. Mongo, Belaid, and Ramdani ( 2021 ) 15 European Union countries (EU15) 1991–2014 Panel ARDL Environmental innovations reduce CO₂ emissions in the long run but increase them in the short run. Wang and Zhu (2020) 30 cities in China 2001–2017 Spatial Regression Analysis Renewable energy technology innovation facilitates CO₂ reduction; fossil energy technology innovation is ineffective in reducing CO₂ emissions in China; impacts vary by region. Khan et al. ( 2021 ) 69 Belt and Road Initiative (BRI) countries 2000–2014 Robust standard error regression and dynamic GMM estimator Technological innovation and economic growth have increased CO₂ emissions. Cheng et al. ( 2021 ) 35 OECD countries 1996–2015 Panel Quantile Regression Technological innovation reduces CO₂ emissions both directly and indirectly through economic growth and renewable energy consumption. Suki et al. ( 2022 ) Malaysia 1971–2017 Bootstrap ARDL Technological innovation reduces both CO₂ emissions and ecological footprint. 3. Data and Methodology This study utilizes annual data from 1996 to 2021 for BRICS countries, including CO₂ emissions, technological innovation, renewable energy consumption, and economic growth. The data were sourced from the World Bank database. The variables employed in the analysis are detailed in Table 2 . Table 2 Variables used in the study Variables Description Data source Co2 Carbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e) World bank www.data.worldbank.org PATENT Patent applications, residents GDPPC GDP per capita REC Renewable energy consumption (% of total final energy consumption) Time Period And Countries Countries Time period Brazil, Russian Federation, India, China, South Africa 1996–2021 The data for carbon dioxide (CO₂) emissions, used as an indicator of environmental pollution; GDP per capita, representing economic growth; patent counts, reflecting technological innovation; and total renewable energy consumption, serving as a proxy for renewable energy usage, were all obtained from the World Bank database. The econometric analysis was conducted using the STATA 18 software. Descriptive statistics of the variables are presented in Table 3 . Table 3 Descriptive Statistics of the Variables Used in the Model Variables Obs Mean Std. Dev. Min Mak LNCO2 130 7.14743 1.100006 5.705793 9.443166 LNPATENT 130 9.163592 2.057362 4.927254 14.17084 LNGDPPC 130 8.212812 0.9897064 5.994078 9.676678 LNREC 130 2.784613 0.9697577 1.163151 3.912023 3.1. Research Hypotheses H1 Technological innovation has a significant impact on CO₂ emissions in BRICS countries. H2 Renewable energy consumption contributes to the reduction of CO₂ emissions in BRICS countries. H3 Economic growth influences the level of CO₂ emissions in BRICS countries. 3.2. Model To investigate the impact of technological innovation, renewable energy consumption, and GDP per capita on CO₂ emissions, an econometric model is constructed in Eq. 1. 𝐶O 2t = 𝐹(PATENT t , 𝐺𝐷𝑃PC t , 𝑅𝐸𝐶 t ) 1 Here, CO₂ represents carbon dioxide emissions; Patent indicates technological innovation; REC stands for renewable energy consumption; GDP refers to gross domestic product per capita; and t denotes the corresponding time period. The following equation represents the logarithmic form of the econometric model constructed above. 𝑙𝑛CO2 t = β 0 + β 1 ∗ 𝑙𝑛PATENT t + β2 ∗ 𝑙𝑛GDPPC t + β3 ∗ 𝑙𝑛REC 𝑡 + ε t 2 In this equation, β₀ denotes the constant term, while β₁, β₂, and β₃ represent the coefficients of the explanatory variables. PATENT, REC, and GDPPC correspond to technological innovation, renewable energy consumption, and gross domestic product per capita, respectively. ln indicates the natural logarithm, and εₜ stands for the error term. 3.3. Methodology 3.3.1. Driscoll-Kraay Estimator Test Since the model exhibits heteroskedasticity, autocorrelation, and cross-sectional dependence, robust estimators were employed to ensure reliable results. Given that the panel structure satisfies the condition T > N, the Driscoll-Kraay (Driscoll J. C., Kraay A. C., 1998) robust estimator was applied to obtain more consistent and efficient estimates. 4. Empirical Results and Discussion In the initial stage of panel data analysis, it is essential to determine whether the appropriate model is a pooled OLS model, a fixed effects model, or a time effects model. Panel data consist of observations from multiple cross-sectional units, each of which may have unique characteristics. These specific characteristics associated with individual units are referred to as “unit effects” (Tatoğlu, Y., 2020 ). To assess the presence of unit effects in the research model, several diagnostic tests are commonly conducted. In order to choose among competing estimators, the Likelihood Ratio (LR) test, the Breusch-Pagan Lagrange Multiplier (LM) test, and the F-test for fixed effects have been applied. The results of these tests are presented in the table below. Table 4 Results of Model Selection Tests Among Estimation Methods Tests Unit and/or Time Effects Unit Effects Time Effects LR 261.93(0.000) 261.93(0.000) 1.13(0.1438) F 9.47(0.000) 285.32(0.000) 2.23 (0.026) LM 424.97(0.000) 0.000 (1.000) Based on the results of the F, LR, and LM tests presented in Table 4 , it is concluded that the model exhibits unit effects but no time effects. Given the presence of unit effects, the classical pooled model is deemed inappropriate, suggesting that either fixed effects or random effects models may be suitable. To determine whether the model should adopt fixed or random effects, the analysis will proceed based on the results of the Hausman test that accounts for unit effects. The outcomes of the standard Hausman test and the robust Hausman test are shown in Table 5 below. Table 5 Robust Hausman Test Results Test Robust Hausman Test P-values 0.02 (0.9993) According to the results of the robust Hausman test presented in Table 5 , the probability values at the 90%, 95%, and 99% confidence levels indicate that the fixed effects model is not appropriate, while the random effects model is valid. Therefore, the analysis will proceed using the random effects approach. In this study, diagnostic tests were conducted to detect violations of key assumptions in panel data models, including heteroskedasticity, autocorrelation, and cross-sectional dependence. Additionally, the Variance Inflation Factor (VIF) criterion was used to ensure the absence of multicollinearity among the variables. The results of these tests are presented in Table 6 . Table 6 Results of Diagnostic Tests for Violations of Classical Assumptions VIF 1.13 Heteroskedasticity Test Levene, Brown and Forstythe Test W0 = 5.7957076 df(4, 125) Pr > F = 0.00025974 W50 = 5.1124827 df(4, 125) Pr > F = 0.000755 W10 = 5.7506283 df(4, 125) Pr > F = 0.00027861 Autocorrelation Tests Bhargava et al. Durbin–Watson 0.31653206 Baltagi–Wu LBI 0.48212939 Correlation Tests Pesaran Test -2.542(0.0110) Friedman Test 8.597(0.0720) Frees Test (1.282) 0.0996 (%10) 0.1297(%5) 0.1870 (%1) According to the results presented in Table 6 , the Levene(1960), Brown and Forsythe(1974) heteroskedasticity tests indicate the presence of heteroskedasticity in the model. Regarding autocorrelation, the Bhargava et al. (Bhargava A., Franzini L., Narendranathan W., 1982 ) Durbin–Watson and Baltagi–Wu ( Baltagi B. H., Wu P. X.,, 1999) LBI test statistics yield values that are not equal to or greater than 2, suggesting the presence of autocorrelation in the model. When examining cross-sectional dependence, the Pesaran test results suggest no significant cross-sectional dependence. However, the Friedman test indicates the existence of cross-sectional dependence within the panel data. Furthermore, the Frees test results, at the 1%, 5%, and 10% significance levels, also confirm the presence of cross-sectional dependence. Lastly, the Variance Inflation Factor (VIF) test, which was conducted to assess multicollinearity among the explanatory variables, shows no indication of multicollinearity in the model. Based on the results of the heteroskedasticity, autocorrelation, and cross-sectional dependence tests, the robust Driscoll-Kraay estimator was employed to ensure more reliable and consistent coefficient estimates. The outcomes derived from this method, which reflect the relationship between the dependent variable and the explanatory variables, are presented in Table 7 . Table 7 Results of the Driscoll-Kraay Estimator Variables Coefficient Driscoll-Kraay Standard Errors T statistic P-value LNPATENT 0. 2003567 0. 0162924 12.30 0.000 LNGDPPC 0.1054418 0.021103 5 0.000 LNREC -0.2190429 0.0657112 -3.33 0.003 Wald chi2(4) = 844.86 Prob > chi2 = 0.0000 R2 = 0.5769 The Wald chi-square statistic, which tests the overall significance of the model, was calculated as 844.86 with a corresponding p-value of 0.000. This result indicates that the model is statistically significant at all conventional significance levels. Moreover, the three independent variables included in the model explain approximately 57.69% of the variance in the dependent variable, suggesting a strong explanatory power and indicating that the model provides a robust fit for capturing the variation in the outcome variable. The variable representing the technological innovation index—measured by patent applications—is found to be statistically significant at the 5% level. The results suggest that a 1% increase in the innovation index leads to a 0.2% rise in environmental pollution. The natural logarithm of GDP per capita (LNGDPPC), representing economic growth, is also statistically significant at the 5% level. The findings indicate that a 1% increase in economic growth results in a 0.11% increase in environmental pollution. The natural logarithm of renewable energy consumption (LNREC) is statistically significant at the 5% level as well. According to the results, a 1% increase in renewable energy use leads to a 0.22% reduction in environmental pollution. According to the obtained findings, the final model constructed with the significant variables is presented below: LNCO2 𝑖𝑡 = 0.2003567 LNPATENT 𝑖𝑡 + 0.1054418 LNGDPPC 𝑖𝑡 − 0.2190429 LNREC 𝑖𝑡 + 𝑢𝑖𝑡 i = 5,…,N ; t=26,….,T 3 5. Conclusion This study provides an empirical analysis of the impact of technological innovation, renewable energy consumption, and economic growth on environmental pollution. In this study, annual data covering the period 1996–2021 were analysed using panel data methodology. The empirical results reveal that in BRICS countries, technological innovation and economic growth are associated with increased CO₂ emissions, whereas renewable energy consumption contributes to a reduction in environmental pollution. The findings of the study reveal that, contrary to expectations, technological innovation in BRICS countries has a positive effect on environmental pollution, contributing to its increase. This outcome can be attributed to the fact that innovation activities in these countries are predominantly geared toward enhancing economic growth and industrial production, rather than promoting environmental sustainability. In particular, the limited investment in research and development (R&D) related to green technologies constrains the potential of innovation to positively influence environmental performance. Furthermore, weak environmental regulations and the integration of innovation processes within fossil fuel-based production structures exacerbate the situation by causing technological advancements to raise, rather than reduce, carbon emissions. Therefore, it is crucial that technological innovation in BRICS countries be redirected to support environmental sustainability. This necessitates the promotion of green innovation policies and the strengthening of the institutional framework to ensure that innovation contributes to ecological well-being alongside economic development. The results of the study indicate that economic growth in BRICS countries contributes to an increase in environmental pollution. As these countries pursue rapid industrialization and higher GDP levels, their economic expansion often relies on energy-intensive and resource-extractive activities, which in turn lead to greater carbon emissions and environmental degradation. This pattern reflects the classical Environmental Kuznets Curve (EKC) hypothesis, where in the early stages of economic development, environmental quality tends to deteriorate due to the prioritization of growth over sustainability. In the context of BRICS economies, the reliance on fossil fuel-based energy sources, insufficient environmental regulation, and limited integration of clean technologies into production systems intensify the negative environmental impacts of economic growth. Moreover, high rates of urbanization and infrastructure development further increase energy consumption and pollutant emissions. Therefore, without a strong emphasis on green growth strategies, sustainable infrastructure, and environmentally responsible policies, economic growth in these countries is likely to continue exacerbating environmental challenges rather than alleviating them. The findings of the study reveal that renewable energy consumption has a statistically significant mitigating effect on environmental pollution in BRICS countries. This outcome underscores the critical role that renewable energy sources—such as solar, wind, hydro, and biomass—play in reducing reliance on fossil fuels and thereby lowering carbon dioxide (CO₂) emissions. The transition towards cleaner energy sources contributes to the decarbonization of the energy mix, which is essential for achieving long-term environmental sustainability. In BRICS economies, where energy demand is rapidly increasing due to industrial expansion and population growth, the adoption of renewable energy technologies offers a viable pathway to balancing economic development with ecological preservation. Furthermore, the reduction in environmental degradation associated with renewable energy use reflects the positive externalities of investing in green technologies and implementing supportive regulatory frameworks. Thus, the study emphasizes the importance of accelerating the deployment of renewable energy infrastructure, enhancing policy incentives, and strengthening institutional capacity to fully realize the environmental benefits of the green energy transition in emerging economies. Human activities and the environment are inevitably interconnected. Therefore, environmental problems cannot be addressed solely by governments. An effective response to ecological challenges requires collaborative efforts among governments, policymakers, producers, and consumers. To utilize innovation as an economic tool in solving environmental problems, it is essential to integrate environmental considerations into the innovation process. In this context, BRICS countries must prioritize the expansion of green innovation, especially by increasing research and development (R&D) expenditures in areas that facilitate access to clean and renewable energy sources. Decision-makers in these economies should carefully implement policies and strategies aimed at transitioning from fossil fuel-based energy systems to clean energy infrastructures. Furthermore, the role of scientists and environmental experts is vital in raising public awareness through academic conferences, public forums, and social media platforms, emphasizing the importance of environmental protection and promoting eco-conscious behaviours. Consumers also play a significant role by making informed choices and favouring environmentally friendly products, thereby reinforcing the demand for sustainable production practices. In conclusion, this study highlights the complex relationship between technological innovation, renewable energy consumption, and environmental pollution in BRICS countries. While innovation and economic growth currently contribute to increased CO₂ emissions, renewable energy consumption offers a promising pathway to reduce environmental degradation. Therefore, aligning innovation policies with sustainability goals and accelerating the transition to clean energy are essential steps for achieving long-term environmental and economic well-being in emerging economies. Future research should continue to explore the mechanisms through which green technologies can be effectively integrated into national development strategies to foster a more sustainable future. References Ahmed A, Uddin GS, Sohag K (2016) Biomass energy, technological progress and the environmental Kuznets curve: Evidence from selected European countries. Biomass Bioenergy 90:202–208 Albino V, Ardito L, Dangelico RM, ve Petruzzelli AM (2014) Understanding the development trends of low-carbon energy technologies: A patent analysis. Appl Energy 135:836–854 Altıntaş H, Kassouri Y (2020) The impact of energy technology innovations on cleaner energy supply and carbon footprints in Europe: A linear versus nonlinear approach. J Clean Prod 276:124–140 Aytun C, Akın CS (2015) Türkiye’de karbondioksit emisyonu, enerji tüketimi ve eğitim ilişkisi: bootstrap nedensellik analizi. İzmir: I. International Symposium on Eurasia Energy Issues Baltagi BH, Wu PX (1999) Unequally Spaced Panel Data Regressions with AR(1) Disturbances. Econometic Theory 15:814–823 Bayar Y, Şaşmaz MÜ (2016) Karbon vergisi, ekonomik büyüme ve CO2 emisyonu arasındaki nedensellik ilişkisi: Danimarka, Finlandiya, Hollanda İsveç ve Norveç örneği. Int J Appl Economic Finance Stud 1(1):32–41 Bhargava A, Franzini L, Narendranathan W (1982) Serial Correlation and Fixed Effect Models. Rev Econ Stud 49:533–549 Bindi G (2019) Technological innovation and the environment, an analysis based on patent counts (Master dissertation, Lund University, Sweeden). Retrieved from http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=8981996&fileOId=8982016 Brown MB, Forsythe AB (1974) The Small sample Behavior of Some Statistics Which Test the Equality of Severa Means. Technometrics 16:129–132 Chen Y, ve Lee CC (2020) Does technological innovation reduce CO2 emissions? Cross-country evidence. J Clean Prod 263:1–11 Cheng C, Ren X, Dong K, Dong X, Wang Z (2021) How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression. J Environ Manage 280:1–11 Cheng C, Ren X, Dong K, Dong X, Wang Z (2021) How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression. J Environ Manage 280:111818 Danish, Ulucak R (2021) Renewable energy, technological innovation and the environment: A novel dynamic auto-regressive distributive lag simulation. Renew Sustain Energy Rev 150:111433 ISSN 1364 – 0321 Driscoll JC, Kraay AC (1998) Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. Review Econ Statistics 80:549–560 Fan H, Hossain MI (2018) Technological innovation, trade openness, CO2 emission and economic growth: comparative analysis between China and India. Int J Energy Econ Policy 8(6):240–257 Fei Q, Rasiah R, Shen LJ (2014) The clean energy-growth nexus with CO2 emissions and technological innovation in Norway and New Zealand. Energy Environment 25(8):1323–1344 H., L. (1960) Robust Tests for Equality of Variances in Olkin I., Ghurye G., Hoeffding W., Madow W. G., Mann H.B., Contribution s to Probability and Statistics . Stanford,California: Stanford University Press, 278–292 Hang G, Yuan-Sheng J (2011) The relationship between CO2 emissions, economic scale,technology, income and population in China. Procedia Environ Sci 11:1183–1188 Hashmi R, Alam K (2019) Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation. J Clean Prod 231:1100–1109 Işık N, Kılıç E (2014) Ulaştırma sektöründe CO2 emisyonu ve enerji ar-ge harcamaları ilişkisi. Sosyoekonomi, 22(22) Johnstone N, Haščič I, Popp D (2010) Renewable energy policies and technological innovation: evidence based on patent counts. Environ resource Econ 45(1):133–155 Khan A, Chenggang Y, Hussain J, Kui Z (2021) Impact of technological innovation, financial development and foreign direct investment on renewable energy, non-renewable energy and the environment in belt & road initiative countries. Renewable Energy 171:479–491 Mongo M, Belaid Fve, Ramdani B (2021) The effects of environmental innovations on CO2 emissions: Empirical evidence from Europe. Environ Sci Policy 118:1–9 Nihat I, Kılıç E (2014) Ulaştırma sektöründe CO2 emisyonu ve enerji arge harcamaları ilişkisi. Sosyoekonomi 22(22):321–346 Samargandi N (2017) Sector value addition, technology and CO2 emissions in Saudi Arabia. Renew Sustain Energy Rev 78:868–877 Suki NM, Suki NM, Afshan S, Sharif A, Meo MS (2022) The paradigms of technological innovation and renewables as a panacea for sustainable development: A pathway of going green. Renewable Energy 181:1431–1439 Tatoğlu Y (2020) Panel Zaman Serileri Analizi: Stata Uygulamalı. Beta Yayını, Istanbul Wang Z, ve Zhu Y (2020) Do energy technology innovations contribute to CO2 emissions abatement? A spatial perspective. Sci Total Environ 726:1–14 Yii KJ, Geetha C (2017) The nexus between technology innovation and CO2 emissions in Malaysia: evidence from granger causality test. Energy Procedia 105:3118–3124 Zhang YJ, Peng YL, Ma CQ, ve Shen B (2017) Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100:18–28 Additional Declarations The authors declare no competing interests. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8315644","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557509552,"identity":"ef12322a-5fcf-4a2a-8c44-d3ef2507ec50","order_by":0,"name":"Alibay MAMMADLI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYHACNhAhByIOPCBFizFYSwIpWhIbQCRRWswlcp89+FBjlz4/7PBDoC12croNBLRYzkg3N5xxLDl34+00A6CWZGOzAwS0GNxIY5PmbWDO3Tg7AaTlQOI2IrXUpxvOTv9AkpbDCfLSOcTacuYZm+SMY8cNN0jnFBxIMCDGL8fT2CQ+1FTLy89O3/zhQ4WdHEEtCL1glQbEKgcB+QZSVI+CUTAKRsGIAgAQU0K1DQ68swAAAABJRU5ErkJggg==","orcid":"","institution":"Azerbaijan State University of Economics (UNEC)","correspondingAuthor":true,"prefix":"","firstName":"Alibay","middleName":"","lastName":"MAMMADLI","suffix":""},{"id":557509553,"identity":"73a56d77-4a87-47a8-a246-2f3dc417991c","order_by":1,"name":"Farrukh MAMMADZADA","email":"","orcid":"","institution":"Azerbaijan Tourism and Management University","correspondingAuthor":false,"prefix":"","firstName":"Farrukh","middleName":"","lastName":"MAMMADZADA","suffix":""}],"badges":[],"createdAt":"2025-12-09 09:21:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8315644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8315644/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97848417,"identity":"128f4265-87e5-4603-a461-60b9ee74fc68","added_by":"auto","created_at":"2025-12-10 06:20:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68663,"visible":true,"origin":"","legend":"","description":"","filename":"TechnologicalInnovationandRenewableEnergyConsumptionasDeterminantsofEnvironmentalPollutionP.docx","url":"https://assets-eu.researchsquare.com/files/rs-8315644/v1/c4100f36495348edbdd0490f.docx"},{"id":97899208,"identity":"33910800-deab-4dbf-b840-4c0903d4a99c","added_by":"auto","created_at":"2025-12-10 15:42:12","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8315644.json","url":"https://assets-eu.researchsquare.com/files/rs-8315644/v1/d46687c98829371178f6c3e8.json"},{"id":97848420,"identity":"bbda759f-cded-48d1-917e-242cbca3829c","added_by":"auto","created_at":"2025-12-10 06:20:40","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86790,"visible":true,"origin":"","legend":"","description":"","filename":"rs83156440enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8315644/v1/8ae562cb7e1d381245ca3334.xml"},{"id":97848419,"identity":"079b52ee-abb0-46c8-84a3-3b9ed1ea9622","added_by":"auto","created_at":"2025-12-10 06:20:40","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85237,"visible":true,"origin":"","legend":"","description":"","filename":"rs83156440structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8315644/v1/dd3bbebfb75cf963d22a8d17.xml"},{"id":97898884,"identity":"10dce1d3-5c56-4c6c-89ee-918db59ee01b","added_by":"auto","created_at":"2025-12-10 15:39:59","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88873,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8315644/v1/8355f71d198eb2d6cbf8169b.html"},{"id":97903256,"identity":"32171293-655e-4f6d-85c5-4a48d86d6d3f","added_by":"auto","created_at":"2025-12-10 15:54:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":775891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8315644/v1/b02952fb-47c0-4a58-a4f8-c48ec1ffa453.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTechnological Innovation and Renewable Energy Consumption as Determinants of Environmental Pollution: Panel Evidence from BRICS Countries\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the last five decades, environmental pollution and climate change have become major global challenges that threaten not only ecosystems but also the well-being of all living organisms (Bayar, Y., \u0026amp; Şaşmaz, M. \u0026Uuml;., 2016). The rapid and often unregulated expansion of economic and social activities has significantly intensified environmental degradation, creating a reciprocal relationship where environmental damage, in turn, impedes sustainable economic progress. A substantial portion of global energy demand continues to be met through the use of fossil fuels. This reliance leads to the emission of harmful gases, such as carbon dioxide (CO₂)\u0026mdash;which accounts for approximately 60% of total greenhouse gas emissions\u0026mdash;and methane (CH₄), resulting in environmental issues such as global warming, ozone layer depletion, and air pollution (Nihat, I., \u0026amp; Kılı\u0026ccedil;, E., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to NASA reports, CO₂ has become the most environmentally damaging greenhouse gas due to its growing concentration in the atmosphere. The accumulation of such gases has been identified as a critical driver of rising global temperatures and other environmental crises. Alarmingly, scientists have warned that by 2050, life on Earth could become unsustainable due to the accelerating pace of environmental degradation (Zhang, Y.J., Peng, Y.L., Ma, C.Q. ve Shen, B., 2017).\u003c/p\u003e\u003cp\u003eIn recent years, both national governments and international organizations have introduced various regulatory frameworks and signed agreements aimed at reducing CO₂ emissions and combating climate change. At the same time, economic instruments have increasingly been utilized as tools for environmental policy. Among the most prominent of these instruments are renewable energy consumption and technological innovation (Altıntaş, H., \u0026amp; Kassouri, Y., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several countries have succeeded in significantly reducing harmful gas emissions\u0026mdash;such as CO₂\u0026mdash;by increasing their level of technological advancement and focusing on the development of environmentally oriented patents (Cheng, C., Ren, X., Dong, K., Dong, X. \u0026amp; Wang, Z., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, the primary aim of this study is to analyze the impact of technological innovation and renewable energy consumption on environmental pollution in selected BRICS countries. As a secondary objective, the study also explores how economic growth may influence CO₂ emissions, both positively and negatively. In this context, renewable energy consumption and technological innovation are treated as the main explanatory variables, while economic growth is considered a control variable. The study employs the panel data analysis method, which is increasingly used in environmental and energy economics literature, to examine the period 1996\u0026ndash;2021 using annual data.\u003c/p\u003e\u003cp\u003eThe structure of the study is as follows: The first section provides an overview of environmental pollution and its underlying causes. The second section presents a comprehensive review of the related literature. The third section details the methodology and data used for the econometric analysis. In the fourth section, the empirical results are evaluated. Finally, the fifth section offers conclusions and policy implications, followed by the list of references.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEnvironmental pollution and rising emissions, as adverse outcomes of economic growth performance, have become significant concerns for both countries and the academic community. Since the early 20th century, various technological developments aimed at protecting the environment have emerged alongside economic expansion, prompting investigations into their effectiveness. Over the last decade, there has been growing scholarly attention on studies that analyse the evolution of emissions and environmental degradation across different countries and time periods, using indicators such as innovation in energy technologies, R\u0026amp;D intensity, and the number of patents. Notably, several empirical studies have employed patent counts as proxies for technological innovation in their analysis of environmental impacts.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of studies on environmental pollution\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethodology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJohnstone et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 High-Income Countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1978\u0026ndash;2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePanel Regression Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental policies significantly promote renewable energy innovation; public R\u0026amp;D spending positively impacts innovation in geothermal, ocean, wind, and solar energy technologies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHang and Yuan-Sheng (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1980\u0026ndash;2006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePanel Regression Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWhile the initial impact of new technology on environmental degradation is positive, in later stages, as technology advances, this effect becomes negative.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbino et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal\u003c/p\u003e\u003cp\u003e(All countries)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1971\u0026ndash;2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePatent Data Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInnovative activity in energy peaked during oil crises and early 1990s; private sector leads in low-carbon energy innovation; patent numbers in alternative energy remain limited; over half of low-carbon energy patents held by the USA.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFei et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorway, New Zealand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1971\u0026ndash;2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eARDL, Granger Causality Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA bidirectional causality exists between environmental degradation and technological innovation in Norway, whereas no significant relationship was found in New Zealand.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIşık and Kılı\u0026ccedil; (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOECD Countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDynamic Panel Data Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR\u0026amp;D expenditures have a negative effect on CO₂ emissions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkın and Aytun (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u0026uuml;rkiye\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1971\u0026ndash;2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBootstrap Causality Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA linear causality relationship was found from higher education levels to CO₂ emissions and energy consumption.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhmed et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 European Countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1980\u0026ndash;2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eARDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTechnological innovation was found to reduce environmental pollution.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamargandi (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1970\u0026ndash;2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eARDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTechnological development was found to have no significant effect on environmental degradation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYii and Geetha (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalaysia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1971\u0026ndash;2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eError Correction Model (ECM) and Granger Causality Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIn the short run, there is a negative relationship between technological innovation and CO₂ emissions; however, no statistically significant relationship exists in the long run. Causality analysis indicates that technological innovation Granger-causes CO₂ emissions in the short run, but no causality is found in the long run.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHashmi and Alem (2019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOECD countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1999\u0026ndash;2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePanel data analysis and GMM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironment-oriented innovation and environmental tax have negatively affected CO₂ emissions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFan and Hossain (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina, India\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1974\u0026ndash;2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eToda-Yamamoto causality test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIn China, there is a bidirectional causality between technological innovation and environmental degradation, while in India, a unidirectional causality is found from technological innovation to environmental degradation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChen and Lee (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1996\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpatial panel data model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCO₂ emissions and R\u0026amp;D intensity are spatially correlated across countries. In high-income, high-tech countries, technological innovation reduces both domestic and neighbouring countries\u0026rsquo; emissions. In low- and middle-income, low-tech countries, innovation increases emissions both domestically and in neighbouring countries.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBindi (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 Countries (Developed and Developing)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1976\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePanel Regression Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInnovation, measured by climate change-related patent counts, reduces carbon emissions in developed countries.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDanish and Ulucak (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA, China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1980\u0026ndash;2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDynamic ARDL simulation method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInnovation significantly reduced CO₂ emissions in the USA but did not have a significant effect in China. Renewable energy consumption reduced CO₂ emissions in both countries. The impact of income level on CO₂ emissions increased from the short run to the long run.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMongo, Belaid, and Ramdani (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 European Union countries (EU15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1991\u0026ndash;2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePanel ARDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnvironmental innovations reduce CO₂ emissions in the long run but increase them in the short run.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWang and Zhu (2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 cities in China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2001\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpatial Regression Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRenewable energy technology innovation facilitates CO₂ reduction; fossil energy technology innovation is ineffective in reducing CO₂ emissions in China; impacts vary by region.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 Belt and Road Initiative (BRI) countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2000\u0026ndash;2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRobust standard error regression and dynamic GMM estimator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTechnological innovation and economic growth have increased CO₂ emissions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCheng et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 OECD countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1996\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePanel Quantile Regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTechnological innovation reduces CO₂ emissions both directly and indirectly through economic growth and renewable energy consumption.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuki et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalaysia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1971\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBootstrap ARDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTechnological innovation reduces both CO₂ emissions and ecological footprint.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study utilizes annual data from 1996 to 2021 for BRICS countries, including CO₂ emissions, technological innovation, renewable energy consumption, and economic growth. The data were sourced from the World Bank database. The variables employed in the analysis are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariables used in the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData source\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCo2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eWorld bank \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.data.worldbank.org\u003c/span\u003e\u003cspan address=\"http://www.data.worldbank.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePATENT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatent applications, residents\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDPPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP per capita\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRenewable energy consumption (% of total final energy consumption)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTime Period And Countries\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCountries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eTime period\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBrazil, Russian Federation, India, China, South Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1996\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe data for carbon dioxide (CO₂) emissions, used as an indicator of environmental pollution; GDP per capita, representing economic growth; patent counts, reflecting technological innovation; and total renewable energy consumption, serving as a proxy for renewable energy usage, were all obtained from the World Bank database. The econometric analysis was conducted using the STATA 18 software. Descriptive statistics of the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of the Variables Used in the Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMak\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLNCO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.14743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.100006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.705793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.443166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLNPATENT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.163592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.057362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.927254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.17084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLNGDPPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.212812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9897064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.994078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.676678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLNREC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.784613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9697577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.163151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.912023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Research Hypotheses\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003eTechnological innovation has a significant impact on CO₂ emissions in BRICS countries.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003eRenewable energy consumption contributes to the reduction of CO₂ emissions in BRICS countries.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003eEconomic growth influences the level of CO₂ emissions in BRICS countries.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Model\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo investigate the impact of technological innovation, renewable energy consumption, and GDP per capita on CO₂ emissions, an econometric model is constructed in Eq.\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u0026#119862;O\u003csub\u003e2t\u003c/sub\u003e = \u0026#119865;(PATENT\u003csub\u003et\u003c/sub\u003e, \u0026#119866;\u0026#119863;\u0026#119875;PC\u003csub\u003et\u003c/sub\u003e, \u0026#119877;\u0026#119864;\u0026#119862;\u003csub\u003et\u003c/sub\u003e) \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHere, CO₂ represents carbon dioxide emissions; Patent indicates technological innovation; REC stands for renewable energy consumption; GDP refers to gross domestic product per capita; and t denotes the corresponding time period.\u003c/p\u003e\u003cp\u003eThe following equation represents the logarithmic form of the econometric model constructed above.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u0026#119897;\u0026#119899;CO2\u003csub\u003et\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003e \u0026lowast; \u0026#119897;\u0026#119899;PATENT\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β2 \u0026lowast; \u0026#119897;\u0026#119899;GDPPC\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β3 \u0026lowast; \u0026#119897;\u0026#119899;REC\u003csub\u003e\u0026#119905;\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003et\u003c/sub\u003e\u0026nbsp; \u0026nbsp;2\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this equation, β₀ denotes the constant term, while β₁, β₂, and β₃ represent the coefficients of the explanatory variables. PATENT, REC, and GDPPC correspond to technological innovation, renewable energy consumption, and gross domestic product per capita, respectively. ln indicates the natural logarithm, and \u003cb\u003eεₜ\u003c/b\u003e stands for the error term.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Methodology\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1. Driscoll-Kraay Estimator Test\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSince the model exhibits heteroskedasticity, autocorrelation, and cross-sectional dependence, robust estimators were employed to ensure reliable results. Given that the panel structure satisfies the condition T\u0026thinsp;\u0026gt;\u0026thinsp;N, the Driscoll-Kraay (Driscoll J. C., Kraay A. C., 1998) robust estimator was applied to obtain more consistent and efficient estimates.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Empirical Results and Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn the initial stage of panel data analysis, it is essential to determine whether the appropriate model is a pooled OLS model, a fixed effects model, or a time effects model. Panel data consist of observations from multiple cross-sectional units, each of which may have unique characteristics. These specific characteristics associated with individual units are referred to as \u0026ldquo;unit effects\u0026rdquo; (Tatoğlu, Y., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To assess the presence of unit effects in the research model, several diagnostic tests are commonly conducted. In order to choose among competing estimators, the Likelihood Ratio (LR) test, the Breusch-Pagan Lagrange Multiplier (LM) test, and the F-test for fixed effects have been applied. The results of these tests are presented in the table below.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Model Selection Tests Among Estimation Methods\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTests\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit and/or Time Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTime Effects\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261.93(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261.93(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13(0.1438)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.47(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e285.32(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.23 (0.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e424.97(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 (1.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBased on the results of the F, LR, and LM tests presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it is concluded that the model exhibits unit effects but no time effects. Given the presence of unit effects, the classical pooled model is deemed inappropriate, suggesting that either fixed effects or random effects models may be suitable. To determine whether the model should adopt fixed or random effects, the analysis will proceed based on the results of the Hausman test that accounts for unit effects. The outcomes of the standard Hausman test and the robust Hausman test are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobust Hausman Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRobust Hausman Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP-values\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02 (0.9993)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAccording to the results of the robust Hausman test presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the probability values at the 90%, 95%, and 99% confidence levels indicate that the fixed effects model is not appropriate, while the random effects model is valid. Therefore, the analysis will proceed using the random effects approach.\u003c/p\u003e\u003cp\u003eIn this study, diagnostic tests were conducted to detect violations of key assumptions in panel data models, including heteroskedasticity, autocorrelation, and cross-sectional dependence. Additionally, the Variance Inflation Factor (VIF) criterion was used to ensure the absence of multicollinearity among the variables. The results of these tests are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Diagnostic Tests for Violations of Classical Assumptions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eHeteroskedasticity Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevene, Brown and Forstythe Test\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eW0 = 5.7957076 df(4, 125) Pr\u0026thinsp;\u0026gt;\u0026thinsp;F\u0026thinsp;=\u0026thinsp;0.00025974\u003c/p\u003e\u003cp\u003eW50 = 5.1124827 df(4, 125) Pr\u0026thinsp;\u0026gt;\u0026thinsp;F\u0026thinsp;=\u0026thinsp;0.000755\u003c/p\u003e\u003cp\u003eW10 = 5.7506283 df(4, 125) Pr\u0026thinsp;\u0026gt;\u0026thinsp;F\u0026thinsp;=\u0026thinsp;0.00027861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAutocorrelation Tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBhargava et al. Durbin\u0026ndash;Watson\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31653206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eBaltagi\u0026ndash;Wu LBI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48212939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCorrelation Tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePesaran Test\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-2.542(0.0110)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eFriedman Test\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.597(0.0720)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrees Test\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(1.282)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.0996 (%10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1297(%5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1870 (%1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAccording to the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the Levene(1960), Brown and Forsythe(1974) heteroskedasticity tests indicate the presence of heteroskedasticity in the model. Regarding autocorrelation, the Bhargava et al. (Bhargava A., Franzini L., Narendranathan W., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) Durbin\u0026ndash;Watson and Baltagi\u0026ndash;Wu ( Baltagi B. H., Wu P. X.,, 1999) LBI test statistics yield values that are not equal to or greater than 2, suggesting the presence of autocorrelation in the model.\u003c/p\u003e\u003cp\u003eWhen examining cross-sectional dependence, the Pesaran test results suggest no significant cross-sectional dependence. However, the Friedman test indicates the existence of cross-sectional dependence within the panel data. Furthermore, the Frees test results, at the 1%, 5%, and 10% significance levels, also confirm the presence of cross-sectional dependence.\u003c/p\u003e\u003cp\u003eLastly, the Variance Inflation Factor (VIF) test, which was conducted to assess multicollinearity among the explanatory variables, shows no indication of multicollinearity in the model.\u003c/p\u003e\u003cp\u003eBased on the results of the heteroskedasticity, autocorrelation, and cross-sectional dependence tests, the robust Driscoll-Kraay estimator was employed to ensure more reliable and consistent coefficient estimates. The outcomes derived from this method, which reflect the relationship between the dependent variable and the explanatory variables, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the Driscoll-Kraay Estimator\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDriscoll-Kraay Standard Errors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLNPATENT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0. 2003567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0. 0162924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLNGDPPC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1054418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.021103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLNREC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2190429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0657112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWald chi2(4)\u003c/b\u003e\u0026thinsp;=\u0026thinsp;844.86 \u003cb\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.0000\u003c/p\u003e\u003cp\u003e\u003cb\u003eR2\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.5769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe Wald chi-square statistic, which tests the overall significance of the model, was calculated as 844.86 with a corresponding p-value of 0.000. This result indicates that the model is statistically significant at all conventional significance levels. Moreover, the three independent variables included in the model explain approximately 57.69% of the variance in the dependent variable, suggesting a strong explanatory power and indicating that the model provides a robust fit for capturing the variation in the outcome variable.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe variable representing the technological innovation index\u0026mdash;measured by patent applications\u0026mdash;is found to be statistically significant at the 5% level. The results suggest that a 1% increase in the innovation index leads to a 0.2% rise in environmental pollution.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe natural logarithm of GDP per capita (LNGDPPC), representing economic growth, is also statistically significant at the 5% level. The findings indicate that a 1% increase in economic growth results in a 0.11% increase in environmental pollution.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe natural logarithm of renewable energy consumption (LNREC) is statistically significant at the 5% level as well. According to the results, a 1% increase in renewable energy use leads to a 0.22% reduction in environmental pollution.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAccording to the obtained findings, the final model constructed with the significant variables is presented below:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLNCO2\u003csub\u003e\u0026#119894;\u0026#119905;\u003c/sub\u003e = 0.2003567 LNPATENT\u003csub\u003e\u0026#119894;\u0026#119905;\u003c/sub\u003e + 0.1054418 LNGDPPC\u003csub\u003e\u0026#119894;\u0026#119905;\u003c/sub\u003e \u0026minus; 0.2190429 LNREC\u003csub\u003e\u0026#119894;\u0026#119905;\u003c/sub\u003e + \u0026#119906;\u0026#119894;\u0026#119905; i\u0026thinsp;=\u0026thinsp;5,\u0026hellip;,N ; t=26,\u0026hellip;.,T \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3\u003c/p\u003e\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study provides an empirical analysis of the impact of technological innovation, renewable energy consumption, and economic growth on environmental pollution.\u003c/p\u003e\u003cp\u003eIn this study, annual data covering the period 1996\u0026ndash;2021 were analysed using panel data methodology. The empirical results reveal that in BRICS countries, technological innovation and economic growth are associated with increased CO₂ emissions, whereas renewable energy consumption contributes to a reduction in environmental pollution.\u003c/p\u003e\u003cp\u003eThe findings of the study reveal that, contrary to expectations, technological innovation in BRICS countries has a positive effect on environmental pollution, contributing to its increase. This outcome can be attributed to the fact that innovation activities in these countries are predominantly geared toward enhancing economic growth and industrial production, rather than promoting environmental sustainability. In particular, the limited investment in research and development (R\u0026amp;D) related to green technologies constrains the potential of innovation to positively influence environmental performance.\u003c/p\u003e\u003cp\u003eFurthermore, weak environmental regulations and the integration of innovation processes within fossil fuel-based production structures exacerbate the situation by causing technological advancements to raise, rather than reduce, carbon emissions. Therefore, it is crucial that technological innovation in BRICS countries be redirected to support environmental sustainability. This necessitates the promotion of green innovation policies and the strengthening of the institutional framework to ensure that innovation contributes to ecological well-being alongside economic development.\u003c/p\u003e\u003cp\u003eThe results of the study indicate that economic growth in BRICS countries contributes to an increase in environmental pollution. As these countries pursue rapid industrialization and higher GDP levels, their economic expansion often relies on energy-intensive and resource-extractive activities, which in turn lead to greater carbon emissions and environmental degradation. This pattern reflects the classical Environmental Kuznets Curve (EKC) hypothesis, where in the early stages of economic development, environmental quality tends to deteriorate due to the prioritization of growth over sustainability.\u003c/p\u003e\u003cp\u003eIn the context of BRICS economies, the reliance on fossil fuel-based energy sources, insufficient environmental regulation, and limited integration of clean technologies into production systems intensify the negative environmental impacts of economic growth. Moreover, high rates of urbanization and infrastructure development further increase energy consumption and pollutant emissions. Therefore, without a strong emphasis on green growth strategies, sustainable infrastructure, and environmentally responsible policies, economic growth in these countries is likely to continue exacerbating environmental challenges rather than alleviating them.\u003c/p\u003e\u003cp\u003eThe findings of the study reveal that renewable energy consumption has a statistically significant mitigating effect on environmental pollution in BRICS countries. This outcome underscores the critical role that renewable energy sources\u0026mdash;such as solar, wind, hydro, and biomass\u0026mdash;play in reducing reliance on fossil fuels and thereby lowering carbon dioxide (CO₂) emissions. The transition towards cleaner energy sources contributes to the decarbonization of the energy mix, which is essential for achieving long-term environmental sustainability.\u003c/p\u003e\u003cp\u003eIn BRICS economies, where energy demand is rapidly increasing due to industrial expansion and population growth, the adoption of renewable energy technologies offers a viable pathway to balancing economic development with ecological preservation. Furthermore, the reduction in environmental degradation associated with renewable energy use reflects the positive externalities of investing in green technologies and implementing supportive regulatory frameworks. Thus, the study emphasizes the importance of accelerating the deployment of renewable energy infrastructure, enhancing policy incentives, and strengthening institutional capacity to fully realize the environmental benefits of the green energy transition in emerging economies.\u003c/p\u003e\u003cp\u003eHuman activities and the environment are inevitably interconnected. Therefore, environmental problems cannot be addressed solely by governments. An effective response to ecological challenges requires collaborative efforts among governments, policymakers, producers, and consumers. To utilize innovation as an economic tool in solving environmental problems, it is essential to integrate environmental considerations into the innovation process.\u003c/p\u003e\u003cp\u003eIn this context, BRICS countries must prioritize the expansion of green innovation, especially by increasing research and development (R\u0026amp;D) expenditures in areas that facilitate access to clean and renewable energy sources. Decision-makers in these economies should carefully implement policies and strategies aimed at transitioning from fossil fuel-based energy systems to clean energy infrastructures.\u003c/p\u003e\u003cp\u003eFurthermore, the role of scientists and environmental experts is vital in raising public awareness through academic conferences, public forums, and social media platforms, emphasizing the importance of environmental protection and promoting eco-conscious behaviours. Consumers also play a significant role by making informed choices and favouring environmentally friendly products, thereby reinforcing the demand for sustainable production practices.\u003c/p\u003e\u003cp\u003eIn conclusion, this study highlights the complex relationship between technological innovation, renewable energy consumption, and environmental pollution in BRICS countries. While innovation and economic growth currently contribute to increased CO₂ emissions, renewable energy consumption offers a promising pathway to reduce environmental degradation. Therefore, aligning innovation policies with sustainability goals and accelerating the transition to clean energy are essential steps for achieving long-term environmental and economic well-being in emerging economies. Future research should continue to explore the mechanisms through which green technologies can be effectively integrated into national development strategies to foster a more sustainable future.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed A, Uddin GS, Sohag K (2016) Biomass energy, technological progress and the environmental Kuznets curve: Evidence from selected European countries. Biomass Bioenergy 90:202\u0026ndash;208\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbino V, Ardito L, Dangelico RM, ve Petruzzelli AM (2014) Understanding the development trends of low-carbon energy technologies: A patent analysis. Appl Energy 135:836\u0026ndash;854\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAltıntaş H, Kassouri Y (2020) The impact of energy technology innovations on cleaner energy supply and carbon footprints in Europe: A linear versus nonlinear approach. J Clean Prod 276:124\u0026ndash;140\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAytun C, Akın CS (2015) \u003cem\u003eT\u0026uuml;rkiye\u0026rsquo;de karbondioksit emisyonu, enerji t\u0026uuml;ketimi ve eğitim ilişkisi: bootstrap nedensellik analizi.\u003c/em\u003e İzmir: I. International Symposium on Eurasia Energy Issues\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaltagi BH, Wu PX (1999) Unequally Spaced Panel Data Regressions with AR(1) Disturbances. Econometic Theory 15:814\u0026ndash;823\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBayar Y, Şaşmaz M\u0026Uuml; (2016) Karbon vergisi, ekonomik b\u0026uuml;y\u0026uuml;me ve CO2 emisyonu arasındaki nedensellik ilişkisi: Danimarka, Finlandiya, Hollanda İsve\u0026ccedil; ve Norve\u0026ccedil; \u0026ouml;rneği. Int J Appl Economic Finance Stud 1(1):32\u0026ndash;41\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhargava A, Franzini L, Narendranathan W (1982) Serial Correlation and Fixed Effect Models. Rev Econ Stud 49:533\u0026ndash;549\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBindi G (2019) \u003cem\u003eTechnological innovation and the environment, an analysis based on patent counts (Master dissertation, Lund University, Sweeden).\u003c/em\u003e Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lup.lub.lu.se/luur/download?func=downloadFile\u0026amp;recordOId=8981996\u0026amp;fileOId=8982016\u003c/span\u003e\u003cspan address=\"http://lup.lub.lu.se/luur/download?func=downloadFile\u0026amp;recordOId=8981996\u0026amp;fileOId=8982016\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown MB, Forsythe AB (1974) The Small sample Behavior of Some Statistics Which Test the Equality of Severa Means. Technometrics 16:129\u0026ndash;132\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, ve Lee CC (2020) Does technological innovation reduce CO2 emissions? Cross-country evidence. J Clean Prod 263:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng C, Ren X, Dong K, Dong X, Wang Z (2021) How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression. J Environ Manage 280:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng C, Ren X, Dong K, Dong X, Wang Z (2021) How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression. J Environ Manage 280:111818\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDanish, Ulucak R (2021) Renewable energy, technological innovation and the environment: A novel dynamic auto-regressive distributive lag simulation. Renew Sustain Energy Rev 150:111433 ISSN 1364\u0026thinsp;\u0026ndash;\u0026thinsp;0321\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDriscoll JC, Kraay AC (1998) Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. Review Econ Statistics 80:549\u0026ndash;560\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan H, Hossain MI (2018) Technological innovation, trade openness, CO2 emission and economic growth: comparative analysis between China and India. Int J Energy Econ Policy 8(6):240\u0026ndash;257\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFei Q, Rasiah R, Shen LJ (2014) The clean energy-growth nexus with CO2 emissions and technological innovation in Norway and New Zealand. Energy Environment 25(8):1323\u0026ndash;1344\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eH., L. (1960) \u003cem\u003eRobust Tests for Equality of Variances in Olkin I., Ghurye G., Hoeffding W., Madow W. G., Mann H.B., Contribution s to Probability and Statistics\u003c/em\u003e. Stanford,California: Stanford University Press, 278\u0026ndash;292\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHang G, Yuan-Sheng J (2011) The relationship between CO2 emissions, economic scale,technology, income and population in China. Procedia Environ Sci 11:1183\u0026ndash;1188\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHashmi R, Alam K (2019) Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation. J Clean Prod 231:1100\u0026ndash;1109\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIşık N, Kılı\u0026ccedil; E (2014) Ulaştırma sekt\u0026ouml;r\u0026uuml;nde CO2 emisyonu ve enerji ar-ge harcamaları ilişkisi. Sosyoekonomi, 22(22)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnstone N, Haščič I, Popp D (2010) Renewable energy policies and technological innovation: evidence based on patent counts. Environ resource Econ 45(1):133\u0026ndash;155\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan A, Chenggang Y, Hussain J, Kui Z (2021) Impact of technological innovation, financial development and foreign direct investment on renewable energy, non-renewable energy and the environment in belt \u0026amp; road initiative countries. Renewable Energy 171:479\u0026ndash;491\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMongo M, Belaid Fve, Ramdani B (2021) The effects of environmental innovations on CO2 emissions: Empirical evidence from Europe. Environ Sci Policy 118:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNihat I, Kılı\u0026ccedil; E (2014) Ulaştırma sekt\u0026ouml;r\u0026uuml;nde CO2 emisyonu ve enerji arge harcamaları ilişkisi. Sosyoekonomi 22(22):321\u0026ndash;346\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamargandi N (2017) Sector value addition, technology and CO2 emissions in Saudi Arabia. Renew Sustain Energy Rev 78:868\u0026ndash;877\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuki NM, Suki NM, Afshan S, Sharif A, Meo MS (2022) The paradigms of technological innovation and renewables as a panacea for sustainable development: A pathway of going green. Renewable Energy 181:1431\u0026ndash;1439\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTatoğlu Y (2020) Panel Zaman Serileri Analizi: Stata Uygulamalı. Beta Yayını, Istanbul\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Z, ve Zhu Y (2020) Do energy technology innovations contribute to CO2 emissions abatement? A spatial perspective. Sci Total Environ 726:1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYii KJ, Geetha C (2017) The nexus between technology innovation and CO2 emissions in Malaysia: evidence from granger causality test. Energy Procedia 105:3118\u0026ndash;3124\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang YJ, Peng YL, Ma CQ, ve Shen B (2017) Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100:18\u0026ndash;28\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Azerbaijan State University of Economics","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":"Technological Innovation, Renewable Energy, CO₂ Emissions, BRICS","lastPublishedDoi":"10.21203/rs.3.rs-8315644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8315644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study empirically investigates the impact of technological innovation and renewable energy consumption on environmental pollution, specifically CO₂ emissions, by controlling for the effect of economic growth in a panel of BRICS (Brazil, Russian federation, India, China, South Africa) countries over the period 1996–2021. Employing panel data analysis techniques, the study aims to uncover the distinct roles of innovation and clean energy in shaping environmental outcomes in a group of rapidly industrializing and technologically advancing economies. The empirical findings indicate that technological innovation has a positive and statistically significant relationship with CO₂ emissions, suggesting that, in the context of the BRICS countries, innovation may still be closely linked to carbon-intensive industrial processes. In contrast, renewable energy consumption exhibits a negative and statistically significant association with CO₂ emissions, highlighting its potential as an effective instrument for mitigating environmental degradation. These results underscore the need to align innovation strategies with environmental objectives and to increase the share of clean energy in the energy mix to promote sustainable development across the BRICS economies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classification: \u003c/strong\u003eO33, Q55, C33\u003c/p\u003e","manuscriptTitle":"Technological Innovation and Renewable Energy Consumption as Determinants of Environmental Pollution: Panel Evidence from BRICS Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 06:20:30","doi":"10.21203/rs.3.rs-8315644/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"0318bbf3-9558-4bdc-a1c1-31ad6cbdc937","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T06:20:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 06:20:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8315644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8315644","identity":"rs-8315644","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}